So, according to these stats, sending 10,000 visitors to a landing page will only result in 59 adopted users. Assuming an optimistic CPC of $5, that\u2019s $841.75 per user who churns<\/a> instead of adopting the product organically.<\/p>\n Despite this, I\u2019ve found that a “leaky” funnel is not about traffic or CPC, but about clarity. Here\u2019s how I analyze, diagnose, and repair broken funnels using a product-led approach:<\/p>\n Conversion funnel optimization is an iterative process of increasing the percentage of users who move from one stage to the next (e.g., visit > signup > activation > retention > expansion). It involves measuring users\u2019 progress (e.g., via funnel or path analyses<\/a>) and optimizing each step in their journey to reduce drop-offs<\/a>.<\/p>\n A conversion funnel might involve very different steps depending on whether you\u2019re in marketing, sales, customer success<\/a>, and so on.<\/p>\n Here are different types of funnels to consider:<\/p>\n Note: <\/strong>The number of funnel stages and their names might vary by company and industry. So for the sake of consistency, we\u2019re going to focus on the AARRR-based funnel for PLG companies<\/a>.<\/p>\n When I talk about SaaS conversion funnels<\/a>, I refer to the specific set of steps a user must take to achieve a goal. For PLG companies, this goal isn\u2019t about achieving a milestone, but about nurturing a cycle of acquisition > retention > referral > more acquisition.<\/p>\n For example, here\u2019s what this funnel looks like in Userpilot<\/a>:<\/p>\n Now that we\u2019re clear about the conversion process, let\u2019s go over the main 9 strategies I follow to minimize friction in our PLG conversion funnel:<\/p>\n Before applying any other strategy, my first action is to diagnose the largest conversion gap in our funnel. This diagnosis dictates whether we focus on fixing acquisition, activation, or free-to-paid conversions<\/a>.<\/p>\n To do this, I love creating funnel reports<\/a>. This gives me a macro view of the necessary steps a user must take to advance in the customer journey. These macro steps might look like this:<\/p>\n This conversion funnel analysis<\/a> lets me isolate specific drop-off points. For example, if 60% verify their email, but only 10% create a project, the design failure may lie in the product\u2019s empty state<\/a>, not the landing page.<\/p>\n The funnel chart also leaves room to track why users drop off. Are they going to the pricing page<\/a>? Are they looping back to the dashboard? Or are they just hitting the documentation? If I see users constantly navigating to the help center<\/a> during onboarding, it\u2019d be a sign of design failure.<\/p>\n For funnel charts, the best is to focus on metrics that reflect stage-to-stage conversion health. This will vary based on your product, but some metrics for this would include:<\/p>\n Besides following the macro steps, I also analyze the micro actions that users perform within a funnel stage.<\/p>\n This is because users will rarely follow the happy path<\/a>. And if I can spot deviation patterns, then I\u2019ll be able to find a solution for it.<\/p>\n To analyze these micro steps, I prefer using path analysis<\/a>. It lets me visualize the sequence of actions that users follow in our product. And within it, I can spot how users are deviating from the happy path and understand their intent. For example:<\/p>\n For the micro steps, I prefer to focus on very specific data points that reflect friction<\/a>. For example:<\/p>\n While quantitative data<\/a> tell what happened, session replays<\/a> show why.<\/p>\n I know seeing a user struggle with a form field I designed is painful, but it\u2019s the fastest way to fix user friction<\/a>. That\u2019s why once I\u2019ve identified multiple friction points, I then watch the recordings to look at:<\/p>\n Cleeng<\/a>, for instance, made a change in their user interface powered by Userpilot’s reporting that led to a 92% drop in feature usage. And the team\u2019s only way to fix the issue was to use session replays to understand that it happened due to a button placement.<\/p>\n \u201cSession replays were super useful when we changed the navigation. We could see what exactly the users were clicking and if they were visiting the pages we want them to visit.\u201d<\/em> \u2013 Anna Sobiak, Product Designer.<\/p><\/blockquote>\n Although session replays are qualitative, there are a couple of metrics that help me analyze user behaviors, including:<\/p>\n A website visitor is only valuable if their intent matches the offer. Bringing cheap website traffic will only bloat the numbers without increasing sales in a significant way.