AI Incentive Presentation for Targeted User Engagement
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Solution Overview
Problem
Conventional systems inefficiently utilize computing resources by presenting data, such as incentives, to users without considering their likelihood of changing engagement levels, resulting in many users not responding to the presented data.
Innovation Solution
Utilizing a trained AI model to analyze user data and predict engagement metrics, allowing targeted presentation of incentives to a subset of users most likely to change their engagement levels, thereby optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is presented to all users without selection, then coverage is maximized, but computing resources are wasted on users unlikely to engage
Solution Approach 1:
The user base is segmented into different groups based on their predicted engagement likelihood. The system divides the population into users who are likely to engage and those who are not, allowing targeted presentation of data only to the relevant segment. This segmentation resolves the contradiction by maintaining comprehensive coverage of the engaged segment while eliminating waste on non-engaged users.
Solution Approach 2:
The system performs preliminary analysis using trained AI models to predict user engagement likelihood before presenting data. By pre-evaluating each user's probability of engagement and pre-selecting the subset of users likely to respond, the system avoids wasting computing resources on users who would not engage, thus resolving the efficiency-resource contradiction.
2Measurement precision
If AI models predict engagement metrics for all users, then prediction accuracy is maximized, but processing time increases
Solution Approach 1:
The system extracts only the necessary subset of users for whom engagement prediction is performed. Instead of running AI models on all users, the system identifies and extracts the subset of users who meet certain criteria or represent the most valuable targets, then applies prediction only to this extracted subset. This maintains prediction accuracy for relevant users while dramatically reducing overall processing time.
Solution Approach 2:
The system performs engagement prediction on a partial subset of users rather than all users. By applying the AI model only to the identified subset of users likely to engage, the system achieves sufficient prediction accuracy for the target population while avoiding the excessive processing time that would result from evaluating the entire user base.
3Reliability
If incentives are offered to maximize engagement, then user retention improves, but cost increases
Solution Approach 1:
The system applies different incentive strategies to different user segments based on their predicted engagement likelihood. High-value users who are predicted to respond well to incentives receive targeted offers, while users unlikely to engage receive no incentives. This local differentiation maintains user retention for valuable users while minimizing overall incentive costs by avoiding waste on non-responsive users.
Solution Approach 2:
The system uses feedback from AI model predictions to guide incentive allocation decisions. By continuously analyzing engagement metrics and updating predictions, the system learns which users respond to incentives and adjusts future incentive distribution accordingly. This feedback loop ensures that incentive costs are invested in users most likely to provide return on investment, thereby improving retention efficiency while controlling costs.
Data Source
AI summary
Optimizing the presentation of data is described. A payment service computing platform may apply a trained artificial intelligence (AI) model to user data associated with users of a payment service, wherein the trained AI model associates a value with each of the users, and wherein the value represents a predicted engagement metric indicating a change in a level of engagement if an individual user of the users is offered an incentive having a particular attribute. The payment service computing platform may generate the incentive having the particular attribute, identify a subset of the users based at least in part on the value, and cause the incentive to be presented via a user interface of a payment application associated with the payment service and executing on a device of a user in the subset.