What is a feature drop-off?<\/strong><\/h2>\nFeature drop-off is the abandonment of a feature or product during a user session. Feature drop-off analysis<\/a> helps identify where user engagement<\/a> dips and which features are leading to friction.<\/p>\nDrop-offs<\/a> also indicate a value gap<\/a> in what your product delivers vs what regular users expect from it.<\/p>\nWhat are the benefits of tracking drop-offs?<\/strong><\/h2>\nTracking where users disengage<\/a> can feel like focusing on the negative, but it’s a proactive way to boost your product’s health<\/a> and optimize engagement<\/a>.<\/p>\nHere are the key benefits:<\/p>\n
1. Identify points of friction to remove them<\/strong><\/h3>\nA feature with declining usage often points to hidden obstacles<\/a> in the user journey. Maybe the feature is hard to find, poorly explained, or buggy.<\/p>\nTracking drop-offs helps you pinpoint these friction points<\/a> so you can strategize to remove them, leading to a much smoother experience<\/a> for users.<\/p>\nFor example, if you notice users are disengaging because the feature is complex, you could deploy tooltips<\/a> to explain the feature better or have your dev team redesign it for simplicity.<\/p>\n2. Increase feature adoption<\/strong><\/h3>\nWhen you remove friction by analyzing user behavior, you remove roadblocks from the user journey. This helps increase user engagement and makes it more likely for users to see the full potential of your features.<\/p>\n
By tracking drop-offs, you can identify the features that contribute to churn and step in to improve the experience.<\/p>\n
3. Prioritize feature development as per user needs<\/strong><\/h3>\nTracking how users behave in-app<\/a> helps you understand their needs better.<\/p>\nYou’ll see the features they use frequently and the ones they ignore. If a feature isn’t gaining traction despite your best efforts, it is likely not a good fit for most users.<\/p>\n
These insights can help you inform feature development<\/a>. You can refine popular features or develop complementary ones and evaluate scaling back<\/a> for those that don\u2019t attract users.<\/p>\n4. Improve user experience and increase retention<\/strong><\/h3>\nContinuously identifying and removing friction with drop-off analysis and user feedback creates a cycle of data-driven product development. This improves the overall user experience<\/a>, leading to more paying customers in the long run.<\/p>\nYou can also expand the same strategy to other channels, such as your landing pages and social media posts, to reduce conversion friction.<\/p>\n
Why do users stop using a feature?<\/strong><\/h2>\nEven well-developed features can suffer from disinterest. Here are five common reasons this happens:<\/p>\n
\n- Friction in user experience<\/strong>: From cluttered interfaces<\/a> to bugs and the lack of proper integrations, many things can cause friction<\/a> in the user experience. The presence of these will keep users from engaging regularly with your product.<\/li>\n
- Poor onboarding<\/strong>: Sometimes, the product is great but the onboarding flows<\/a> confuse users. When customers struggle to understand each feature and how it works, the result is reduced engagement.<\/li>\n
- Steep learning curve<\/strong>: Some features may have a steep learning curve due to their complexity or advanced capabilities. If users feel that learning to use a specific feature is too time-consuming or difficult relative to the perceived value<\/a> it offers, they may choose not to use it.<\/li>\n
- Inadequate customer support<\/strong>: When users encounter issues or have questions about a feature, prompt and effective customer support is crucial. Lack of adequate support<\/a> can leave users feeling frustrated and abandoned, pushing them to stop using the feature entirely.<\/li>\n
- Long time to value<\/strong>: Users often look for quick wins<\/a>. If a feature takes too long to deliver tangible value or the benefits are not immediately apparent, users may lose interest and disengage.<\/li>\n<\/ul>\n
How to identify drop-offs for features<\/strong><\/h2>\nAnalytics tools play a critical role in identifying drop-off points. While you can use a single method to identify friction points, combine different methods to glean the most value from your analysis.<\/p>\n
