Churn Prediction Value Calculator
Work out churn prediction value instantly with clear inputs, formula shown and shareable results.
About Churn Prediction Value Calculator
The value of a churn model is not its AUC but the customers a retention action actually saves. That chain has three multipliers: how many churn, what share the model catches (recall), and what share of contacted churners the intervention rescues — typically only 20 to 30 percent. Multiplying through usually reveals a much smaller prize than the model's accuracy metrics suggest.
Formula
Retention value
churners = customers x churn rate; identified = churners x recall; saved = identified x save rate (25%); value = saved x value per retained customer
Worked example
With the default values below, Churn Prediction Value Calculator returns Annual value retained of $240,000.00. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Customers in the baseminimum 1
- 50,000
- Annual churn rateaccepts 0.01–100 %
- 8 %
- Share of churners the model identifiesaccepts 0–100 %
- 60 %
- Value of retaining one customerminimum 0
- 400
Results
- Annual value retained
- $240,000.00
- Customers expected to churn
- 4,000
- Churners the model flags
- 2,400
- Customers actually saved at a 25% save rate
- 600
Step by step
- 1Values used
- 2Retention value
- 3Annual value retained= 240,000.00
- 4Customers expected to churn= 4,000
- 5Churners the model flags= 2,400
- 6Customers actually saved at a 25% save rate= 600
Assumptions and limits
- The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax or fees or charges into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why assume only a 25 percent save rate?
Most flagged customers have already decided, and outreach converts a minority. Measure your own save rate with a randomised holdout rather than assuming the intervention always works.
Should I chase higher recall?
Only up to the point where precision keeps the contact cost sensible. Contacting the whole base gives 100 percent recall and destroys the economics.
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