Explore Conversion Rate using your PostHog data
Conversion Rate in PostHog
Conversion Rate is critical for PostHog users because it transforms raw event data into actionable business insights. PostHog captures detailed user behavior across your entire product funnel—from initial page views and feature interactions to completed purchases or sign-ups. This rich event data makes conversion rate analysis particularly powerful, enabling you to identify exactly where users drop off, which features drive conversions, and how different user segments perform across your product journey.
However, calculating conversion rates manually from PostHog data quickly becomes overwhelming. Spreadsheet analysis requires exporting event data across multiple timeframes and user segments, creating complex formulas that are prone to errors, and manually updating calculations as new data arrives. With dozens of potential conversion paths and user segments to analyze, maintaining accurate spreadsheet models becomes virtually impossible.
PostHog’s built-in reporting, while useful for basic metrics, offers limited flexibility for deeper conversion analysis. You can’t easily explore “what if” scenarios, segment by multiple dimensions simultaneously, or drill down into specific user cohorts that drive your conversion rates. When stakeholders ask follow-up questions about seasonal trends or cohort-specific performance, rigid dashboards leave you scrambling to find answers.
Count eliminates these pain points by automatically applying the conversion rate formula and letting you explore PostHog data through natural language queries, making it effortless to calculate conversion rate across any dimension or timeframe.
Questions You Can Answer
What’s my overall conversion rate from signup to purchase?
This reveals your core business performance by showing how effectively you turn new users into paying customers using PostHog’s event tracking data.
How do I calculate conversion rate for my checkout funnel steps?
Understanding the conversion rate formula between each funnel step helps identify where users drop off most frequently, enabling targeted optimization efforts.
What’s the conversion rate difference between mobile and desktop users?
This analysis leverages PostHog’s device property data to uncover platform-specific performance gaps that might require different user experience strategies.
How has my feature adoption conversion rate changed over the last 3 months by user cohort?
Tracking conversion rates across cohorts and time periods reveals whether product improvements are actually driving better user engagement and feature uptake.
What’s the conversion rate from trial start to paid subscription for users who completed onboarding versus those who didn’t?
This sophisticated segmentation combines multiple PostHog events to understand how onboarding completion impacts your most critical business conversion.
How do conversion rates vary across different UTM campaigns and user properties like company size?
Cross-cutting analysis using PostHog’s campaign tracking and custom properties helps optimize marketing spend and identify your highest-converting user segments.
How Count Analyses Conversion Rate
Count transforms your PostHog conversion rate analysis from basic event tracking into comprehensive business intelligence. Unlike rigid dashboards that show standard conversion rate formulas, Count’s AI agent writes custom SQL tailored to your specific questions—whether you’re calculating conversion rates across complex user journeys or analyzing how to calculate conversion rate for multi-step funnels.
Count runs hundreds of queries in seconds across your PostHog data, automatically segmenting conversion rates by user properties, feature flags, and cohorts simultaneously. For example, Count might analyze your signup-to-purchase conversion rate while simultaneously breaking it down by traffic source, A/B test variant, device type, and geographic region—uncovering patterns like mobile users from paid ads converting 40% better in specific markets.
Your PostHog data isn’t perfect, and Count knows it. The platform automatically handles missing events, duplicate users, and inconsistent property formatting while calculating conversion rates, ensuring clean analysis without manual data preparation.
Count’s transparent methodology shows exactly how it calculated each conversion rate—every filter, assumption, and transformation is documented. You can verify the conversion rate formula used and modify the logic as needed.
The analysis arrives presentation-ready with visualizations and insights, while your team can collaboratively explore follow-up questions. Count also connects your PostHog conversion data with other sources like your CRM or payment processor, providing complete conversion attribution across your entire customer journey.