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.