Let’s explore how this is done with the best tools for the job.<\/p>\n
1. Identify where users drop off with funnel analysis<\/strong><\/h3>\nFunnel Analysis<\/a> tracks the user journey<\/a> through a series of steps toward a defined goal, such as discovering and using a feature.<\/p>\nIt helps to identify at which stage users drop off<\/a>, providing insights into potential issues or friction areas within that journey.<\/p>\nAnalytics tools such as Userpilot let you conduct funnel analysis<\/a> and generate reports to spot drop-offs across various features.<\/p>\nFor example, by examining a funnel analysis report, you can see a sharp decline in progression between steps, suggesting friction.<\/p>\n![\"drop-offs_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/drop-offs-feature-drop-offs_a0247c225da445842a6778ad8e957992_800.png\")
<\/picture>Funnel report generated with Userpilot<\/a>.<\/figcaption><\/figure>\n2. Track user paths for your feature with path analysis<\/strong><\/h3>\nPath analysis<\/a> visualizes the various routes users take through your product, revealing how they navigate towards and within specific features. Each stage in this analysis comprises a feature users engage with, an event they complete, or a page they view.<\/p>\nYou can also track drop-offs for every path<\/a> as users move from one stage to another. This helps identify exactly where they face trouble.<\/p>\n![\"path-analysis_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/path-analysis-userpilot_a7e8a566218396076736f51946aa3294_800.png\")
<\/picture>User path analysis in Userpilot<\/a>.<\/figcaption><\/figure>\n3.<\/strong> Track feature retention with cohort analysis<\/strong><\/h3>\nCohort analysis<\/a> divides users into groups based on shared characteristics (such as sign-up date) and tracks their behavior patterns over time.<\/p>\nThis analysis helps you measure<\/a> how many users return to perform the same event. By conducting regular cohort analysis, you can identify which features have high and low retention rates<\/a> and how they contribute to overall product retention.<\/p>\n![\"cohort-analysis_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/cohort-analysis-userpilot_7cbb41ac729d8cd8aec75fb36c229f2e_800.png\")
<\/picture>Cohort analysis with Userpilot<\/a>.<\/figcaption><\/figure>\n4. Collect user feedback to ask users where they face friction<\/strong><\/h3>\nFeedback surveys<\/a> help you hear from your users directly. Gather both quantitative and qualitative data regarding how your users feel and identify friction areas other report types might be missing.<\/p>\nFor example, a feature might be popular with your users but still be hard to use. In such a scenario, a funnel report cannot highlight the perceived difficulty. A CES survey<\/a>, on the other hand, can help you collect direct feedback regarding how easy it is to use.<\/p>\nYou can also ask users to expand on their responses with open-ended questions, giving you deeper insights related to their experiences.<\/p>\n![\"customer-effort-score-survey_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/customer-effort-score-survey-feature-drop-offs_d8b08e8b421bc6dcffc477d60fe46c96_800.png\")
<\/picture>Customer effort score survey created with Userpilot<\/a>.<\/figcaption><\/figure>\nRelated metrics that help measure drop-offs<\/h2>\n
Track the following metrics to spot drop-offs in your product easily:<\/p>\n
\n- Product stickiness<\/strong>: This metric measures how often users return to your product over a specific period. The easiest way to track product stickiness<\/a> is to find your daily active users to monthly active users ratio. A high stickiness score suggests users find continuous value in your features, meaning you see fewer drop-offs.<\/li>\n
- Feature activation rate<\/strong>: This measures the percentage of users who start using a feature after discovering it for the first time. It assesses the initial appeal and ease of using your features. Low activation rates<\/a> may signal usability issues or a lack of a clear value proposition, leading to drop-offs.<\/li>\n
- Feature adoption rate<\/strong>: This metric tracks the percentage of users that incorporate your features into their workflows and continue using them over a specific period. If you have a good activation rate but poor product adoption<\/a>, your feature might have usability issues.<\/li>\n
