Dynamic Loyalty Earn Rate Adjustment System
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Solution Overview
Problem
Current loyalty programs face challenges in efficiently utilizing budgets due to fixed earn rates, leading to potential financial losses from overspending or underspending, and lack of user participation due to diluted impact from numerous programs with little differentiation.
Innovation Solution
A system and method for dynamically updating the earn rate for disbursing loyalty points in real-time using real-time and predictive transaction data, market disbursement data, and user preferences, ensuring efficient budget utilization and encouraging user participation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed earn rate is used for disbursing loyalty points, then the budget can be easily managed and tracked, but the budget may be overspent or underspent due to estimation errors in transaction volumes
Solution Approach 1:
The earn rate is transformed from a static fixed value to a dynamic variable that adjusts in real-time based on transaction volume analysis. The system continuously monitors actual transaction patterns and modifies the earn rate accordingly, allowing the budget utilization to adapt to actual user behavior rather than relying on preliminary estimates.
Solution Approach 2:
The system implements a feedback mechanism where actual transaction data is continuously fed back into the earn rate calculation algorithm. This closed-loop system uses real-time transaction volume information to adjust the earn rate, ensuring that the loyalty points budget is disbursed at an rate that matches actual spending patterns, thereby preventing both overspending and underspending.
2Adaptability or versatility
If many loyalty programs are offered to users, then program coverage and accessibility are improved, but the impact and differentiation of each program is diluted
Solution Approach 1:
Instead of applying a uniform earn rate across all users and transactions, the system implements local quality by tailoring the earn rate to specific users, merchants, and transaction types. This allows each loyalty program instance to have customized parameters that enhance its unique value proposition and differentiation, while still operating within the broader multi-program ecosystem.
Solution Approach 2:
The system enables dynamic parameter changes in the earn rate based on multiple factors including user segmentation, merchant category, transaction amount, and time of day. This flexibility allows each loyalty program to optimize its effectiveness by adjusting parameters in real-time, maintaining high impact despite the presence of multiple programs.
3Reliability
If the earn rate is updated in real-time based on transaction data, then budget utilization is optimized and overspending/underspending is prevented, but the system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating earn rate adjustments based on projected transaction patterns and budget constraints. Before actual transactions occur, the system establishes preliminary earn rate parameters that are then refined in real-time, reducing the computational burden during high-volume transaction processing while maintaining accuracy.
Solution Approach 2:
The system introduces an intermediary layer (the dynamic earn rate calculation engine) that sits between the fixed budget parameters and the actual transaction processing. This intermediary continuously analyzes transaction data and adjusts the earn rate, simplifying the overall system architecture by centralizing the complexity in a dedicated module rather than distributing it across the entire transaction processing infrastructure.
Data Source
AI summary
A method and system for disbursing loyalty points is provided. A server receives loyalty points disbursement (LPD) parameters including at least an initial earn rate, a loyalty points budget, and a disbursement period. The server receives an LPD request corresponding to a transaction performed by a user. The server updates the initial earn rate based on at least one of real-time transaction data, predictive transaction data, real-time market disbursement data, and predictive market disbursement data. The server disburses a first set of loyalty points, from the loyalty points budget, to the user based on at least the updated earn rate and a transaction amount of the first transaction.