How Mosaic Group grows

Across Mosaic apps, price increases lowered retention less than one-to-one: roughly a 10% price rise cost a couple percent of retention, so LTV usually rose if conversion held. Sensitivity differs by product; apps with a thin layer of value have a lower price ceiling than deep-content products like language learning.

Screen each app or category on three lenses: (1) size: the product must plausibly reach tens of millions of dollars in revenue, avoiding a long tail of zombie apps doing a few hundred thousand each; (2) competition: a 'Goldilocks' market where you can take real share, e.g. RoboKiller was the leading independent spam blocker with carriers not invested and no big adjacent entrants; skip VPNs (everyone spends heavily on paid, so unit economics fail) and personal finance/budgeting (banks bid on the same keywords with far higher LTV); (3) existing demand vs. new category: existing demand allows ASO and performance marketing to pay back right away, while a new category means running at a loss to build demand.

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.

Always cap LTV at a set number of years (e.g. start at 2 years, extend to 3-4 as the company matures) because you must get paid back and future dollars are worth less. Early-stage: payback period is the primary filter (make money back in month 1 or within 3 months), with LTV:CAC as a secondary check; a high LTV:CAC under a tight payback signals that finding liquidity would unlock growth. Later-stage: manage to an LTV:CAC threshold (e.g. 2-3x) and treat payback (possibly 12+ months) as a sanity check.

Annual retention isn't known when a price test ends, so use the monthly plan's retention change as a proxy, or past price rises ('last time I raised annual 10%...'), and trial-to-paid as a leading indicator. Then sensitize: would the winner still win if retention comes in 10%, 20% or 50% worse? If only a ~5% buffer, don't roll out or roll out cautiously; if it survives a 50% hit, ship it. The proxy is weakest when one plan dominates (e.g. a 70/30 annual/monthly split).

Since Google and Meta are black boxes where you only set bid and budget, creative is the main lever. Always keep a share of creatives in each campaign experimental/unproven. Because every designer has a personal style that leads to 5-10% tweaks, rotate creative team members across products every couple of months (within brand guidelines) and occasionally bring in a new designer told to 'go wild', then double down on styles that win.

Accept that most apps cap out when top-3 keyword rankings max out organic and CAC hits the (usually sub-$100) subscriber LTV, after which only single-digit growth comes from optimization. Grow by adding products instead: e.g. three $30M apps rather than one $90M app, with diversification as a bonus. A portfolio with a consolidated tech stack is also what makes a business sellable to private equity.

Before building, talk to users about what they want. Then ask whether the idea is a feature or a product: on a 6-inch screen, eight features confuse users, so prefer two or three slim apps with two or three features each and build a net-new product when there is demand. Every feature that does get built must be labeled up front as either an ARPU feature (e.g. an upsell) or a retention/engagement feature, because they are built and marketed differently.

Tested early cancellation (turning off auto-renew) as a predictor of churn for LTV modeling. Trial-to-paid conversion and usage of the one key value action proved better leading indicators.