- Retention rate<\/strong>: The retention rate<\/a> is the percentage of users who continue using your features long after adoption. Declining retention is a sign of drop-offs.<\/li>\n
- Churn rate<\/strong>: This metric is the opposite of the retention rate; it measures how many users stopped engaging<\/a> with your app in a given period.<\/li>\n<\/ul>\n
4 Ways to reduce feature drop-offs for your SaaS company<\/strong><\/h2>\nNow that you’ve seen how to identify drop-offs and the metrics to track, it’s time to learn how to boost engagement<\/a>. Here are four ways to lower friction for your users.<\/p>\n1. Personalize the onboarding flow<\/strong><\/h3>\nOne size does not fit all. If you have a single onboarding flow for all your new users, you’re likely showcasing irrelevant features and delaying their time to value<\/a>.<\/p>\nDifferent segments care about different features. Use personalization<\/a> to introduce only the features relevant to each use case and speed up product adoption.<\/p>\nWelcome surveys enable<\/a> you to collect user data upfront. Use this information to segment users, understand their needs, and trigger the right onboarding flow<\/a> for the right user.<\/p>\n![\"welcome-survey-userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/welcome-survey-userpilot_ee7be42ac0ee8c60b1adda1ad55c23fa_800.png\")
<\/picture>Welcome surveys built with Userpilot<\/a>.<\/figcaption><\/figure>\n2. Offer secondary onboarding for every new feature<\/strong><\/h3>\nDon’t assume existing users will independently discover new features<\/a> and magically know how to use them right away.<\/p>\nInstead, trigger detailed secondary onboarding<\/a> each time you roll out features so existing users can learn about them and use them in the right way. This has dual benefits:<\/p>\n\n- It quickly draws users\u2019 attention to the new release<\/a>.<\/li>\n
- It shows them how to engage and maximize it, eliminating friction and improving user satisfaction.<\/li>\n<\/ul>\n
![\"feature-release_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/feature-release-feature-drop-offs_375347555427362efc25e1178e909a1c_800.png\")
<\/picture>Feature release announcement created with Userpilot<\/a>.<\/figcaption><\/figure>\n3. Deliver proactive customer support<\/strong><\/h3>\nProactive support<\/a> anticipates and addresses user issues or questions before they become significant problems. So, how can you offer proactive help?<\/p>\nUse contextual tooltips<\/a> to provide in-app support when users explore a feature for the first time or if they struggle to get things done.<\/p>\nIn addition, create a robust resource center<\/a> that contains helpful content for users at different journey stages. This allows users to troubleshoot issues themselves without waiting for hours to get live help.<\/p>\n![\"resource-center-editor_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/resource-center-editor-userpilot_f0bfd4bf20abac0eb3ee160bfeae99bb_800.png\")
<\/picture>An in-app resource center built<\/a> with Userpilot<\/a>.<\/figcaption><\/figure>\n4. Improve feature experiences with A\/B testing<\/strong><\/h3>\nIterative testing<\/a> allows your product to stay competitive and ensure user loyalty. A\/B testing can help you test multiple variations of your UI to find the ones that users prefer the most.<\/p>\nTo run an A\/B test, define a hypothesis that could help reduce feature drop-offs. Then, use A\/B testing<\/a> to validate your ideas<\/a>. If a new variant results in better engagement, that’s a sign you’ve addressed the source of friction in your UX.<\/p>\nNow, all you need to do is iterate<\/a> to keep improving the user experience.<\/p>\n![\"ab-testing-complete-experiment_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/ab-testing-complete-experiment-feature-drop-offs_27d59dad207e0e99c18a75825f889730_800.png\")
<\/picture>A controlled A\/B test in Userpilot<\/a>.<\/figcaption><\/figure>\nUserpilot: The best tool for monitoring feature engagement<\/strong><\/h2>\nUserpilot<\/a> is a product growth tool with powerful features to help you understand user behavior and trigger in-app experiences to boost engagement.<\/p>\nHere’s how Userpilot can help:<\/p>\n
