Cross-functional collaboration in app development is about bringing together members from various departments, each contributing their unique perspective and expertise for the collective good.

Although it seems reasonable in theory, building a successful cross-functional team is a serious challenge for leaders. Over one-third of employees worldwide say their colleagues don’t collaborate enough. And data silos are the top reason for collaboration failure.

In this article, we’ll go over what cross-functional collaboration means in the SaaS environment. You’ll also learn how to implement the latest trends and best practices to ensure data remains at the center of your collaborative decision-making process.

• What we mean by collaborative product development
• Latest trends and best practices in collaborative product development
• How to build a data-driven product development process as a cross-functional team
• 3 Collaborative development challenges in the SaaS environment and how to overcome them

What we mean by collaborative product development

Collaborative product development involves people with different roles and backgrounds coming together to create (ideation), develop, and optimize a product. 

In the center of this process stands a cross-functional team — a group of experts across disciplines required to build a balanced product, including UX design, marketing, customer success, development, etc.

Product development lifecycle

Before we dive into the peculiarities of collaborative product development, let’s go over the 7-step framework of software development:

• Ideation: this is where a product team comes up with the concept of a product or major update. It’s about identifying the problems worth solving or the needs that must be fulfilled with the help of market research, competitor analysis, or historical data analysis (in the case of a product update)

• Conceptualization: once an idea takes root, it’s time to shape it into a tangible concept. This involves defining a product’s scope, target market, and core features

• Design and planning: here, a product begins to take shape visually and structurally. Designers map out user interfaces, workflows, and architectural plans

• Development: this is where the coding happens. Developers bring the design and plans to life, transforming concepts into a minimum viable product (MVP)

• Testing and quality assurance (QA): testing is how you know a product works as intended, ensuring bugs are ironed out, and quality benchmarks are met

• Launch: the product is ready to be introduced to potential customers

• Post-launch optimization: the launch is just the beginning. Continuous improvement is essential, with feedback guiding updates and enhancements

Armed with an understanding of your product’s development lifecycle, you’re ready to collaborate across each stage of the process.

Is collaborative product development right for you?

Any SaaS product will benefit from collaborative product development. Among the countless positive outcomes of cross-functional product teams, the most significant include:

• Customer-centricity. Collaborative product development teams have access to insights from different touchpoints along the customer journey, ensuring that a product addresses real user needs
• Reduced silos. Cross-functional collaboration breaks down silos between departments and promotes transparency. Breaking the silo mentality encourages knowledge sharing and reduces delays associated with data hoarding and departmental bottlenecks
• Faster iterations. With a shared sense of purpose and easy access to necessary resources, collaborative teams can resolve bottlenecks and progress through SaaS projects faster
• Data-driven product decisions. Collaborative teams have access to a wealth of data from various sources, including user behavior, SaaS market trends, and performance metrics. This data empowers teams to make informed decisions at every stage of development

According to Lukasz Dec (a lead product designer), cross-functional collaboration is key to designing great products for the following reasons. 

➡️ More innovative and user-friendly products. When different business functions work together, various perspectives, skills, and experiences come together.

➡️ Time and cost-effective. Cross-functional collaboration helps you identify and address potential problems early. This saves time and money in the long run.

➡️ Builds trust and empowers teams. When people from different departments work together, they learn to trust one another. This creates a more positive and productive work environment.

➡️ Magnifies your voice as a designer. When you collaborate with individuals across functions, you have the opportunity to share your design expertise and influence the whole company.

Lukasz Dec

Lead product designer

To reap the benefits of collaborative product development, you need to be ready to adopt its best practices and address any challenges your cross-functional team may face. The following will arm you with the necessary tools to build a consistent collaborative product development process.

Latest trends and best practices in collaborative product development

Let’s explore some of the key aspects shaping today’s SaaS collaborative product development process.

Assembling a cross-functional team

Cross-functional teams are at the heart of collaborative product development. These teams bring together experts from various departments, each contributing their unique skills and perspectives to the SaaS product development process.

Eighty-three percent of “digitally maturing” companies report using cross-functional teams. Yet you shouldn’t necessarily rush into a complete organizational shift — such a drastic change may seriously threaten your company’s productivity. Start by forming a cross-functional team for a specific project or initiative (e.g., user interface (UI) redesign) to test the waters and identify team challenges.

Here are some best practices to help you build a successful cross-functional team:

• Develop a team charter. This is a document outlining project goals as well as the roles and responsibilities of teams
• Assign a team leader. Cross-functional teams require a coordinator. Their job is to set a project’s direction, facilitate communication, and keep the team focused on its objectives
• Establish a conflict resolution mechanism. Conflict is inevitable in collaborative product development. Be sure to put together communication guidelines, including a plan for what happens when teams disagree, and appoint a reliable person to de-escalate conflict

Creating a digital collaboration workspace

The digital collaboration technology stack has evolved immensely since the rise of remote work, reaching a point where it’s no longer exclusive to remote teams.

You probably rely on a few collaboration platforms, but your existing system may not be fully equipped to support the needs of cross-functional teams. For instance, tools like Jira are a common choice for developers but aren’t very intuitive for non-software teams. 

The easiest way to overcome communication challenges in cross-functional teams is by implementing a shared digital collaboration workspace such as:

• Switchboard — a simple collaboration platform that combines whiteboard and video conferencing with existing software solutions (e.g. Figma, Notion, Google Drive, etc.)
• Nifty — a hybrid collaboration workspace that brings different teams together
• Monday.com — project management software offering customizable templates for collaborative product development

Include your customers in the process

Customers are as valuable to your collaborative development process as cross-functional teams. In fact, bringing your target audience into the design and development process is a new trend in software development called co-creation.

The following are some basic steps you can take to involve your customers in the collaborative development process:

• Identify a group of customers representing various target user personas to bring different perspectives into your product plan
• Set up a collaborative workspace where customers can interact with your team 
• Conduct interviews, surveys, and focus groups with selected customers to understand their needs. Use these insights as a foundation for co-creation
• Allow them to interact with wireframes, test product prototypes, and provide user feedback
• Establish a continuous feedback mechanism with your co-creation group. Keep them informed regarding the progress of their product ideas, including how their input influences the development process
• Recognize and reward customers who actively participate in the co-creation process. This includes early access to new features, exclusive access to beta versions, and other incentives

Co-creation results in SaaS platforms that meet customer expectations and foster a sense of ownership and loyalty among your user base. 

Fostering data-driven collaboration

We’ve finally arrived at the biggest trend in collaborative product development — integrating data insights into collaborative decision-making.

You already know that one of the biggest advantages of cross-functional teams is reduced data silos. But how exactly does it help in collaborative SaaS software development?

When data silos are eliminated, team members can access a centralized pool of data, giving them a 360-degree view of product performance, including user behavior and market dynamics. In other words, developers can incorporate critical customer data into product roadmaps by sharing a data hub with customer support, marketing teams, and other sectors.

To foster data-driven collaboration, you need technology that makes it easy for different teams to access insights. It’s crucial that your software features collaboration capabilities so cross-functional teams can work together.

Smartlook allows you to create shared and private product analytics dashboards to control who sees what so you can avoid overloading staff from different sectors with unnecessary data.

Shared and private dashboards in Smartlook

How to build a data-driven product development process as a cross-functional team

Easier said than done, huh? 

You can’t just give everyone in your organization access to the available data and let them sort it out. So what should you do to foster data-driven decision-making across your cross-functional team? Here are five steps to get you started.

Define project objectives

When we say “collaborative software development,” we don’t mean an entire organization should come together to develop each aspect of a product. We recommend that you focus on project-based collaboration.

What task do you want your team to focus on? This could be developing a new feature, a UI update, funnel optimization, or anything that improves the user experience (UX).

Once you’ve defined a goal, you need to specify the milestones you’ll be working toward. If your objective is to release a new product tour, a simple action plan may look something like this:

• Customer support: suggest pain points new customers experience during the onboarding process
• Product manager: define the scope and features of the guided product tour
• Executives: determine whether or not the project is aligned with company goals
• UI/UX designers: develop wireframes, prototypes, and visual design elements, and conduct usability testing to refine the design
• Developers: build and deploy the guided product tour feature. Closely collaborate with designers to implement the user interface
• QA testers: develop test cases, perform testing, and provide feedback for improvement
• Business analysts: implement data collection, analyze user behavior, and provide insights for optimization

Define and monitor cross-functional KPIs

Next, identify the specific data points and metrics that will help you measure the success of your project. These metrics should align with your project objectives. For a guided tour project, key data points may include user engagement, feature adoption, and time-to-value metrics:

• “Launch the guided product tour, including design, development, testing, and adoption, within four months from today”
• “Engage at least 50% of new users in a product tour”
• “Increase the user activation rate by 25% within three months of launching the guided product tour”
• “Reduce the average time-to-value for new users by 20%”
• “Reduce support tickets by 10%”

Set up a system for data collection

When you have an idea of your goals and KPIs, you’ll better understand the tools you’ll need to measure them. The next step is setting up a robust system for data collection.

Determine the primary data sources required to track and measure your chosen KPIs. These sources may include user interactions, customer feedback, and other relevant data points. For instance, setting up a guided tour will require the following analytics tech stack:

• A product management platform like ProductPlan to monitor project progress and track the contribution of everyone involved
• A product analytics tool like Smartlook where team members can see real-time statistics regarding user interactions, watch session replays, and collect critical metrics in private or shared dashboards
• A customer support analytics suite like Intercom that collects data regarding customer requests before and after a product tour release 

A Smartlook funnel representing the user flow through a product tour

It’s also a good idea to create a centralized data integration hub or platform where data from various sources can be aggregated in real time. This hub will serve as a single source of truth for cross-functional teams, eliminating data silos. 

Schedule regular data reviews

Regular data reviews are essential for keeping cross-functional teams aligned with project objectives and ensuring that data-driven insights are actively incorporated into decision-making. 

Here’s how to establish effective data review sessions:

• Set a recurring schedule for data review meetings. These meetings can be weekly, bi-weekly, or monthly, depending on the project’s timeline and complexity
• During these meetings, present key data metrics and insights relevant to your project objectives and KPIs. Use data visualization tools and dashboards to make data more accessible and understandable for all team members
• Identify trends, patterns, and areas where adjustments can be made based on the data. Use this information to prioritize project tasks and strategies
• Assign action items and responsibilities to team members based on insights derived from the data 

Iterate based on data insights

Data-driven collaboration is an ongoing process that involves continuous improvement based on project insights.

Encourage a culture of experimentation and empower cross-functional teams to propose and test adjustments based on data. Remain open to tweaking strategies and making further iterations based on the outcomes of implemented changes. 

Data-driven decision-making is iterative. As such, learning from success and failure is essential.

3 Collaborative development challenges in the SaaS environment and how to overcome them

No matter how professional your team members are, they will have to learn how to effectively collaborate. You can shorten this adaptation period and avoid conflict situations by anticipating the following challenges.

Misalignment 

Misalignment can occur anywhere in cross-functional teams — in communication, goal setting, daily procedures, etc.

Team members from different departments have varying communication styles, terminologies, and habits, so you’ll need to find a way to bridge the gap between them.

Solution: Start by aligning cross-functional teams around objectives. Beginning with your organizational goals (a.k.a. primary goals), move down the milestones of each department. Make sure people in different roles understand the goals of their colleagues, including their own.

Next, establish clear communication protocols. These include preferred channels, meeting schedules, and expectations for responsiveness.

Data silos

Data silos occur when information is isolated within specific departments or teams. This usually happens when an organization lacks a standard data management approach.

Solution: Develop a consistent data governance strategy. Define data ownership, access permissions, and data quality standards. Schedule regular data reviews to keep everyone involved updated regarding the state of the project (from different perspectives) and align collaborators around a shared strategy.

Lack of accountability

When many people are involved in a project, tasks and responsibilities can fall through the cracks, leading to project delays and decreased overall efficiency.

Solution: Use project management software that allows you to assign tasks, set deadlines, and track progress. These tools allow you to automatically route tasks, set reminders, and create KPIs to hold team members accountable.

Improve your SaaS solution with better cross-functional collaboration

Just like a top pit stop crew, your SaaS app development journey depends on how well your teams collaborate.

The trends and best practices we’ve covered (digital teamwork tools and co-creation) bring data to the table and give people outside the development team more say.

Incorporate Smartlook into your collaborative arsenal to break down data silos and put customer experience-related data at the center of your collaborative decision-making strategy. Smartlook’s real-time statistics and session replays provide cross-functional teams with product analytics insights, allowing everyone to focus on what matters most — the end-user experience.

Book a demo or start your free 30-day trial today!

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Collaborative product development: Improving SaaS products with data-driven insights appeared first on Smartlook Blog.

Read on to learn how to set up customer experience analytics that gauge user happiness and give you the necessary tools to optimize your product for maximum customer satisfaction. 

Here’s everything we’ll cover in this guide:

• What is digital experience analytics?
• The correlation between user happiness and business impact
• User happiness metrics
• How to set up digital experience analytics to understand user happiness
• Using data analytics to improve customer experience

What is digital experience analytics?

Digital experience analytics is the practice of monitoring and interpreting customer behavior to drive informed decisions regarding the further direction of product development and customer experience strategies. 

To paint a complete picture of the UX, product teams collect and analyze both quantitative and qualitative data on user behavior within a website or app. You can turn to any means of data collection to obtain the necessary information — from running customer surveys to monitoring user activity with a product analytics solution.

This in-depth analysis helps to identify user pain points, friction points in the product experience, and opportunities for enhancing user satisfaction and engagement. 

“Data is the means by which the consumer shares their story. As we read their story, we will probably see something that makes us uncomfortable.

It could be a bad website experience, email, or customer service interaction. Whatever it is, it’s our obligation to listen, respond, and optimize.

[…] Not just for leads. Not just for sales. But for the relationship.

If we focus on defining and seeking such a relationship, we will have more than a portfolio of satisfied customers.”

Joseph Parker

VP of Digital Experience at JDA Worldwide

The correlation between user happiness and business impact

In the realm of digital experience analytics, user happiness isn’t just another aspect of product performance. It has a profound impact on user engagement and, therefore, the overall success of your product.

“What if, and I know this might sound crazy, instead of thinking about adoption plans, we actually tried to have good ideas💡 and lean towards global end users’ happiness 😍”

Philippe Jost

Head of Customer Obsession at Computacenter

Engaged users are more likely to:

• Convert and upsell: satisfied users willingly convert from free users to paying customers. They are also receptive to upsell offers because they trust the value your product provides

• Stay longer: users who are happy and find value in your product are more inclined to stick to it (and spend more with your business) 

• Advocate and refer: happy users are your best brand advocates. They share their positive experiences with others, contributing to word-of-mouth marketing and driving organic growth

The longer your users stay with you, the higher your ROI. Just a 5% increase in customer retention can lead to a 25% growth in profit. 

So how do you create happy customers?

The first step toward happier users is assessing the current levels of satisfaction with your product to create a baseline for improvement. This involves building a digital experience analytics system to capture valuable insights into how users perceive your product and what they like and dislike about it. By doing so, you can craft a strategic approach to enhance user happiness and drive tangible business outcomes.

Let’s begin with the user happiness metrics you need to monitor.

User happiness metrics 

These quantitative metrics serve as the initial touchstone, helping you gauge user satisfaction and setting the stage for comprehensive root cause analysis and subsequent UX improvements.

You’ll also be using them to track the results of your product optimization efforts. By monitoring changes in quantitative data over time, you’ll be able to assess your progress and gauge the impact of your initiatives. 

Customer Satisfaction Score (CSAT)

CSAT measures the satisfaction level of customers based on a specific interaction or experience.

To calculate CSAT, you need to run a CSAT survey asking users to rate their satisfaction with your product — it’s best to focus on a particular aspect of the product experience, such as the onboarding process or a recent feature update. When you have the answers, you’ll divide the number of satisfied customers by the number of total respondents. 

Net Promoter Score (NPS)

NPS gauges customer loyalty and the likelihood of your users recommending your product to others. 

To measure it, ask your users a simple question: “On a scale from 0 to 10, how likely are you to recommend our product to a friend or colleague?” 

NPS categorizes respondents into Promoters (9-10), Passives (7-8), and Detractors (0-6), providing a snapshot of overall customer sentiment.

Customer Effort Score (CES)

CES measures the ease with which customers can complete specific tasks or interactions within your product. The goal is to assess the level of effort customers feel they need to make to achieve their goals.

To gather CES data, ask a question like: “How easy was it for you to [complete a specific action]?” Respondents typically provide answers on a scale such as “Very easy,” “Somewhat easy,” “Neutral,” “Somewhat difficult,” and “Very difficult.”

CES is particularly important for ensuring long-term user happiness. Products that consistently require low effort will keep users engaged and loyal over time. High-effort interactions, on the other hand, can lead to user frustration, decreased engagement, and, eventually, churn.

User Retention Rate

User retention rate is the percentage of customers who continue to use your product over a specific period. This metric reflects how well your product meets ongoing user needs. 

To calculate the user retention rate, use the formula:

User Retention Rate = ((Number of Users at the End of a Period – New Users Acquired During the Period) / Number of Users at the Start of the Period) * 100

Customer Churn Rate

The churn rate represents the percentage of customers who stop using your product over a specific period. It’s the inverse of the user retention rate and provides insights into how many users are leaving your product. 

Here’s how you calculate the churn rate:

Churn Rate = (Number of Customers Lost During a Period / Number of Customers at the Start of the Period) * 100

How to set up digital experience analytics to understand user happiness

Pursuing user happiness goes beyond measuring satisfaction metrics. You need to establish a consistent digital experience analytics system that not only provides quantitative UX data but also pinpoints the user emotions tied to those metrics and uncovers the “why” behind the numbers.

Choose the right analytics methods

You need to select analytics methods that align with your objective. And since your objective is to enhance UX by quantifying and interpreting user happiness, your chosen methods should focus specifically on monitoring user frustrations and sentiments.

Get ready to use a mix of quantitative and qualitative data: 

Quantitative data collection

• User surveys: design surveys that capture specific aspects of the user experience, such as CSAT, NPS, and CES. These surveys can be distributed through various digital channels, including email, in-app prompts, or after specific interactions

• User behavior tracking: use analytics tools to collect quantitative use behavior data like page views, clicks, navigation paths, and feature usage 

Qualitative data collection

To understand what user experiences affect those happiness metrics you’ve just collected,  incorporate qualitative data collection methods:

• Feedback forms: encourage users to share their suggestions and challenges directly within your app. Place feedback forms at various touchpoints to gather customer insights at relevant moments

• User interviews: conduct one-on-one interviews with selected users to delve deeper into their experiences. These interviews allow you to explore user perspectives and pain points in greater detail

“Here, I’d like to highlight something: sometimes, customers don’t really know what they want/need, even if they come with a specific feature request. That’s why it’s essential to get to the bottom of their business processes, deconstruct the challenges they face, and ask additional questions that help to understand their expectations from the product. That way, there will be significantly less friction later in the process, and the customers will reach the “aha!” moment much quicker.“

Anastasia Tatsenko

Chief Customer Officer at NetHunt CRM

• Session recordings and heatmaps: Visualize real user experiences through session recordings and heatmaps. These tools offer visual insights into how users navigate, interact, and engage with different elements of your product

Only by combining both quantitative and qualitative methods will you gain a holistic understanding of user happiness. 

