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Customer Retention Rate Calculator

Calculate customer retention while excluding customers newly acquired during the period.

Free business calculator

Customer Retention Rate Calculator

Calculations and What-If scenarios run in your browser. Borkish does not require you to submit these values to calculate the result.

What the Customer Retention Rate Calculator measures

Retention helps distinguish growth from the ability to keep existing customers. Measure ecommerce performance, order economics, marketplace costs and store growth without spreadsheets.

The result becomes more useful when every input follows the same definition and reporting period. That keeps comparisons between campaigns, products, customers and time periods meaningful.

When to use this calculator

  • Use the Customer Retention Rate Calculator to review store economics using order, revenue and customer data.
  • Compare product or marketplace performance using the same reporting period.
  • Model how changes in conversion, fees or order value affect the business.

Formula

Retention Rate = ((End Customers − New Customers) ÷ Start Customers) × 100

Percentage results are most useful when the numerator and denominator come from the same population and reporting period.

How to use this calculator

  1. Customers at StartUse the value from the same reporting period or scenario as your other inputs.
  2. Customers at EndUse the value from the same reporting period or scenario as your other inputs.
  3. New CustomersUse the value from the same reporting period or scenario as your other inputs.
  4. Complete the required inputsThe result updates automatically as the values become valid.
  5. Compare the resultUse a previous period, target or relevant internal benchmark before making a decision.

Worked example

Using the demonstration values — Customers at Start = 1000, Customers at End = 1100, New Customers = 250 — the calculator returns 85%. The example shows how the formula behaves; replace the demonstration data with your own before using the result for planning.

How to interpret the result

Retention helps distinguish growth from the ability to keep existing customers. Use these calculators for store planning, product economics, checkout performance and marketplace decision-making.

Check the definition of Customers at Start, Customers at End, New Customers, the attribution or accounting rules behind those inputs, and any important costs or outcomes that the formula does not include.

Common mistakes to avoid

  • Using Customers at Start and Customers at End from different reporting periods or definitions.
  • Ignoring marketplace, payment or fulfilment costs when they materially affect the result.
  • Using order and customer counts from different periods.

Frequently asked questions

What does this calculator do?

Calculate customer retention while excluding customers newly acquired during the period.

Where should I get the input values?

Use your own advertising platform, ecommerce system, accounting report, CRM, analytics platform or forecast — whichever source is authoritative for the metric. Keep all inputs on the same basis and date range.

Is there one good result I should target?

Usually not. A useful target depends on your margins, acquisition model, operating costs, channel, market and business goals. Your own historical performance is often a better starting benchmark than a generic number.

Can I use this for forecasting?

Yes. Enter forecast values to model a scenario, but treat the output as an estimate based on those assumptions rather than a prediction of future performance.

For formulas, rounding and limitations, see our Calculator Methodology.
Decision path

What to calculate next

Retention helps distinguish growth from the ability to keep existing customers. A single metric rarely explains the whole decision, so compare this result with the related cost, conversion, margin or growth metrics below before acting on it.

Keep reporting periods and metric definitions consistent when moving between calculators. That makes the comparison more useful than treating each result as a standalone benchmark.

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