<\/p>\n So, to optimize the top of the funnel (TOFU), I actually add more<\/em> steps to bring users down the funnel.<\/p>\n Usually, adding more steps will reduce conversions because of friction. But by segmenting users and using personalized content, I\u2019ve found that adding the right step increases the quality of conversion.<\/p>\n An example of this is when we trigger a welcome screen<\/a> after a user logs in, asking, “What is your main goal today?”<\/p>\n Then, depending on their answer, they\u2019ll follow a personalized onboarding flow<\/a>. If they say that they want to send email marketing campaigns, then the flow will lead them directly to the email feature. Ultimately, the result is improved customer satisfaction compared to a non-personalized product tour<\/a> with \u201cless friction\u201d.<\/p>\n Personalization will usually improve conversion rates<\/a>, but there are other metrics to determine if it\u2019s working, including:<\/p>\n In the PLG funnel, activation is the inflection point that sets the user on the path to becoming a life-long customer.<\/p>\n This is why we\u2019re obsessed with providing value as soon as the user logs into our product. For which I always ask myself these three questions:<\/p>\n To optimize activation, many people think adding in-app guidance<\/a> will only annoy users and create friction.<\/p>\n But what I\u2019ve found is that, instead of bombarding users with pop-ups upon login, triggering flows<\/a> based on specific in-app actions leads to shorter time-to-value<\/a>. For instance, if a user clicks the “Reports” tab, then showing a native tooltip<\/a> on how to export data adds more value rather than being an obstacle.<\/p>\n The key difference is that most product tours are passive. They sit there waiting for the user to pay attention. Whereas contextual guidance<\/a> requires the user to take action, building familiarity and reducing friction.<\/p>\n Besides activation rates<\/a>, there are more sensitive metrics that might tell different stories, including:<\/p>\n You might think that gamification<\/a> belongs to the domain of B2C apps like Duolingo.<\/p>\n But some of its practices are also very useful for B2B SaaS: adding scores based on completed tasks, streaks, achievements, or simply adding funny animations are perfectly mappable to B2B software.<\/p>\n Plus, the best stage to add gamification is during onboarding<\/a>. My go-to tactic is to embed an onboarding checklist that leverages the Zeigarnik effect<\/a>: our psychological need to finish incomplete tasks.<\/p>\n This means adding pre-completed steps (mark “Create Account” as done) to create immediate momentum, including progress bars so users feel motivated to finish the task, and giving rewards to users who achieve the activation task (i.e., the final item).<\/p>\n Sked Social<\/a>, for example, tripled<\/em> its free-to-paid conversion rates when adding a gamified checklist that included the three elements I just mentioned.<\/p>\n To measure customer engagement, track metrics that reflect how users interact with gamified elements, such as:<\/p>\n Users will<\/em> get stuck in the funnel, and depending on how you manage it, you might send them to a competitor. In fact, 73% of consumers<\/a> will switch to a competitor after multiple bad support experiences.<\/p>\n To avoid this, support must be both instant and self-serve, with a human safety net for high-value accounts.<\/p>\n My best recommendation is to implement an in-app resource center<\/a> widget for self-service support<\/a>. This will connect the knowledge base to the product, and as a result, users will stay inside the app rather than opening a new tab to Google the documentation.<\/p>\n Plus, you can even allow the most impactful customers to book a call with their customer success<\/a> manager right inside the product.<\/p>\n Use any metrics related to resource center performance, including:<\/p>\n When a user activates a core feature, it doesn\u2019t mean that they\u2019ve adopted the product.<\/p>\n Once a user starts to use the product consistently, our focus shifts toward expansion revenue<\/a> and support.<\/p>\n For this, my first step is to use behavioral segmentation<\/a> to identify users who are ready for advanced features. For example, if a user has logged in 10 times but hasn’t used our “Team Collaboration” feature, I trigger a slideout<\/a> showcasing the benefits of inviting their team.<\/p>\n This is secondary onboarding<\/a>, and it\u2019s how you drive expansion revenue. Companies like Chargezoom<\/a> manage to triple expansion growth (from 1,38% to 4% in one quarter) this way. They added tooltips<\/a> and step-by-step flows to nudge users toward features they would have otherwise missed.