\n- Analytics tools<\/strong>: Userpilot lets you generate trends<\/a>, funnels<\/a>, cohorts<\/a>, and path<\/a> analysis reports to gain a comprehensive understanding of in-app user behavior and understand why users abandon the journey.<\/li>\n<\/ul>\n
![\"trend-analysis_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/trend-analysis-userpilot_059c5e2f3214b51848b79a9a140364f3_800.png\")
<\/picture>A trends analysis report in Userpilot<\/a>.<\/figcaption><\/figure>\n\n- Feature tagging<\/strong>: Tag existing and new features to measure associated user interactions<\/a>. You can visualize the result in a comprehensive dashboard like the one below, to see the total number of events<\/a> and interactions.<\/li>\n<\/ul>\n
![\"feature-events-dashboard_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/feature-events-dashboard-feature-drop-offs_0b29925570e576f5b30b1c1496b47b00_800.png\")
<\/picture>Userpilot’s<\/a> features and events dashboard.<\/figcaption><\/figure>\n\n- A\/B testing for flows<\/strong>: Userpilot’s A\/B testing<\/a> feature lets you test different variations of in-app flows and UX enhancements to discover what works best for your users. You can conduct controlled A\/B tests, head-to-head tests, and multivariate tests<\/a>.<\/li>\n<\/ul>\n
![\"ab-testing-types_\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/ab-testing-types_9860e3868954248c0e3d73814d5b2426_800.png\")
<\/picture>Types of A\/B tests available in Userpilot<\/a>.<\/figcaption><\/figure>\n\n- Analytics dashboards<\/strong>: Userpilot’s analytics dashboards<\/a> let you track key features relating to user experience and product performance in one place. To track feature engagement, you can view the core feature engagement dashboard. It lets you track usage trends<\/a>, adoption rates<\/a>, and retention<\/a> for your most vital features.<\/li>\n<\/ul>\n
![\"core-feature-engagement-dashboard_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/core-feature-engagement-dashboard-feature-drop-offs_b1fd5ec6850c3ca16eca44864eb1d3d4_800.png\")
<\/picture>Start tracking your core features code-free with Userpilot<\/a>.<\/figcaption><\/figure>\n\n- Session recordings<\/strong>: Watch video recordings of how users interact with your features. Observe their navigation patterns<\/a> and the points where drop-offs occur. Then, use the insights to determine how best to boost engagement.<\/li>\n<\/ul>\n
Conclusion<\/strong><\/h2>\nThe best way to deal with feature drop-offs is by continuously studying user behavior, and making data-driven enhancements in the UX. This helps you understand what your users need, track changes in their preferences, and identify where your product may be lacking.<\/p>\n
Drop-offs<\/a> also indicate a value gap<\/a> in what your product delivers vs what regular users expect from it.<\/p>\n Tracking where users disengage<\/a> can feel like focusing on the negative, but it’s a proactive way to boost your product’s health<\/a> and optimize engagement<\/a>.<\/p>\n Here are the key benefits:<\/p>\n A feature with declining usage often points to hidden obstacles<\/a> in the user journey. Maybe the feature is hard to find, poorly explained, or buggy.<\/p>\n Tracking drop-offs helps you pinpoint these friction points<\/a> so you can strategize to remove them, leading to a much smoother experience<\/a> for users.<\/p>\n For example, if you notice users are disengaging because the feature is complex, you could deploy tooltips<\/a> to explain the feature better or have your dev team redesign it for simplicity.<\/p>\n When you remove friction by analyzing user behavior, you remove roadblocks from the user journey. This helps increase user engagement and makes it more likely for users to see the full potential of your features.<\/p>\n By tracking drop-offs, you can identify the features that contribute to churn and step in to improve the experience.<\/p>\n Tracking how users behave in-app<\/a> helps you understand their needs better.<\/p>\n You’ll see the features they use frequently and the ones they ignore. If a feature isn’t gaining traction despite your best efforts, it is likely not a good fit for most users.<\/p>\n These insights can help you inform feature development<\/a>. You can refine popular features or develop complementary ones and evaluate scaling back<\/a> for those that don\u2019t attract users.