Adapty Predictions are designed to help you answer the following questions:

1. What is the predicted lifetime value (LTV) of your user cohorts?
2. Which cohorts are likely to generate the highest revenue in the future?
3. How much can you invest given the predicted payoff?

With Adapty Predictions, you can make data-driven decisions about revenue and growth.

Adapty’s prediction model estimates the long-term revenue potential of your app’s user cohorts. For each cohort, it projects how revenue, the number of paying subscribers, and average LTV will evolve over time. This helps you make informed decisions about user acquisition, marketing strategies, and product development.

Adapty offers predicted lifetime value (LTV) and predicted revenue for cohorts of paying subscribers. Predictions are displayed on the cohort analysis page for 3, 6, 9, 12, 18, and 24 months after cohort creation.

For apps with very limited history, the model falls back to cross-app averages, so predictions for newer apps may not fully reflect their specific user behavior.

How the model works

Adapty’s prediction model uses retention patterns from historical cohort data to project future revenue and LTV.

For each combination of app and subscription type, the model measures how paying subscribers and total revenue change from one renewal period to the next. It calculates two retention rates — one for subscribers, one for revenue — based on the app’s past cohorts. These rates are then applied to new cohorts to project their growth over 3, 6, 9, 12, 18, and 24 months after cohort creation. The data used is completely anonymized.

The model produces two values for each cohort:

• Predicted revenue: The total revenue a cohort is projected to generate within the selected horizon.
• Predicted LTV: The predicted revenue divided by the predicted number of paying subscribers in the cohort.

App-specific and cross-app weights

By default, predictions for a cohort use retention weights learned from that app’s own historical cohorts, reflecting its specific user behavior.

When an app doesn’t have enough history for a particular prediction horizon, Adapty falls back to retention weights averaged across all apps of the same subscription type. For example, a 12-month projection for an app that’s only six months old uses the cross-app fallback. This fallback is applied independently per horizon, so the same cohort can use the app’s own weights for the 3-month prediction and cross-app weights for the 12-month prediction.

Availability and updates

Predictions become available after a cohort completes its first renewal period — typically one week after creation for weekly subscriptions and about four weeks for monthly subscriptions. After that, predictions are updated daily using the latest transactional data, so they stay current with the cohort’s behavior.

Limitations

• Data quality: Unusual cohort behavior or cohorts with very few paying subscribers reduce accuracy. Cohorts with fewer than 100 paying subscribers are excluded from the model’s training data.
• New apps: Apps without sufficient history use cross-app fallback weights, which may not reflect the app’s specific user behavior.
• Cohort age: Predictions for a given horizon are hidden once the cohort exceeds that horizon. For example, 3-month predictions stop showing after three months, and no predictions are shown for cohorts older than 24 months.

In the Dashboard

To view predictions, navigate to the Cohort analysis page in your Adapty dashboard. For details on cohorts, see Cohort analysis.

The Predicted revenue column shows the estimated total revenue a cohort of subscribers is expected to generate during the selected time frame after cohort creation. This value is calculated using Adapty’s prediction model, based on the app’s historical cohort retention patterns.

The Predicted LTV column shows the estimated lifetime value of each user in the selected cohort. This value is calculated by dividing the predicted revenue by the predicted number of paying users in the cohort.

Select the horizon

To change the prediction horizon, select a value from the Predictions dropdown. The available options are 3, 6, 9, 12, 18, and 24 months after cohort creation.

Filter by product

You can filter predicted revenue and LTV by product. By default, predictions are built from all purchase data — filtering by product shows how each product contributes.

When predictions are unavailable

When a prediction can’t be produced for a cohort, the Predicted Revenue and Predicted LTV columns show em-dashes (—) instead of values. This can happen for several reasons:

• Insufficient time since cohort creation: Predictions become available only after the cohort completes its first renewal period — around one week for weekly subscriptions and about four weeks for monthly subscriptions.
• Small cohort size: Too few paying subscribers to produce a reliable projection.
• Unusual cohort behavior: The cohort deviates significantly from the patterns the model expects. Waiting a few weeks may resolve this as more data accumulates.
• Horizon exceeded: The cohort is older than the selected prediction horizon. For example, the 3-month prediction is hidden after three months, the 12-month prediction after twelve months, and no predictions are shown for cohorts older than 24 months.