Retention and Churn
Test your retention and churn against a simulated population modelled on your customers, and launch the version you already know works.
Bring any of these decisions
- renewal reminders01
- win-back campaigns02
- loyalty offers03
- subscription changes04
- service-recovery messages05
- customer follow-up06
The customers most likely to leave, the reason behind the risk, the intervention most likely to work, and the customers you should leave alone.
- Recommendation
- the option to launch
- Expected impact
- conversion, revenue, retention
- Segments
- who it works for, who it costs you
- Friction
- where customers stop, and why
- Changes
- what to fix before launch
- Rollout
- how to validate it safely
How do you predict customer behaviour?
Historical customer data is used to estimate who is likely to buy, what they may buy, when they may buy, and who may churn. Results are given as probabilities with confidence levels, never as guarantees.
How do you predict customer churn?
Declining activity, spending, visits, and engagement are used to flag customers at risk of leaving, with the likely reason behind each risk and the intervention most likely to work.
How do you reduce customer churn?
Test renewal reminders, win-back campaigns, loyalty offers, and service-recovery messages before sending them. You receive the customers most likely to leave, the intervention most likely to retain them, and the customers who should be left alone.
How do you improve customer retention?
Retention journeys are tested segment by segment so you can see which message retains which group, and where a discount is unnecessary spend on customers who were staying anyway.
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