Marketing analytics

Stopping tests at first significance inflates false positives from 5% to over 26%, producing wins that never replicate in production.

Pricing, value prop, and onboarding tests move revenue materially, while button colors optimize finish on a broken engine.

Teams with shared repositories run 40% fewer redundant tests; losses eliminate dead ends and compound institutional knowledge.

Point estimates destroy credibility when actuals diverge; ranges with assumptions make you look scientific and defensible.

Last-year-plus-percent anchoring locks in historical inefficiency and blocks reallocation toward emerging opportunities.

Teams that did not build the forecast will not defend it, producing misalignment and finger-pointing when numbers miss.

Static annual budgets get outmaneuvered; markets saturate and shift, making January forecasts outdated by March.

A 4x revenue ROAS at 25% margin means losing money at scale; revenue multiples hide true profitability.

Using projected LTV to justify aggressive CAC today creates budget crises later, as the Peloton situation demonstrates.

Marginal CAC rises as you exhaust high-intent audiences; using early-stage CAC to justify scaling is a costly math error.

TV drives measurable search lift within 24-48 hours; measuring offline in isolation systematically undervalues it and triggers harmful cuts.

Agency-owned models carry conflicts of interest, and insufficient data yields garbage seasonality coefficients; holdout tests confirm causation.