Real-Time Sequential Purchase Recommendation Engine
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Solution Overview
Problem
Existing systems fail to provide real-time purchase recommendations that are actionable and tailored to a customer's immediate needs, as they struggle to determine when a consumer is transitioning from one activity to another, leading to timely and relevant suggestions being difficult to deliver.
Innovation Solution
A method that receives customer profile data and authorization request data from financial transactions to generate sequential purchase recommendations based on merchant location, purchase category, and customer preferences, transmitting these recommendations to the customer's device for immediate action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time recommendations are provided based on transaction data, then the timeliness and relevance of recommendations improve, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary actions by integrating with authorization terminals and continuously monitoring transaction data in real-time. This allows the system to detect consumer state transitions immediately when they occur during the authorization process, rather than waiting for post-purchase data processing. The recommendation engine is pre-configured with purchase sequence patterns and consumer preference data, enabling it to generate actionable recommendations instantly upon detecting a relevant transaction event.
2Measurement precision
If the system monitors consumer state transitions in real-time, then the accuracy of recommendation timing improves, but the difficulty of detecting and measuring consumer state changes increases
Solution Approach 1:
The system uses authorization terminals as intermediaries to detect consumer state transitions. Instead of directly monitoring complex consumer behavior and inferring state changes, the system leverages the authorization terminal's existing transaction data as a reliable proxy indicator. When a purchase is authorized, it objectively signals that the consumer has completed an activity and may be ready for the next activity, providing an accurate and easily measurable state transition event.
3Adaptability or versatility
If personalized recommendations are generated based on customer profile data and purchase history, then the relevance of recommendations improves, but the computational requirements and system complexity increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing consumer profile data, purchase history, and preference information in structured formats before real-time recommendation generation. Purchase sequence patterns are pre-identified from historical data, and consumer segments are pre-defined based on spending habits and demographics. This pre-prepared data structure enables the recommendation engine to quickly match current transactions with relevant patterns and generate personalized recommendations without heavy computational overhead during real-time operation.
Data Source
AI summary
A method for providing a real-time purchase recommendation includes receiving customer profile data associated with a customer and authorization request data associated with a request to authorize an attempted purchasing using a financial account associated with the customer. The method includes determining attempted purchase data including an identity of a merchant, a merchant location, and a purchase category associated with the attempted purchase. The method includes generating a sequential purchase recommendation based on the attempted purchase data, wherein the sequential purchase recommendation is a real-time recommendation for one or more goods or services for future purchase sequentially following the attempted purchase. The method further includes transmitting a message including the sequential purchase recommendation to a customer computing device associated with the customer.