Real-Time Loyalty Prediction via Data Ecosystem
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Small businesses lack the resources and data to identify potentially loyal customers in real-time during sales transactions, limiting their ability to respond effectively and market to valuable customers.
Innovation Solution
A data ecosystem processes transaction data in conjunction with historical and environmental data using a logistic regression algorithm to predict customer behavior, providing real-time insights for merchants to offer promotional incentives during payment processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional customer identification methods are used, then customer loyalty can be identified, but real-time identification at point of sale is not achieved due to resource limitations
Solution Approach 1:
The system pre-processes and stores customer transaction data, behavioral patterns, and purchase history in advance before the point-of-sale moment. This preliminary data preparation enables instant retrieval and analysis during the actual transaction, resolving the contradiction between identification accuracy and time loss by doing the heavy lifting before it's needed.
Solution Approach 2:
The patent replaces manual or conventional mechanical customer identification methods with an automated digital data processing system. The system uses electronic data ecosystems, algorithms, and automated decision-making to identify loyal customers instantly, substituting slow manual processes with fast computational analysis that provides both accuracy and real-time performance.
2Measurement precision
If comprehensive data processing is implemented to identify loyal customers, then customer value identification improves, but system complexity increases
Solution Approach 1:
The system segments the complex data processing task into distinct modular components: data collection modules, data processing modules, analysis modules, and output modules. Each segment handles specific aspects of customer data, making the overall complex system manageable, maintainable, and scalable while achieving high prediction accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediary data structures and processing layers that mediate between raw customer data and final loyalty predictions. These intermediaries organize, normalize, and structure data in ways that simplify subsequent analysis while maintaining comprehensive information, thereby reducing system complexity without sacrificing prediction accuracy.
3Speed
If real-time customer behavior prediction is provided, then promotional offer timing is optimized, but data processing requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources on analyzing only the most relevant customer data elements needed for prediction rather than processing every available data point. This selective approach delivers real-time predictions with optimized resource consumption by doing just enough processing to achieve the required speed and accuracy threshold.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as analysis depth, data sampling rates, and computational thresholds based on transaction context and time constraints. This allows the system to optimize the balance between prediction speed and resource consumption by changing operational parameters rather than maintaining fixed high-resource settings for all scenarios.
Data Source
AI summary
Systems and methods are provided to predict customer behavior during a digital transaction at a point of sale. The disclosed systems and methods can collect information regarding a merchant and the merchant's business as well as information about the current sales environment in which the merchant is operating. From the collected information, the disclosed systems and methods can process the collected information to generate a prediction of future customer behavior in real-time or near real-time.