Your best subscribers don’t look special at trial start.

Trial start and trial-to-paid are short-window proxies. Two users can look identical at trial start, then diverge on paid conversion, renewal, and early churn.

Churney predicts per-user subscriber pLTV from your first-party data and sends that as the bid signal, so you are not just scaling cheap trials that never retain.

Cohort and retention reports explain who was valuable. They do not teach your ad platforms who to acquire next.

Churney activates pLTV as a live optimization signal so bidding learns from predicted paid conversion, renewal, and retention, not only the first in-window event.

Ad platforms learn on installs and trial starts inside a short window. Your best subscribers often reveal their value after trial-to-paid and Month 2-3 renewal.

If early spend (days 1-7) does not rank the same as late subscriber value (day 30-90+), trial CPA misallocates budget toward users who churn before payback. Churney targets users predicted to convert and retain, not just start trials.

Yes, when retention and renewal materially change subscriber value. Churney models expected long-term value from early behavior, so acquisition can favor users predicted to renew and retain, not only users who convert once.

Retention improvements then show up as better cohort quality from paid channels, not only lifecycle fixes downstream.

Structured pilot: Churney pLTV signal vs. your business-as-usual setup (for example trial start, install, or trial-to-paid optimization), with holdouts and cohort readout when paid conversion and early renewals mature.

Churney fits businesses where paid conversion, renewal, or retention meaningfully changes subscriber value, and paid acquisition is held back because platforms only see early proxy events.

Quick test: If your best subscribers only show their true value after trial-to-paid or the second billing cycle, you are in the zone. If trial-to-paid already tells the full story with stable retention, standard value bidding may be enough.

You can test your growth predictability here: https://gpt.churney.io/

Onboarding aside, usually 60-120 days from campaign launch, because subscription value often appears after trial-to-paid and early renewals, not at install or trial start.

You are comparing Churney vs. your current setup over a window long enough for paid conversion and renewal cohorts to mature, not judging it on week-one CPI or trial CPA alone.

The aim is better subscribers, not fewer. You keep scaling acquisition while the bid model favors users predicted to convert, renew, and retain over 30-90 days.

Success looks like steady or growing trial or install volume with stronger trial-to-paid, higher renewal rates, and unit economics that hold up once renewals land.

Yes. That is often where the pain is sharpest. On conversion-optimized campaigns you must pick an event: subscribe (quality, low volume), trial (volume, mixed cohort), or a DIY proxy. Churney sends user-level predicted subscriber value so the platform gets enough events to learn while ranking users by expected convert-and-retain value.

You do not have to stay stuck on trial volume because subscribe events are too sparse, or on subscribe-only optimization that never leaves learning phase.

Rule-based proxies compress correlation into fixed thresholds. They look fine in a spreadsheet until creative, geo, or product mix shifts. pLTV models expected subscriber value from behavioral and revenue history and refreshes as your business changes.

Churney replaces brittle proxy rules with a calibrated value signal you can test against business as usual in a structured pilot.

Yes. Churney serves subscription apps (mobile and web), SaaS with trial or freemium models, and other recurring-revenue businesses where paid conversion and retention decide unit economics.

Setup varies by stack: MMP and SKAN for mobile, product analytics and warehouse data for SaaS. Signals send through server-side paths like Meta CAPI and Google Ads Conversion API. We map your architecture in the Growth Predictability Test.