<\/p>\n Continuously identifying and removing friction with drop-off analysis and user feedback creates a cycle of data-driven product development. This improves the overall user experience<\/a>, leading to more paying customers in the long run.<\/p>\n You can also expand the same strategy to other channels, such as your landing pages and social media posts, to reduce conversion friction.<\/p>\n Even well-developed features can suffer from disinterest. Here are five common reasons this happens:<\/p>\n Analytics tools play a critical role in identifying drop-off points. While you can use a single method to identify friction points, combine different methods to glean the most value from your analysis.<\/p>\n Let’s explore how this is done with the best tools for the job.<\/p>\n Funnel Analysis<\/a> tracks the user journey<\/a> through a series of steps toward a defined goal, such as discovering and using a feature.<\/p>\n It helps to identify at which stage users drop off<\/a>, providing insights into potential issues or friction areas within that journey.<\/p>\n Analytics tools such as Userpilot let you conduct funnel analysis<\/a> and generate reports to spot drop-offs across various features.<\/p>\n For example, by examining a funnel analysis report, you can see a sharp decline in progression between steps, suggesting friction.<\/p>\n Path analysis<\/a> visualizes the various routes users take through your product, revealing how they navigate towards and within specific features. Each stage in this analysis comprises a feature users engage with, an event they complete, or a page they view.<\/p>\n You can also track drop-offs for every path<\/a> as users move from one stage to another. This helps identify exactly where they face trouble.<\/p>\n Cohort analysis<\/a> divides users into groups based on shared characteristics (such as sign-up date) and tracks their behavior patterns over time.<\/p>\n This analysis helps you measure<\/a> how many users return to perform the same event. By conducting regular cohort analysis, you can identify which features have high and low retention rates<\/a> and how they contribute to overall product retention.<\/p>\n Feedback surveys<\/a> help you hear from your users directly. Gather both quantitative and qualitative data regarding how your users feel and identify friction areas other report types might be missing.<\/p>\n For example, a feature might be popular with your users but still be hard to use. In such a scenario, a funnel report cannot highlight the perceived difficulty. A CES survey<\/a>, on the other hand, can help you collect direct feedback regarding how easy it is to use.<\/p>\n You can also ask users to expand on their responses with open-ended questions, giving you deeper insights related to their experiences.<\/p>\n Track the following metrics to spot drop-offs in your product easily:<\/p>\n Now that you’ve seen how to identify drop-offs and the metrics to track, it’s time to learn how to boost engagement<\/a>. Here are four ways to lower friction for your users.<\/p>\n One size does not fit all. If you have a single onboarding flow for all your new users, you’re likely showcasing irrelevant features and delaying their time to value<\/a>.<\/p>\n Different segments care about different features. Use personalization<\/a> to introduce only the features relevant to each use case and speed up product adoption.<\/p>\n Welcome surveys enable<\/a> you to collect user data upfront. Use this information to segment users, understand their needs, and trigger the right onboarding flow<\/a> for the right user.<\/p>\n Don’t assume existing users will independently discover new features<\/a> and magically know how to use them right away.<\/p>\n Instead, trigger detailed secondary onboarding<\/a> each time you roll out features so existing users can learn about them and use them in the right way. This has dual benefits:<\/p>\n Proactive support<\/a> anticipates and addresses user issues or questions before they become significant problems. So, how can you offer proactive help?<\/p>\n Use contextual tooltips<\/a> to provide in-app support when users explore a feature for the first time or if they struggle to get things done.<\/p>\n In addition, create a robust resource center<\/a> that contains helpful content for users at different journey stages. This allows users to troubleshoot issues themselves without waiting for hours to get live help.