# User Retention Prediction Accurate predictions of user retention are critical for the success of incentive programs. OpenBlock Labs uses sophisticated models to predict how long users are likely to remain engaged with a protocol, helping protocols better allocate rewards to retain high-value participants. #### **Methodology** Our retention prediction model analyzes user behavior over time, identifying patterns that suggest whether a user is likely to remain active or become inactive. We look at factors such as transaction frequency, asset holding patterns, and overall protocol engagement to forecast retention rates. By focusing on metrics like TVL-days (the duration assets are locked in the protocol) and user engagement levels, we predict long-term user retention with a high degree of accuracy. This enables protocols to tailor their incentives, ensuring they retain users who provide ongoing value and avoid wasting resources on users likely to churn. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.openblocklabs.com/intelligence-models/user-retention-prediction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.