How Does User Behavior Analytics Work in SaaS
User behavior analytics helps you understand how users engage with your SaaS. By digging into the user flow data, you can spot friction points and identify improvements to enhance the user experience.
In this article, we answer the question: how does user behavior analytics work? Read on to learn:
- Behavior metrics to track.
- Tools for insightful user behavior analytics.
- A step-by-step process for analyzing user behavior.
TL;DR
- User behavior analytics focuses on understanding how users interact with websites and apps. Tools such as Userpilot and Google Analytics are effective for this purpose.
User behavior analytics helps you access key insights to:
- Improve free trial to paid conversion rate.
- Increase product adoption and stickiness.
- Identify and remove friction.
- Create data-driven product strategies.
- Reduce customer churn.
- Delight customers.
Metrics to track when conducting behavior analytics:
- User activation rate.
- Free to paid conversion rate.
- Feature usage.
- Product/feature adoption rate.
- Stickiness rate.
- Funnel drop-offs.
- Retention rate.
- Churn rate.
8 types of behavior analytics methods to track user activity:
- Trend analysis.
- Funnel analysis.
- Path analysis.
- Cohort analysis.
- Feature and events reports.
- A/B testing.
- Heatmaps.
- Session recordings.
Step-by-step process for effective behavior analytics:
- Define your objective for analyzing customer data.
- Select the relevant metrics for tracking user behavior.
- Choose the right type of analytics report.
- Collect feedback from individual users.
- Act on the collected data to improve user experience.
Ready to start tracking user behavior for your SaaS? Book a demo now to see how Userpilot lets you do it easily – without coding or the help of a data scientist.
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What is user behavior analytics?
User behavior analytics focuses on understanding how users interact with websites and apps.
This analytics data helps identify changes that you can make to improve the customer experience, create better product campaigns, and boost conversions across the customer journey.
The importance of analyzing user behavior
When done well, behavior analytics helps you:
- Improve free trial to paid conversion rate: Analyzing how users interact during free trials reveals what features drive value and which ones lead to drop-offs. This allows you to optimize the trial experience to encourage more users to become paying customers.
- Increase product adoption and stickiness: Behavior analytics shows where new users succeed or stumble, helping you spot areas needing better onboarding or in-app guidance. Optimizing these elements makes your product more intuitive and increases adoption and long-term engagement.
- Identify and remove friction: Friction in the user journeys can be identified by behavior analytics. Fixing it creates a seamless user experience.
- Create data-driven product strategies: Understanding how users actually use your product prevents you from making decisions based on assumptions or what you think is best. Data-driven insights from behavior analytics ensure your product roadmap is based on real needs and user preferences.
- Reduce customer churn: The data from behavior analytics lets you spot churn trends. That way, you can view customer segments and understand if they’re at risk of churn and what proactive solutions you can provide to retain them.
- Delight customers by constantly improving user experience: Continuous user behavior analytics helps you understand changing user needs and position your product experience to exceed customer expectations and foster loyalty.
Metrics to use when tracking user behavior data
Knowing the right user behavior metrics to track helps you identify areas of success and friction, informing product improvements and growth strategies. Let’s dive into a few of the most important ones:
- User activation rate: This metric measures the percentage of new users who take a specific desired action (e.g., completing a profile setup) within a certain timeframe after signing up. It’s used to gauge how effectively users see value in your product.
- Free to paid conversion rate: This is the percentage of free users that converted to paying customers over a given period. Your free-to-paid conversion rate says a lot about the initial onboarding process, your pricing, and how valuable free users perceive the product to be.
- Feature usage: This metric tracks user interactions, clicks, and actions that users complete for each feature. It helps you understand which features are most and least popular, guiding development resources towards what matters most to customers.
- Product/feature adoption rate: This measures the rate at which new users start incorporating your features into their daily lives. A good adoption rate indicates that you’re attracting the right customers and have an effective onboarding flow.
- Stickiness rate: This metric tracks how frequently and consistently users return to your product (commonly measured as the ratio of Daily Active Users to Monthly Active Users).
- Funnel drop-offs: This metric tracks the number of users who exit at various stages of the conversion funnel, helping you spot where users lose interest or encounter issues. Understanding funnel drop-offs is crucial for optimizing the user journey to maximize conversions.
- Retention rate: This measures the percentage of users who continue using your product over a given period. It’s a key indicator of how well the product fits into the users’ needs and habits.
- Churn rate: This is the opposite of the retention rate. Customer churn rate tracks the percentage of customers who stop using your product (or parts of it) over time.
You can measure each of these metrics manually, but it will be time-consuming and prone to errors. Instead, use product usage dashboards from behavior analytics software such as Userpilot to view all your key metrics in one place.
8 types of behavior analytics tools to track user activity
Combine two or more behavior analytics tools to gain a comprehensive understanding of user behavior and needs. Let’s explore each of the available options.
1. Trend analysis
Trend analysis examines changes in user behavior over time, identifying patterns or shifts in engagement, feature usage, or customer satisfaction.
This behavior analytics approach has many applications. A good use case is during product roadmapping. You can implement it to understand current user needs and whether customer preferences have changed over time. This will help you focus on the right features to prioritize.
