GTM Sauce

Mosaic Group · B2C · app portfolio company

Estimate lifetime value as a range of weighted guesses, then narrow it monthly

Lets new products act on LTV before years of data exist; no numbers

Workedanalytics-attribution

What they did

Early in a product's life, run a range of LTV scenarios (e.g. plus or minus 20% in 5% steps), assign probabilities, pick a best estimate and act on it. Re-check every 1-2 months as renewal data comes in, dropping scenarios (six to four to two) until one precise estimate remains, which takes multiple years. At Mosaic's scale an in-house model could predict the next ~20 renewals from the first one or two, since most churn happens in the first renewals.

What happened

Guest says it lets you make decisions under uncertainty; mature portfolio data made predictions reliable.
34:39
“run a range of scenarios and say, you know, plus or minus 20% and maybe 5% increments, what are my possible LTVs, assign some probabilities to them, come up with your best estimate, start making decisions and acting on that, and then just go back and check it every month or 2 months”
Patrick Falzon
41:04
“if we got, you know, an initial renewal point or an initial two renewal points from a given cohort, we could pretty reliably predict out then what the next 20 would look like from there.”
Patrick Falzon

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