Uplift Modeling for Targeted User Engagement Incentives
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Solution Overview
Problem
The challenge of optimizing user engagement in Internet-accessible services while minimizing resource usage and costs associated with incentives is not effectively addressed by existing technologies, as they often offer incentives to all users indiscriminately, leading to inefficient resource utilization.
Innovation Solution
A user engagement modeling system employs causal machine learning techniques to identify users whose engagement is likely to improve via incentives, using historical data and counterfactual modeling to generate uplift scores, allowing targeted incentive offers based on individual user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If incentives are offered to all users indiscriminately, then user engagement metrics improve, but resource usage and costs increase
Solution Approach 1:
The patent segments users into different groups based on their engagement characteristics and responsiveness to incentives. By dividing the user base into segments with different uplift potentials, the system can target incentives only to those users who are most likely to respond, rather than applying incentives universally. This segmentation enables differentiated treatment that improves engagement efficiency while reducing overall resource consumption.
Solution Approach 2:
The patent applies local quality by customizing incentive strategies for specific user segments rather than applying a uniform approach. Each user segment receives tailored incentives based on their individual characteristics and predicted responsiveness. This localized approach ensures that resources are allocated to users where they will have the greatest impact, thereby improving engagement metrics while minimizing wasted resource expenditure on users unlikely to respond.
2Use of energy by moving object
If targeted incentive offers are made based on individual user behavior, then resource usage decreases, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary component in the form of a machine learning model that bridges the gap between raw user behavior data and incentive decision-making. This intermediary automatically processes user data, predicts uplift scores, and generates targeted incentive recommendations. By delegating the complex analysis and decision-making to this intermediary system, the patent reduces manual complexity while enabling sophisticated targeted incentives that optimize resource usage.
Solution Approach 2:
The system implements self-service by automatically analyzing user behavior data and generating targeted incentive strategies without requiring manual intervention. The machine learning models autonomously process user data, identify responsive segments, and recommend optimal incentive allocations. This automation handles the complexity internally, allowing the system to deliver sophisticated targeted incentives while presenting a simplified interface and reducing operational burden.
Data Source
AI summary
Methods, systems, and computer-readable media for user engagement modeling for engagement optimization are disclosed. Based (at least in part) on one or more user engagement models, a user engagement modeling system determines an uplift score for a user of an Internet-accessible service. The uplift score comprises an estimated effect on one or more user engagement metrics of an incentive to interact with the service. The uplift score is determined based (at least in part) on values of the user engagement metric(s) for the user in a treatment group and values of the metric(s) for the user in a control group, in view of propensity score to be in either group. The treatment group is offered the incentive, and the control group is not offered the incentive. The system determines that the user is or is not offered the incentive based at least in part on the uplift score.