Consumer Behavior Prediction Using Cohort Segmentation and Decay Factors
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Solution Overview
Problem
Existing electronic marketing services face challenges in accurately and efficiently predicting consumer behavior, leading to inefficient allocation of resources and reduced revenue due to irrelevant promotions and increased processing power requirements.
Innovation Solution
A machine learning model is employed to classify consumers into cohorts based on purchase frequency, using different attributes for predicting purchase likelihood and quantity, and applying decay factors to adjust predictions over time, allowing for targeted advertising and optimized marketing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model classifies consumers into cohorts and uses different attributes for prediction, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments consumers into distinct cohorts based on purchase frequency (e.g., frequent buyers vs. infrequent buyers). Each cohort is then predicted using attributes specifically selected for that segment, rather than applying a single uniform model to all consumers. This segmentation allows the system to achieve higher prediction accuracy by tailoring the prediction approach to each group's characteristics while managing complexity through modular cohort-specific models.
2Reliability
If decay factors are applied to adjust predictions over time, then prediction reliability is improved, but processing power requirements increase
Solution Approach 1:
The patent applies decay factors that periodically adjust prediction values based on the recency of consumer behavior. Instead of continuously recalculating predictions, the system uses time-based decay mechanisms that periodically update prediction reliability. This approach maintains prediction reliability by accounting for changing consumer behavior over time while reducing processing power requirements compared to continuous real-time updates.
3Productivity
If targeted advertising is implemented based on consumer cohorts, then revenue is improved, but marketing service complexity increases
Solution Approach 1:
The patent implements targeted advertising by delivering different promotional content to different consumer cohorts based on their specific characteristics and predicted behaviors. Rather than using a uniform marketing approach, the system tailors the quality and type of advertising content to each cohort's preferences and purchase patterns. This local quality approach increases revenue by improving ad relevance while managing complexity through standardized cohort definitions and automated delivery mechanisms.
Data Source
AI summary
Embodiments provide methods, systems, apparatuses, and computer program products for predicting behavior. An example method includes determining a classification for a first consumer, where the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.