User Behavior Patterns: 8 Examples & How to Analyze Them
Are you on the verge of launching a new product? Or maybe you’ve been monitoring product analytics for a while and are looking for ways to boost growth. In either case, monitoring and analyzing user behavior patterns can help drive more user engagement and minimize churn.
In this article, we’ll take a closer look at different examples of behavioral patterns.
TL;DR
- User behavior patterns refer to the common behaviors people demonstrate while using an app. Behavioral analysis aims to identify these trends to improve the user experience.
- Monitoring patterns in user behavior offers several benefits, including improved product adoption and reduced churn.
- Examples of user behavior patterns include deferred choices, progressive disclosure, safe exploration, instant gratification, and more.
- You can use various analytics techniques to identify similar usage patterns.
- Trend analysis helps identify changing user needs over time. Identifying and adapting to these shifts helps you keep up with your users’ expectations.
- Funnel analysis gives a granular view into how users move through your funnels to accomplish particular goals you’ve set.
- Path analysis helps you access all the various paths users take as they complete their tasks.
- Cohort analysis enables you to see how different segments engage with your product.
- Feature and event reports help you dive deep into the performance of individual features and custom events.
- A/B tests help you test two or more variations of UX to find the one that’s better aligned with your users’ preferences.
- Heatmaps and session replays show you how users interact with your UI.
- To analyze user behavior, start by identifying user personas and analyzing their journey map. Then, choose a relevant report for the analysis from options such as funnel analysis, and path analysis.
- Next, get qualitative user data through surveys to back your reports up and uncover missing information. Use the collective insights to improve the user experience.
- Userpilot offers powerful user behavior tracking along with in-app surveys and more. Book a demo to understand how you can increase user engagement with user behavior analysis.
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What are user behavior patterns?
User behavior refers to the way users interact with your app and its features. Likewise, user behavior patterns are common behaviors people exhibit when interacting with a product’s user interface (UI). Understanding these patterns gives you insights into what users expect from your product, so you’re able to fine-tune the user experience (UX) and increase product adoption.
Monitoring different user behavior patterns comes in handy for analyzing and understanding customer behavior. Moreover, if your product supports these patterns, you can ensure that users achieve their goals without friction, which in turn improves customer satisfaction levels.
Why is monitoring user behavior patterns important?
Whether you’re looking to boost free-to-paid conversions or drive product adoption, meeting user expectations is crucial. Monitoring and analyzing patterns in user behavior helps you do just that.
Key benefits include:
- Increase product stickiness – Understanding user behavior helps you identify and implement features that address common pain points. It helps users accomplish crucial tasks and encourages them to keep coming back.
- Keep up with changing user behavior – Track user behavior trends and preferences over time so you’re always meeting changing user expectations.
- Identify and remove friction – Identify precise points where users struggle in their journey. This enables you to find ways to eliminate friction from the UX and improve conversion rates.
- Create data-driven product strategies – Usage pattern data helps understand how people navigate your product so product teams can design features and user experiences accordingly.
- Reduce customer churn – When you eliminate friction and adapt your product to changing user needs, they’re less likely to churn. It helps boost retention and drives customer loyalty.
8 examples of behavior patterns in SaaS
Now that you know why patterns in user behavior are crucial to product development, let’s discuss a few examples.
- Deferred choices: If products ask for too much information, users tend to defer the decisions due to analysis paralysis. That’s why SaaS companies now have shorter sign-up forms.
- Progressive disclosure: Too much information overwhelms users. They expect product experiences that reveal information gradually according to a user’s needs.
- Satisficing: The word “satisficing” is a combination of the words “satisfying” and “sufficing”. It indicates that users prefer to use the most accessible and satisfactory option, even if it isn’t the best one.
- Instant gratification: Users get hooked to products that reward them to encourage engagement. For instance, Asana uses the element of gamification to delight users every time they complete a task.
- Habituation: Users expect similar behaviors as observed in popularly used apps. This is why product teams try to build user interfaces that accommodate existing user habits, such as supporting the “Ctrl + C” and “Ctrl + V” keyboard shortcuts for copy-pasting.
- Safe exploration: Users expect the freedom to explore new products without suffering from irreversible consequences. They expect to explore the interface without losing any progress they’ve made.
- Incremental construction: Users prefer to build things in their sequence of choice. Take HubSpot’s drag-and-drop email builder, for example, which lets you design emails in any order to reduce friction.
- Easy repetition: SaaS users expect to speed up their work with easy repetition. Think about how PayPal lets you create a new invoice by copying the previous one to improve the overall experience.
User behavior analytics that help recognize patterns
Analyzing user behavior is an integral part of improving product adoption along with other product growth metrics. But how do you identify and leverage these patterns? The answer lies in monitoring user behavior and analyzing the available data.
Here are a few types of analysis reports that help you get there.
1. Trend analysis
Trend analysis is a vital tool for recognizing changes in user behavior and preferences. It offers insights into how user needs evolve and helps inform product development decisions.
With detailed trend analysis reports, you also get an idea of when shifts occur in user behavior. That, in turn, can help you dig deeper into potential reasons behind those changes. You can even identify recurring/seasonal changes in user behavior.
For instance, a company can predict the popularity of its invoice creation feature based on past seasonal trends.
