Table & Till

Intelligence for independent restaurant owners

Repeat Visit & Loyalty Economics Calculator

Test how many additional paid visits would cover all reward and program costs.

Use one cohort and one observation period. B and P are assumptions. This calculator does not predict return visits or prove that rewards caused them.

Your scenario

Use a label without customer names or personal data.
Counterfactual assumption, not an observed fact.
Free reward-only pickups do not count.
Do not deduct the reward here and again in D.
Includes rewards for guests who would have visited anyway; include displaced paid sales where applicable.

Saving is optional, stays in this browser on this device, and does not sync. Only save on a device you trust.

Your results

Choose Calculate to evaluate the illustrative inputs or your own scenario.

What the result means

A positive difference means the assumed extra paid-order contribution covers the entered reward burden and program cost. A negative result is retained. A break-even count is an arithmetic hurdle, not a forecast.

Assumptions & methodology

Additional paid orders = P − B. Contribution difference = (P − B) × m − D − K. For m > 0, break-even additional paid orders = ceil((D + K) / m).

D covers all rewards, including those given to people who would have returned anyway. Count lost revenue for cash discounts; for a free item, count incremental costs and any displaced paid-sale contribution consistently. Do not automatically use the retail menu price as its incremental cost.

Do not count a reward-only free pickup as a paid order. Do not deduct the same reward in both m and D. Extra orders may have a different contribution from the average; test a lower m if the offer changes mix.

If m is zero or negative with positive program costs, no finite positive number of additional identical orders recovers those costs. With zero program cost, the additional-order hurdle is zero, but negative-margin orders can still worsen results.

A comparison group or other appropriate research design is needed to estimate the counterfactual B. Selection bias, displaced full-price purchases, and purchases pulled forward can change the interpretation.

Related owner questions

  • Would these customers have bought anyway?
  • Does the observation window include later purchases displaced by the offer?

Related articles

Data & privacy

Calculations run in this browser. Financial inputs are not sent to a server, AI model, advertising service, analytics endpoint, or URL. Optional saved settings use browser storage; Reset deletes this tool’s saved settings. No login, account, or spreadsheet download is required.

A worked example

Hypothetical example: 130 program paid orders versus an assumed 100 without the program, at $12 contribution per paid order, less $150 reward loss and $30 program costs, gives $180 additional contribution. Covering the $180 costs requires 15 extra paid orders. The assumed 30 extra orders are not proof of causation.