Consumer Behavior Prediction Using Demographics And Product Features
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Solution Overview
Problem
Existing technologies struggle to accurately predict consumer behavior towards new products due to the difficulty in accounting for differences in demographics and features, leading to inaccurate predictions of spending behavior.
Innovation Solution
A system using machine learning models to analyze spending behavior data, payment device feature data, and transaction data from existing payment devices, training models to predict transaction metrics based on proposed product features and demographics, and transmitting communications based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained using only past observations of existing products, then the prediction process is simple, but the accuracy of consumer behavior predictions deteriorates due to inability to account for demographic and feature differences
Solution Approach 1:
The patent segments the prediction problem into multiple components: demographic segmentation (age, income, location) and feature segmentation (product attributes, pricing, design). This allows the machine learning model to process and weigh different factors separately, improving prediction accuracy by accounting for diverse consumer characteristics without overwhelming complexity
Solution Approach 2:
The patent introduces new dimensions to the prediction model by incorporating demographic data and detailed product feature data alongside traditional transaction history. This dimensional expansion enables the model to capture nuanced relationships between consumer characteristics, product attributes, and spending behavior, thereby improving prediction accuracy
2Loss of information
If multiple product features are considered in the prediction, then the comprehensiveness of analysis improves, but the difficulty of identifying which features impact consumer behavior increases
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously learns from actual consumer behavior data and adjusts its understanding of feature importance. This feedback loop enables the system to identify which features truly drive consumer behavior while maintaining comprehensive analysis of all product attributes
Solution Approach 2:
The patent uses machine learning algorithms as intermediaries to process the complex relationships between multiple features and consumer behavior. The model acts as a mediator that synthesizes information from demographics, product features, and transaction history to identify meaningful patterns and feature impacts without manual analysis
3Measurement precision
If demographic data and product feature data are integrated into the prediction model, then the accuracy of transaction metric predictions improves, but the amount of data processing and computation increases
Solution Approach 1:
The patent performs preliminary data processing and feature engineering before the main prediction computation. By pre-processing demographic data, product feature data, and transaction history to create meaningful features and representations, the system reduces the computational burden during actual prediction while maintaining high accuracy in transaction metric forecasts
Data Source
AI summary
Systems, methods, and computer program products are provided for predicting consumer behavior based on demographics and new product features using machine learning models. An example method includes receiving spending behavior data, payment device feature data, and transaction data associated with existing payment devices and proposed spending behavior data and proposed payment device feature data associated with a proposed payment device. A machine learning model (MLM) is trained to predict a transaction metric associated with the transaction data based on the spending behavior data and the payment device feature data for the existing payment devices. The trained MLM predicts a predicted transaction metric based on the proposed spending behavior data and the proposed payment device feature data associated with the proposed payment device. A communication is transmitted based on the predicted transaction metric.