Map out user journeys

To help you fully understand your customers’ digital experience, your analytics tools should capture data at every touchpoint users encounter during their journey.

Start by creating visual maps that outline the steps users take along these journeys. Document all the touchpoints, interactions, and decision points users face. 

When you’ve outlined user journey maps, define milestones that will indicate users have achieved success in a particular stage. For instance, in the early stages of the customer journey, a milestone could be completing the onboarding flow and reaching the “aha!” moment.

Configure your analytics tools to gather user happiness metrics data the moment a user completes a milestone. This could involve displaying a survey or feedback prompt immediately after the milestone is achieved.

As users progress through their journey and reach different milestones, you’ll see how user happiness metrics vary. Look for trends, patterns, and fluctuations in satisfaction scores. Do certain milestones consistently correlate with higher or lower satisfaction scores? These are the areas you should focus on for now.

Watch users interact with your app to spot points of frustration

You can’t collect real-time feedback on each and every interaction between your users and your app. Well, technically, you can, but it will create unnecessary friction. 

Instead, set up a digital customer experience monitoring system that allows you to observe user behavior unobtrusively. We’re talking about session recordings and heatmaps.

Session recordings capture real-time user interactions, allowing you to replay sessions and see exactly how users navigate, click, scroll, and interact. When watching session recordings, look for:

• Repetitive actions: users attempting the same action multiple times could indicate difficulty understanding a feature
• Abandoned interactions: abruptly abandoned actions might reveal points where users encounter challenges or lose interest
• UI confusion: detect instances where users seem uncertain about the purpose or functionality of certain elements

To make it easier to analyze session recordings, Smartlook allows you to highlight events in your session replays that can then be filtered, segmented, and quantized.

Heatmaps visually represent aggregated user interaction data. They showcase which areas of your app receive the most engagement (hotspots) and which areas receive less attention (cold spots).  

There are three types of heatmaps:

• Click heatmaps point out non-clickable elements that users are trying to click
• Scroll heatmaps show how many people make it to certain points on a page, making it easier to decide where to place critical elements
• Mouse movement heatmaps identify areas that distract or confuse visitors

Smartlook collects heatmap data and presents it in your preferred format — all you need is to know what you’re looking for.

Identify recurring patterns of frustration and focus on them. These patterns will guide your efforts to improve specific aspects of your product that are causing user discomfort.

Capitalize on positive user experiences

While identifying and addressing points of friction is essential, it’s equally important to learn from positive user interactions and experiences. Positive data can provide insight into what’s working well and help you leverage those aspects to enhance product growth.

Look for instances where users achieve their goals seamlessly and exhibit positive emotions. This could be when they complete a task, navigate smoothly, or express satisfaction. These moments are valuable because they signify that your product is delivering value as intended.

Examine the features or touchpoints that consistently lead to positive user experiences: 

• What elements are contributing to their happiness? 
• What aspects of your product are resonating with them?
• Are there specific user segments or personas that tend to exhibit high satisfaction?
• How could you replicate those experiences in other areas of your product?

Use these insights to incorporate elements contributing to positive user experiences throughout the entire user journey. 

Interpret data to drive actionable insights

No matter how deep the data you collect — if you don’t act on it, it’s useless. To connect your user happiness insights to business outcomes, you need to start interpreting your data. 

Look for connections between quantitative and qualitative findings. For instance, if users are consistently giving low scores for a particular aspect of your product (quantitative insight), delve into qualitative data to see if there are recurring issues or pain points related to that aspect (qualitative insight).

Next, develop hypotheses about the potential solutions to the identified issues. These hypotheses will guide your experimentation and optimization efforts. Whether it’s tweaking a feature, improving onboarding, or refining a specific interaction, use data-driven hypotheses to guide your changes.

Using data analytics to improve customer experience

There are no limits to what you can achieve with the help of digital experience analytics. These are the most common use cases illustrating how data helps to enhance user journeys.

Addressing pain points based on rage clicks

Rage clicks occur when users repeatedly click on the same element of an app out of frustration, usually because they expect it to perform a specific action that it doesn’t. These issues usually lead to customer frustration and low satisfaction levels. So if you spot your CSAT dropping abnormally, follow this process:

• Set up tracking for rage clicks in Smartlook
• Use heatmap and session replay tools to identify instances of rage clicks
• Dig deeper into the elements that are causing rage clicks. Is the design misleading? Are there broken links? Are users trying to access information that’s hard to find?
• Based on your findings, take action to optimize the user experience and eliminate rage clicks. This could involve making clickable elements more prominent, fixing broken links, or enhancing error messages to provide clear guidance and assistance

Analyzing rage clicks can provide valuable insight into areas of your digital product that need immediate improvement.

Driving personalization with cohort analysis

Different user segments may require a tailored approach to satisfy their unique needs. This simple strategy helps gauge the happiness of separate customer cohorts and craft experiences that resonate with them most:

• Group users based on attributes like sign-up date, behavior, or demographics
• Monitor how each cohort engages with your product or service, including how their behavior evolves
• Implement in-app surveys targeting specific cohorts to learn about the pains of particular user groups
• Use insights to create personalized user flows, offers, or features for different cohorts

Tip: Use Smartlook to run a cohort analysis. You can group users based on common characteristics or events and track engagement statistics for different cohorts in Retention Tables. When you spot engagement anomalies, set up targeted in-app surveys with Survicate — the platform integrates with Smartlook enabling you to connect survey responses to user sessions later.

Understanding user emotions with sentiment analysis

Say you receive hundreds of reviews across various online channels — how do you make sense of the piles of customer feedback? Sentiment analysis is the answer.

Sentiment analysis involves analyzing text data, such as customer reviews or social media posts, to determine user sentiment (positive, negative, neutral). It helps to identify trends in user feedback and address issues more effectively. 

• Collect user-generated content like reviews, comments, and support tickets
• Use natural language processing (NLP) tools to analyze and categorize text data by sentiment
• Identify recurring themes, positive and negative sentiments, and areas of concern

“What we must not forget in trying to increase end-user global happiness through transformation and changes is “sentiment data”, gathered from end users themselves telling us about how they feel.”

Philippe Jost

Head of Customer Obsession at Computacenter

Using positive user experiences to develop new features

Another powerful strategy is using positive experience data to develop new features or enhance existing ones. This approach involves using the data you’ve collected from users who have had positive interactions with your product to shape the direction of your development efforts. 

Here’s how you can do it:

• Analyze user feedback, surveys, session recordings, and other qualitative and quantitative data to identify instances where users have had positive experiences
• Identify the elements that resonate well with users and contribute to their happiness
• Think of how these enhancements can further amplify the positive experiences for a broader user base
• Focus on the ones that address pain points or provide value to multiple user segments

Proactively addressing churn with predictive analytics

Churn is a direct indicator of user happiness. When users choose to discontinue using your product, it’s a sign that they’re not finding the value they expected. But the problem isn’t always in the product itself — it could as well be caused by poor personalization or lack of user guidance. 

If the churn issue isn’t related to product functionality, you can address it by:

• Collecting relevant data on user behavior, interactions, and engagement with your product
• Setting up algorithms to identify common characteristics, behaviors, or actions associated with users who have churned in the past. No need to be a data analytics pro. Just use a predictive analytics tool like Churn360 — it will do all the heavy lifting for you
• Keep track of customer health scores and spot early signs of customer churn in your predictive analytics platform
• For users in the high-risk segment, proactively implement personalized interventions aimed at improving their experience and addressing potential pain points

Customer experience analytics tools

You don’t need a massive analytics kit to measure and enhance user happiness. In fact, these three digital experience analytics tools will help you gain a comprehensive view of your user experience, enough to build smooth product journeys and achieve customer loyalty.

Smartlook

Smartlook is a digital experience analytics platform that connects quantitative (revenue insights, UX metrics, etc.) and qualitative (session recordings) data that’s easy to interpret and act on. Its funnels, session recordings, heatmaps, and other features help product managers understand how users interact with their products so they can make informed decisions and enhance product experiences.

With Smartlook, you can uncover user behavior patterns or dive into individual user experiences — whatever you need to build stronger relationships with your customers. 

Survicate

Survicate is a survey and feedback platform that enables you to gather qualitative insights directly from users. 

It integrates seamlessly with other analytics tools like Smartlook, allowing you to connect survey responses to specific user sessions. This synergy between quantitative and qualitative data helps you better understand user emotions and create more satisfying user interactions.

Lexalytics 

Lexalytics is a leading sentiment analysis tool that focuses on understanding user emotions from qualitative data sources like customer feedback, reviews, and social media conversations.

Aside from sentiment analysis, Lexalytics can help you automatically categorize outstanding tickets and reviews, allowing you to focus on the most pressing topics.

Gauge product experience and maximize user happiness with Smartlook

Digital experience analytics help you understand user emotions, experiences, and frustration behind your product performance data. Only by uncovering and acting on these insights can you go beyond your hypotheses and create a product that meets the needs of the target audience.

User happiness is an asset you can convert into tangible business outcomes with the right tools in your tech stack. 

Smartlook empowers you to gain deep insight into user behavior by capturing and visualizing their interactions in real time. Through session recordings and heatmaps, you can see how users navigate, click, scroll, and interact with your product. This level of visibility allows you to identify pain points, areas of frustration, and moments of joy within the user journey.

Book a product demo to see Smartlook in action, or start your free, full-featured 30-day trial now.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Quantifying User Happiness: The Science of Digital Experience Analytics appeared first on Smartlook Blog.

When users interact with your SaaS product, they leave behind a wealth of data that you can harness to lead your product development strategy. 

The problem is that this wealth of data is just too wealthy to easily make sense of. At least not until you establish a reliable SaaS analytics system where each piece of data has its own purpose. And this is exactly what we’re going to help you with.

In this guide, you’ll learn how to turn SaaS analytics into a constant flow of insights to inform product optimization and foster relationships with users. 

• What is SaaS analytics?
• Types of SaaS analytics
• How to use data analytics to drive SaaS growth
• Best SaaS analytics software to collect meaningful data

What is SaaS analytics?

SaaS analytics is the act of collecting, processing, and analyzing product performance data (in any vertical) to gain meaningful insights and make strategic decisions regarding further product development.

It involves using analytics tools to collect and visualize data relating to user interactions, application usage, business processes, and more — the type of data depends on your area of focus and the outcomes you wish to achieve.

Types of SaaS data analytics

The first step toward successful SaaS analytics is revealing what kind of data you need to collect to draw the right conclusions (including identifying the means of data collection). 

It’s important to clearly distinguish between types of data analytics to avoid getting lost in piles of data. 

Journey analysis

A journey analysis, or funnel analysis, involves tracking and analyzing the paths that users take as they interact with your SaaS product. A funnel is a quantitative representation of the user journey, showing the statistics on how many users make it through various stages along the way.

By visualizing this journey, you can identify areas where users drop off or struggle, allowing you to enhance the user experience.

To create a reliable system for journey analytics, you’ll need to map out your ideal customer journeys first. 

When developing your product’s functionality and user interface (UI), there’s a good chance you designed intuitive, logical flows that take users toward their goals. Now, it’s time to map those flows out in your product analytics tool to verify whether your users are following the paths you’ve designed for them. 

To build a funnel, you’ll need to define the key actions or milestones that have successfully helped users reach their goals. These can be onboarding completion, profile creation, report export, and other milestones that indicate users are getting closer to realizing the value of your product. 

Mind that every key action within your product will require a separate funnel. 

Once you’re done, the next step is to identify the touchpoints where users interact with your app to reach a destination. If the target action is completing onboarding, the touchpoints might be: (1) filling in the required information about the user’s business, (2) starting a product tour, and (3) checking off all the items on the onboarding checklist.

And where do you define all these touchpoints and build funnels? Your product analytics solution should handle it. For instance, Smartlook builds event-based funnels. All you need to do is define the touchpoints, a.k.a. events, and add them to a new funnel.

The resulting funnels will look like this:

What if journey analytics discover that too many users aren’t following the pre-defined paths?

In this case, you’ll need to switch to Behavior Flows to see the real paths users take. This is where you’ll be able to spot deviations from intended paths and explore why they occur.

User experience analytics

A user experience (UX) analysis evaluates how users feel while using your SaaS product. UX insights must power your customer retention strategy to ensure continuous growth. 

Through granular customer experience analysis, you can spot pain points that hinder seamless interaction with your product. These pain points often contribute to user dissatisfaction and, if left unaddressed, can lead to higher churn rates. 

There are various methods of performing a UX analysis, each serving different research needs:

Usability testing 

This method involves observing users as they interact with your product to identify usability issues and areas of confusion. Session recordings provide an unfiltered view of how users navigate, click, scroll, and engage with your product.

Session recordings in Smartlook

Best for: Identifying specific usability issues and gathering qualitative insights.

User surveys and interviews

Collecting feedback directly from users through surveys helps you understand their perceptions and capture opportunities for improvement.

In-app surveys by Survicate

Best for: Gaining an understanding of user satisfaction.

Heatmaps

Heatmaps help you visualize user interactions on particular pages and understand where users engage the most.

Heatmaps in Smartlook

Best for: Detecting and resolving UI issues.

Revenue analytics

Revenue analytics in the SaaS environment involves the systematic analysis of financial data and performance metrics to gain insight into the financial health of your SaaS business. It goes beyond just tracking revenue figures, delving into understanding the drivers of revenue, optimizing pricing strategies, and maximizing customer lifetime value.

Depending on your objectives, you may turn to various aspects of revenue analytics:

1. Subscription metrics: analyzing subscription-based metrics such as Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), and Average Revenue Per User (ARPU) to get a clear picture of your recurring revenue streams.

2. Churn analysis: examining churn rates to understand how many customers are canceling their subscriptions over time. From here, you can identify reasons for customer churn (just turn to UX analysis) to implement strategies to minimize it.

3. Expansion and contraction revenue: monitoring the revenue generated from upselling (expansion) and downsizing (contraction) activities to gain insight into the effectiveness of your cross-selling and retention efforts.

4. CLV analysis: estimating the average value a customer brings to your business throughout their engagement to measure the real impact of your customer retention efforts.

5. Cohort analysis: segmenting customers based on their sign-up dates to analyze how revenue and churn behavior differs among different cohorts. 

Let’s pay particular attention to cohort analysis as it’s the most granular analytics method. It gives you deep insight into how different customer groups engage with your SaaS product, including how their revenue contributions evolve.

Are customers acquired during a specific period more likely to churn than others? Are there cohorts that demonstrate higher engagement and revenue growth?

You cannot access those insights in top-level reports and metrics alone. Say your MRR has plummeted, and you’re digging into churn analytics and CLV data only to see your key metrics provide no apparent trends. Only by running cohort analysis can you uncover the hidden patterns that impact your top-level SaaS metrics. 

With Smartlook, you can run a cohort analysis using retention tables. You just need to create customer segments and define the events you want to track for those segments. 

Predictive analytics

Reactive analytics is good, but preventing issues before they become problems is better. 

Predictive analytics uses historical data and statistical algorithms to forecast future outcomes and trends. In the SaaS environment, predictive analytics help to anticipate customer behavior, identify potential opportunities, and make proactive decisions that drive growth.

You can use predictive analytics to:

• Identify customers who are at risk of churning based on their historical behavior
• Predict which customers are most likely to respond positively to upselling or cross-selling efforts
• Forecast future demand for your SaaS product, aiding in resource planning, capacity management, and meeting customer needs more effectively
• Estimate the potential lifetime value of new customers, helping you prioritize acquisition efforts and tailor retention strategies

The good news is you don’t have to be a data analytics pro to benefit from predictive insights. There are predictive analytics tools that collect, process, and interpret historical data to detect early troubling signs in user behaviors. For example, SubcriptionFlow offers retention management functionality that uses historic behavioral data to estimate customer health scores and identify users that are most likely to churn.

How to use data analytics to drive SaaS growth

Not only does analytics help product teams spot gaps in product experience, but it also sets the trajectory for product growth. Let’s walk through some scenarios where SaaS analytics drives immense value. 

Building data-driven product roadmaps

A product roadmap without data is just a hypothesis. 

“Do you really know…

– What’s going on in the market?

– What worldviews your buyers share?

– What they believe? What they don’t believe?

– The inflection points?

– Where the urgency is today?

– Why customers are churning?

– The problems, signals, and roadblocks?

– The strongest drivers today? Why?

You can’t guess this stuff.

Otherwise, you’re just living in the company bubble. And that’s a terrible place to be.

There needs to be constant dialogue between sales, marketing, success, and product so that you can reprioritize the roadmap.”

Danny Asling

SaaS Marketing Leader

Are you moving in the right direction? Are customers ready for new features? You can only answer these and other similar questions if you incorporate SaaS user analytics into your decision-making process. This is how to do it:

• Prioritize features — use data regarding feature adoption, user behavior, and satisfaction to prioritize roadmap items. Features that align with user preferences and have higher adoption rates are more likely to drive growth

• Validate ideas — turn to customer feedback to predict the potential impact of feature enhancements. Simply ask users whether they see value in new features or if there are other areas that require your attention. Always A/B test changes and closely monitor product performance after releasing new features

• Iterate based on feedback — user surveys and feedback provide valuable insights into feature requests and pain points. Prioritize the most frequent requests to make iterative improvements

• Adjust based on performance — continuously monitor the impact of roadmap initiatives. If a feature doesn’t drive expected growth, consider adjusting it or rolling back

Cohort analysis plays a significant role in prioritizing product roadmap. It’s no secret that startups make a good effort to meet the needs of the most “expensive” customers, namely high-ticket user cohorts. 

So if you aren’t sure where to start with feature prioritization, dive into the behavior of high-value cohorts. Watch them interact with your product, including which features they use most and what pain points they encounter. Use those insights to inform your roadmap, and you’re all set.

Fostering product and feature adoption

Even if your SaaS product is filled with groundbreaking features, they won’t contribute to growth if they’re not adopted by your users. This is where SaaS product analytics comes to the rescue, ensuring that your offerings are not only built but also embraced by your user base.

