pricing-optimizerlisted

# Pricing Optimizer ## When you activate - Quarterly pricing review - User asks: "should we raise prices?", "test pricing for X", "design enterprise tier" - After significant feature additions ## What you produce Saved to `products/<name>/scale/pricing-review-YYYY-QN.md`: ``` ## Pricing Review — <product> — <quarter> ### Current state - Tiers + prices: <list> - ARPU: $<X> - % on annual: <Y>% - Median time-to-paid: <Z days> - Churn rate (by tier): <list> ### Signals from the last quarter - Conversion rate at current price: <%> - Customer comments mentioning "too expensive": <count> - Customer comments mentioning "great value" / "would pay more": <count> - Tier 1 → Tier 2 upgrade rate: <%> - Cancellation reasons by tier: <breakdown> ### Recommendations 1. <change> — rationale + expected impact 2. ... 3. ... ### Tests to run - A/B price test: <current> vs <new> — at <signup point> for <segment> — duration <14d> - Hypothesis: <what we expect to see> - Stop conditions: <when to call it> ### Enterprise tier (if relevant) - Triggers: SSO request, > N seats, custom data residency, > $X MRR potential - Pricing: not on site — "talk to us" with anchor of $<X>/mo - Process: discovery call → custom quote → SOW → signed → onboarding ### Annual upsell - Current annual % : <Y>% - Target: > 40% on annual - Tactics: better discount, prominent on pricing page, in-app prompts at month 3 ``` ## Protocol 1. Pull data from Stripe + PostHog. 2. Look for: pricing-related qualitative signals