Funnel Analysis AI Prompts

Audit the conversion funnel for {{product_flow}} and identify the highest-impact drop-off points. Funnel stages provided: {{stages_and_counts}} 1. Compute conversion rates: - Step-by-step conversion rate: users_at_step_N / users_at_step_N-1 - Cumulative conversion rate: users_at_each_step / users_at_top_of_funnel - Overall funnel conversion: bottom_of_funnel / top_of_funnel 2. Identify the biggest drop-offs: - Rank steps by absolute user loss (not just % drop) - Rank steps by % conversion rate (lowest = most leaky) - Flag any step with conversion rate below {{threshold}}% 3. Benchmark against industry standards: - What is a typical conversion rate for each step in {{industry}}? - Which steps are performing below benchmark? 4. Segment the funnel: - Break conversion rates by: new vs returning users, device (mobile/desktop), traffic source, user cohort - Which segments have the lowest conversion at the biggest drop-off step? - Are any segments converting exceptionally well? (Best practice to replicate) 5. Qualitative context: - For the top 2 drop-off steps: list 3 possible reasons users are leaving - What data would confirm or rule out each reason? 6. Prioritized recommendations: - Top 3 interventions ranked by expected impact on overall funnel conversion - For each: hypothesis, test design, and expected lift Return: funnel table with conversion rates, drop-off ranking, segment breakdown, and prioritized recommendations.

Analyze how conversion rates differ across key user segments in this funnel. Funnel data: {{funnel_data}} Segmentation dimensions: {{dimensions}} (e.g. acquisition channel, device, plan type, geography, user tenure) 1. Per-segment funnel tables: For each dimension, produce a funnel table showing conversion at every step broken out by segment value. Highlight: which segment has the highest overall conversion? Which has the lowest? 2. Segment-step interaction: - Are drop-off patterns consistent across segments, or does one segment struggle at a specific step? - Example: mobile users may convert well at sign-up but drop at payment entry - Identify any step where segment A converts at more than 2x segment B 3. Volume-weighted impact: - A segment with 5% conversion but only 2% of volume has low total impact - Compute: (segment volume %) x (conversion gap vs best segment) = impact score - Rank segments by impact score to prioritize where improvement matters most 4. Cohort conversion analysis: - Do users acquired in recent months convert better or worse than older cohorts? - Is there a trend suggesting the product is getting easier or harder to convert? 5. Statistical significance: - For the largest conversion gap between segments: run a proportion z-test - Is the difference significant (p < 0.05) or within random variation? 6. Recommendations: - Which segment should be targeted for conversion improvement first and why? - What product or UX change would most help the lowest-converting high-volume segment? Return: per-segment funnel tables, segment-step interaction analysis, impact scores, significance test, and top recommendations.

Analyze which in-product touchpoints and features most contribute to conversion or activation. User journey data: {{journey_data}} (user_id, touchpoint_type, touchpoint_timestamp, converted: Y/N) Conversion event: {{conversion_event}} (e.g. first purchase, plan upgrade, feature activation) 1. Touchpoint inventory: - List all unique touchpoints users encounter before the conversion event - Count how often each appears in converting vs non-converting journeys - What % of converters touched each touchpoint? 2. Attribution models - compare all three: First touch: - 100% credit to the first touchpoint the user interacted with - Best for: understanding what initiates the conversion journey Last touch: - 100% credit to the touchpoint immediately before conversion - Best for: understanding what closes the conversion Linear: - Equal credit to all touchpoints in the path - Best for: understanding overall touchpoint contribution 3. Path analysis: - What are the top 10 most common touchpoint sequences for converters? - What sequences do non-converters follow? Where do they diverge? - Is there a specific touchpoint combination that strongly predicts conversion? 4. Time-to-conversion by path: - Do users with certain touchpoint paths convert faster? - Is there a touchpoint that accelerates conversion when added to the path? 5. Recommendations: - Which touchpoints should be promoted (high attribution, currently under-used)? - Which touchpoints appear to delay or interrupt conversion? - What is the optimal path to guide new users through? Return: touchpoint attribution table (all three models), top conversion paths, path divergence analysis, and recommendations.