Watch every member's journey.

The simulation runs twice, with and without the retention levers, and both runs use exactly the same random numbers for every member and every month. So the member who would have cancelled in month seven still decides to cancel in month seven in both runs. The only thing that differs is whether a lever was there to catch them, which means any difference between the runs is caused by the levers, not by chance.

Re-rolling picks a new seed, so different people have different luck. Small memberships move more between rolls because a handful of members either way is a big share. If a lever's impact holds up across several rolls, it's real; if it swings between positive and nothing, it's within the noise at your size.

Failed payment recovery uses Memberstack's published figures: 67% of failed payments recovered, against a 35% industry average. Every other lever's effect is an assumption, labelled as one, and you can edit it on the lever's card. The defaults are deliberately conservative (for example, one in ten would-be cancellers pausing, and one in ten abandoned checkouts finishing after a reminder), so the result is a cautious estimate rather than a best case.

No. The whole simulation runs in your browser and nothing is sent to a server. If you copy a link, your numbers and the dice go into the URL so whoever opens it sees exactly the same simulation.

As accurate as the numbers you put in. It's a model, not a forecast: it assumes your rates stay steady, and real memberships have seasons, launches and price changes. Use it to compare choices, like which lever is worth setting up first, rather than to predict an exact revenue figure.

Look at the impact on each lever's card: it's that lever on its own against none at all. The biggest number is usually the best place to start, but check what each one costs you to set up. Most are a Memberstack setting or a free MemberScript, so the effort is small.