A conversion funnel analysis tracks this journey step by step to identify weak links—like where users drop off. It pinpoints where, between the consideration and decision stages, users leave (and why) so you can improve them for better conversion.
Let’s recap the typical stages of a SaaS conversion funnel:
- Stage 1: Signup/ Registration
- Stage 2: Onboarding
- Stage 3: Engagement
- Stage 4: Activation/ Conversion
Calculating your trial-to-paid-conversion rate
How effective is your conversion funnel? To measure its efficiency, you need to know how many customers start and how many make it through to the end of the funnel. That’s your trial-to-paid conversion rate.
Consider this funnel analysis example. Your SaaS company promotes a new feature to drive trial users to paid plans. From 200 free trials, 30 convert to paid subscriptions.
To find the trial-to-paid conversion rate, you’d use the following formula:
The data shows that out of 200 free trial users, 15% upgraded to a paid plan, meaning 85% didn’t.
A conversion funnel analysis will then track app user behaviors through the different stages to identify where 85% of users left—whether it was during onboarding, exploring key features, or choosing the paid plan so you can focus and improve the obstacles for better conversions.
Key objectives of conversion funnel analysis
A low trial-to-paid conversion rate points to friction in the user journey, possibly from unclear onboarding, misaligned product value, or even a misplaced CTA. A high rate shows everything, from onboarding to CTAs and engagement, is on point.
And by using the right tools throughout your analytical process, like heatmaps, triggered session recordings, and user feedback, you get crucial insights into user behavior and pain points, which allows you to make scalable improvements.
Here are five outcomes that a conversion funnel analysis helps you achieve:
1. Understand user behavior at each stage of the funnel
New users, free trial users, and returning customers interact with your product differently—each group has unique needs and wants different things at each stage.
For instance, new visitors look for basic information, free trial users look at functionality, and returning customers want more advanced tools or support.
Knowing and understanding these behaviors allows you to tailor the user experience to each group’s specific needs.
2. Identify and reduce friction points that lead to drop-offs
Customers abandon the funnel when they feel stuck, overwhelmed, or confused. A funnel analysis shows you the exact moments this happens.
For instance, heatmaps and session recordings pinpoint where potential customers drop off, giving you the chance to remove obstacles and create smoother experiences.
3. Improve trial-to-paid conversion through data-driven insights
Precise insights let you focus on what matters most so you can deliver better outcomes for both users and your business.
When you know exactly where and why customers leave, you're no longer guessing. This precision lets you make decisions based on facts and data and cut irrelevant actions (and costs).
This isn’t just about increasing conversions. When you address specific pain points, you also improve the customer experience and lifetime value, enhance retention, increase customers, and drive repeat business.
4. Optimize onboarding processes to enhance user activation
Onboarding is your first impression with customers—it can make or break your relationship.
And when it’s optimized for better engagement, it’s easier for users to get started. It also reduces early drop-off rates, minimizes the need for customer support, and guides potential customers through key steps—increasing product usage and adoption.
5. Scale the funnel analysis as your user base grows
You can’t manually track and analyze thousands of user interactions. The beauty of implementing automated processes and AI-driven analysis lies in its scalability.
You can gather insights across all interactions and apply these systematically as your user base grows. This lets you maintain strong performance metrics like customer acquisition cost (CAC) and customer lifetime value (CLV).
Product analysis tools to use to optimize conversion
While a basic funnel analysis highlights drop-offs, it doesn’t explain the causes. Understanding the "why" requires diving deeper with analytics tools like:
- Triggered session recordings
- Heatmaps
- Targeted surveys
Triggered recordings of specific user sessions
Sprig Replays capture targeted session recordings based on specific user actions (or events) to give precise insights—without the need to sift through full-length sessions.
The playback page of a Replay study in Sprig, where you can watch all of the targeted session recording clips you’ve captured.For example, if users abandon their cart during a checkout process. Instead of reviewing hours of footage, Sprig Replays record just the interaction where the user stops engaging with the form. This helps to quickly identify what they were doing when they left.
Record specific clips of users’ web and mobile experience based on their unique attributes and in-product actions.
Heatmaps to visualize the user experience in your app or website
Sprig Heatmaps visually aggregate in-product activity, highlighting popular sections and less-engaged areas by showing where users click, scroll, and hover.
The results page of a Heatmap study in Sprig — including the AI Summary and heatmap, scrollmap, and clickmap data.Together, replays and heatmap examples collect data at scale and provide detailed insights into customer behavior analysis.