<\/p>\n \u201cMost customers only used our product for one thing. They didn\u2019t even realize all the other features that could make their lives easier until we started guiding them with tooltips.\u201d<\/em> \u2013 Erin Gordy, Director of Customer Success at Chargezoom.<\/p><\/blockquote>\n Key performance indicators for secondary onboarding include:<\/p>\n In general, closing the feedback loop<\/a> means collecting customer voice and responding to it by addressing user pain points. It\u2019s a form of customer retention strategy that \u201creacts\u201d to the user\u2019s sentiment<\/a>.<\/p>\n The common practice is to measure NPS<\/a> to gauge user sentiment. But that misses the \u201creaction\u201d part of the cycle, and what I do to close the loop is to automatically trigger tasks based on NPS responses. For instance, if a user is a “promoter,” I automate a follow-up message asking for a G2 review. If they are a “detractor,” I send it to the success team so they can reach out personally.<\/p>\n An example of this strategy is Unolo<\/a>. They started regularly running NPS surveys<\/a> and alerting the success team about detractors. The result? Response rates increased to 43.65%, and churn decreased by almost 1% after addressing negative feedback.<\/p>\n \u201cI started using NPS, which was a really great feature because it gave us feedback almost instantly. This helped us get in touch with customers more quickly and understand their concerns with the product, which, in turn, helped us reduce our churn rate.\u201d \u2013Subhash Yadav, Product Marketer at Unolo<\/em><\/p><\/blockquote>\n Besides NPS, key metrics that help close the feedback loop include:<\/p>\n Effective conversion funnel optimization is a continuous loop of hypothesis, design, and analysis. You\u2019ll never have a perfect funnel simply because products and users are constantly evolving.<\/p>\nWhat is conversion funnel optimization?<\/h2>\n
Marketing funnel vs. PLG funnel vs. revenue funnel<\/h3>\n
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What are the conversion funnel stages?<\/h2>\n
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9 High-impact strategies to optimize your conversion funnel<\/h2>\n
1. Map the happy path (macro view)<\/h3>\n
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![\"Userpilot](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/funnel-analysis-userpilot_1ab2e3c70829fe5d4407d77ac2bb2675_1600.png\")
Metrics to track<\/h4>\n
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2. Track the actual path (micro view)<\/h3>\n
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![\"Userpilot](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/userpilot-path_3cc113a3c07f13e86b3a3b61206d79c8_1600.png\")
Metrics to track<\/h4>\n
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3. Watch the struggle (identify the \u201cwhy\u201d)<\/h3>\n
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![\"Userpilot](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/session-replays_e46cb28b0418bc6694651381c8a5fe1e_1600.png\")
Metrics to track<\/h4>\n
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4. Segment users to personalize their experience<\/h3>\n
![\"Segmentation](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/segmentation_c4576f16498d1fcc84a0a4e87935445c_1600.png\")
Metrics to track<\/h4>\n
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5. Engineer the Aha! moment<\/h3>\n
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Metrics to track<\/h4>\n
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6. Gamify the onboarding sequence<\/h3>\n
![\"Customizing](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/gamification_73ae604ee6e3d77d350091ae21645bf7_1600.png\")
Metrics to track<\/h4>\n
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7. Embed an always-on support layer<\/h3>\n
![\"In-app](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/resource-center_23ef82b827885875b37c10a69f3a5a17_1600.png\")
Metrics to track<\/h4>\n
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8. Deploy secondary onboarding<\/h3>\n
Metrics to track<\/h4>\n
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9. Close the feedback loop<\/strong><\/h3>\n
![\"NPS](\"https:\/\/blog-static.userpilot.com\/blog\/wp-content\/uploads\/2026\/04\/nps-survey_5ada259d1141c042b44d748760c1d15c_1600.png\")
Metrics to track<\/h4>\n
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Sustaining funnel performance over time<\/h2>\n