<\/p>\n Iterative testing<\/a> allows your product to stay competitive and ensure user loyalty. A\/B testing can help you test multiple variations of your UI to find the ones that users prefer the most.<\/p>\n To run an A\/B test, define a hypothesis that could help reduce feature drop-offs. Then, use A\/B testing<\/a> to validate your ideas<\/a>. If a new variant results in better engagement, that’s a sign you’ve addressed the source of friction in your UX.<\/p>\n Now, all you need to do is iterate<\/a> to keep improving the user experience.<\/p>\n Userpilot<\/a> is a product growth tool with powerful features to help you understand user behavior and trigger in-app experiences to boost engagement.<\/p>\n Here’s how Userpilot can help:<\/p>\n The best way to deal with feature drop-offs is by continuously studying user behavior, and making data-driven enhancements in the UX. This helps you understand what your users need, track changes in their preferences, and identify where your product may be lacking.<\/p>\nWhat are the benefits of tracking drop-offs?<\/strong><\/h2>\n
1. Identify points of friction to remove them<\/strong><\/h3>\n
2. Increase feature adoption<\/strong><\/h3>\n
3. Prioritize feature development as per user needs<\/strong><\/h3>\n
4. Improve user experience and increase retention<\/strong><\/h3>\n
Why do users stop using a feature?<\/strong><\/h2>\n
\n
How to identify drop-offs for features<\/strong><\/h2>\n
1. Identify where users drop off with funnel analysis<\/strong><\/h3>\n
![\"drop-offs_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/drop-offs-feature-drop-offs_a0247c225da445842a6778ad8e957992_800.png)
<\/picture>2. Track user paths for your feature with path analysis<\/strong><\/h3>\n
![\"path-analysis_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/path-analysis-userpilot_a7e8a566218396076736f51946aa3294_800.png)
<\/picture>3.<\/strong> Track feature retention with cohort analysis<\/strong><\/h3>\n
![\"cohort-analysis_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/cohort-analysis-userpilot_7cbb41ac729d8cd8aec75fb36c229f2e_800.png)
<\/picture>4. Collect user feedback to ask users where they face friction<\/strong><\/h3>\n
![\"customer-effort-score-survey_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/customer-effort-score-survey-feature-drop-offs_d8b08e8b421bc6dcffc477d60fe46c96_800.png)
<\/picture>Related metrics that help measure drop-offs<\/h2>\n
\n
4 Ways to reduce feature drop-offs for your SaaS company<\/strong><\/h2>\n
1. Personalize the onboarding flow<\/strong><\/h3>\n
![\"welcome-survey-userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/welcome-survey-userpilot_ee7be42ac0ee8c60b1adda1ad55c23fa_800.png)
<\/picture>2. Offer secondary onboarding for every new feature<\/strong><\/h3>\n
\n
![\"feature-release_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/feature-release-feature-drop-offs_375347555427362efc25e1178e909a1c_800.png)
<\/picture>3. Deliver proactive customer support<\/strong><\/h3>\n
![\"resource-center-editor_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/resource-center-editor-userpilot_f0bfd4bf20abac0eb3ee160bfeae99bb_800.png)
<\/picture>4. Improve feature experiences with A\/B testing<\/strong><\/h3>\n
![\"ab-testing-complete-experiment_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/ab-testing-complete-experiment-feature-drop-offs_27d59dad207e0e99c18a75825f889730_800.png)
<\/picture>Userpilot: The best tool for monitoring feature engagement<\/strong><\/h2>\n
\n
![\"trend-analysis_Userpilot\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/trend-analysis-userpilot_059c5e2f3214b51848b79a9a140364f3_800.png)
<\/picture>\n
![\"feature-events-dashboard_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/feature-events-dashboard-feature-drop-offs_0b29925570e576f5b30b1c1496b47b00_800.png)
<\/picture>\n
![\"ab-testing-types_\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/ab-testing-types_9860e3868954248c0e3d73814d5b2426_800.png)
<\/picture>\n
![\"core-feature-engagement-dashboard_feature-drop-offs\"](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/03\/core-feature-engagement-dashboard-feature-drop-offs_b1fd5ec6850c3ca16eca44864eb1d3d4_800.png)
<\/picture>\n
Conclusion<\/strong><\/h2>\n