--- title: "Mail Cadence Calculator" category: Direct Mail area: "Direct mail" canonical: https://plato.ai/tools/mail-cadence/ --- # Mail Cadence Calculator How many repeat drops to the same households pay for themselves before response falls below break-even? An interactive analytical framework at https://plato.ai/tools/mail-cadence/ (runs in the browser). ## How it works A second piece can remind a household that ignored the first, but each repeat drop to the same people responds less than the one before. This calculator follows a mail sequence drop by drop: households that respond leave the list, each repeat keeps a share of the previous drop's response, and a drop is worth sending only while it responds above the break-even rate. Use it to see how many drops a sequence should have, what each extra drop adds and when the next one would lose money. The first drop goes to every household in the sequence. Households that respond leave the list, so each later drop goes to the households that have not responded yet. Each repeat drop keeps a fixed share of the previous drop's response rate, measured at the spacing you use now; a different gap between drops changes that share. Each drop earns the households it reaches times (response rate × share of responders who fund × value per funded loan − cost per piece). A drop is worth sending when its response rate is above the break-even rate, the cost per piece divided by the value of a responder; because response only falls from drop to drop, sending every drop above break-even is also the sequence with the highest total profit. Every response is counted as caused by the mail. `Response to drop k: r_k = r_1 × retention^(k−1); households mailed: M_1 = households, M_(k+1) = M_k × (1 − r_k); profit of drop k = M_k × (r_k × fund rate × value per loan − cost per piece); break-even response rate = cost per piece ÷ (fund rate × value per loan); send every drop with r_k above break-even` ## What a real decision adds - Fatigue differs by household. Some respond only after several pieces and others never will, so one sequence length for everyone mails too much to some households and too little to others. - The gap between drops changes how much response each repeat keeps, and the right gap varies with the offer and the season. Spacing has to be tested, not assumed. - Other channels change the value of a repeat piece. A household that has just responded to email or a call may not need another letter, and the mail may get credit for a response another channel earned. - Later drops carry the most response that would have happened anyway. Without a holdout that skips the extra piece, a long sequence can look profitable while adding little. ## Frequently Asked Questions ### How many times should I mail the same household? Keep sending drops while each one responds above the break-even rate, the cost per piece divided by what a responder is worth. In the default example the first drop responds at 0.8%, each repeat keeps 70% of the previous response, and the break-even rate is about 0.17%, so five drops pay for themselves and the sixth would lose money. ### What does "response kept by each repeat drop" mean? The second drop's response rate as a share of the first's, the third's as a share of the second's, and so on. At 70%, a 0.8% first drop is followed by about 0.56%, then 0.39%. Measure it from past sequences: compare each drop's response among households that had not yet responded. ### How far apart should drops be? The calculator takes the share of response each repeat keeps at the spacing you use now; it does not model the gap itself. Closer drops usually keep less response and longer gaps keep more but take longer to pay back. Test two or three spacings on random splits of the same list and enter each one's retention to compare. ### Are later drops really adding response? Not all of it. Some households who respond to drop four would have responded anyway, to the earlier pieces or another channel. A holdout group that skips the extra drop shows what it really adds; use that incremental response here if you have it. The default inputs are an illustrative example, not a benchmark. ## Read the article [Optimizing the Cadence: How Often to Send Direct Mail](https://plato.ai/md/direct-mail-cadence-optimization.md)