Consumer Spending Prediction via Transaction Progression Analysis
Find Innovative SolutionsGenerate Solutions
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, leading to a disconnect between online activity and brick-and-mortar retail purchases.
Innovation Solution
A system and method that predicts consumer spending behavior by analyzing historical purchase activity progressions, linking transaction data and environmental/behavioral data from past and current transactions to identify trends and attribute online exposure to subsequent purchases, using a profiler computing system to provide indications of predicted purchase transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If companies utilize multiple marketing channels (TV, radio, Internet) to attract new business and increase revenue, then marketing reach and potential customer acquisition improve, but the ability to correlate advertising expenditures to subsequent purchase events deteriorates
Solution Approach 1:
The patent introduces a profiler computing system as an intermediary that collects, processes, and analyzes transaction data from multiple marketing channels. This system acts as a mediator between diverse advertising sources and purchase event tracking, enabling correlation by standardizing and analyzing data from TV, radio, Internet, and other channels through a unified analytical framework
Solution Approach 2:
The patent segments the marketing attribution problem by analyzing transaction data in distinct progressions and patterns. It divides the complex correlation task into manageable segments by examining specific purchase event sequences, temporal patterns, and channel-specific behaviors, allowing targeted attribution analysis for each segment of the marketing funnel
2Adaptability or versatility
If purchase events occur through different sales channels or subsequent to initial exposure to marketing communications, then customer conversion flexibility improves, but the ability to track and attribute online activity to brick-and-mortar purchases deteriorates
Solution Approach 1:
The patent creates a universal attribution system that handles multiple sales channels (online, offline, brick-and-mortar, mobile) through a single profiler computing system. This multi-functional system processes transaction data regardless of purchase channel, applying consistent analytical methods to attribute marketing exposure across diverse sales environments, thereby maintaining measurement precision while supporting channel flexibility
Solution Approach 2:
The patent implements feedback mechanisms by continuously analyzing transaction data and refining attribution models based on observed purchase patterns. The system uses historical transaction information to improve future attribution accuracy, creating a closed-loop system that learns from cross-channel purchase behaviors and enhances its ability to track online-to-offline conversions over time
Data Source
AI summary
Systems and methods are disclosed for predicting consumer spending behavior based on historical purchase activity progressions. One method includes: receiving transaction data related to two or more past payment transactions of a consumer; receiving environmental and/or behavioral data associated with each of the past payment transactions; determining, based on the transaction data and environmental and/or behavioral data, historical purchase activity progressions, wherein each of the historical purchase activity progressions identifies one or more trends in environmental and/or behavioral data; receiving transaction data related to a current payment transaction of the consumer; receiving environmental and/or behavioral data associated with the current payment transaction; comparing the environmental and/or behavioral data associated with one or more of the past payment transactions with environmental and/or behavioral data associated with the current payment transaction; and determining whether a progression of one or more of the past payment transactions to the current payment transaction maps to one of the historical purchase activity progressions.