There are many ways to measure feature adoption — the right one depends on the context:

• Method #1: tracking events (i.e. feature interactions) in your product analytics tool and measuring how many active users have adopted specific features
• Method #2: mapping out the user journey that leads to adopting a new feature and analyzing the drop-off rates
• Method #3: running a cohort analysis to track feature adoption among specific user groups
• Method #4: tracking time to value (TTV) to understand how much time it takes for users (or even specific cohorts) to adopt a feature

With cohort analysis in Smartlook, you can see how many new users interact with specific features within the first days, allowing you to gain insight into their initial adoption rate. This is particularly valuable as it helps you understand if a feature immediately resonates with users or if you need to make adjustments to enhance its appeal.

Nurturing customer relationships

SaaS analytics indirectly drives startup growth by helping you build stronger customer relationships and increase retention. 

How?

By giving you the tools to understand your customers better and deliver an experience that makes them stick.

UX analytics and predictive insights are your must-have tools here. User behavior tracking gives you invaluable insight into what engages users most and what pain points they face. You can use this data to tailor your product to the needs of your customers.

With SaaS predictive analytics, you can analyze historical data to forecast user behavior. This means you can proactively offer solutions, whether it’s suggesting relevant features, addressing potential issues, or creating targeted in-app messaging. By proactively addressing user needs, you’ll anticipate churn and build long-lasting customer relationships.

Informing product optimization and continuous enhancement

Your SaaS strategy isn’t set in stone. You need to consistently enhance and refine it to sustain growth and keep your users engaged.

Successful startups make changes before users ask about them. Sometimes they roll back, but changes that stick drive innovation and help companies keep their position in the competitive market. 

Take Instagram, for example. Users hadn’t been bombarding developers with requests to release Instagram Stories back in 2016. Yet the feature has brought immense success to the company. 

You may suggest that any update brought by such a huge company is destined for success, but that’s not the case.

In 2022, Instagram made another major release — the full-screen home feed. After getting loads of negative feedback, the company rolled back the new feature. 

Innovation involves risks, but data analytics helps you test and validate your ideas with confidence. Before a full-scale rollout, conduct A/B tests on a subset of users. Analyze how different versions of a feature perform, track engagement levels, and gather feedback. This validation process minimizes the chances of releasing unsuccessful changes.

“Let users inform your ideas, but don’t let them dictate them. Here’s how you can do that:

𝗗𝗶𝗴 𝗗𝗲𝗲𝗽𝗲𝗿: Don’t just ask what users want; find out why they want it. Understanding the underlying problem helps you craft a solution that actually works.

𝗨𝘀𝗲 𝗬𝗼𝘂𝗿 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: You know your product better than anyone else. Use that knowledge to interpret what users are really asking for.

𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹𝗹𝘆: Before making big changes, try out new ideas on a small scale. See how they resonate with users without committing too many resources.”

Carl Vellotti

Senior Product Manager at GoodRx

Best SaaS analytics software to collect meaningful data

With these SaaS analytics tools, you’ll get a complete picture of your product performance, user experience, revenue insights, and future forecasts.

Smartlook for product analytics and user behavior insights

We’ve mentioned Smartlook a lot in this guide, and for good reason. 

Smartlook is a versatile product analytics platform that offers session recording tools, heatmaps, event tracking, and funnel analysis. It combines quantitative and qualitative data analytics to give you a 360-degree view of your product experience.

From top-level dashboards to individual session replays, Smartlook provides a comprehensive perspective on how users interact with your SaaS product and helps you spot opportunities for SaaS growth.

Price: Free for up to 3,000 monthly sessions. From $55/month for analyzing 5,000 monthly user sessions and more. 

Adobe Experience Cloud for customer journey analytics

Adobe Analytics is a part of the Adobe Experience Platform designed to help organizations track, measure, analyze, and optimize their digital marketing efforts and online user experiences.

Not only does the platform uncover insights into your customer journeys, but also provides revenue analytics and predictive intelligence insights.

Price: Available on request.

Profitwell for revenue analytics

Profitwell specializes in subscription analytics, making it an excellent choice for SaaS businesses focused on revenue optimization. 

It provides insight into key subscription metrics like MRR, Churn Rate, and CLV. The platform also offers revenue optimization tools that help automate retention and billing efforts.

Price: Free for tracking financial metrics. Advanced features like automated customer recovery, pricing optimization, and billing solutions are available in custom plans.

Survicate for customer feedback

Survicate is a user feedback and survey tool that enables you to gather insights directly from your users. It offers in-app surveys, NPS (Net Promoter Score) surveys, and user feedback forms.

Survicate integrates with Smartlook, enabling you to connect your customer feedback to user behavior insights. You can filter events or session recordings by negative feedback and see what might have triggered such a negative experience.

Price: Free for tracking financial metrics. Advanced automation features like customer recovery, pricing optimization, and billing solutions are available in custom plans.

SubscriptionFlow for predictive insights

SubscriptionFlow assists subscription-based businesses with predictive insights and subscription management.

It offers retention management functionality, allowing you to estimate customer health scores and predict churn. By analyzing user behavior and subscription data, it identifies at-risk customers and suggests retention strategies to drive growth.

Price: Starts from $99/month for up to 3 users.

Make strategic decisions with Smartlook’s PX insights

SaaS analytics isn’t just about solving problems; it’s about guiding startups toward future success. It has a lot to unveil, and you need to find a way to make those insights work for you.

Smartlook deciphers user behavior and empowers startups with actionable insights to make data-driven decisions and drive SaaS growth.

SaaS analytics starts with Smartlook. Book your free demo or sign up for a free, full-featured 30-day trial today.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post SaaS analytics insights unveiled: The key role of analytics in startup growth appeared first on Smartlook Blog.

But that’s only the tip of the iceberg—interpreting and incorporating them into your product strategy is where the real challenge begins. 

Thankfully, we’re here to help product teams along the way. Be sure to continue reading to learn more about:

• Key product metrics in the B2B SaaS environment
• Vanity metrics product teams should avoid
• How to incorporate product metrics into the product development process
• How to use metrics to inform product development: 3 use cases

Key product metrics in the B2B SaaS environment

As Jason Cohen of WP Engine pointed out, choosing which metrics to track depends on who you’re presenting them to:

“Executives want financial outcomes, strategists want systemic impact, managers want team accountability, teams want credit for executing work, planners want to track progress, ops wants to know that systems are stable and secure.

They’re all correct, so how do we select metrics that satisfy everyone?”

JASON COHEN

Founder at WP Engine

In this article, we’re focusing on the metrics product teams should rely on to inform their roadmaps. Below is a list of metrics that should be included in every product dashboard.

Acquisition Metrics 

You don’t have to wait around for product usage statistics to accumulate before you can evaluate the performance of your product. Acquisition metrics will give you a good idea of how an audience perceives your product, including whether it meets their needs (or whether you’ve chosen the right audience).

What acquisition metrics say about your PX: While typically used to assess the effectiveness of marketing and sales activities, these metrics are valuable assets for young product teams as they’re good indicators of product-market fit — whether or not your product is aligned with the needs and expectations of your target market.

Conversion Rate

The conversion rate is a metric that product teams, in addition to marketers and salespeople, should pay attention to. It’s an early indicator of whether your SaaS product resonates with your target audience or not. 

Note: What you define as a ‘conversion’ depends on your customer journey and business strategy. Any key action to be a conversion, from a free trial sign-up to upgrading to a premium package. 

How to measure it: Divide the number of conversions (e.g., sign-ups, free trials, demos, etc.) by the total number of visitors to your website or landing page, and multiply it by 100 to get a percentage.

Customer Acquisition Cost

The Customer Acquisition Cost (CAC) is a financial indication of whether your product is product/market fit.

How to measure it: Add up all sales and marketing expenses involved in acquiring new customers and divide the sum by the number of new customers acquired over a specific period.

Activation and Engagement Metrics

While valuable early in the product cycle, acquisition metrics don’t say much to product teams that have already found their product-market fit—activation and engagement metrics do.

But these metrics can be tricky as every company has its own definition of product value. This is why it’s paramount that you define your own. 

“Activation isn’t the goal. It’s the leading indicator of the goal. Conversion, $$, retention, PQLs, etc. — that’s what you really care about. Your growth team might be laser-focused on improving activation only to find out that all of their work didn’t improve business outcomes.

As you scale, start to segment users and personalize activation paths to different audiences.

Not all of your users are trying to achieve the same thing. So why do you push them down the same onboarding path?

Folks often focus on simplifying onboarding, reducing friction, etc., but can’t crack the code on improving their activation rate. The problem: you shouldn’t have only had one definition of activation.”

KYLE POYAR

Operating Partner at OpenView

What activation and engagement metrics say about your PX: These metrics show how well users engage with your product features. They usually correlate with the quality of your onboarding process and your ability to understand and meet your customers’ needs in regard to product functionality.

You should use activation and engagement metrics to identify friction points users face early in their product journey.

Activation Rate

The activation rate measures the percentage of users who take a specific action or reach a milestone early in their product journey. Depending on your product, actions can include completing a tutorial, setting up a profile, or performing a key task.

How to measure it: Divide the number of activated users by the number of registered users. It’s also a good idea to calculate the activation rate by separating users into different cohorts (e.g. by registration date, customer demographics, etc.) to gain insight into the behavior of specific segments. 

“Our activation moment is when a new user sends their first document. We’ve taken three key steps to define it:

1. Identify actions/events with the ability to predict when users will convert to paid members. 

2. Conduct interviews to understand when users truly grasp the value of PandaDoc.

3. Summarize findings to select a metric related to a predictive event supported by user interviews.”

KONSTANTIN VALIOTTI

Product Director at PandaDoc

What it says about your PX: The activation rate measures how well your onboarding process guides users toward success.

Free-to-Paid Conversion Rate

A free-to-paid conversion rate represents the percentage of free users who transitioned to paid customers.

How to measure it: Divide the number of users who transition from the free version of your product to the paid version by the total number of free users, then multiply that figure by 100 to get a percentage.

A low free-to-paid conversion rate may indicate a range of issues:

• Your product hasn’t delivered on your marketing/sales messaging promise
• The free version doesn’t fully showcase the value of your paid features
• Users face friction or barriers during the upgrade process

Onboarding Completion Rate

The onboarding completion rate measures the percentage of people who have successfully gone through your onboarding flow (and thus are most likely to become loyal users).

How to measure it: Divide the number of users who have successfully completed the onboarding flow by the total number of new users over a specified time.

A low onboarding completion rate may indicate several issues:

• The flow is too long and time-consuming
• Your content is irrelevant to the needs of users
• It’s displayed at the wrong time in the wrong place
• Users are proficient enough to use your product without basic guidance

Time to Value

Time to Value (TTV) measures how fast users can get value from your product after registration.

How to measure it: First, define an action that indicates a user has realized the core value of your product. Next, create an event for this action in your product analytics software and start tracking it specifically for new users. Do this with Smartlook by creating a cohort of users who registered on the same day, then turning to Retention tables to see the statistics for the specified event day by day.

New Customer Churn Rate

New customer churn rate is a metric that focuses on understanding the churn rate as it relates to new SaaS customers.

How to measure it: Divide the number of customers who canceled or stopped using your product during a period by the total number of customers at the beginning of the said period, then multiply it by 100 to get a percentage.

Feature Adoption Rate

Feature adoption rate measures the percentage of users that actively engage with specific product features.

How to measure it: Divide the number of users who interacted with the feature over a specific period by the total number of users who interacted with your app over the same period.

Retention Metrics

Retention metrics help you assess the long-term health of your B2B SaaS product. 

What retention metrics say about your PX: These are direct indicators of how satisfied customers are in the long term. 

Customer Retention Rate

The customer retention rate gauges the ability of your product to gain long-term users.

How to measure it: To calculate the customer retention rate, you’ll need to collect data on the number of customers at the beginning of a specific period (let’s say a month) and the number of customers at the end of that period. Divide the number of customers who remained active by the total number of customers, then multiply by 100 to get a percentage.

Customer Lifetime Value

Customer Lifetime Value (CLV) is a crucial metric that quantifies the total value a customer brings to your business throughout their entire relationship with your company. It assesses the long-term revenue potential of each customer.

How to measure it: Calculate the average revenue generated from a single customer throughout their entire relationship with your company.

Customer Satisfaction Score

A Customer Satisfaction Score (CSAT) measures how satisfied your customers are with your product after interacting with it for some time.

How to measure it: Run an in-app survey asking users to rate their satisfaction with your product on a scale (typically from 1 to 5 or 1 to 10).

Tip: You can build in-app CSAT surveys with Survicate. Target various customer segments for a comprehensive understanding of customer satisfaction across the entire customer journey. 

Survicate integrates with Smartlook allowing you to back customer feedback with real-time user behavior insights. 

Net Promoter Score

A Net Promoter Score (NPS) measures customer loyalty, including the likelihood of customers recommending your product or service to others. 

How to measure it: Ask your customers how likely they are to recommend your product or service to others on a scale from 0 to 10. Based on their response, you can segment customers into Promoters (score from 9-10), Passives (score from 7-8), and Detractors (score from 0-6).

It’s worth noting that, on their own, CSAT, NPS, and other metrics don’t offer much value to product teams. You’ll need to take these metrics in context and analyze them alongside other relevant data points to see their value. 

Vanity metrics product teams should avoid

So, why not just incorporate as many metrics as possible into your dashboard? Although it may sound like a good idea, there is a risk of falling into the trap of vanity metrics.

Vanity metrics are data points that appear significant at first but fail to provide valuable insight into the actual performance/health of your product. These metrics are not directly correlated with your objectives and therefore will only divert your attention from actionable insights.

The most common examples of vanity metrics for product teams include:

• Total sign-ups 
• Total conversions
• Session duration

Instead of fixating on vanity metrics, product teams should prioritize insights into user behavior and product performance. 

Here are some tips to avoid falling into the vanity metrics trap:

• Identify your product’s primary objectives 
• Select metrics that directly relate to those goals
• Group the metrics that relate to different stages of the customer journey to make it easier to analyze the data
• Ignore metrics that aren’t aligned with your objectives

That’s it. You’re ready to track and use your product metrics to inform your product map and optimize your product for maximum customer satisfaction. Just stick to the instruction below.

How to incorporate product metrics into the product development process

Choosing the right product metrics is only the beginning. You still need to incorporate them into your product management routine. 

Set up a monitoring system 

When setting up or revisiting your product tracking system, you should know what you’re looking for. As mentioned, compiling as many product metrics as possible into one dashboard won’t help you make smarter product decisions—it will only distract you from what really matters.

These are some rules that will help you keep your product data organized and aligned with your goals:

• Map out product user journeys (you may have different user journeys for different custom segments) and define key milestones throughout
• Set up events with your product analytics software to keep track of milestones. Smartlook can track any website or in-app event, from landing page visits to saving a project in your product
• Identify the key metrics that align with each dashboard’s goal
• Make sure to set up a system for tracking each metric. For instance, while you can track conversion rates and activation metrics using Smartlook, you’ll need to add Survicate into the mix to collect user feedback 
• Stick to the “one goal = one dashboard” rule. With Smartlook, you can create  granular dashboards that focus on specific user segments, product aspects, and teams

Understanding metric hierarchy

Understanding metric hierarchy will help you allocate your resources and efforts effectively so you can focus on the metrics that will have the most impact.

To make sense of the metrics you’re tracking, try splitting them into three levels:

• North Star—this is the ultimate measure of product success. It can be MRR, CLV, or any other revenue-related metric
• Key influencers of North Star—these are the secondary metrics that have a direct impact on the North Star metric 
• Levers—these are the individual initiatives you can employ to improve Key Influencer metrics

By organizing your metrics into the levels described, you’ll create a clear hierarchy to guide your decision-making process. Now, your product team can prioritize their efforts based on the impact each metric has on the North Star.

It also helps to understand the difference between leading and lagging indicators. Leading indicators, like activation and product adoption, predict future success. Lagging indicators, such as revenue or CLV, measure the outcome of past actions. 

“Revenue is a multi-input, lagging indicator of success.

So, even though it is the ultimate measure of success for a product, you have to put it in context with other metrics to run your product properly.”

JASON COHEN

Founder at WP Engine

Compare the metrics against your goals/benchmarks

All these metrics only make sense when viewed in the context of your business goals and benchmarks. 

Once you have your North Star, you’ll need to set goals for Key Influencer metrics and levers—the milestones you’ll need to reach to achieve your primary goal. These are the milestones you’ll be comparing your actual product performance against. 

Combine quantitative and qualitative insights

You’ll need to go beyond the numbers. While quantitative data provides numerical metrics and statistics, qualitative insights will show you the user behavior and experiences behind the numbers.

Use quantitative product metrics to spot potential issues in the customer experience. Instead of coming up with hypotheses regarding what may have caused an issue, turn to qualitative insights like funnel reports and session recordings to detect real customer struggles. 

Here are a few examples of quantitative and qualitative data working together:

• Activation. Your TTV metric is too high, and you don’t know why. You proceed to analyze other activation metrics only to find out that your onboarding completion rate is low. In Smartlook funnels, you review session replays from individuals who drop from the onboarding flow. It turns out they were closing guidance pop-ups and then struggling to find help content within your UI.

Session recordings in Smartlook

• Feature adoption. Quantitative data shows that a certain feature has a low adoption rate. To understand why users aren’t engaging with the feature, you perform a funnel analysis and discover that your users aren’t following the flow you’ve laid out for them. With help from in-app surveys, you ask users about their experience with the feature only to learn they’re unaware of it.

Funnel view in Smartlook

• Customer satisfaction. Recent surveys indicate poor customer satisfaction scores within a customer segment. Through contextual micro surveys, you discover that customers in this segment are experiencing issues with a new product update.

Survicate’s in-product surveys

Revisit your product development cycle regularly

Revisiting your product development cycle regularly is a critical practice that allows product teams to stay agile, responsive, and aligned with the ever-changing needs and expectations of their customers.

Create a schedule for periodic reviews and updates to your product roadmap. Depending on the complexity of your product and the pace of the market, you may choose to conduct these reviews monthly, quarterly, or on a more frequent basis.

Prioritize friction points

Friction points are areas where users encounter difficulties, obstacles, or frustrations while using your product. While developing new features is important, focusing on resolving existing issues will lead to more immediate and tangible results.

Here’s what you need to do:

• After identifying friction points using quantitative and qualitative product data, assess the impact of each issue on the UX
• Connect the identified friction points with your overall business goals and product strategy
• Evaluate the effort and resources required to address each friction point
• Prioritize the friction points that have a negative impact on the UX but are feasible to address quickly

Use the insights to forecast future performance

Product metrics not only help product teams address issues but they also allow them to gauge a product’s future performance and anticipate potential challenges.

Use product metrics to spot early signs of UX issues and address them proactively. The most insightful metrics in this instance are customer satisfaction scores and Net Promoter Score (NPS) over time. If these begin to decline, you can still proactively investigate and address the root cause before it impacts other product performance metrics.

Collecting historic product data will help you understand cause-and-effect relationships and forecast future performance with accuracy. By analyzing past trends and patterns in product metrics, you can identify the factors that have historically influenced your product’s success and failures and address them more effectively in the future.

