Diagnose DTC retention health and generate ICE-prioritized action plans.
npx skills add https://github.com/afadtc/afa-dtc-skills --skill afa-retain
Please help me install this Agent Skill. Skill: afa-retain Source: https://github.com/afadtc/afa-dtc-skills/tree/main/afa-retain Command: npx skills add https://github.com/afadtc/afa-dtc-skills --skill afa-retain
DTC brands often struggle to understand and act on retention, leading to squandered LTV opportunities. This Skill provides a structured, data-driven framework to diagnose retention health, prioritize actions, and orchestrate cross-functional efforts across cohorts, subscriptions, and loyalty programs.
Activate the retention engine with your brand data to generate a prioritized, ICE-scored action plan tailored to your data maturity.
High-intent search queries and answers about installing and using this skill.
You can diagnose DTC retention by analyzing customer lifecycles, churn patterns, and LTV drivers through cohort analysis. This process applies RFM and JTBD frameworks to identify retention health issues and generate ICE-prioritized action plans for maximizing long-term value.
The best way to reduce subscription churn is by applying subscription defense and win-back playbooks based on churn pattern diagnostics. This approach uses RFM scoring to identify at-risk subscribers and prioritize cross-channel retention actions to recover squandered LTV.
You prioritize non-subscription retention actions by applying ICE scoring to your RFM and LTV analysis. This framework evaluates retention opportunities across repurchase rates, value distribution, and engagement dimensions to output a data-driven, prioritized action plan.
Yes, you can analyze loyalty program effectiveness by running a 5-dimension retention health check against category benchmarks. This evaluates retention economics, engagement, and value distribution using your existing brand data maturity to design ongoing loyalty optimization playbooks.
To start automated retention cohort analysis and LTV forecasting, you need your standard brand data exports containing customer transaction histories. The retention engine uses this input to generate diagnostic reports, ICE-scored action plans, and data-driven LTV forecasts.
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