2. Funnel analysis
Funnel analysis helps track the progression of user journeys within your product. It involves setting up a conversion funnel, i.e., a series of tasks a user must complete to result in a conversion.
Use this report to identify where users drop off within a conversion funnel. It’ll help you determine points of friction so you’re better able to eliminate them from the user journey.
Let’s say a company wants to improve its user activation rate. A quick funnel analysis reveals that new users drop off before they send the first message in the app. This helps you identify the point in the user journey where you need to dig deeper and improve the user experience.
3. Path analysis
Path analysis involves tracking all the paths users take to complete an action. It offers a detailed glimpse of how users prefer to navigate your product when completing a specific task. That data comes in handy for identifying the shortest path to an action (also known as the happy path).
Additionally, you can use path analysis to drill down into specific user flows. It’ll help you focus on specific steps within a path and gain a deeper understanding of user friction.
For instance, you can use path analysis in Userpilot to identify the fastest way to a free-to-paid conversion. Then, you can build in-app engagement flows to drive new free trial users down the same path and convert them into paid users.
4. Cohort analysis
A cohort is a group of users with shared characteristics. Cohort analysis refers to the process of tracking product engagement and retention over time for specific cohorts. It offers insights into how different cohorts engage with your product.
Cohort analysis comes in handy when monitoring the performance of different user retention strategies and understanding what makes users stick. It can also provide you with a deeper understanding of how different user personas behave.
For instance, you can compare two different retention strategies implemented in two different quarters to understand which one resulted in lower churn.
5. Feature and events reports
Event tracking is a crucial part of monitoring in-app user activity. You can set up custom events to monitor feature usage and identify features that resonate with users. Enhancing these features or introducing similar ones will improve the product experience. On the other hand, you can also pinpoint features with low user engagement and decide whether you should sunset them.
For instance, you can use feature usage reports to track and improve adoption for a newly released feature.
6. A/B testing
A/B testing lets you test your UX by comparing user responses on two or more variations of in-app experience. You can use it to optimize various UI elements, including calls-to-action (CTAs), banners, and more. Also, you can experiment with different variations of product tours or walkthroughs to identify the best-performing ones.
You can run different types of A/B tests, such as:
- Controlled A/B test – Compares the performance of an in-app flow with that of a control group that doesn’t see the flow.
- Head-to-head A/B test – Compares the performance of two variations of an in-app flow to select the most effective one.
- Multivariate test – Compares the performance of multiple variations of an in-app flow to select the best-performing one.
For instance, you can set up an A/B test with different tooltip variations and track relevant metrics to select the most effective ones.
7. Heatmaps
Heatmaps help you identify user hotspots in your app. Analyzing heatmaps helps you identify parts of the UI that get high engagement in terms of clicks and hovers. It can also pinpoint in-app elements that don’t garner traction.
You can choose from different types of heatmaps, such as:
- Feature click heatmaps pinpoint areas of user activity and engagement in the UI. They help identify which CTAs, ads, or links get the most clicks.
- Scroll heatmaps offer insight into how far users scroll down a page and help identify sections of the UI that lead to abandonment.
- Mouse-tracking heatmaps highlight areas of a page where users hover and move their cursor. They help you understand where users focus their attention and what they don’t find interesting.
- Eye-tracking heatmap tools use sensors to monitor a user’s eye movements, blinking, and pupil dilation and understand where they focus on a page.
8. Session recordings
Session recordings go a step beyond heatmaps and track user interactions across dynamic elements and device types. They’re ideal for understanding how users interact with different parts of the UI in terms of clicks, hovers, scrolling, and even zoom-in.
Session recordings come in handy for digging deeper into user paths and identifying points of friction. You can use them to optimize the UX and improve engagement.
Step-by-step guide for analyzing user behavior to gain actionable insights
Here’s a breakdown of how you can analyze user behavior.
1. Identify your user persona and analyze their journey map
Start by outlining the user persona you want to observe and drill down into their needs, pain points, and motivations. List down what you already know about them to provide context for your analysis.
Next, take a closer look at the user journey map for the selected persona to understand how they move through your product.
2. Choose the relevant report for your user behavior analysis
Use your selected tool to collect user data from different reports, heatmaps, and user session recordings. This data will help you understand how users navigate and engage with different features so you’re better able to develop strategies for improving the customer experience.
You can also combine all these reports with a detailed analytics dashboard to study them all at once and gain more comprehensive insights.
3. Supplement your reports with qualitative user behavior data
Analytics reports can only help you spot trends and points of friction in the user experience. But you need to talk to your users to understand the “why” behind their actions.
You can collect qualitative data from different sources, including email and in-app surveys. Include open-ended questions in your surveys to allow users to give you in-depth feedback.
4. Act on the user behavior insights to improve user experience
This is where you implement suitable measures to improve UI/UX based on the analyzed user behavior data and feedback. Then, you can run product experiments to enhance the customer journey.
For instance, if your data indicates that many users are dropping off before activation, you can use an onboarding checklist that walks them through key activation steps.
Conclusion
Patterns in user behavior offer an in-depth understanding of user expectations, needs, and preferences. From funnel analysis and A/B testing to heatmaps, session recordings, and cohort analysis, you can use different tools to monitor user behavior patterns.
Ready to get started with user behavior analysis? Get a Userpilot demo and see how the powerful user behavior analytics tool can help you dig deep into user pain points and enhance their experience.
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