Transaction Pattern Prediction for Personalized Loyalty Offers
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Solution Overview
Problem
Loyalty programs often provide benefits to cardholders who have shown no interest in the related goods or services, leading to a sub-optimal user experience and diminished customer engagement.
Innovation Solution
A method and system for managing a payment network that learns transaction patterns over time, generates a transfer function using a neural network's probability density function, predicts future transactions, and automatically transmits personalized offers or incentives to cardholders based on their behavior, linking accounts to relevant merchant category codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If loyalty offers are extended to all cardholders, then coverage and reach are improved, but user experience quality and customer engagement deteriorate due to irrelevant offers
Solution Approach 1:
The patent applies local quality by transitioning from uniform offer distribution to personalized offer targeting based on individual cardholder transaction patterns. The system analyzes each cardholder's specific spending behavior, preferences, and historical data to deliver customized offers that match their local (individual) characteristics, thereby improving user experience while maintaining broad reach through automated segmentation.
Solution Approach 2:
The system changes parameters by using machine learning models to dynamically adjust offer relevance based on cardholder behavior parameters such as transaction frequency, spending categories, and temporal patterns. This allows the offer distribution strategy to adapt continuously to changing cardholder preferences and behaviors, resolving the contradiction between broad coverage and personalized relevance.
2Ease of operation
If personalized offer targeting is implemented using transaction pattern analysis, then user experience and offer relevance are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system applies self-service by implementing automated machine learning models that continuously learn from transaction data and autonomously generate personalized offer recommendations. The system serves itself by automatically updating transaction patterns, retraining models, and adjusting offer strategies without manual intervention, thereby managing complexity through automation rather than human analysis.
Solution Approach 2:
The patent replaces manual offer curation and analysis with computational machine learning systems. Instead of human analysts manually reviewing transaction data to create personalized offers, the system uses automated algorithms to process transaction patterns, predict cardholder preferences, and generate targeted offers, substituting mechanical human processes with computational ones.
3Ease of manufacture
If traditional loyalty programs provide benefits without behavioral analysis, then implementation simplicity is maintained, but customer engagement and program effectiveness diminish
Solution Approach 1:
The system applies preliminary action by analyzing transaction patterns and predicting cardholder preferences before offers are delivered. The machine learning models continuously pre-process transaction data, build behavioral profiles, and prepare personalized offer recommendations in advance, enabling the system to provide relevant offers proactively rather than reactively, thereby enhancing engagement while maintaining automated simplicity.
Data Source
AI summary
An aspect of the present disclosure is drawn to a method for managing a payment network, including: learning a transaction pattern of an account over time; generating a transfer function based on the transaction pattern; predicting information for the account based on the density function; changing a state of the account based on the predicted information; and automatically transmitting a notification of a feature to an owner of the account based on the changing of the state of the account, wherein the transfer function is a probability density function of a neural network and wherein the information includes a time of a future transaction linked to a correlated marker of the payment network.