Suppose most users click the “Help” button on a specific page. Replays allow you to zoom into those sessions and see what the users are doing or trying to do just before they click “Help.” Heatmaps consolidate all interactions so you see where users engage the most and which areas they ignore.
The data collected also supports customer churn analysis and customer retention analysis. They identify user frustration points to reduce attrition and pinpoint high engagement areas, showing which features users prefer for targeted optimization.
In-app surveys and feedback buttons
Timely feedback and surveys act as a live diagnostic tool, capturing users' immediate reactions, emotional responses, and moments of frustration with specific features or processes.
Imagine potential customers are dropping off during the signup process in your app. By pairing Sprig in-app surveys with session Replays, you can capture real-time feedback and see exactly how users interact throughout the signup flow to understand where, and more importantly why, they are leaving the signup process.
Survey results page in Sprig — Sprig AI analyzes all responses to generate a study summary and key takeaways.This helps to uncover user sentiment with open-ended survey questions or targeted closed-ended responses, which will help you eliminate friction points in your customer journey and offer insights for future product development. Here’s an example of Sprig AI analyzing and grouping over 3,000 of Coinbase’s open-text survey responses:
Coinbase uses Sprig AI to synthesize and group open-text responses into key themes to uncover patterns in their users’ feedback.Read how Coinbase used Sprig AI to turn user feedback into actionable recommendations to improve their product interface.
AI insights and recommendations
Capture experience data at scale via Replays, Heatmaps, Surveys, and Feedback — Sprig AI instantly analyzes all data to uncover trends in your product experience.Sprig AI analyzes all your product experience data for you in real-time. It synthesizes data from Replays, Heatmaps, Surveys, and Feedback studies to summarize and identify patterns in you users' behavior and sentiment—also enabling you to segment data by customer journey flow, product area, device type, and more to give you a deep understanding of how and where specific user groups are engaging with your product.
For example, Sprig AI groups similar Replay clips into themes like recurring issues with form validation or confusing UI elements, unclear design, or pricing issues. It also summarizes the data into one concise report highlighting key trends for easy analysis. Here’s an example of Sprig AI’s analysis of 100+ session replay clips:
Results page of a Replay study in Sprig — Sprig AI instantly analyzes and groups session clips into themes to uncover patterns in your users' behavior.Once it pinpoints the exact issue, Sprig AI recommendations give you clear, actionable ways to fix friction areas.
For instance, if users are abandoning the signup process, Sprig AI will identify the exact issue—like a button not being clearly visible on mobile and suggest adjusting the layout for optimal customer experience.
Advanced guide to conversion funnel analysis: 6 Steps
Now, let’s move from theory to action and unpack funnel stages one by one so you know precisely what to do at each step—for conversion funnel optimization.
Step 1: Map the funnel stages
A clear funnel map tracks user progress so you can set conversion goals at each stage. Also, by visually outlining the funnel steps, your marketing team can align their marketing strategies with product teams to guide users through the process.
First, track every stage for user interaction, from landing pages and CTAs to sign-up forms and onboarding screens.
Common stages include:
- Signup/Registration: Where users first engage like trial sign-ups.
- Onboarding/Activation: Key actions like profile completion or product integration.
- Engagement: Ongoing usage, where users interact with features.
- Conversion: When users make a purchase or upgrade to a paid plan.
Do this for different customer segments. Then, break down each stage further into specific touchpoints where users might drop off—to avoid any chance of blind spots.
Here are the key touchpoints for each stage:
- Signup/Registration: Landing pages, call to action (CTA) buttons, sign-up forms, and social media sign-in options.
- Onboarding/Activation: Welcome emails, tutorial walkthroughs, feature demonstrations, and account setup guides.
- Engagement: Regular newsletters, in-app notifications, user support channels, and feedback forms.
- Conversion: Upgrade prompts, special offer notifications, pricing page interactions, free trial expiration alerts.
Step 2: Define KPIs for each stage
What are you measuring at each stage? You need to track everything from significant events to smaller engagements to get a detailed and nuanced understanding of user actions throughout the conversion funnel.
