Wagering Incentive System for User Cohort Segmentation
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Solution Overview
Problem
Current wagering applications lack tailored incentives to increase user engagement, particularly for casual or beginner users, as they primarily focus on high-engagement expert users with limited options and chances to win.
Innovation Solution
A system that uses a base module to initiate an incentive correlation module to determine appropriate incentives based on user behavior, filtering users into cohorts and setting thresholds for incentive eligibility, offering incremental rewards to modify user behavior and increase engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic incentives are provided to all users, then implementation is simple, but user engagement increases minimally
Solution Approach 1:
The system segments users into different cohorts (e.g., new users, active users, dormant users) based on their behavior patterns and engagement levels. Each cohort receives customized incentives tailored to their specific needs and motivations, rather than applying a single generic incentive to all users. This segmentation enables the system to address diverse user motivations effectively while maintaining manageable complexity through automated cohort assignment.
Solution Approach 2:
The incentive system dynamically adapts to user behavior changes by continuously monitoring engagement metrics and adjusting incentive offerings in real-time. As users transition between engagement states (e.g., from dormant to active), the system automatically modifies the incentives presented to match their current motivations and behaviors, ensuring incentives remain relevant and effective without requiring manual reconfiguration.
2Reliability
If big incentives are offered to expert users, then expert user retention improves, but casual and beginner users have limited chances to win
Solution Approach 1:
The system applies different incentive qualities and magnitudes to different user cohorts based on their specific characteristics and needs. Expert users receive high-value incentives such as large bonuses or exclusive perks, while casual and beginner users receive appropriately scaled incentives like smaller bonuses, free bets, or achievement badges. This localized approach ensures each user group receives incentives matched to their engagement level and motivations, maximizing overall system effectiveness.
3Device complexity
If limited incentive options are provided, then system complexity is reduced, but user motivation and engagement are insufficient
Solution Approach 1:
The system employs a unified incentive platform that serves multiple user cohorts and objectives through a single multi-functional architecture. This universal system can automatically generate, manage, and distribute various types of incentives (bonuses, credits, perks, achievements) across different user segments without requiring separate systems for each user type. The multi-functionality reduces overall complexity while maintaining diverse incentive offerings through automated cohort-based customization.
Data Source
AI summary
This invention provides a method of determining appropriate incentives to users of a wagering platform or application by tailoring the incentive to the type of user that they are and provides incentives to increase the user's engagement with the platform or application to modify the user's behavior to allow them to become more experienced users.