Why it matters
pLTV is not one universal number. A D7 IAP forecast supports mobile UA with fast feedback; a D180 net revenue forecast fits ecommerce with long repeat cycles. Mismatched horizons create false confidence: the model optimizes the wrong economic question, signal transformation sends values platforms cannot validate, and finance measures payback on a different clock than bidding.
Horizon also interacts with delayed conversions. Platforms learn inside an optimization window (delivery impact), not the attribution window (reporting credit). Your horizon should inform what early value means, not pretend late revenue already happened. See canonical-attribution-vs-optimization-window.md.
Explicit horizon documentation prevents teams from comparing incompatible scores across channels or replatforming events.
Prediction horizon
Horizon is chosen before activation, then proven in market:
- Define economics: Set target payback period and finance LTV definition on first-party data in the data warehouse.
- Select horizon: Pick D7/D30/D90 (or blended) based on leading indicators and label availability at cohort maturity.
- Model: Train user-level pLTV for that horizon; document features and leakage guards.
- Calibrate and transform: Tune scale and caps for the chosen horizon before Meta CAPI / Google Ads Conversion API delivery.
- Validate: Holdout tests and cohort readouts at the same horizon; revisit when model drift or mix shifts.
Signal optimization may use multiple horizons by vertical or platform path (for example, shorter on iOS degraded signal paths).
Category variants
| Vertical | Typical horizon | Why |
|---|---|---|
| Ecommerce | D30 to D180 net revenue | Repeat and returns need time |
| Subscription | Trial to D90 paid LTV | Paid conversion and early churn |
| Mobile app | D7 to D90 blended IAP | Faster feedback, payer sparsity |
Common mistakes
- Using lifetime labels. without enough mature cohorts to train or calibrate.
- Changing horizon mid-pilot. without resetting BAU comparisons.
- Horizon longer than optimization potency. without an early-score strategy (Churney internal: ~36–48h post anchor).
- Skipping holdout readout. at the same maturity as the horizon.
Advertiser lens
| Role | Cares about |
|---|---|
| Finance | Payback alignment with forecast window |
| Data science | Label availability and leakage at score time |
| UA / performance | Whether horizon supports fast enough learning |
| Growth analytics | Experiment length tied to maturity calendar |
FAQ
What is the most common horizon for ecommerce pLTV?
Often D90 net revenue or similar; exact choice depends on repeat cycle and return windows.
Can you send multiple horizons to platforms?
Usually one primary value per event; internal models may blend horizons before signal transformation.
How does horizon affect calibration?
Calibration panels must use the same horizon as live sent values, or readouts will disagree.
Should horizon match platform attribution window?
No. Attribution window governs reporting credit. Prediction horizon is the model label length (D7, D30, D90). Optimization potency (Churney internal: ~36–48h post anchor) governs how early you must send value for delivery to learn. Three different clocks.
When should horizon change?
After proven mix shift, new product economics, or drift; validate with holdout tests.
Who approves horizon choice?
Finance plus analytics align economics; data science implements; UA agrees on learning implications.
Not the same as
| Term | Difference |
|---|---|
| Attribution window | Platform click/view reporting credit lookback; not model label length |
| Optimization window | Period when new events still influence learning and delivery; not the same as attribution |
| Maturity window | Experiment readout timing, not necessarily training label |
| Customer lifetime value (LTV) | Often realized full relationship value, not a fixed forward horizon |
