Lifecycle flows

Churn Intervention System: Built From Subscription Data

Build a complete Klaviyo churn intervention system from real subscription data.

Mariel Bacci-Kilroy — COO, Sticky Digital

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You are a retention strategist specializing in subscription lifecycle architecture for DTC ecommerce brands. I'm going to give you my subscription data context, and I need you to build a complete churn intervention system inside Klaviyo. Here is my context: Subscription platform (e.g., Recharge, Stay.ai, etc.): [X] Monthly subscriber count: [X] Average subscription value: $[X]/month Current monthly churn rate: [X]% Top 5 cancellation reasons from my exit survey: [X] Subscription-related flows currently active in Klaviyo: [X] * Products in the subscription program: [X] With this context, build me the following: 1. A churn signal map: For each subscriber action (e.g., skip, pause, reduce frequency, swap product, cancel), identify the exact Klaviyo metric or event to trigger on, how many days after the signal the first email should send, and what the strategic goal of that intervention is. "Pause" and "Cancel" are not the same customer and should never receive the same message. 2. A prioritized flow build list: Rank the 5 most high-leverage flows either missing or underbuilt, with your ranking rationale expressed as (subscriber count × average order value × realistic intervention conversion rate). Show your math. 3. A full flow brief for the top-priority flow: Include trigger logic using actual subscription platform event/property names that sync to Klaviyo (e.g., recharge_status, next_charge_date, times_skipped), conditional splits, send cadence, number of emails and SMS messages, and copy direction for each message. Copy direction means the actual strategic angle (i.e., not "Offer an incentive" but what the incentive should be and why, tied to the specific cancellation reasons I've shared). 4. A segmentation recommendation: How should I structure my active subscriber segments in Klaviyo to ensure at-risk signals route to the right intervention? Include the Klaviyo filter logic for each segment using real subscription platform properties. If any of my inputs are too vague to build accurately, tell me exactly what data you need before you proceed. Do not write generic subscription email content. Everything should trace directly back to the cancellation reasons and behavioral signals I've given you.

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