Predicted LTV models vs SKAN conversion windows: two ways to value an install early

Predicted LTV models vs SKAN conversion windows: two ways to value an install early

You can't wait 90 days to bid. Both approaches estimate value fast, differently.

pLTV models (in-house or vendor)
— ✓ Use rich first-party behavior, predict D90 from D1-D3 signals
— ✓ Channel-agnostic, works across all your reporting
— ✗ Needs data-science investment and constant retraining as cohorts drift

SKAN conversion-value mapping
— ✓ No model needed, encode revenue or event tiers directly into 6 bits
— ✓ Network-native, networks optimize toward your value
— ✗ Crude buckets, locked timers, crowd-anonymity can null your signal

Use SKAN value mapping as the floor everyone must have on iOS. Layer a pLTV model on top once you have enough first-party data to beat the 6-bit ceiling, especially for high-LTV apps where bucket coarseness costs real money.

Verdict: SKAN value mapping as baseline, pLTV models when 6 bits can't capture your revenue spread.
Best for: subscription and IAP-heavy apps with wide LTV distributions.