# Using Predictions

Understand the time windows, probabilities, and tiers for purchase and churn predictions.

Source: https://cue.airbridge.io/docs/en/guides/reading-predictions
Language: en
Updated: 2026-10-16

Context API predictions estimate how likely a user is to purchase or churn. The response includes a probability and tier for each model under `predictions`. This guide explains each model's predicted event and time window, and how to interpret probabilities and tiers.

## Prediction targets and time windows

The purchase and churn models predict different events over different time windows, both measured from app installation.

Prediction windows measured from app installation

App installation0

1
2
3

…

Days30

Purchase prediction
Within 3 days after installation

`predictions.d3_purchase`

Predicts the likelihood of the standard order-completed event, `airbridge.ecommerce.order.completed`, occurring within three days after app installation.

Churn prediction
After day 3 through day 30

`predictions.d3_churn`

Predicts the likelihood of no activity events being observed after day three through day 30 after app installation.

The timeline after day 3 is compressed.

The models predict different events, so both tiers can be `high`.

## Probabilities and tiers

Each model returns a `probability` and a `tier`.

| Result field  | How to read it                                                                                                                            |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------- |
| `probability` | Represents the model's predicted probability as a number between 0 and 1. A value of `0.723` means 72.3%.                                 |
| `tier`        | Groups results using the distribution of predicted probabilities for that model's training data. The value is `low`, `medium`, or `high`. |

A purchase tier of `high` means a high predicted likelihood of purchase. A churn tier of `high` means a high predicted likelihood of churn. Each model has its own tier criteria, so the same probability can fall into different tiers in different models. There is no fixed probability threshold that applies to all models.

The response rounds probabilities to three decimal places. Each model determines tiers according to its own criteria. If your rule uses a tier, use the returned `tier`. Recalculating it from the rounded probability can produce a different tier from the one the model assigned.

## Predictions and personalization effects

A predicted probability estimates how likely an event is to occur. It does not measure an increase in purchase probability caused by a message or additional results caused by personalization. To measure how much personalization improves results, run an experiment with defined comparison conditions.

## Unavailable predictions

Treat unavailable predictions as a separate state from probability `0` or tier `low`. If an API error prevents you from receiving predictions, follow [Error Handling](/docs/en/reference/errors) and apply the default behavior you defined.

See [Using Context](/docs/en/guides/using-context) to design your rules, or [Context API](/docs/en/reference/context-api) for the full response and field definitions.