Search for continuous opportunities for growth

Nothing is worse for product development than sticking to a static strategy. To achieve sustainable growth, you should not only focus on addressing immediate issues but also proactively identify and capitalize on growth opportunities.

Keep a direct line of communication open with your users. Customer feedback is the best way to unearth growth prospects aligned with user needs.

Use historic product insights to make hypotheses and develop experimentation plans. These insights unveil trends, behaviors, and patterns that lay the foundation for hypotheses. 

What worked before? What didn’t? 

Use this knowledge to predict how proposed changes might impact user experiences. 

Most importantly, before pursuing growth opportunities, be sure to check whether they align with your long-term business objectives. While it might be tempting to aim for quick victories, it’s smart to ensure your efforts guide your product toward long-term success.

How to use metrics to inform product development: 3 use cases

Product metrics serve a greater purpose than simply displaying team performance during routine reviews. They should inform product team decisions throughout the entire development lifecycle.

Here are the most common use cases for incorporating product metrics:

Validating hypotheses

When considering product changes, product teams speculate about how these updates will impact UX and key performance indicators (KPIs).

By setting up clear success criteria and defining the right product metrics, product teams can test hypotheses and determine whether proposed changes are likely to yield desired outcomes.

Prioritizing feature enhancements

In a dynamic product development environment, teams are often bombarded with ideas for new features and improvements. However, it’s essential to prioritize these ideas based on their potential impact on the user experience.

Product metrics provide objective data to evaluate the potential impact of feature adjustments. By analyzing metrics like customer feedback, feature adoption rate, and customer retention, product teams can identify which enhancements are likely to foster the most user value. 

Addressing product friction

By analyzing quantitative data like activation and engagement metrics and retention rates, as well as qualitative data from user feedback and support tickets, teams can pinpoint the specific pain points users face.

With this data in hand, product teams can develop targeted solutions to alleviate them. Whether it’s improving the onboarding process, simplifying complex features, or streamlining the checkout flow, data-driven insights help teams prioritize the most critical friction points and focus on improving the UX.

Back your product metrics with PX insights

With Smartlook, you’ll bridge the gap between quantitative product metrics and qualitative PX insights so you can:

• Collect the most important product metrics including conversions, retention, and feature adoption stats 
• Analyze funnels and review session recordings to understand user behavior, including the “why” behind the numbers
• Dig deeper into CSAT and NPS scores with Survicate & Smartlook integration
• Accumulate enough historic data to make informed decisions regarding product performance and anticipate potential challenges

Book a free demo or start your free, full-featured 30-day trial today!

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Product metrics that matter: Optimizing Product management in B2B SaaS appeared first on Smartlook Blog.

Competition in the B2B SaaS sphere is about to reach boiling point as customers are becoming increasingly discerning about the products they invest in. Providing a frictionless product experience (PX) is no longer an additional perk but a key factor in the success and sustainability of your B2B product.

But is it possible to eliminate all points of friction in your SaaS product, or is that more of a pipe dream?

In short, although you can’t (and shouldn’t) get rid of product friction completely, you can minimize it.

In this article, we’ll cover what constitutes ‘good’ and ‘bad’ product friction and equip you with a strategy to help you spot and eliminate harmful friction. In addition, we’ll also cover the following topics:

• What is product friction?
• 3 Types of friction in the SaaS environment
• How to identify friction points in your product
• 5 Examples of points of friction and how to overcome them

What is product friction?

Product friction refers to any obstacle that hinders a user’s experience while interacting with a product. This includes any points of resistance that make it difficult for prospects and customers to complete desired actions.

Product friction can be the result of various factors, including a slow or glitchy user interface (UI), complex navigation, excessive steps to complete actions, slow loading times, confusing terminology, a lack of clear instructions, and more.

3 Types of friction in the SaaS environment

Friction comes in several forms, including ‘good’ and ‘bad’ friction.

Wait, is product friction ever a good thing…?

In short—yes! Good friction refers to intentional design elements that are strategically implemented to achieve specific goals. Good friction should help you guide users, prevent errors, and deliberately create delays regarding specific actions.

Examples of good friction in the SaaS environment include:

• Confirmation dialog boxes. When a user is about to perform a crucial or irreversible action, such as deleting data, a confirmation dialogue box introduces friction by popping up and asking the user to confirm their decision

Source: Slack

• Progressive onboarding. By gradually introducing users to features and offering tooltips, guided tours, and contextual help, you’re introducing good friction by enabling users to navigate your product more effectively

Source: Wingspan

• Setting password complexity. Requiring users to create strong passwords with a combination of letters, numbers, and special characters is an example of good friction that enhances security

Source: Microsoft

Bad friction, on the other hand, is any unintended or excessive obstacle that negatively impacts the user experience. There are three major types:

• Emotional friction
• Cognitive friction
• Interaction friction

Emotional friction 

Emotional friction refers to obstacles that transform the users experience into an intimidating and discouraging ordeal.

This is the hardest type of friction to describe, grasp, and combat due to its complex and subjective nature. You can only address this type of product friction if you have a deep understanding of end-user emotions and struggles.

Here are some examples of emotional friction:

• Unmet expectations—i.e. misleading ads or promises that don’t align with a product’s actual capabilities
• Loss of control—i.e. unexpected changes or actions that users can’t oversee or undo, such as accidental data deletion
• Lack of empathy—i.e. robotic interactions, especially regarding customer support and error messages

Interestingly enough, it’s emotional friction that can give you the biggest competitive advantage. 

In order to gain traction in the competitive market, you should identify friction points in your competitors’ products that bother your target audience the most—this is what we refer to as emotional friction. Only then can you create a solution that appeals to them functionally and emotionally.

“In the context of product-market fit, friction is what’s in the way of a perfect product experience. Traction is the outcome of enough people adopting a product that it removes enough friction to be worth adoption costs (time+money).

A new product won’t gain traction unless the people that come before them experience friction.

You can’t get traction without friction.”

Kenny MacKenzie

Founder & CEO Acen.ai

Source: LinkedIn Post

Cognitive friction 

Cognitive friction occurs when excessive mental effort is required to complete a task within a product. Complex workflows, difficult-to-understand instructions, and overwhelming information all contribute to cognitive friction. 

Examples of cognitive friction in the SaaS environment include:

• Unclear guidance—i.e. complex or technical jargon that makes it hard for users to understand product features
• Over-complicated workflows—i.e. too many steps to complete a task
• Product not performing as expected—i.e. error messages appear in response to certain actions

Interaction friction

Interaction friction refers to the difficulties that arise when users interact with a product’s UI. It can be caused by: 

• Ambiguous navigation—i.e. a cluttered or poorly organized UI
• Unresponsive controls—i.e. buttons or links that don’t work as expected
• Performance issues—i.e. slow loading times for pages or features

How to identify friction points in your product

Let’s walk through a simple yet actionable strategy for identifying points of friction in your SaaS product.

1. Map out the user journey 

You know there are problems somewhere in the PX, but where? 

Your first step toward answering this question is identifying specific touchpoints that may cause friction throughout the customer journey. 

User journey mapping is the process of identifying the key stages and milestones users go through, from product discovery and the conversion stage to ongoing product adoption. 

Here’s what you need to do:

1. Define user personas. You already have user personas in place, so it’s time to revisit them. Each persona should represent a specific customer segment. This will make it easier for you to visualize customer journeys for users with various needs and pain points. 

2. Outline the key touchpoints for each persona. Make a list of the critical touchpoints where user personas interact with your product. These touchpoints can include visiting your website, signing up for a trial, using specific features, seeking customer support, and more.

3. Create user journey maps. Visualize user journeys with maps and flowcharts. The number of customer journeys should correlate with the number of customer personas.

4. Identify gaps and discrepancies. Surprise-surprise, your ideal customer journey and reality may fail to align. Analyze the differences between the outlined journey and the actual customer journey—this is where you’ll identify the biggest friction points. 

Once you’ve broken down the user journey(s) into smaller steps, it’s much easier to analyze your product usage data and identify trends in the PX.

2. Look at your product metrics at each stage of the journey

KPIs like conversion rates, bounce rates, feature adoption rates, churn rates, and others will indicate whether you’re dealing with product friction and whether you need to do anything about it.

To interpret the metrics, look for patterns, trends, and anomalies that indicate areas of concern, for instance:

• Low conversion rates combined with a high bounce rate suggest friction during the onboarding process
• A low feature adoption rate may be caused by different factors—from friction in the onboarding process to unclear communication regarding new features
• A low task completion rate indicates friction in the user interface, such as confusing workflows

Once you spot an alarming pattern, you can dive deeper into the root cause by looking at the user experience from a user’s perspective. 

3. Collect user feedback

You already have some quantitative insight into your product’s performance. Now it’s time to collect some user feedback to gain access to the qualitative insights that metrics alone don’t reveal.

By asking open-ended questions, running surveys, and analyzing customer support interactions, you’ll be able to see your product through your customers’ eyes and uncover the type of friction you’re dealing with. Here’s an action plan to help you on your journey:

• Create targeted surveys to gather specific feedback from users regarding their experiences with your product. Consider running in-app surveys triggered by specific user actions
• Conduct one-on-one interviews with loyal customers. What do they have to say?
• Analyze customer support tickets and interactions. What are the most frequent issues or common user frustrations?

A combination of product metrics and user feedback will give you a good idea of the pain points your users experience. That said, you’ll still lack the necessary context to come up with a solution to minimize product friction. 

4. Look into your product analytics data

First you used product metrics to detect signs of friction. Then you turned to user feedback to understand the customer’s point of view. Now, it’s time to look at the PX through your customers’ eyes. 

These tools will help you collect the PX insights that will provide you with more context and reveal underlying patterns: 

• Session recordings—by watching these recordings, you can see firsthand how users interact with your UI, including moments of hesitation and where they get stuck
• Heatmaps—use heatmaps to pinpoint elements that users attempt to interact with but are not clickable or functional
• User paths—also known as a funnel analysis, a user path analysis highlights common sequences of actions users take within your product, including if and where they deviate from the intended flow

This is where you’ll need the user journey maps you built earlier. Once you understand how different customer segments should behave inside your product, it’s easier to spot the behaviors that don’t align with your user journeys.

Combining the insights from session recordings, heatmaps, and user paths with quantitative data and user feedback will help you spot potential bad friction points in your SaaS product.

5. Translate the data into friction points

Now that you have gathered valuable insights from various sources, it’s time to turn it into product friction points—the good kind.

For best results, consider the following steps:

1. Look for common issues that users repeatedly face, such as difficulty with specific screens or interactions
2. Group friction points into emotional friction, cognitive friction, and interaction friction to understand their nature
3. Prioritize the friction points that have the most impact on the user experience and overall product success
4. Segment friction points based on user demographics or behavior to tailor solutions to specific user needs

With this data in hand, you’re ready to design and implement the solutions that will address your friction points.

5 Examples of friction points, including how to overcome them

Friction points come in all shapes and sizes. Here are the most common friction points SaaS customers face.

1. Overcomplicated sign-up flow

Problem: An overcomplicated sign-up process is the first major source of friction in the user experience. Often, it includes too many steps making it challenging for users to register.

“It doesn’t matter how great your marketing is if your buying process is a nightmare.”

Diego Oquendo

Performance Marketing Manager at Catalyst Consulting

Source: LinkedIn Post

Identifying the problem: Analyze drop-off rates along each step of the sign-up flow to identify where users are struggling. Perform a funnel analysis with Smartlook to spot friction points in your checkout funnel.

Pair a funnel analysis with screen recordings (also accessible in Smartlook) to see what the user experience looks like behind each drop-off.

Addressing the problem: If possible, remove all friction from the registration process. There’s no place for ‘good’ friction in the sign-up flow. Leading product experts recommend that SaaS companies minimize friction in the buying process by showcasing the product first:

“Spotify helps you experience the product’s value with minimal friction.

You don’t even need to sign up—their landing page is their product”

MATT HINDS

Co-Founder at Sauce

2. Lack of user guidance

Problem: A lack of user guidance can be a significant friction point for SaaS customers. 

Identifying the problem: There are quite a few indicators of poor onboarding. Start with measuring the time it takes for users to unlock the core value of your product. If it takes too long for users to achieve value, it’s a sign they’re lacking proper guidance.

In Smartlook, you can assess your product’s time-to-value (TTV) by running a cohort analysis. This involves setting up tracking for a specific group of people (e.g. users that signed up over the last month) to identify how many of them completed the first key action (e.g. save the project). All you need is to define a cohort, a.k.a. a customer segment, and define the event you want to track.

Once you’re all set, you can access detailed stats regarding the cohorts’ activity over a specified timeframe in the retention table:

In the Y-axis in the above table, you can see users split into groups by the day they signed up for a free trial. The X-axis refers to the number of users who took the specified action (in this case, file upload) nine days following their registration. To assess whether your new users are achieving the first milestone fast enough, compare the number of users taking action to the benchmark TTV (oh yea, you also need to set a benchmark).

“If you’re a product manager that keeps an eye on conversions and new business in regards to a product, cohorts will help you a lot.

Every time you put a cut in each of the steps your users make along their journey, you make the data set smaller and more controllable. It’s like freezing a group of users in time.

Cohorts also give you more influence. So the smaller the parts that you chop, the better.

If you look at users that convert and bring money to your business, every chop you make, the more control you have. Let’s say your business has 100 new users. They may convert. But, so what?

If you build a cohort and aggregate, let’s say, 100 users from this campaign on this channel, it gives you more control over what you can analyze. For example, you can dig deeper into:

The time to value (how much time it takes for your free users to turn into paying clients). Do we have the right amount of free features, including behind-the-paywall features? How is our product performing over a specific period of time?

So, cohorts make it possible to improve your product in a product-led growth framework. Cohorts are actually the only way to make an impact on a product and have better control over it.”

MICHIEL VERMEULEN

Growth & revenue manager

Addressing the problem: Unlike the conversion journey, an onboarding flow benefits from ‘good’ friction (adding a layer of educational content before letting new users explore your product).

3. A confusing user interface

Problem: A cluttered UI is a common point of product friction. It often happens when a company incorporates too many features or wishes to create a design that “stands out.”

Identifying the problem: Turn to heatmaps and session recordings. Heatmaps allow you to visualize quantitative data in regard to recurring patterns in user behavior, such as areas of interest and the ones that get ignored. From here, you can dig deeper into the exact reasons behind these trends by reviewing session replays.

Session recordings represent qualitative data that helps uncover the behavior of individual users (which make up patterns when put together). These recordings will show you how users interact with your UI and point out where visitors get angry, lost, or confused.

Addressing the problem: The solution depends on the exact problem you’ve identified during your analysis. But you’re likely to start by improving the visual hierarchy of your UI and creating a consistent layout. You should also consider simplifying the navigational flow by reducing the number of clicks required to reach essential features.

“Recently, we had a major UX challenge with our product: users were finding certain flows unintuitive. Here’s what we did to fix it:

We built and maintained a “core flows” library. Examples for us included setting up alerts, dashboards, investigating outages, etc.

User testing—we started internally since our product is used by engineers. Now every new employee is put into a pool for testing, and we actively recruit our users

We systematically grade and improve the UX towards A’s

A = new user can complete with no guidance 

B = users can complete with conceptual instructions 

C = users require step-by-step instructions 

This way, all major flows are tested, and friction is minimized before GA release.”

ROSS LAZEROWITZ

Head of Product at Observe, Inc.

4. Failure to reach the “aha!” moment

Problem: The “aha!” moment is the point in the user onboarding process where users experience the true value and benefits of your product. In other words, it’s the moment they decide to stick with you. Failing to reach this moment as early as possible in the user journey can lead to user disengagement and churn.

Identifying the problem: This is a difficult one. First off, you need to define when the “aha!” moment happens along the user journey. To validate it, you need to trace the correlation between this moment and user retention—do users that reach it stay with you longer?

Once you’ve defined the “aha!” moment, you can set it as an event in Smartlook and track how many people (if any) reach it. 

The funnel view will work best for uncovering insight into the user journey. Since the “aha!” moment is rarely just one event but rather a sequence of actions, a funnel analysis will help you understand whether users are completing the path you’ve outlined for them, including where they’re experiencing problems. 

Addressing the problem: If the data shows you need to get your users to reach the “aha!” moment faster, you should focus on optimizing the onboarding process. You can start by tailoring the onboarding experience based on user personas and their specific needs. Walk users through the most critical aspects of your product with interactive guides to ensure they understand and experience its value early on.

5. Too much information

Problem: While providing detailed onboarding guidance is helpful, logging into a new app and suddenly being bombarded with multiple instructions can be irritating. Failing to strike that balance can lead to information overload and visual noise.

Identifying the problem: Observe user behavior to see if visitors appear confused or spend excessive time trying to process information. Also, be sure to pay attention to how many users abandon certain screens or features quickly, as it may indicate information overload.

In Smartlook, you can create a funnel for tracking the onboarding process—just define the milestones, a.k.a. events, for your funnel and activate monitoring. 

Addressing the problem: Simplify the screen and trigger one experience at a time. Avoid bombarding users with multiple prompts or options at once, and give them time to absorb the information and take action.

“An example of product friction we recently overcame is overwhelming users with too many options. By breaking down our user onboarding checklist into two parts, we saw double-digit gains in our completion rate—from less than 2% to about 25%.

Initially, our onboarding checklist had several steps and was the same for all user groups. So we decided to split the list out across post-purchase and trial, creating separate lists for each phase of the experience. 

The first checklist now focuses on installing the Appcues Builder. Once the first checklist has been completed, it triggers the second checklist, which includes 3 items: create a flow, track an event, and create a goal. 

The idea is to daisy-chain the checklists together so folks don’t have a 45-page to-do list. They have just a couple of little setup tasks—truly bite-sized tasks, teeny little things. This makes each step more palatable and easy to complete.”

RAMLI JOHN

Content Director at Appcues and the author of Product-Led Onboarding

Identifying and reducing user friction with Smartlook

The goal of product designers and developers is to strike a balance between good and bad friction. While good friction can guide users and enhance PX, bad friction creates unnecessary barriers to the user experience. 

To draw a line between these two and minimize bad friction, turn Smartlook’s PX insights. The platform offers a set of product analytics tools to help you go as deep as possible into the points of friction in your PX and the context behind them. With event funnels, heatmaps, and session recordings, you’ll be able to access a visual representation of your product’s quantitative and qualitative data helping you understand and resolve user pain points.
Schedule a Smartlook demo or start your free, full-featured 30-day trial today.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Create a Seamless User Experience: 5 Ways to Overcome Product Friction in B2B Saas appeared first on Smartlook Blog.

That said, creating product dashboards and reports chock-full of data that matters is a challenge for many product teams. It’s easy to cut and paste default dashboards provided by your product analytics software, but they often lack the specificity and depth required to truly understand your product’s performance.

