The App Pricing Signal Map: 7 Prelaunch Tests That Predict LTV, CAC & 30‑Day Payback

What it predicts: willingness‑to‑pay and likely optimal list price band; best used to estimate average initial ARPU and the psychological anchor that frames downstream conversion. This is a classic low‑friction test: show multiple price options (including a ‘too cheap’ and ‘too expensive’ anchor) and capture selection without requiring payment.

How to use the signal: convert chosen price bands into ARPU scenarios and model LTV given your expected retention. Remember this is behavioral intent, not actual purchases—use it to set price anchors and the range you should A/B test with real money later.

What it predicts: real conversion velocity to paid, first‑purchase ARPU and early CAC — the single most reliable prelaunch signal for whether paid demand exists. A refundable deposit converts intent into monetary skin in the game and filters out casual interest that price surveys miss.

How to interpret: deposit conversion rate maps closely to first‑month revenue and early CAC expectations when paired with the acquisition channel used in the test. If you buy traffic for the test, you can compute channel‑level CAC and estimate payback time at the deposit→full‑pay conversion rate.

What it predicts: steady‑state free→paid conversion rate, downstream CAC when free users are counted as funnel costs, and realistic LTV once churn is estimated. Freemium tests reveal whether your product’s value hook triggers meaningful upgrades over time.

How to use the signal: measure conversion at the natural upgrade trigger (usage threshold or time). Then include all acquisition spend used to acquire free users when calculating CAC for paid cohorts—this is the correct way to estimate CAC from freemium funnels.

What it predicts: price segmentation, preferred bundles, and the mix of ARPU across tiers—this helps forecast blended LTV under different tier mixes. Presenting 2–3 preorder bundles with different price/feature sets forces real choices that map to future revenue mix.

How to interpret results: use the observed distribution of choices to model a blended ARPU and sensitivity to introducing a middle vs. premium tier. When combined with deposit or paid preorder flows you also get early paid conversion and CAC.

What it predicts: relative shifts in conversion and ARPU when you change the anchor or introduce a decoy—useful when deciding whether to push customers toward a higher ARPU plan without harming conversion.

How to use the signal: run an A/B test where one variant includes a high‑price anchor or decoy and the other shows only base/mid options. Measure paid conversion and average order value (AOV); anchoring often raises AOV while producing small conversion tradeoffs.