pLTV tools and the cold-start problem nobody warns you about
Predictive LTV modeling (native in AppsFlyer, Adjust, or your own warehouse) promises early ROAS calls from D0-D3 signals. The catch is the cold start, and it quietly wrecks new-campaign decisions.
✓ Mature campaigns with months of history get accurate pLTV from a few early events
✗ Brand-new creatives or geos have no behavioral baseline — the model borrows from your portfolio average and is confidently wrong
✗ Models trained on whales over-predict for broad campaigns; trained on broad users they under-predict premium ones
✗ SKAN's coarse data starves iOS pLTV further
Gate pLTV-driven scaling decisions until a campaign clears a minimum cohort size — usually a few hundred converters — and always sanity-check predicted vs realized LTV monthly.
Verdict: trust pLTV on established campaigns, ignore it on cold-start ones until the cohort matures.
Best for: growth teams automating ROAS-based budget shifts.
In-App Bench
@InAppBench
pLTV tools and the cold-start problem nobody warns you about
Этот пост опубликован в Telegram-канале In-App Bench. Подписаться можно по ссылке: @InAppBench.