Throughout this article, we’ll provide you with practical tips for creating custom product dashboards that align with your product objectives. We’ll also explore how to turn product data into reports that resonate with C-level executives.

• Dashboards or reports?
• Types of product analytics dashboards
• How to build zero-fluff product dashboards
• 21 metrics you’ll want to include in your product report
• How to turn your product data into reports for C-level executives

Dashboards or reports?

There’s a good chance you already know the difference between dashboards and reports. That said, it’s important to draw a line between them so you (and your team) know when to use what. 

What are product dashboards?

Product dashboards are dynamic, visual representations of data that provide an at-a-glance overview of a product’s key performance indicators. They offer real-time (or near-real-time) insights and are typically presented in a visually appealing and intuitive format.

Dashboards make use of charts, graphs, gauges, and other graphics to present complex information in a clear and understandable manner. They also include interactive features like filtering, drill-down functionality, and dynamic data updates that enable product teams to access deeper insights.

Dashboard example in Smartlook 

How to access product dashboards

You can build product dashboards within your product analytics platform. Smartlook offers interactive, customizable dashboards that allow you to visualize all sorts of user experiences and product-related data.

When you need product dashboards

Dashboards are ideal for tracking high-level metrics that provide an overview of your product’s performance. 

They’re particularly valuable for real-time monitoring and obtaining quick insights when it’s imperative for teams to have their finger on the pulse of key metrics to assess their progress.

What are product reports?

Product reports provide a more comprehensive and structured analysis of data over a specific period. They enable more in-depth data exploration, often facilitating retrospective analysis and long-term trend identification, and allow you to share insights with stakeholders. 

Reports typically focus on specific areas of product performance and go into more detail compared to high-level dashboards. It’s also common for people that deliver reports to interpret the data, offer explanations, and provide their own perspective. 

How to access product reports

The data that makes up a product report is usually extracted from the same analytics tool used for product dashboards. In Smartlook, you can activate reporting from any dashboard — all you need to do is specify the reporting frequency and period.

When you need product reports

Product teams use reports for strategic decision-making and communicating key findings to a broader audience.

The most common use case for reports is regular performance reviews when a product manager (PM) needs to present and interpret product analytics data for stakeholders and management.  

Types of product analytics dashboards

There’s no such thing as a one-size-fits-all product dashboard. You’ll need different types of dashboards for different needs. 

1. Operational dashboards

Operational dashboards (or product operations dashboards) monitor KPIs related to operational efficiency, system health, and service reliability. They often include metrics like uptime percentage, response time, error rates, and system resource utilization.

This type of dashboard helps product teams quickly identify and address any operational issues, such as server downtime, latency spikes, or service disruptions. 

Source: Sentry

2. Strategic dashboards

Strategic dashboards display high-level insights and metrics aligned with the long-term goals of the product. These dashboards help monitor the overall health and success of a product by displaying metrics related to user acquisition, conversion rates, revenue growth, and customer satisfaction. 

Management is primarily responsible for maintaining strategic dashboards. Product leaders turn to strategic dashboards to make informed decisions surrounding a product’s direction and resource allocation.

Source: Geckoboard

3. Project management dashboards

Project management dashboards allow you to visualize the progress and status of product development projects. They include information relating to project timelines, task completion, resource allocation, and team productivity. 

You can build these dashboards using your product management software. For instance, Jira offers dashboards that provide a comprehensive overview of project status at a glance, including to-do list tasks, team member progress, and more. 

Source: Atlassian

4. User experience dashboards

User experience (UX) dashboards provide a view into how users interact with a product. They visualize user paths, points of friction, and other metrics that reflect the UX.

This is perhaps the most insightful type of dashboard for product-led teams, as they enable you to see your product from the user’s perspective. 

User analytics dashboards take many forms:

• Funnel visualizations: The visualization of the user journey, including the paths users take as they navigate a product

• Heatmaps: Insight into where users click, scroll, and spend the most time in a product’s interface 

• In-app events: Statistics on specific user actions and events within a product  — best for identifying the most/underused features

Smartlook supports all three types of UX dashboards, giving you a comprehensive overview of how your users experience your product.

How to build zero-fluff product dashboards 

There’s a good chance your product analytics software has already prepared a default dashboards tab for you. But to gain meaningful insights from your product analytics, you’ll need to go beyond the pre-built options and create custom dashboards tailored to your project objectives.

1. Use product analytics software with data visualization capabilities

Not all product analytics tools are equally as effective when it comes to dashboards. Some offer limited customization capabilities, and others struggle to provide up-to-date insights. 

Look for software that allows you to easily create, customize, and update visualizations to suit your needs.

We’ve already touched upon software solutions that will help you create different types of dashboards, but let’s bring them all together:

• Geckoboard — dedicated dashboard software that combines data from multiple analytics platforms to centralize your KPIs
• Smartlook — a product analytics platform that features UX dashboards for sharing insight into user behavior
• Sentry — an application performance tracking tool that provides you with a broad overview of your application’s health by collecting and visualizing statistics relating to product issues
• Jira — a product management system that allows you to create custom dashboards to monitor project progress, track tasks, and visualize key metrics related to your projects

“Smartlook is a tool that helps product managers make the right product decisions. Instead of relying on your intuition, you have access to clear data regarding user behavior and how your product works. PMs can set up custom views of specific product data in Smartlook, save it to a dashboard, and view the current status in moments. With Smartlook, you can place virtually any view of the data on the dashboard, such as DAU, WAU, MAU metrics, time charts for key product events, or funnels, for example.” 

Tomáš Bia

Senior Product Manager at “Smartlook”

2. Create different types of dashboards based on projects and objectives

Assuming you already have the right analytics software in your tech stack, let’s move on to the best practices for creating dashboards that make a difference.

As there’s no shortage of things you can put on your dashboards, choosing from a variety of metrics, time ranges, and types of widgets can be overwhelming.

That’s why we recommend one major objective per dashboard. Think of each dashboard as a focused lens through which you can examine specific metrics and data points related to a particular goal or area of analysis. 

For instance, you can have a UX dashboard that sheds light on user behavior and product adoption metrics, a strategic dashboard that provides high-level insights on product and business KPIs, and a sales dashboard that tracks revenue and conversion rates.

This approach helps you focus and maintain clarity when analyzing your product data.

In Smartlook, you can create an unlimited number of dashboards and manage them autonomously. This allows you to create unique dashboards across teams that include only the most relevant tiles (widgets).

Consider creating really granular dashboards that focus on specific user segments, product aspects, or teams. 

With Smartlook, you can segment your data based on different parameters, such as user demographics, traffic sources, or user behavior. Segmenting your data can provide deeper insight into specific user groups and behaviors, allowing you to tailor your product reports accordingly. 

3. Select metrics and key performance indicators (KPIs)

Let’s say you have an empty custom dashboard tab in front of you. What should you add to it?

First, you’ll need to determine the objective and KPIs for your dashboard.

A clear objective sets the direction and focus of your analysis, guiding you in selecting the right metrics and visualizations to include.

Ask yourself, “What specific question or area of performance do I want to understand or improve?”

Keep your objective SMART: specific, measurable, attainable, relevant, and time-bound.

If you want to boost user engagement among high-ticket customers, your objective may sound something like “Increase the average session duration of Enterprise plan users by 20% within the next quarter.”

Once you have a defined objective, break it down into specific components or sub-objectives. These sub-objectives should align with your overall goal and focus on different aspects or dimensions related to your product or business.

For instance, if your objective is to increase user engagement, your sub-objectives might include understanding user behavior patterns, identifying points of friction in the user journey, or analyzing the impact of specific features on engagement metrics.

These sub-objectives will form the tiles of your dashboard. Each tile helps you visualize the user experience from a different angle, allowing you to gain insight into different aspects of user engagement.

4. Limit the number of tiles per dashboard

When designing your dashboard, remember that less is more. Resist the temptation to overload your dashboard with excessive metrics and visualizations. 

Cluttering your dashboard will only confuse your goals and make it difficult to find the information you’re looking for. Instead, focus on including only the most important tiles.

Keep the number of tiles per dashboard to a manageable range, ideally no more than 5-7 tiles. 

5. Configure dashboard access

Configure access permissions to ensure that the right people have the appropriate level of visibility into your product data. 

When managing permissions, consider the following factors:

• User roles. Identify the different user roles within your organization that will need access to the dashboard. This may include executives, product managers, analysts, or specific team members. Determine the level of access and functionality each role requires

• Access levels. You may have read-only access for stakeholders who only need to view the dashboard, while product managers or analysts may require editing capabilities to customize the dashboard and explore the data further

6. Activate reporting for events in your project

You’re all set. What’s next?

The next step is to activate reporting for the dashboards you’ve built. Regular reporting allows you to receive and share updates regarding the metrics and insights displayed in your dashboard.

And when it’s time for your performance review, you can use those reports as a base for your presentations. Speaking of which… 

21 Metrics for your product reports

You shouldn’t include every possible metric in your product analytics reports just to make them look more solid. Similar to dashboards, your reports should focus on specific aspects of your product growth.

We’ve compiled a list of must-have metrics based on the angle of your reports:

1. Revenue-related metrics

C-level executives, finance teams, and stakeholders want insights into the financial performance of a product. Include the following metrics in your reports to meet their expectations:

• Monthly Recurring Revenue (MRR) measures the predictable, recurring revenue generated by your product on a monthly basis. It provides insight into the stability and growth of your subscription-based business
• Average Revenue per User (ARPU) calculates the average revenue generated per user or customer. It helps evaluate the monetization potential of your user base and the effectiveness of your pricing strategies
• Customer Lifetime Value (CLTV) estimates the total value a customer brings to your business over their lifetime. It assesses the long-term profitability of acquiring and retaining customers
• Revenue Churn Rate measures the rate at which your revenue is lost due to customer cancellations or downgrades. It highlights the health of your customer base and identifies opportunities for reducing churn

2. User adoption and engagement metrics

When reporting to product managers, marketing teams, and customer success teams, you’ll need to cover the following metrics:

• Active Users represent the number of users actively engaging with your product within a specific time period 
• User Retention Rate calculates the percentage of users who continue to use your product over a given period
• Time on Task measures the average time users spend performing specific tasks or actions within your product. It gives you a better idea of the usability of your product
• Feature Adoption Rate tracks the rate at which users adopt specific features of your product. It’s helpful for evaluating the success of feature releases and making product improvement decisions
• Customer Churn Rate measures the percentage of customers who stop using or cancel their subscription to your product over a given period
• Churn Rate Cohort Analysis segments customers based on their acquisition cohorts and tracks their churn rates over time

3. Conversion and funnel metrics

Product teams need to work closely with sales departments to align their efforts and build a product that meets users’ needs. Therefore, you may need to collaborate on reports that dig into the conversion funnel. 

Among other KPIs, these reports should cover the following:

• Conversion Rates: measures the percentage of users who complete a desired action, such as making a purchase or signing up for a subscription. Product teams usually track free trial conversion rates to assess the effectiveness of their onboarding flows and overall user journeys
• Funnel Drop-off Rate: tracks the percentage of users who abandon the conversion funnel at various stages. It helps identify potential bottlenecks and points of friction in the user journey and suggests areas for optimization
• Customer Acquisition Cost (CAC): calculates the cost incurred to acquire a new customer

4. Performance and efficiency metrics

Product teams need to analyze performance and efficiency metrics internally and with product ops to identify opportunities for optimizing operational efficiency. 

These reports focus on the technical aspects and operational performance of a product, including:

• Server Response Time: measures the time it takes for your server to respond to user requests
• Application Load Time: represents the time it takes for your product to load and become usable
• Error Rate: tracks the frequency and occurrence of errors or issues faced by users while interacting with your product
• Uptime and Downtime: displays the percentage of time your product is available and accessible to users (uptime) versus the time it is unavailable or experiencing issues (downtime)

5. Customer satisfaction metrics

This is where customer support teams, customer success managers, and product managers join their efforts. 

A customer satisfaction analysis report includes metrics that assess how users feel about their overall product experience:

• Net Promoter Score (NPS): measures customer loyalty. Users need to rate their likelihood of recommending your product or service on a scale of 0-10. Based on their responses, customers are categorized as Promoters (9-10), Passives (7-8), or Detractors (0-6)
• Customer Satisfaction (CSAT) Score: measures customer satisfaction. It typically involves asking customers to rate their satisfaction on a scale or provide feedback relating to specific aspects of their experience
• Customer Effort Score (CES): measures the ease of doing business with your company, including using your product
• Support Ticket Resolution Time: tracks the average time it takes for your support team to resolve customer issues or inquiries. For product teams, it’s also an indicator of the intuitiveness and usability of the product

How to turn your product data into reports for C-level executives

The last skill to master is communicating your reports to management and C-level executives.

1. Focus on high-level strategic insights

C-level executives are primarily interested in the big picture and strategic decision-making. 

When creating reports, focus on providing high-level insights that align with the company’s goals and objectives. Highlight key metrics and trends that directly impact the overall product strategy.

2. Simplify complex data into actionable summaries

You surely have a lot of product analytics data, but unfortunately, that’s not what C-level executives want to see. They want you to present your findings in a concise and easily digestible format. 

After covering the numbers, summarize and present your reports in the form of actionable insights and recommendations. Use clear and straightforward language to communicate your findings, avoiding jargon and technical details that may obscure the main message.

3. Present data visually using charts and graphs

Visualizing data through charts and graphs enhances understanding and makes it easier for executives to grasp important information quickly. 

Consider using tiles from your dashboards if it helps you present key points. 

4. Provide ideas for improvement and next steps

Go beyond data presentations and offer recommendations for improvements based on your analysis of the product data. 

Present ideas for optimizing performance, addressing challenges, and capturing opportunities. Include specific next steps and strategies that can be implemented based on the insights you’ve discussed.

Build customized product dashboards with Smartlook

Product analytics doesn’t have to be difficult. If you don’t overcomplicate your dashboards and stick to the “one objective=one dashboard” rule, it should be easy to obtain the necessary insights the moment you turn to your product data.

With Smartlook, you can build customized product dashboards effortlessly. We offer a user-friendly interface that enables you to turn your UX data into insightful visualizations and reports. 

Sign up for a free 30-day trial to see for yourself. 

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Product dashboards & reports: how to create an insightful report as a PM appeared first on Smartlook Blog.

A customer won’t abandon a product that delivers a seamless user experience. And while it may sound like a pipe dream for product teams, it’s more than possible to thrill users with the right analytics tools combined with a reliable user journey map.  

In this guide, we’ll provide you with an actionable strategy for optimizing the user journey of your SaaS product (from activation to advocacy), including some tips that will help you deliver a seamless product experience throughout each stage.

• What is user journey mapping?
• Identifying opportunities for user journey optimization
• How to optimize the SaaS user journey
• 13 user journey optimization ideas (by stage)
• 3 best practices for SaaS user journey optimization

What is user journey mapping?

User journey mapping is a technique that involves understanding and visualizing key user interactions within your product, from the initial point of contact to when users stop interacting with your software (churn).

In simple terms, it’s the process of mapping the various touchpoints users interact with when navigating your software. Its purpose is to look at the product experience (PX) from the user’s perspective, identify pain points, and uncover opportunities for improving PX. 

User journey mapping helps product teams:

• Better understand user motivations, goals, and pain points
• Identify gaps and areas for improvement
• Align business objectives with user needs
• Enhance the overall user experience, including customer satisfaction
• Generate ideas for new features and enhancements
• Improve usability and conversion rates
• Reduce customer churn by creating a smooth user experience

User journey maps may take different forms — from timelines and flowcharts to storyboards and custom maps. The style you adopt will depend on the complexity of the user journey and the specific needs of your business.

In this article, we’ll focus on user journey optimization as it relates to improving flows you’ve already established by analyzing user behavior and identifying areas for optimization. Let’s get going.

Identifying opportunities for user journey optimization

You should base all product changes on data-driven insights combined with a deep understanding of your users. Before making any changes, gather and analyze user insights to identify opportunities for optimization. Here’s how to do it. 

1. Map out user journey stages and key milestones

What stages does your user journey include? 

A standard product journey map begins with signing up. It usually looks like this:

• Sign-up
• Onboarding
• Adoption
• Advocacy

You may wish to add more steps to your user journey map based on the peculiarities of your product.

Within each user journey stage, there are specific milestones or touchpoints that users must reach to get to the next stage. These milestones represent significant actions, events, and interactions that shape the user experience.

Once you’ve identified the key stages, you can take a more granular approach and add milestones such as the “aha!” moment, activation, training completion, etc.

By doing so, you’ll better understand the steps your customers need to take to reach the advocacy point, including how you can make their journey smoother. It’s much easier to analyze your product usage data once you’ve broken down the entire user journey into digestible chunks.

From here, you can focus your analysis on specific stages and milestones, making it easier to identify patterns, trends, and anomalies within each segment.

“When optimizing your product user journey, try to clearly define the desired user behavior at each step of the journey. For example, you may want users to complete the registration process or create an account.

Once you’ve defined the desired user behavior, you can then identify any roadblocks that may prevent users from achieving it. You can then optimize the user journey to remove such roadblocks and increase the likelihood of your users reaching the desired behavior.”

Matthew Ramirez

Founder at “Rephrase”

2. Implement event tracking

Events include any user interactions within your product. To collect quantitative data relating to how users make it through the different stages of the journey, you’ll need to set up event tracking (if you haven’t done so yet).

Here’s how you can set up event tracking based on the user journey stages and milestones you defined earlier:

• Track the activation process. Implement event tracking to capture the user’s sign-up journey, including form submissions, account creation, reaching the “aha!” moment, and any additional steps involved 
• Monitor onboarding progress. Track user interactions during the onboarding phase, such as completing setup steps, tutorial views, feature activations, etc. 
• Track feature usage. Capture user interactions with key features and functionalities. Monitor events such as feature activation, usage frequency, and duration to gain insight into how users engage with your product’s core offerings 

With Smartlook’s Event Analytics tool, you can set up custom events for any user action within your product. 

You can then dive deeper into your product analytics and create user paths based on the data Smartlook collects for you. 

3. Conduct a funnel analysis

A funnel (aka user path) analysis refers to a process aimed at understanding how users navigate your product. By analyzing user paths, you can uncover patterns and bottlenecks and spot areas where users face challenges throughout their journey.

This is where you have a chance to validate your user journey stages to ensure your users are actually making it through the paths you’ve mapped out for them. 

Are users skipping certain steps and failing to reach the “aha!” moment? A funnel analysis will provide you with answers to these questions and more.

“Instead of relying on assumptions, Smartlook enables PMs to see user behavior for what it really is. For example, instead of assuming your users are following the path you’ve laid out for them, Smartlook’s “Funnels” feature will give you definitive proof. It’s not uncommon for our assumptions to trump reality. As a PM, it’s your responsibility to sort fact from fiction and continuously improve your product to achieve your desired goals.”