2. Funnel analysis
This analysis report tracks the funnel progression for all user journeys in your app, such as free-to-paid conversion or customer onboarding.
Funnel analysis helps monitor how many users move on from one stage to the other and how many abandon the journey mid-way. This feature makes it ideal for identifying friction points and removing them to improve the user experience.
3. Path analysis
Path analysis examines all the various sequences of user actions within your product, highlighting common navigation patterns and detours. The report from this analysis helps you see what percentage of users are on the happy paths and those deviating from normal behavior.
By studying the analysis report, you can decide the best ways to help users stick to the happy paths, improving your engagement and retention rates.
4. Cohort analysis
Cohort analysis groups user accounts based on shared characteristics or behaviors over time.
By analyzing cohorts, you can uncover specific trends that might not be visible when examining the user base as a whole. This specificity allows you to make precise adjustments to your product experience and drive better engagement.
For instance, cohort analysis helps you understand retention rates across different user segments. Armed with this insight, you can deploy personalized retention strategies that work for each of your personas.
5. Feature and events reports
These reports show you how often users engage with specific features or complete specific actions (“events“). They help you spot the parts of your tool that are most valuable to users and what might need improvements.
However, you’ll have to dig further to know why users behave the way they do. For example, low engagement with a new feature doesn’t automatically mean users don’t like the value it provides; it’s possible there’s a feature discovery issue.
6. A/B testing
A/B testing helps you understand users better by testing two or more variations of your UX to understand which version resonates better with users.
A tool like Userpilot allows you to conduct both a controlled A/B test and a head-to-head A/B test, depending on your needs. A controlled A/B test compares a new variation against a control group (the original version), while a head-to-head A/B test directly compares two new variations against each other.
7. Heatmaps
Heatmaps visually represent where users click, scroll, and focus the most on a page, using color intensity to indicate activity levels.
This behavior analytics tool lets you understand what elements of a page or which specific features draw user attention and the parts of your tool they are ignoring.
For example, a heatmap report can help you see that many users unknowingly ignore a button that will make their work easier. You can then deploy tooltips to provide better guidance and improve the user experience.
8. Session recordings
Session recordings are more granular than heatmaps and are more suitable for tracking user interactions across dynamic elements and different device types.
By watching how users scroll, click, hover, and zoom in on different parts of your tool, you can glean valuable insights that will help you improve their experience.
Userpilot’s screen recording feature will be available before the end of Q1 2024.
How to analyze user behavior? Step-by-step process
Here’s a breakdown of the process for analyzing user behavior, along with some refinements to make it even more effective.
1. Define your objective for analyzing data
Start with clear business goals. Are you aiming to increase conversions, improve feature adoption, or identify churn risk?
Knowing exactly what you want before you begin gives your analysis direction and ensures you obtain the right insights.
For example, if your goal is to improve your activation rate, a good objective will be to boost the number of free trial activations—meaning your analysis will focus more on trial users.
Using a goal-setting framework helps to increase your productivity and keep your team accountable. Some of these frameworks include backward goals, goal pyramid, B.H.A.G (Big, Hairy, Audacious Goals), and S.M.A.R.T goals.
2. Select the relevant metrics for tracking user activity
Next, you need to identify the metrics that will assist you in tracking user behavior.
There are multiple user behavior metrics to track (we covered some earlier), and you might not know what to measure. Let your objectives and the user journey stage guide your decisions.
For example, if you aim to improve the acquisition funnel, you’ll track the following metrics:
3. Choose the behavioral analytics tool for your analysis
The behavioral analytics method you’ll use to measure user behavior depends on your objectives and KPIs.
For example, funnel analysis lets you understand and improve critical funnels such as user activation and free-to-paid trial conversion.
If your objective is to boost user retention, the retention cohort analysis works best; it will help you identify the factors driving retention, and you’ll be able to understand which areas to test new strategies.
4. Collect feedback from individual users
Once you’ve identified the source of the problem, supplement data from your analytics dashboard with qualitative data to understand the “why” behind user behavior patterns.
You can do this by collecting user feedback. Here are a few ways to do so:
- Conduct user interviews with a few selected target users to gather qualitative insights.
- Hold focus groups where a moderator guides users to discuss specific topics related to your product experience.
- Use in-app surveys to collect contextual feedback as users interact with your tool.
5. Act on the collected data to improve user experience
Combine the quantitative data from behavior analytics and qualitative insight from user feedback to spot friction areas and understand user behavior.
Use the insights you gathered to enhance the product experience with improved features, better UX, or contextual user guidance, depending on user needs.
Here is an example. Attention Insights noticed a low activation rate for their product. They analyzed new user behavior to diagnose it and, in turn, discovered that many users weren’t performing the primary actions that led to activation.
To counter this, they utilized Userpilot to add:
- An interactive product walkthrough to introduce new users to key features.
- An onboarding checklist to encourage new users to complete key activation steps.
- Hotspots to draw attention to UI elements that users were ignoring.
- A resource center to offer self-service support and increase user satisfaction.
Conclusion
Make user behavior analytics a continuous process so you can stay in the loop of changing user needs and create proactive solutions.
Userpilot can help you streamline and automate the process. Book a demo now to see how you can start collecting quantitative and qualitative user insights code-free.
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