E-commerce & Digital
Subscription churn prediction and save offers
Predict which subscribers are likely to cancel and select the intervention — a pause option, a plan change, a targeted offer — most likely to retain each one at acceptable cost.
10–20%
reduction in subscriber churn
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Offering a discount to someone who was never going to leave is pure margin loss, and it is the most common failure. Model uplift — the change in behaviour caused by the offer — rather than churn probability alone.
A pause option often retains more value than a discount and costs nothing. Test non-financial interventions before assuming money is the answer.
Cancellation flows are subject to consumer protection rules on subscription cancellation. Retention offers must not obstruct a customer who wants to leave.
Experiment starter: Score the base, then randomly withhold intervention from 20% of high-risk subscribers. Compare retention and margin between the treated and held-out groups after two billing cycles.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.More in E-commerce & Digital
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