Tomáš Bia

Senior Product Manager at “Smartlook”

Use an analytics tool like Smartlook to track and visualize user paths within your product. The platform will help you spot any deviations or drop-offs that indicate potential pain points or areas for improvement. 

The Smartlook team recently used its own funnel analytics feature to redesign its Signup flow, with a primary focus on acquisition. To ensure seamless implementation, the product team defined the key objectives and closely monitored each step, leveraging the funnel view for analysis and improvement.

With Smartlook’s funnel view, the team was able to gain valuable insight into the user journey and identify potential issues and high dropoff areas. 

4. Drill down to get granular user data

Noticing high drop-off rates? Then it’s time to look into the reasons behind them. 

With Smartlook, you can view session recordings for every event along the user path, drop-offs included. Just click the “play” button next to the event you want to dig into, and you’ll see a list of every session replay associated with it. 

Click on any recording to watch a replay of a user interacting with your product prior to an event.

Within each session recording, pay attention to user interactions, navigation patterns, clicks, and scrolling behavior. Look for any usability issues, confusion, or roadblocks that users may be facing.

Analyze multiple session recordings to identify common pain points or challenges faced by users. Are there specific features or steps that consistently lead to confusion or frustration? These are the areas you should prioritize in your roadmap.

5. Run a cohort analysis 

At the moment, you have an incomplete view of your user data. In other words, you have a unified view that will drive you to wrong conclusions if your user base consists of more than one customer persona. 

The thing is, different types of users will experience your product differently. That’s absolutely normal. Running a cohort analysis will give you a deeper understanding of user behavior, allowing you to optimize the user journey for different user segments or personas.

Cohort analysis is a part of behavioral analytics that involves grouping audiences based on shared characteristics. 

“Every time you put a cut in each of those steps your users make along their journey, you make the data set smaller and more controllable. Think of it as freezing groups of users in time.

Cohorts also help you gain more influence. The smaller the parts that you chop, the better.”

“Micheil Vermeulen”

Growth & revenue manager

Here’s how to perform a cohort analysis to identify opportunities to optimize the user journey:

• Segment your user base into cohorts based on different attributes such as acquisition source, subscription plan, user behavior, or other relevant criteria. These cohorts should represent distinct user types or personas within your user base
• Compare the behavior of different cohorts to identify patterns and understand the impact of specific variables on the user journey
• Look for opportunities to optimize the user journey based on the behaviors and experiences of potentially high-value cohorts

Performing a cohort analysis isn’t as difficult as it may seem, especially with quality software. You can use Smartlook’s Retention Tables to see how specific user categories progress through the product journey. 

Let’s take a quick look at how a cohort analysis for a cloud drive product could look. Say a product team’s objective is to drive 50% of free users to upload at least 1 file to their cloud storage within 10 days of usage. 

In the Retention Table, rows represent users grouped by day of sign-up, and columns represent the first 10 days of product usage. The analysis is done both horizontally and vertically, looking at the percentage of users performing the event each day after their initial interaction with the product.

The table reveals that on day 0, 96% of free users uploaded at least 1 file to their cloud storage. However, the trend shows a significant decrease in the event’s occurrence over the following days. This indicates potential issues or challenges that are causing users to discontinue completing the desired action.

The product team may draw the following conclusions from this cohort analysis:

• Users faced errors that deterred them from continuing to perform the file upload event
• Users faced difficulty uploading files due to a lack of understanding or friction within the product
• The design process for the file upload feature is not optimal, leading to user dissatisfaction
• Users don’t see the benefits of uploading files if they are unable to share them afterward

Based on the analysis, the team can come up with hypotheses regarding how they can optimize new user journeys. They’ll need to implement those changes one by one until they see a significant improvement in user behavior. But that’s a subject for the next section. 

6. Gather user feedback

The easiest way to determine customer pains within product flows is with direct feedback from users.

With a survey tool like Survicate, you can collect user feedback through in-product microsurveys and connect responses with the user data obtained with Smartlook (check out Survicate & Smartlook integration). This approach combines quantitative data with qualitative insights, allowing you to incorporate the voice of the customer (VoC) into your user journey optimization strategy.

Design questions that focus on specific stages or milestones in the user journey to get insight into issues troubling your customers along user paths. 

It’s also a good idea to uniquely approach separate user segments. For instance, send loyal customers open-ended questions to encourage detailed product feedback. Use microsurveys to reach users in the early stages of their product journey.  

How to optimize SaaS user journeys: Put action to your insights

By now, you should have enough data and insight to come up with ideas for improving the user experience. In the following steps, we’ll teach you how to turn those insights into actionable steps to enhance the user journey.

1. Identify areas for improvement by user journey stage

Use your product analytics data to brainstorm ideas for addressing the pain points you’ve identified at each stage of the user journey.

Think about potential solutions and enhancements that could improve the user experience and drive better outcomes at each stage. Consider both small tweaks and significant changes to address pain points effectively.

For instance, what would you do if you discovered that a large chunk of your audience wasn’t completing the target action within your product? If you dive deep into the data, you may discover that perhaps they haven’t completed in-app training.

From here, you may wish to consider various action plans, including:

• Shortening your training workflows (but make them obligatory)
• Adding tooltips so users can learn about your product features on the go
• Making the user path smoother and more intuitive 
• Setting up targeted notifications or email campaigns to remind users about underutilized features

Not all ideas will be equally effective or feasible, but you can always assess and prioritize them later.  

This stage requires a collaborative effort. Consult various teams, including designers, customer service representatives, salespeople, and others who have direct interaction with your users. Each team brings a unique perspective regarding customer behavior and pain points. This way, you can tap into their collective knowledge and generate valuable solutions for optimizing the user journey.

2. Prioritize optimization opportunities

Once you have generated a list of potential solutions to address the identified pain points in the user journey, it’s time to prioritize them. 

If you’ve been developing your product for some time, there’s a good chance you already follow a framework dedicated to product enhancement. But if you don’t have a specific framework, the Value vs. Effort prioritization matrix is a good place to start. 

Here’s how you can approach it:

• Assess the potential impact of each change on the user experience, including user satisfaction, and desired outcomes
• Determine the level of effort and resources required to implement each change. Consider factors such as development time, complexity, dependency on other features or systems, and other potential risks and challenges
• Create a matrix with the “Value” on the vertical axis and “Effort” on the horizontal axis. Plot each proposed change on the matrix based on its potential impact and the amount of effort required

Prioritization matrix by Productboard

Once you’ve placed your optimization ideas on the matrix, prioritize the changes that fall in the high-value/low-effort quadrant. These are the changes you should start with.

Don’t forget that although some changes may have a lower immediate impact, they may still be necessary for future improvements (they may also have synergistic effects with other optimizations). Be sure to balance your priorities accordingly.

3. Prototype and testing

We’re talking about reshaping the product journey, not simply fixing bugs. So before you make any significant changes to your UX, test and validate them first.  

Here’s what you’ll need to do:

• Develop prototypes or wireframes for the proposed optimizations
• Conduct usability testing
• Show your prototypes to loyal users and stakeholders 
• Look for patterns and common themes in their feedback
• Refine prototypes and wireframes based on their suggestions
• Iteratively test and refine your designs until you achieve the desired user experience

Only after validating your ideas will you be ready to release them to the public. 

4. Implement changes

We’ve reached the stage where you can finally take action and optimize your product’s user journey. That said, there’s no need to rush. 

Instead of implementing all changes at once, consider a phased or incremental rollout. 

It’s also a good idea to A/B test updated journeys. Release the optimized version of your product to a portion of your user base while keeping the original version for comparison.

To get reliable insights from such a test, be sure to integrate your A/B testing software and your product analytics tool (remember Smartlook?). While tools like Optimizely or Firebase A/B testing will allow you to collect quantitative data for different versions of your product, Smartlook will display exactly how users interact with each variant.

5. Continuously monitor and iterate

Product user journey optimization is literally a neverending journey. 

You need to monitor user behavior and engagement post-implementation to assess the impact of the optimizations. Smartlook visualizes historical data in its dashboards, allowing you to track how product changes affect user behavior. 

Continue to gather feedback from users through surveys, interviews, or review channels to further refine the user journey.

13 User journey optimization ideas (by stage)

Still not sure what kind of improvements you can make to optimize the user journey? We’ve hand-picked the best user journey optimization examples to help you out.

1. Sign-up

In the sign-up stage, your key task is simple: to encourage more users to sign-up. 

Consider the following tips:

1. Simplify the sign-up process. Remove any unnecessary steps or information fields to make the sign-up process quick and easy 
2. Collect user data to customize onboarding flows. Display a new user questionnaire right after a person logs into your app for the first time, and offer personalized product flows to different user cohorts 
3. Guided tours and feature showcases. Provide in-app tours or interactive walkthroughs during the activation process to introduce users to the key features of your product

“At Testlify, we needed to improve the user journey. To do so, we simplified the registration steps, reduced the number of required fields, and introduced a guided tour to familiarize new users with key features.

We also added progress indicators to help users understand where they are in the onboarding process, minimizing confusion and increasing completion rates.”

ABHISHEK SHAH

Founder at “Testlify”

2. Onboarding

The onboarding stage sets the foundation for a positive user experience.  

You can optimize product journeys for newly activated users by:

1. Implementing on-demand tutorials or tooltips within your product to guide users through key features and functionalities
2. Proactively reaching out to users who might be experiencing difficulties or haven’t yet engaged with critical features
3. Providing contextual prompts or messages based on user actions to offer assistance with underutilized features and workflows 

3. Adoption

This is where users decide if they’ll stick with your product or not. And it’s in your best interest to make sure they stick.

Here’s what you can do:

1. Offer personalized recommendations on how users can get the most out of your product, highlighting features that align with their needs
2. Provide easily accessible self-help resources, such as a knowledge base or interactive FAQs, to assist users as they explore and adopt more advanced features
3. Simplify the user experience in collaboration with your design team by removing unnecessary steps or features and improving the user interface (UI)

4. Advocacy

Even when it seems you’ve won over your loyal audience, you need to nurture and empower them to become advocates for your product. 

The following are some ideas for optimizing the user journey for long-lasting customers:

1. Develop advanced educational resources, such as webinars, video tutorials, or blog articles. HubSpot Academy is a perfect example of an educational resource built into the user journey 
2. Provide personalized support to long-lasting users, fostering a strong relationship between your brand and the user
3. Seek customer input through beta testing programs and early access opportunities to involve them in the product development process
4. Optimize performance and loading times to ensure a smooth and efficient user experience

3 Best practices for SaaS user journey optimization

These simple yet effective tips will help you smoothly optimize the user journey.

1. Define your goals first

Before you dig into your product analytics data, clearly define your goals and objectives. 

What are you aiming to achieve through optimizing the user journey? It doesn’t make sense to make changes when you’re perfectly happy with your product performance (which is hardly the case for product teams, though).

Are you looking to increase user conversion rates, improve user retention, or enhance user satisfaction? By setting clear goals, you can focus on the aspects that matter most and prioritize your optimization initiatives accordingly. 

2. Foster cross-functional collaboration

User journey optimization is a collaborative effort that requires involvement from multiple teams within your organization. 

You need to bring together teams such as product management, design, development, marketing, customer support, and data analytics. Each team will bring unique expertise and insight into the user journey. 

3. Document the process to measure outcomes

It’s best to document every step of the process to make it easy to trace changes and evaluate the outcomes. 

Start by capturing the initial state of the user journey before implementing any changes. Then describe the optimizations, their objectives, and their expected impact on the user experience and business goals.

Record the implementation details relating to each change, such as the timeline, resources involved, and any technical considerations. Continuously track and measure the impact of the optimizations on relevant metrics and key performance indicators (KPIs). Document the data collected and analyze it to assess the effectiveness of the changes.

Improve the user journey with Smartlook analytics

User journey optimization is no small feat. But its long-term benefits, such as an improved user experience, higher customer satisfaction, and increased revenue, are well worth it.

Turn to Smartlook for reliable user journey analytics. Smartlook allows you to gain a deep understanding of how users interact with your product at every stage of their journey.

From identifying opportunities for optimizing the user journey to measuring the impact of changes, Smartlook is your partner in product analytics. 

Book a free Smartlook demo or start a full-featured, 30-day trial today. 

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post From first customer clicks and on: How to optimize the user journey throughout your SaaS product appeared first on Smartlook Blog.

That being said, achieving product-led growth in a SaaS environment will help you create an excellent customer experience, drive retention, and grow your product faster. Not sure how to do it? Fear not, we’ve got you covered.

In this guide, we’ll cover the following:

• What is PLG?
• Why product-led growth is the answer for SaaS companies
• How PLG compares to other product growth strategies
• How to implement a product-led growth strategy in SaaS
• How to measure the effectiveness of your PLG strategy

What is product-led growth?

Product-led growth (PLG) is a business strategy where all strategic decisions are driven by product usage. 

The PLG framework focuses on building a product that’s valuable and intuitive enough for customers to find, adopt, and use without extensive marketing or sales efforts. When following this strategy, product teams should prioritize the user experience to create products that customer love, filled with features and benefits that are easy to understand and use.

Why product-led growth is the answer for SaaS companies

PLG is particularly effective for SaaS companies for a range of reasons:

• PLG lowers the entry barrier. In the product-led growth framework, SaaS applications are usually self-serve, allowing users to navigate interfaces and workflows on their own 
• PLG reduces acquisition costs. Product-led companies require fewer resources to market and sell their products
• PLG boosts CLV.  Due to the high cost per acquisition, long-term retention is one of the primary objectives for software companies. PLG helps to build apps that provide real value to customers, thus driving retention and increasing CLV
• PLG drives virality. The strategy relies heavily on viral customer acquisition (aka word-of-mouth). For instance, loyal customers typically have access to a referral program and receive rewards for inviting others to join the app
• PLG supports cross-functional alignment. The strategy creates a shared sense of ownership around a product (which can’t be said about traditional sales-led or marketing-led growth strategies)
• PLG supports upselling and expansion. Users of intuitive products are more likely to adopt new features or upgrade to higher-priced plans

These were the benefits of product-led growth for SaaS — but is it really the only strategy that works for SaaS startups?

PLG and other product growth strategies

PLG isn’t the only growth strategy that proves effective. There are at least three more strategies that are popular among SaaS businesses:

• Sales-led growth
• Marketing-led growth
• Customer-led growth

Let’s discover what these are and how they compare to the product-led strategy.

Product-led vs sales-led 

Sales-led growth (SLG) is a business strategy that emphasizes direct sales to drive revenue growth. This strategy requires building a strong sales team to successfully move prospects through the sales funnel. 

Salesforce is an example of a successful sales-led organization. The company’s primary focus is enterprise-size organizations that require personal assistance throughout the buying journey. 

While PLG strives to create self-service customer journeys, sales reps in sales-led companies assist their prospects throughout the buying journey.

Although sales-led growth isn’t as good at driving long-term retention as product-led growth is, there are quite a few reasons many SaaS companies prefer it to other growth strategies. Sales-led growth is perfect for:

• Selling expensive products
• High-touch sales where products require a lot of customization
• Companies with a one-time fee pricing model (e.g. for selling licenses)

Product-led vs marketing-led

Marketing-led growth (MLG) involves running extensive marketing initiatives to drive customer acquisition, retention, and expansion.

Companies like HubSpot and Ahrefs have achieved great success with a marketing-led growth strategy. Both companies have built large content libraries to generate interest in their products with top-funnel content.

Qwirl is an example of a successful marketing-led company:

“Qwilr has an inbound-driven motion, where prospects discover Qwilr’s proposal software through thought leadership content, proposal templates, performance marketing, and targeted webinars.”

Brendan Connaughton

Head of Growth Marketing at “Qwilr”

In comparison to product-led growth, MLG is mainly focused on creating awareness around a product and generating short-term interest. The growth strategy aims at bringing the prospects into the sales funnel and turning them into qualified leads. 

Despite a lot of SaaS companies actively moving away from this strategy, MLG is still a go-to-market strategy for:

• Winning competitive markets
• Rapidly entering new markets
• Short-term customer acquisition

Product-led vs customer-led

Customer-led growth (CLG) is often confused with the product-led approach. Indeed, these two strategies have a lot in common, but, in essence, they’re different.

Due to the very fine line between them, it’s hard to tell a PLG-driven company from a CLG-driven company from a distance. But upon closer examination, we can trace the features of customer-led growth within brands like Slack and Airbnb. These companies put the customer at the center of their growth strategies.

CLG is a growth strategy built around serving customer needs. It heavily relies on customer data and feedback to drive product development. The goal of this strategy is to build strong customer relationships, deliver a great user experience, and boost retention.

Similar to CLG, product-led growth also prioritizes retention as a means to drive business growth. However, the two strategies use different mechanisms to reach their goals. While customer-led companies analyze the needs and preferences of their existing customers (through customer interviews, surveys, etc.) to guide product development, product-led companies rely on product experience data to make strategic decisions. 

Choosing between CLG and PLG isn’t easy. Customer-led growth is great for companies that have the resources to maintain direct communication with users, collect customer insights, and deliver personalized experiences throughout the entire customer journey.

At the end of the day, none of these strategies are mutually exclusive. If you choose to pursue product-led growth, it doesn’t mean you don’t need to run advertising campaigns or invest in your sales team anymore. It only means that from now on, all your teams should be focusing on highlighting your product value and helping your audience to realize it. 

For instance, Serpstat is a PLG-driven company with CLG features. The company uses both product data and customer feedback to drive continuous product improvement:

“We strive to remain relevant and valuable in the ever-evolving digital marketing landscape by constantly developing our product based on user feedback and product data analysis. As a result, we can make sure our customers have access to cutting-edge features and functionality that meet their evolving needs and allow them to achieve their goals effectively.”

Daria Ahieieva

Content marketer at Serpstat

8 Steps to implement a product-led growth strategy in SaaS

If you’ve chosen PLG as your main business growth strategy, you’ll need to implement it. Luckily, the process is quite straightforward:

1. Create a self-serve buying experience

As has been said, PLG is perfect for products that are intuitive enough for users to navigate without much help from your end. And it all starts with a self-serve buying experience.

Customers should be able to discover your product and sign up for it on their own, without the need for sales or support assistance. This means you need to create an intuitive website that clearly communicates your product’s value proposition and pricing plans. You should also create a simple and short registration process and provide clear instructions for getting started with your product.

Here’s how the EmbedSocial team has created a self-serve onboarding experience for their customers:

“From the start, our focus has been addressing high-intent questions people search for on Google. We create content tailored to their needs based on these searches.

For instance, if someone is searching for a way to embed multiple TikTok videos on their website, they’ll land on a page that offers them the relevant widget and a free trial. It’s worth noting that our onboarding flow is complemented by a live chat feature where our customer success team is readily available to help users who may encounter difficulties with self-onboarding.”

