# Waitlist growth simulator: project your signups week by week A waitlist growth simulator projects how big your list is after a set number of weeks. This one adds your own weekly signups to referrals, where each week's referred signups equal K times the previous week's new signups, and K fades each week by the rate you set. You get the list size with and without referrals, and the K value behind it. Use it free, with no signup, at https://getlaunchlist.com/tools/waitlist-growth-simulator. Updated 2026-10-15. ## What you enter - **New signups per week from your own channels**: Posts, ads, outreach, communities: signups that did not come from a referral. - **People already on your list**: They can refer too. Leave at 0 if you are starting from nothing. - **Share of signups who refer someone (i)**: Out of 100 signups, how many bring in at least one friend who joins. An example value: replace it with your own. - **Successful referrals per referrer (c)**: Friends who actually joined, averaged over the people who referred. - **How much sharing fades each week**: 0% holds K steady. At 10%, K is 10% lower each week than the week before. - **Weeks to project**: How many weeks ahead to run the projection. ## How it's calculated The simulator works in weekly cohorts. Each week's new signups, whether they came from your own channels or from a referral, share your link during that week, and the friends they bring join the week after. - **K = i × c.** i is the share of signups who refer, and c is the successful referrals each of them brings in. This is the [K-factor](https://getlaunchlist.com/glossary/k-factor). - **K(t) = K × (1 - d)^(t - 1).** d is how much sharing fades each week, so K is lower the longer the list has been running. - **Referred signups in week t = K(t) × all new signups in week t - 1.** The people already on your list count as week 0. - **New signups in week t = your own signups + referred signups.** The list is the running sum. - **Without referrals** the list is your starting list plus your own signups each week. ## How to read the result The gap between the two totals is what the referral loop is worth over the weeks you chose. K below 1 means every cohort is smaller than the one before it, so referrals multiply your own effort but do not replace it. K at 1 or more means each cohort is at least as large as the last, so the loop sustains itself while sharing holds. The model has no limit on how many people exist to join, so the longer the horizon and the higher K, the more the later weeks are an upper bound. ## What moves it Your own weekly signups set the base every referral builds on. Raising i (asking at the moment of signup, making sharing one tap) and raising c (a reason to keep sharing after the first friend) both raise K, and K is what compounds. The fade rate matters most over long horizons: a loop that fades by 20% a week is worth far less in week 20 than in week 4. None of the example inputs is a benchmark. Measure i and c on your own first cohorts and replace them. Counting only signups that joined, not links shared or clicked, keeps K honest. ## Frequently asked questions ### What is a K-factor for a waitlist? K-factor is the average number of new signups each existing signup brings in through referrals. It equals the share of people who refer (i) multiplied by the average number of successful referrals per referrer (c). At 1.0 or more, the list keeps growing from referrals alone; below 1.0, referrals multiply the signups you bring in yourself. ### How do I find my own K-factor? Take a fixed window, such as your first two weeks, and divide the signups that arrived through a referral by all signups in the same window. That equals i × c. Count people who joined, not links shared or clicked, and leave out self-referrals and fake signups. ### Why does the simulator show less than a simple K times signups? A simple calculation assumes every generation shares at full strength forever. This one applies the fade you set, so K falls each week, and referrals land the week after the cohort that caused them. Set the fade to 0% to see the simpler, more optimistic result. ### What is K-factor decay? It is the way sharing can slow as a list ages, so each week's signups bring in fewer friends than the week before. The simulator models it as a percentage that K loses each week. The right value is your own: compare K in your first week with K in your third and set the fade from the difference. ### What does it mean when K is above 1? Each cohort brings in a larger one, so referrals grow week over week while the loop holds. The simulator has no limit on how many people exist to join, and real audiences are finite and sharing fades, so a K above 1 is worth treating as a burst, not a steady rate. ### What values should I enter for sharing and fade? Use your own numbers. After two weeks of signups, count the share who referred and the friends they brought, and set the fade by comparing K in week one with K in week three. Before you have data, try a low and a high value and look at the range, not a single number. ## Glossary terms - [K-factor](https://getlaunchlist.com/glossary/k-factor.md) - [Viral coefficient](https://getlaunchlist.com/glossary/viral-coefficient.md) - [Viral loop](https://getlaunchlist.com/glossary/viral-loop.md) ## Related tools - [Referral program ROI calculator](https://getlaunchlist.com/tools/referral-reward-calculator.md): Price a referral reward and see the ROI of a referral program before you launch it. - [Waitlist benchmark](https://getlaunchlist.com/tools/waitlist-benchmark.md): Compare your referral share, sharers and confirmation rate with real LaunchList waitlists of your size. - [Product Hunt upvote calculator](https://getlaunchlist.com/tools/launch-day-forecaster.md): Estimate the upvotes your own list and network bring to a Product Hunt launch, and what closes the gap. - [Pre-launch MRR calculator](https://getlaunchlist.com/tools/prelaunch-mrr-projector.md): Project launch-day MRR and the first year of revenue from your waitlist, price and churn.