StakeSage-L

Overview

StakeSage-L is our liquid staking lifetime value (LTV) estimation model built on the Ethereum network. This model accurately predicts the total lifetime staking amount that a blockchain address is likely to commit in the future, with a precision of up to 0.01 ETH. By providing actionable customer intelligence, StakeSage-L empowers staking platforms to offer revenue-maximizing incentives to high-value users and optimize their purchase funnels, all while enhancing the overall user experience.

Designed as a turnkey solution, StakeSage-L brings advanced AI capabilities to your application without the need for building an in-house data science team. Its precise predictions enable dynamic adjustments in staking offers, ensuring that both platform operators and their customers benefit from tailored incentives that drive long-term engagement and improved operational metrics.

Key features

StakeSage-L delivers high-resolution insights by accurately estimating the lifetime staking value for each address. It identifies high-value stakers with pinpoint accuracy, allowing platforms to design and deploy dynamic staking terms that reward users based on their predicted commitment levels.

In addition, the model is engineered to enhance customer intelligence by providing granular data on user behavior. This supports the optimization of marketing strategies and product design, leading to more personalized user experiences. The model’s ability to process large volumes of on-chain data ensures it remains robust, scalable, and continuously updated with the latest market dynamics.

Data sources & inputs

StakeSage-L is powered exclusively by on-chain data from Ethereum. For training, the model leverages a comprehensive dataset that includes 212,293 wallets, 2,723,000 token features, and 344,729 transactions, with predictions scored for 194,318 wallets. These detailed inputs enable the model to capture the intricate nuances of staking behavior, from initial participation to lifetime commitment.

This high-fidelity data set ensures that the model’s predictions are grounded in real, verifiable blockchain activity. By using such specific and extensive inputs, StakeSage-L is able to deliver insights that are both accurate and actionable, providing a solid foundation for forward-looking staking strategies.

Methods & technical details

In developing StakeSage-L, we rigorously evaluated multiple regression techniques including Linear Regression, Decision Tree Regressors, Random Forest Regressors, XGBoost, and CatBoost. CatBoost emerged as the superior technique, outperforming XGBoost by 12% and linear regression by 61%, thanks to its effective regularization methods that better suit our training set's characteristics.

The model employs advanced feature engineering to quantify key staking behaviors and predict future staking amounts with a high degree of precision. By blending state-of-the-art algorithms with robust on-chain data, StakeSage-L transforms raw transaction data into a reliable prediction of lifetime staking value, providing deep insights into user behavior for dynamic decision-making.

Performance & accuracy

StakeSage-L boasts a prediction accuracy within 0.01 ETH, making it one of the most precise models in its category. This level of accuracy is achieved through rigorous model development and extensive testing, ensuring that the predictions are both reliable and actionable for high-stakes financial decisions.

To validate the model, we employ out-of-sample testing, where the model is evaluated on data it has never seen before. This process simulates real-world conditions and confirms that the model maintains its accuracy beyond the training dataset. By comparing predicted values against actual lifetime staking amounts, we continually refine StakeSage-L to ensure it meets the high standards required for strategic decision-making.

Use cases

StakeSage-L is primarily used to drive LTV-driven dynamic staking terms, enabling platforms to strategically offer incentives tailored to each user’s predicted lifetime value. This approach helps maximize average order value (AOV) and secures long-term loyalty from high-value stakers.

Beyond optimizing staking offers, the model enhances overall customer intelligence. By providing precise insights into user behavior, StakeSage-L supports targeted marketing campaigns, refined product design, and improved operational strategies. This comprehensive view of user engagement allows platforms to dynamically adjust their user interfaces and purchase journeys, ultimately leading to better customer retention and increased profitability.