Kate Bojkov

Head of Growth at EmbedSocial

Simple sign-up process in Embed Social

2. Offer a full-featured free trial or freemium

Letting potential customers explore your app is the easiest way to communicate the value of your product. It’s also one of the pillars of PLG.

“We have a 14-day free trial period for our users, which helps us drive product-led growth. With this free trial, we are able to collect valuable user data from our customers that help us understand how they use the platform and identify areas of improvement.”

Will Yang

Head of Growth & Customer Success at Instrumentl

PQLs, or product-qualified leads, are free trial and freemium users who have experienced the benefits of your product first-hand, realized its value, and are ready to become paying users. You can identify PQLs by looking into your product analytics.

For example, if you notice a particular user has been logging in frequently and spending a lot of time on a specific feature or page, that could be a strong indicator of interest and potential qualification as a PQL.

You can track user activity in Smartlook. Define actions that you want to track as events and see how often users complete these events in your app.

In PLG, free trials have a bigger purpose than simply converting new users into paying customers. When watching new users experience your product for the first time, you can collect a wealth of useful data. By tracking user behavior during the trial period, you’ll identify friction in the product experience and find a way to eliminate it. 

3. Reshape your product onboarding experience

The first experience with your product creates a lasting impression on your customers. A smooth and intuitive onboarding experience will help you make a great first impression and drive product-led growth.

Onboarding sets the tone for the entire product experience and determines whether your end users will fully adopt your product or not. While a sales-driven growth strategy involves high-touch, personalized onboarding by human agents, PLG involves creating a consistent self-serve onboarding process and continuously improving it.

As you collect data on the first-time user experience (see step 2), think about how you can act on it to create a better onboarding process. This may involve implementing in-app guidance, minimizing user interface (UI) complexity, or simplifying workflows. Use product analytics data to drive your decision-making.

You can use Smartlook’s product analytics to watch new users interact with your product for the first time and drive informed conclusions. Just connect the tool to your app and watch session recordings to see what users do, including why they don’t follow the paths you’ve built for them. 

With Smartlook, Hookle, a social media engagement app, has been able to improve the user experience and optimize the onboarding process: 

“Instagram and Facebook didn’t make it clear to users how to sync their accounts. It required a certain amount of time and was troublesome for users. We wanted to make it more frictionless. With Smartlook’s screen recordings, we improved app onboarding. Now, there are no failed onboardings and account connections.”

Jere Seppälä

Chief technology officer and a Co-founder at Hookle Inc.

Pricing is becoming more of a focal point of the PLG flywheel. By establishing a clear pricing roadmap, you’ll align with your customer demand and effectively encourage product growth.

Here’s how you can create a consistent product-led SaaS packaging strategy: 

• Choose one of the following pricing models: flat-fee, per-user, usage-based, or hybrid? The choice is yours
• Align pricing with value by understanding the features that are most important to users
• Offer tiered pricing plans so that customers can easily upgrade when needed
• Design a product package that serves the needs of each of your customer segments. Yes, you may end up with 10 packages, but that’s all right (look at Hubspot!)
• Use historical data to inform pricing decisions and determine the optimal pricing model for your product

5. Collect usage data with a product analytics tool

Product data is the main driver in PLG. You need to collect data on how customers are interacting with your product and use that data to inform your business decisions.

You should use a product analytics tool to track user behavior, identify patterns, and spot points of frustration. These insights will help you set the right direction for product development and create the smoothest user experience possible. 

Smartlook will help you collect and analyze critical product usage data. Use it to:

• See how your customers are progressing through the system by analyzing user paths
• Watch session recordings and understand the reasons behind why users don’t take the actions you want them to
• Spot points of user frustration by tracking rage clicks
• Analyze heatmaps and see which areas of your screen generate the highest user engagement

Yet you shouldn’t rush to collect and analyze all the data your product analytics tool provides you with. First, define a problem you want to fix in your product and then dig into the analytics to find the reason behind it. For example, you may be checking user paths (aka funnels) and discover the vast majority of users don’t complete a specific workflow. From here, you’ll need to review session recordings to see what issue might have preceded drop-offs.

Analyzing session recordings in Smartlook 

6. Gather user feedback 

Similar to CLG, the voice of the customer (VoC) is important in PLG. Collect customer feedback to discover your users’ reactions to product features, UI, or updates. 

By integrating Smartlook with Survicate, a survey platform, you can gather user feedback and send it directly to your Smartlook account to link the feedback to your product usage data. This way, you’ll have a 360-degree view of the product experience without leaving your product analytics tool. 

Here are just a few of the ways to collect customer feedback with Survicate:

• Place an in-app survey to ask users what they think about a specific feature and whether it meets their needs
• Reach users that have shown early signs of customer churn with targeted in-app surveys 
• Run an NPS survey to discover how your major product update has affected the product experience You’ll need to calculate NPS before you make changes and then measure it a month after the update

7. Build the data into your product roadmap

User research should set the direction for your product development process, ensuring that you stay aligned with your customer needs and preferences. 

When you have the product usage data and customer feedback at your disposal, build it into your product roadmap. To do so, you’ll need to be consistent:

• Identify user needs and pain points. Analyze the usage data to identify the features that are most used and the ones that users struggle with. Based on this data, you can identify the changes required to address user pain and frustration

• Evaluate the potential impact. Consider the potential impact of each feature on user satisfaction, retention, and revenue. Prioritize the features that have the potential to make the most significant impact on the business

• Estimate the effort required. Evaluate the number of resources you’ll need to develop each feature throughout the design, development, testing, and release phases. Prioritize the features that require less effort and can be delivered quickly

• Align with the company’s goals. Ensure that the changes you’re about to make align with the company’s overall business goals and strategy. Prioritize the features that help the company achieve its strategic objectives

• Iterate based on feedback. After making changes, collect feedback from users to evaluate their effectiveness and make adjustments as needed. Iterate the roadmap using product feedback

8. Focus on continuous product adoption

Product-led growth is all about driving continuous product adoption and usage. To achieve this, you should never stop looking for ways to provide ongoing value to your customers. This means regularly monitoring product performance, releasing new features and updates, and incentivizing users to continue using your product.

How to measure the effectiveness of your PLG strategy

There are two ways to track the performance of your growth strategy, and you need them both:

• Monitoring the key product-led growth metrics (aka quantitative data)
• Analyzing the user experience with qualitative data

Monitor the key product-led growth metrics

Start by collecting quantitative insights to measure the impact of your PLG strategy. These include:

Customer acquisition cost

Customer acquisition cost, or CAC, measures how much your company spends to acquire each new SaaS user. CAC includes all the marketing and sales expenses related to acquiring new customers, such as advertising, events, and sales team salaries. 

Since the calculation doesn’t include the product team’s efforts and other expenses that affect customer acquisition directly or indirectly, CAC is usually very low in product-led companies.

Ideally, an effective PLG strategy should allow you to invest less in marketing and sales activities, resulting in even lower CAC. 

User activation rate

This is the percentage of new users who become active product users. You should use this to determine how successful your in-app onboarding process is at converting new users into active customers. 

“One of the key product-led growth metrics we use is the Activation Rate. This helps us understand how users engage with our platform and measure whether or not they are finding value in it.”

Will Yang

Head of Growth & Customer Success at Instrumentl

You can monitor user retention dynamics with Smartlook’s retention tables. The rates in the table represent the percentage of users that stick with your app, week over week:

This feature is particularly effective for spotting changes in activity for particular customer segments (e.g. small businesses vs enterprises).

Customer churn rate

The customer churn rate helps you measure how many users stop using your product over a certain period in comparison to the total size of your customer base. It’s another indicator of how well your company is retaining customers. 

Customer lifetime value

Customer lifetime value (CLV) measures the total amount of revenue a customer is expected to generate for your company over their lifetime. You can calculate CLV by dividing the monthly average revenue per account (ARPA) by the user churn rate. 

A product-led CLV should continuously grow as a result of increased retention and reduced churn. Its dynamics are perhaps the best monetary indicator of the success of your PLG strategy.

Monthly recurring revenue

Monthly recurring revenue, or MRR, is the amount of revenue a company generates from its subscription-based products every month. This metric helps PLG companies assess their strategy health and revenue stability.

Referrals

Remember when we said PLG heavily relies on virality? Here’s the way to measure it.

Referrals are the number of new users or customers that are acquired through existing users or customers referring them to your product. It helps determine the level of satisfaction and loyalty of the user base. 

Analyze the user experience with qualitative data

This was quantitative data — numbers that hardly mean anything without context. Now, you need to bring in qualitative data to understand what drives those metrics and what steps you can take to improve your strategy.

• Watch session recordings (remember, you can do this in Smartlook)
• Collect user feedback with in-app surveys
• Run 1-on-1 interviews with customers

You can also add some quantitative survey metrics to your data mix, like:

• Net Promoter Score (NPS)
• Customer Satisfaction Score (CSAT)
• Customer Effort Score (CES)

Share the insights with your company to support cross-functional collaboration and align your next steps to improve your product strategy.

Become product-led SaaS

Adopting a product-led growth model isn’t difficult, you just need a lot of data to rely on. You should use your product usage data and customer insights to guide product development and create an app that users are perfectly comfortable with. This is basically the essence of  PLG.

Great news — Smartlook can provide you with quantitative and qualitative data to help you grasp the product experience and create a data-driven roadmap. Request a demo now or start your free, full-featured 30-day trial today to drive business growth.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

The post Product-led growth (PLG): how to apply it in SaaS (strategy guide included) appeared first on Smartlook Blog.

A misleading UX, friction points, and poor onboarding all contribute to low user activation rates.

On the bright side, product managers can locate and address all of the friction points that impair user activation in a timely manner. 

Throughout this guide, we’ll delve into 6 tactics that have been proven to boost user activation rates and drive revenue for SaaS products. Moreover, you’ll learn how to measure user activation and act on the data collected.

• What is user activation in SaaS?
• What are the consequences of a low activation rate?
• How do you measure user activation?
• A 6-step approach to boost user activation in SaaS
• Best tools for user activation

Smartlook records user actions and is equipped with session replays to help you unlock opportunities for improvement. Request a free demo or try Smartlook with a full-featured, 30-day trial: no credit card required.

What is user activation in SaaS?

User activation in SaaS refers to the pivotal moment when a new user successfully experiences the core value of your product for the first time. In other words, an activated user is someone who has realized the benefits of your software and, as a result, is more likely to continue using it and become a paying customer.

For example, regarding a project management SaaS, an activated user could refer to someone who has created their first project, added team members, or assigned tasks, thereby experiencing the software’s collaborative capabilities.

What are the consequences of low activation?

Reading about the positive side of measuring user activation is always inspiring. But let’s flip the coin and explore 5 major repercussions of low user activation. This better reflects the reality of product development.

• High churn rate. Users who fail to activate are more likely to abandon your product
• Low customer satisfaction rate. Unsatisfied users who struggle to realize your product’s value will have a lower satisfaction rate, negatively impacting brand reputation and referrals
• Negative marketing ROI. When customers stop using your product before reaching activation milestones, the resources spent acquiring them go to waste
• Decreased customer lifetime value (CLV). Users who don’t fully engage with your product are less likely to convert into paying customers or upgrade to higher-tier plans. This directly affects your revenue stream
• Increased customer acquisition costs (CAC). You’ll need to invest more to acquire new customers to compensate for the ones you’ve lost. As a result, you’ll drive up your customer acquisition costs and potentially strain your budget

“At pgMustard, we measure user activation as a proxy for the user experience. We track how many folks successfully review at least 1, 2, and 5 query plans and review these numbers by weekly cohorts. 

A while back, the number of people submitting at least one query plan (within a week) was as low as 50%. We tried several iterations relating to the improvement of our docs, the error messages we gave, and our welcome emails… but still only managed to get user activation up to about 55%. 

Eventually, after watching new users try and fail multiple times, we bit the bullet and started improving the product to accept badly formatted inputs (the primary cause of issues). This almost instantly took us to around 75%, a number we’ve not managed to improve much since!”

Michael Christofides

Founder at pgMustard

In other words, low activation rates can spell disaster for your SaaS business, resulting in a domino effect of negative consequences. At Smartlook, it’s our mission to equip you with the best tools on the market for maximising user activation.

But first, you’ll need to understand your current user activation rate.

What is the user activation rate?

The user activation rate is a metric that quantifies the percentage of activated users after signing up. It’s a crucial indicator of how well your product delivers value to new users and helps identify areas for improvement in the onboarding process and overall user experience.

What is the formula for calculating the user activation rate?

User Activation Rate = (The number of activated users / Total signups) x 100

For example, if 1,000 users sign up for your SaaS product in a given month, and 300 of them become activated users, your user activation rate is:

(300 Activated Users / 1,000 Total Users) x 100 = 30%

This means that 30% of your users became activated (e.g., experienced the first real value).

What is a good user activation rate? 

For SaaS companies, the average activation rate is 36% (the median is 30%).

How do you measure user activation?

Knowing the formula is not enough. If you find a ratio of new sign-ups, including those who reached the activation point, then you know you’re looking at high-level data. Dig deep to understand how different customer segments adopt your product to make meaningful adjustments.

Step 1. Define activation points

Activation points are specific milestones or actions that indicate a user is deriving value from your product during the onboarding process. These events vary depending on the nature of your SaaS product and its core features. Examples include:

• Collaboration tools: Creating a workspace or team, inviting team members, and engaging in conversation
• CRM software: Importing customer data, creating a sales pipeline, and logging a deal or opportunity
• Social media management: Connecting social media accounts and scheduling the first post

Step 2. Track in-app events and segment customers 

Once you have laid out your activation points, the next step is to set up a product analytical platform to monitor these events. This is important for several reasons:

• By monitoring in-app events, you can get a better understanding of user behavior and identify friction points in the UX and the onboarding process, including what’s causing users to drop-off 
• Segmenting customers enables you to analyze activation rates across different user groups, helping you tailor your strategies to meet the unique needs of each segment

Use Smartlook to assign custom in-app events and analyze the activation rate across segments. Here’s how to do it.

Define key moments in your user onboarding flow and set them as activation events. A set of key moments in the onboarding process could consist of users starting and finishing a product tour (including if they skipped any steps). Smartlook users can create funnels based on specific events around product tours such as start_ProductTour, ProductTour_step1, ProductTour_stepN, and end_ProductTour to quickly monitor drop-off rates between steps (i.e., those that haven’t completed the product tour). 

Once you’ve collected enough data, you can delve into user segmentation to find anomalies in particular segments. Smartlook offers event breakdowns by technologies, location, and event properties. 

Other important user activation metrics to track

In this section, we’ll explore 6 more user activation metrics so you can get a better picture of user behavior to optimize your app. To make things easy, we’ve grouped the following metrics into short-term and long-term objectives.

Short-term user activation objectives

Short-term user activation objectives focus on the initial stages of user engagement within a product. These metrics help evaluate the effectiveness of the onboarding process, the speed at which users realize the value of your product, and the level of early user activity. 

By optimizing short-term objectives, you can quickly address friction points, enhance the user experience, and increase user activation rates.

Onboarding completion rate

This metric measures the percentage of users who complete the entire onboarding process. It helps you identify areas for improvement in the onboarding flow, contributing to higher user activation.

For instance, Vertigo Games keeps a close eye on the onboarding process to quickly locate bugs and other issues that prevent new users from completing the onboarding process. The company relies on Smartlook’s screen recordings and funnel analytics to spot drop-offs, including the reasons behind them. These features allow Vertigo Games to save 10-12 hours of development time.

Onboarding Completion Rate = (The number of users completing onboarding / Total signups) x 100

Time-to-value (TTV)

TTV refers to how much time it takes for a new user to realize your product’s core value. By reducing TTV, you can accelerate user activation and improve user satisfaction.

TTV = Time between user signup and reaching activation milestone.

“At Practice, we measure activation to understand two important points:

1. How quickly a user gets to value

2. If and where there are roadblocks that prevent them from using the product

Within our product, we track the time to value in certain product loops. For example, how long it takes a customer to invite a client or set up their profile. We use tools to easily watch session replays and create funnels to track objectives toward a certain product loop.”

Jeremy Ross

Growth Lead at Practice

Active users

This metric refers to the number of users who actively engage with your product within a specific time frame. Monitoring active users can help you gauge the overall effectiveness of the user activation process.

Active Users = The number of users engaging with an app over a specific time period.

Long-term user activation objectives

Long-term user activation objectives contribute to sustained user engagement and the broader adoption of your product’s features over time. Use insights to inform your product roadmap and refine marketing strategies to encourage ongoing user activation, improve retention, and maximize CLV.

Feature adoption rate 

This is the percentage of users who adopt new product features. Typically, it’s measured across all users to understand whether a feature rollout is successful. But this metric can also point out problems in the onboarding process when measured across users who reach the “aha” moment or activation milestones. 

For example, you can tell if some features are being abandoned by the signup ratio. To drive feature discovery, consider adding some additional steps to your product walkthrough.

Feature Adoption Rate = (The number of users adopting a feature / total number of users (or within a specific user segment)) x 100

StoragePug collects feature adoption insights with Smartlook’s funnel analytics and session replays. These insights validate the feature adoption rate and tell you if a new feature is stable and ready to be promoted to the entire user database.

Product adoption rate

This refers to the percentage of customers who regularly use your product’s core features. Monitoring this metric can help you identify trends and opportunities to improve user activation over time.

Product Adoption Rate = (The number of new active users / Total signups) x 100

User churn rate

This represents the percentage of users who drop off (stop using) your product within a specific time period. To calculate this, you can measure the overall churn rate or examine segments to locate specific friction points.

User Churn Rate = (The number of churned users / Total users at the beginning of a time period) x 100

Analyze your key metrics often to refine and improve user activation tactics. With that, let’s explore 6 proven user activation techniques.

A 6-step approach to boost user activation in SaaS

Ready to drive user activation rates through the roof? Let’s explore six powerful tactics, complete with real-life examples. We recommend employing them one-by-one for maximum results.

1. Use in-app and website heatmaps to identify friction points

The first step toward increasing user activation is to pair in-app and website heatmaps to locate friction points at the beginning of the user journey. Heatmaps allow you to visualize user interactions, such as clicks, taps, hovers, and scrolls. Warm and cold zones show how users navigate your product and website, including where they encounter difficulties.

Smartlook’s in-app and website heatmaps help you uncover areas where users are struggling or dropping off. By analyzing this data, you can pinpoint issues such as confusing navigation, non-intuitive design elements, and poorly placed CTAs.

Case in point: Sewio incorporated Smartlook’s heatmaps to declutter its home page and increased its CTR by 276%.

“With the insights from Smartlook, we’ve achieved our goal of increasing the visibility of our e-shop and customer references. This has helped increase the overall business efficiency of our homepage.”

Petr Passinger

CMO at Sewio

Once you’ve taken care of all the technical issues, the next step is to spend some time creating an outstanding user onboarding experience.

