Consumer Spend Behavior Tracking for Attrition Prediction
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Solution Overview
Problem
Companies face difficulties in correlating online advertising and marketing expenditures with subsequent purchase events, especially when purchases occur through different sales channels or after initial exposure to marketing communications, and in tracking consumer spending behavior to predict attrition.
Innovation Solution
A computer-implemented method and system that tracks consumer spend behavior by analyzing past and current transaction data, environmental, and behavioral data to determine a spend behavior model, predicting future spend behaviors and identifying the likelihood of attrition, and attributing online activity to subsequent purchase events across various sales channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If companies utilize multiple marketing channels to attract new business and increase revenue, then marketing effectiveness and customer acquisition improve, but the ability to correlate advertising expenditures with subsequent purchase events deteriorates
Solution Approach 1:
The patent segments the tracking system into multiple components: device fingerprinting modules that track individual devices across channels, attribution servers that process and correlate data, and database systems that store transaction and marketing data. This segmentation enables effective correlation tracking across multiple marketing channels by dividing the complex tracking task into manageable, specialized components.
Solution Approach 2:
The patent introduces an intermediary attribution server that acts as a mediator between marketing channels and purchase events. This server receives data from various sources including device fingerprints, marketing campaign data, and transaction information, then correlates them to attribute purchases to specific marketing exposures across different channels.
2Measurement precision
If companies track consumer spending behavior across different sales channels, then understanding of consumer behavior improves, but system complexity increases
Solution Approach 1:
The patent implements a universal device fingerprinting system that functions across multiple sales channels and marketing platforms. The fingerprinting technology creates unique identifiers for devices that can be recognized and tracked regardless of which channel or platform the consumer interacts with, enabling multi-functional tracking without requiring separate systems for each channel.
Solution Approach 2:
The patent utilizes parameter changes in device characteristics to create and update fingerprints. By monitoring changes in device parameters such as hardware configurations, software versions, and network characteristics, the system can track consumers across channels while adapting to device modifications, maintaining tracking accuracy without increasing system complexity.
3Measurement precision
If companies analyze past transaction data and environmental/behavioral data to create spend behavior models, then prediction accuracy of consumer attrition improves, but data processing requirements and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing transaction data and environmental/behavioral data into structured formats before model creation. The system pre-segments consumer data, pre-calculates relevant features, and pre-establishes data relationships, which reduces the computational burden during actual model training and attrition prediction, improving efficiency while maintaining accuracy.
Data Source
AI summary
Systems and methods are disclosed for tracking consumer spend behavior to predict attrition. One method includes: receiving past transaction data related to a plurality of past payment transactions of a consumer; receiving environmental and/or behavioral data associated with each of the past payment transactions of the consumer; determining a spend behavior model of the consumer; subsequent to determining the spend behavior model of the consumer, receiving transaction data related to one or more current payment transactions of the consumer; receiving environmental and/or behavioral data associated with the one or more current payment transactions; determining, based on an analysis of the current transaction data and environmental and/or behavioral data associated with each of the current payment transactions, a current spend behavior of the consumer; and determining, based on a comparison of the current spend behavior with the spend behavior model, the likelihood of an attrition of the current spend behavior.