Here's a list of KPIs for each stage and what they tell you:
Funnel Stage |
Key Performance Indicator (KPI) |
What high values tell you |
What low values tell you |
Registration / Signup |
Signup completion rate: Percentage of users who complete the sign-up process |
Acquisition efforts and easy user conversion |
Sign-up process issues or unclear value proposition |
Onboarding / Activation |
Onboarding completion rate: Users who finish the onboarding process |
Users are engaging well with the onboarding process |
Confusion or friction during onboarding |
Engagement |
Feature usage frequency: How often users interact with key features |
Strong product adoption and long-term engagement |
Lack of communication, ease, or value in key features |
Conversion |
Free-to-Paid conversion rate: Users upgrading from free to paid plans |
Users see value in upgrading |
Pricing issues or a non-compelling free-to-paid transition |
Retention |
Churn rate: Users who stop using your product before the trial ends |
Problems in the user journey or users don't find value in upgrading |
Users are happy with the product and see the value |
Step 3: Set up tracking tools to capture user behavior
With the customer journey mapped, stages and touchpoints identified, and KPIs set. You can now apply Sprig's suite of tools to collect detailed data on how users interact with your product.
For example, you notice that users are abandoning the onboarding process. Here’s exactly how you set up:
Trigger session replays to capture the exact moments when users hesitate or disengage from the onboarding flow, and use Sprig's heatmaps to see which areas they give the most attention to. Deploy in-product surveys that pop up precisely when a user clicks 'exit' during form filling, asking for their reasons for leaving.
By timing your surveys just right, you can tap into emotional and honest feedback.
This quick setup and targeted tools collect the data from when it matters the most.
Now, let's talk analysis.
Step 4: Analyze drop-off points to identify friction
Once the user experience data is collected via Replays, Heatmaps, Surveys, and Feedback studies, you can analyze it all instantly and at scale using Sprig’s AI,
It identifies common patterns in your users’ sentiment and behavior, and gives you actionable insights to pinpoint exactly what aspects of your product need attention.
Sprig AI Recommendations then springs into action, giving you specific suggestions on what updates can be made to optimize the overall product experience—like simplifying complex steps, clarifying instructions, or adding engaging elements at points where users typically drop off.
Step 5: Use AI to segment users by behavior, traffic source, and demographics
For clearer insights, Sprig AI sorts user data into themes and segments. As we’ve mentioned earlier in this article, Sprig’s AI breaks down behaviors from session replays into themes, highlighting frequent issues, and segments users by criteria like device type or new or returning.
This allows you to customize the experience for each group at all funnel steps.
“The themes are always abundantly clear with Sprig AI, making it easy for product teams to see how feedback applies directly to new features, upcoming features, features that are not on the roadmap yet.”
Read Ward, Product Operations Lead at Ramp
Step 6: Use A/B testing and experiments to optimize the funnel
Before applying all insights, test and validate the effectiveness of proposed changes. Use A/B testing to assess each adjustment methodically.
For example, if the AI recommends simplifying the sign-up process, create two simplified versions of the page. Run these tests across different user segments to compare performance. Focus on key metrics like completion rates for each version.
Quickly analyze the results with Sprig AI. See if there is a statistically significant improvement in user behavior and how close you are now to meeting your goals.
This iterative process will confirm that every adjustment you make improves user engagement and boosts conversion.
Plann used Sprig to cut onboarding drop-off by 24% and boost MRR by 8%
Plann used Sprig to cut onboarding drop-off by 24% and boost monthly recurring revenue (MRR) by 8%. Here's how they did it:
Plann needed to understand why many users who signed up were not completing the onboarding for their desktop app. To address this, Plann deployed two targeted Sprig Surveys to identify onboarding drop-offs and website conversion barriers.
Plann used open-ended questions to capture user feedback at drop-off pointsBy engaging users directly at critical moments with Sprig Surveys, Plann quickly pinpointed crucial friction points, reduced onboarding drop-offs by 24%, and increased MRR by 8%.
Plann used Sprig Surveys to cut onboarding drop-off by 24% and boost MRR by 8%
Maximize your conversions through smart funnel analysis
If users aren't converting, you'll likely fall short of your KPIs.
Knowing where users drop off and understanding why they leave is the only way to refine your conversion funnel. But, identifying drop-off points is just the beginning—using the right tools to dig deeper into user behavior is necessary for improving user experiences, driving higher conversions, and retaining customers.
Follow our simple guide to scale your conversion funnel analysis effectively.
Optimize your funnel with Sprig’s heatmaps and session replays for pinpoint accuracy. Then, let Sprig AI automate and speed up data analysis, reveal hidden trends, and provide clear data-driven recommendations to boost conversions.