2. Use welcome screens to personalize onboarding

Picture this: a new user stumbles upon your SaaS product and can’t wait to explore its features. But they’re greeted with a generic, one-size-fits-all onboarding experience — not the warmest of welcomes.

Welcome screens in the sign-up flow are your chance to roll out the red carpet for new users, making them feel valued and understood from the get-go. 

Ask users about their jobs to be done, their industry, and their roles. Use this information to customize their onboarding experience. With tailored content and guidance, users will become more engaged.

For example, Lemlist collects your goals and then explores whether you’ve worked with the tool before. If you’re a first-time user, you’ll be taken on an interactive product tour.

Another great example of a welcome screen is by Later. It’s equipped with a social media scheduling platform that studies new signups by asking them questions, including:

• Their field
• Social media marketing goals
• The tasks they want to resolve 
• How they heard about Later

Not only can you use this customer data to personalize the onboarding flow, but you can also use it to determine your top-performing marketing channels.

“In the sign-up flow, we provide users with a mini survey asking them why they joined Reply (JTBDs). Based on this info, we show them a different set of pop-ups that showcase the key values of a product tailored to their needs. With that, we try to push them to their “Aha” moment as quickly as possible.”

Eugene Nechvoloda

Product Manager at Reply.io

3. Implement interactive walkthroughs to guide users to the activation point

Interactive walkthroughs consist of step-by-step, in-app guidance that helps users explore your product’s features by completing actions themselves. They beat passive product tours when it comes to user involvement and shortening time to value.

The third step is to couple interactive walkthroughs with welcome screens to build a highly-personalized and cohesive user experience. Do this, and your user activation rates will soar.

For instance, Lemlist engages signups with cold email campaign creation guidance after they complete a microsurvey on the welcome screen.

Platforms like WhatFix or Appcues allow you to build interactive product walkthroughs and show how different customer segments adopt your product. 

4. Use NPS and CSAT surveys to uncover customer pain points

Dig deeper to find out what users like/dislike. Use NPS (Net Promoter Scores) and CSAT (Customer Satisfaction) surveys to get a feel of user sentiment and uncover areas for improvement.

NPS surveys measure the likelihood of users recommending your product to others, while CSAT surveys assess their overall satisfaction. By collecting feedback through these surveys during the onboarding stage, you can identify and troubleshoot the issues that are preventing users from becoming fully activated.

To implement in-app NPS and CSAT surveys, consider using a tool like Survicate, which offers an easy way to trigger surveys based on specific events and user actions. To capture the most valuable insights, time your surveys strategically. For example, you may wish to trigger an NPS survey after a user has completed an activation milestone or launch a CSAT survey after they’ve interacted with a specific feature.

“Use session recordings to see what a user did prior to/after submitting a satisfaction survey (to better learn what made them happy/unhappy). This can be done in connection with our Survicate integration.”

Renata Ekine

Head of Demand Generation at Smartlook

5. Watch session recordings to understand how users interact with your app

Imagine being able to see your product through your users’ eyes, witnessing firsthand how they navigate your app and where they stumble. With Smartlook’s advanced session recordings, you can do just that.

We recently released a free tool designed to help users find and fix their CRM data challenges. As part of the process of building the app, we had a “soft launch” followed by extensive testing with both live and recorded sessions. 

By watching recordings, we found that users struggled to understand their results in CRM Health Grader. Users were presented with a detailed report of their sales data, but without someone on the call to walk them through the results, they were lost. 

The fix? Pulling out the most important bits and presenting them right at the top. We made the change, tested it again, and our users reported a much better experience.

With such analytics, we exposed user concerns early on and were able to course-correct, leading to greatly improved conversion rates. As a byproduct, we were also able to open the tool up to a wider audience.

Sam Hillestad

Senior Product Marketing Manager at SetSail

Session recordings capture every click, swipe, and scroll, offering a detailed, real-time view of how users interact with your app. By watching these recordings, product managers can gain invaluable insights into user behavior and uncover pain points and usability issues.

You can also filter session recordings by event occurrence, location, duration, and technology and skip idle time.

Here are some of the insights you can glean from session recordings:

• Identify confusing UI elements
• Analyze the onboarding flow
• Observe feature adoption
• Discover user preferences

The StoragePug Product team uses session replays to see how customers adopt new features and locate friction points and unexpected user paths. They also utilize session recordings to analyze and improve product UI.

6. Re-engage inactive users with email marketing

Even with the best onboarding experience, some users might still slip through the cracks and become inactive. With email marketing, you can re-engage these dormant users and reignite their interest in your product.

Types of content you may wish to send include: 

• Product updates
• “We miss you!” emails
• Success stories
• Incentives
• Educational content

Here’s what a re-engaging email by WordTune looks like:

Best tools for user activation

To spot drop-offs and see how users behave within your app and website, product managers use a range of product analytics tools. Below, you’ll see the 3 best tools on the market for collecting quantitative and qualitative data and for performing an in-depth user behavior analysis.

1. Smartlook

Smartlook combines qualitative and quantitative analytics about user behavior at different stages throughout the customer journey. The following features are of utmost importance for optimizing user activation rates:

• Use session recordings to see why users don’t activate, including why they leave negative NPS scores
• Heatmaps will help you visualize and quantify UX issues across your app and website
• Incorporate funnel analytics to break down the user activation stage and identify which steps cause friction
• Get the numbers regarding how many users become activated over a given period with the events feature

Book a personal demo and start your free 30-day trial: no credit card required.

2. Survicate

Survicate is a powerful tool for building in-app microsurveys and collecting quantitative and qualitative user feedback. Trigger surveys by event occurrence, hovers, clicks, time period, etc. Create follow-up questions to get a sense of why users leave positive and negative scores.

3. Appcues

Appcues includes tools for driving product adoption and user activation. With it, you can create interactive onboarding flows, announce new features with modals, and deliver contextual help with tooltips — all of which are code-free.

Drive user activation rates with Smartlook

In a nutshell, user activation can make-or-break your SaaS product’s success. With Smartlook’s qualitative and quantitative app analytics, you can effortlessly measure, monitor, and optimize your user activation strategies for both short-term and long-term success. 

Don’t let low user activation rates hinder your growth – equip yourself with the right tools and tactics from this guide to create an engaging, user-centric experience that drives product growth. 

Schedule a Smartlook demo and start your free 30-day trial today!

Jenny Romanchuk

Jenny is a freelance writer with 6+ years of experience in product marketing & sales for B2B SaaS. She writes for HubSpot, Smatlook, MarTeach, Weflow, and more. In her spare time, Jenny enjoys hiking and reading The Witcher Saga.

The post The ultimate guide to user activation in SaaS, including how to improve it [+6 Tactics] appeared first on Smartlook Blog.

When running a product experiment, you need to consider feature rollout, troubleshooting, user behavior findings, and decision-making practices while maintaining your product and supporting users that aren’t affected by the recent changes.

Before you even get into this whirlpool, you need to determine whether you need to perform a product experiment at all. Not every idea needs to be tested. Often, releasing a change without a rigorous validation process is the best way to go. So how do you know when product experimentation is required or not? 

Throughout this guide, we’ll answer this question and more, including tips on preparing and running experiments, collecting data, and evaluating the results. We’ll also cover the following:

• What is product experimentation?
• When is the right time to run a product experiment?
• 7 Steps to building a robust product experimentation framework

Smartlook records user actions to help you identify opportunities for improvement and evaluate the effectiveness of your experiments. Request a free demo or try Smartlook with a full-featured, 30-day trial: no credit card required.

What is product experimentation?

Product experimentation is the process of testing and trying out variations of a product or feature to see how they perform with users. 

Think of it as conducting an experiment in a laboratory, but instead of chemicals and test tubes, you’re experimenting with different aspects, such as design, user interface, features, pricing, and even messaging.

The goal of product experimentation is to collect data to make informed decisions about whether or not you should release a new element at full scale. 

Product experimentation allows you to validate your assumptions, test new ideas, and iterate quickly based on user feedback. It’s all about constantly refining and improving your product to deliver the best user experience possible.

3 Benefits of product experimentation for product teams

Product experimentation has a tremendous impact on your product development cycle as it:

• Reduces risk. Product teams can test new ideas on a small scale before investing significant time and resources into a full-scale release. It helps reduce the risk of launching a feature that doesn’t meet the needs of the target audience and hurts the user experience
• Increases innovation. You’ll foster a culture of experimentation and innovation by encouraging teams to explore new ideas and approaches to solving problems in a risk-free environment
• Improves customer knowledge. By regularly testing your hypothesis with real users, you will gain a better understanding of what your customers like and don’t like so you can build a more customer-centric strategy with time

Despite all the significant advantages experimentation brings, it’s not a panacea — sometimes, it’s not necessary. Below, we’ll examine cases when product experimentation is a good idea and when it’s not. 

When is the right time to run a product experiment?

Determining the right time is the first step to launching a successful product experiment. It’s very easy to get stuck in a constant loop of experimentation, leading to a strain on your company’s resources.

The right time to run a product experiment depends on several factors, including the stage of product development, the goals of the experiment, and the resources available. 

It’s worth stating that a product experiment is a way to validate your ideas with the help of quantitative data. If you don’t have enough users yet, it’s best to focus on qualitative research before you attempt to run experiments. Barada Sahu of Mason suggests hitting at least 10,000 monthly users before you begin experimenting.

Once you have a large enough user base, you can run product experiments whenever you’re considering making significant changes, such as adding new features, changing the pricing model, or redesigning the user interface. These experiments will help you determine whether these changes will be well-received by your users and can help you make data-driven decisions about how to proceed.

No matter what you plan on testing, you should only proceed when the data tells you to do so. Your product team should be constantly monitoring product performance and user behavior to detect issues and spot opportunities for improvement. Only when the data points to customer frustration or hardships can you form a hypothesis regarding what could help fix the problem and run an experiment for validation.

Keep in mind not every idea has to be tested. Say you notice that a lot of people are straying from your user path, failing to complete an intended action. With help from a session replay tool like Smartlook, you might discover they were simply failing to locate the right button. Minor changes like this will fix the issue without requiring you to spend time on experimentation.

There’s no sense in overdoing it with product experiments — the only thing you’ll achieve is wasting time.

A few years ago, Instagram made changes to its interface, switching to a full-screen style feed (apparently, without testing the update with a focus group). After learning that many users resented the new interface, the company reverted back to the original model and “made Instagram normal again.” This story proves that you shouldn’t make changes for the sake of making changes. If something works well, it’s often best to leave it alone.

“We try to run as many tests as possible. But running a thousand tests per month simply makes no sense. If the change has a significant impact or carries a high or medium level of risk, I recommend running an experiment. 

However, if the change is low risk and has a minimal impact on the user experience, it may be appropriate to roll it out immediately to save time and resources. In these cases, we use historical data, common sense, and even predictive analytics to categorize the changes.”

Vladislav Podolyako

Founder & CEO at Folderly

7 steps to building a robust product experimentation framework

Follow these steps to prepare and run a successful product experiment.

1. Look at your product analytics, and spot opportunities for improvement

One doesn’t run an experiment without data at hand. You need to identify a real user problem that needs your attention first. But before you do it, you’ll need to spot problems with your company’s performance first. 

In other words, you should start with money.

Is it the customer lifetime value (CLV) that you need to increase? Is it a low activation rate that you need to fix? Look at your top-level performance indicators and focus on one problem at a time.

From here, drill down on product analytics to identify areas that require improvement. Tools like Smartlook can help you track user behavior, pinpointing areas where users are struggling or dropping off.

Start with top-level user engagement metrics and then move on to analyzing user paths with heatmaps and session recordings. 

For example, if you are looking to increase your CLV, there’s a good chance you’re dealing with poor customer retention and high churn rates. In this case, you’ll want to identify what’s causing your customers to feel frustrated with your product. To answer this question, it’s best to ask them about it. With exit surveys, you’ll determine what aspects of your product aren’t meeting user expectations. You can then turn to session recordings to see how users are interacting with the features they reported they liked least. From here, you can make assumptions regarding how you can improve your product’s functionality or UI.

You can also run in-app surveys from Survicate that ask active users to rate specific features. This way, you’ll collect valuable quantitative insights that highlight areas that require special attention. By integrating Survicate with your Smartlook account, you can drill down into the data by viewing the session recordings behind each response.

2. Develop a hypothesis

Once you’ve identified areas for improvement, it’s time to develop a hypothesis relating to optimizing the product experience. This hypothesis should be based on your understanding of the problem your users are facing and should clearly state how you plan to address it. 

For example, if your CLV is low because users are having trouble using your product, you could hypothesize that by adding interactive product walkthroughs, you’ll improve retention and ultimately increase CLV. Just make sure your hypothesis is specific, measurable, and testable.

3. Choose a testing model

How exactly are you going to run an experiment? There are several testing models to choose from, including:

• A/B testing. This involves creating two versions of a product and randomly assigning users to one or the other. Once the test is complete, compare the performance of each version to see which performs best
• Multivariate testing. Instead of implementing one change at a time, change multiple variables at once. Multivariate testing allows you to test various combinations to see which performs best
• Funnel testing. This type of product experimentation involves making changes to different product pages to create and test new user paths. This is a perfect experimentation technique when you need to adjust the entire user flow but want to test it first

Smartlook helps you validate your experiments. Simply set up your variations in Firebase, Google Optimize, or another product testing tool and specify your variation IDs in Smartlook. You can then group sessions based on experiment and variation IDs and evaluate the impact of your tests using funnels and events.

“The scope of what you would like to test during product experimentation is the single most significant determiner of which testing technique to use. I recommend using A/B testing and Split testing if you are focusing on a single aspect of the product, while a multivariate technique works best if testing multiple product elements.”

Alvin Wei

Co-founder and CMO at SEOAnt

“We’ve found that the most important thing when it comes to testing techniques is to make sure you’re getting good data. You want to be able to measure and track the results of your tests so you can see what’s working and what isn’t.

That said, there are a lot of different ways you can do this. For example, A/B tests are great because they let you compare two different versions of something (like button color and font) and see which one performs better.

But we don’t think any one technique will work for every product team. It really depends on what kind of product you’re trying to build, your goals for testing, etc.

One thing we always recommend is using real users as part of the testing process. This way, you’ll be able to get feedback from the people who actually use your product — not just from yourself or other members of your team!”

Will Yang

Head of Growth at Instrumentl

4. Define KPIs for your test

To measure the success of your product experiments, you’ll need to specify your KPIs early. Consider the following criteria when defining your experimentation KPIs:

• Alignment with goals: The KPIs you choose should align with your experimentation goals. For example, if you’re testing a new onboarding flow, your KPIs might include metrics such as the percentage of users who complete the onboarding process, the time it takes to complete the process, and the drop-off rate

• Focus on user behavior: Your KPIs should focus on user behavior in addition to metrics like pageviews and session length. User behavior metrics will help you understand how users interact with your product and provide insights into how you can improve the user experience

• Set benchmarks: It’s important to set benchmarks for your KPIs so you can measure progress over time. This will help you understand whether your experiments are having an impact

Once you know which metrics will help you evaluate your experiment results, you can set up tracking.

5. Set up tracking

To do this, you’ll need to choose a tracking tool. Select a tool that supports the metrics you want to track and the testing method you’ve chosen. Tools like Firebase and Google Optimize support different experimentation forms and deliver insightful reports on the performance of your tests.

Monitoring test performance with Firebase.

Consider adding another layer of qualitative insight to your experiments. While your experimentation tool will help you determine a winning version of your product, there should be a visual insights tool that will help you understand the reasons behind the results. For instance, if you choose to run tests with Firebase, integrating it with a tool like Smartlook will help you get more granular insights into how users are responding to the changes.

Next, add the tracking code and set up events and goals to start collecting data. This will allow you to track specific user actions, such as clicking a button or filling out a form. 

Test your tracking to ensure it’s working properly, and validate your data to make sure it’s accurate.

6. Determine what you consider to be statistically significant results

How do you know if your test results are significant enough to make conclusions on the effectiveness of the experiment? The short answer is that you’ll need to determine testing parameters beforehand.

Testing parameters include audience sample size and the length of the experiment required to achieve statistically significant results.

Statistical significance is a measure of whether the difference between two variations is real or simply due to chance. For example, if you try to test a new UI against ten users split into two groups, the results won’t be statistically significant due to the small size of the sample audience. 

The larger the audience sample size and the longer the test lasts, the more reliable the results your experiment will deliver.

“In simple words, it should become evident that the test had a strong impact on your key metrics. For example, you did not change anything with your product, and the external factors (competition, PPC, seasonality, etc.) are super stable. The maximum expected deviation of your conversion rate is 5%. However, your CR jumped to 145% after moving the “buy now” button on the first screen. 

The significance level or alpha indicates the probability that the observed difference between groups is due to chance. If the change is higher than alpha, it is statistically significant. The bigger the difference, the better.”

Vladislav Podolyako

Founder & CEO of Folderly

7. Run the experiment and evaluate the results

When you’re all set, you can launch your experiment. Don’t wait for the test to end to monitor its performance — keep an eye on the metrics all the time to spot issues as they arise. 

After the test is over, collect and analyze the data to determine whether the experiment was successful or not. Your experimentation tool will provide you with a clear verdict on which product version won — this is where you discover whether your hypothesis is confirmed or not. 

Defining a winning variant with Google Optimize.

Regardless of the verdict, go beyond the metrics and look into the behaviors users exhibited during the experiment. Watch how they interacted with new features or workflows to spot moments of frustration or positive experiences. 

Document the results once you’ve finished your analysis. Share your findings with your team and stakeholders. This will help you create more successful experiments in the future.

Remember, you can’t improve everyone’s experience. If you see a significant improvement in KPIs after the test, it’s a win — even though some users may be failing to accept the change. 

Run data-driven product experiments with Smartlook

Product experimentation is an easy way to validate your ideas for improvement. That said, it can be tricky. Overdo it, and you’ll waste time and money instead of quickly making necessary adjustments. Underdo it, and you’ll waste time and money releasing features users don’t need. 

The best way to understand when to run new product experimentation is by looking into your product data. Smartlook will provide you with quantitative and qualitative insights to not only spot opportunities for improvement but also to figure out whether the improvements require a rigorous experimentation process or not.

Furthermore, Smartlook helps you evaluate the effectiveness of your experiments with granular user behavior insights. For example, you can set up events to see how your test group interacts with a new feature or monitor heatmaps to see how a UI change affects the user experience.
To learn more about how Smartlook can help you with product experimentation, schedule a free demo with our team today. You can also try Smartlook for free with a full-featured, 30-day trial.

Adelina Karpenkova

is a freelance writer with a background in SaaS marketing. She loves discovering new product marketing strategies, gaining insights for product experts, and turning her knowledge into helpful content. When she's not writing, she plays tennis or knits cozy sweaters.

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