ML Offer Recommendation System for Consumer Relevance
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Solution Overview
Problem
Consumers are overwhelmed with irrelevant product and service offers, leading to annoyance and a high probability of ignoring desirable offers, especially in the financial sector where timely responses are crucial.
Innovation Solution
A method and system using machine learning techniques to identify and recommend relevant offers by correlating user attributes with offer attributes, training models with historical data, and providing real-time recommendations based on user interest prediction algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If offers are widely distributed to many consumers, then the reach and potential customer base is improved, but the relevance and acceptance rate deteriorates due to information overload
Solution Approach 1:
The patent segments the consumer base into distinct groups based on demographic, behavioral, and preference attributes. By dividing the broad consumer market into smaller, more homogeneous segments, the system can tailor offers to each segment's specific characteristics, thereby maintaining high relevance while distributing offers widely across different segments.
Solution Approach 2:
The patent applies local quality by customizing offer attributes (such as product type, pricing, timing) to match the specific characteristics of each consumer segment or individual consumer. This ensures that each consumer receives offers with locally optimized qualities that align with their preferences, rather than a uniform offer approach.
2Duration of action of moving object
If multiple identical offers are presented to consumers repeatedly, then the reinforcement of the offer message is improved, but the consumer annoyance and negative perception worsens
Solution Approach 1:
The patent implements periodic action by scheduling offer presentations at optimal intervals based on consumer engagement history and offer importance. Rather than continuous or repetitive display, offers are presented periodically with varying timing and frequency, maintaining consumer interest while avoiding the negative effects of over-exposure and annoyance.
Solution Approach 2:
The patent applies dynamics by making the offer presentation frequency and timing adaptive rather than static. The system dynamically adjusts how often and when offers are shown based on real-time consumer behavior data, engagement levels, and offer performance metrics, allowing the offer strategy to evolve and prevent consumer fatigue.
3Productivity
If blanket offer distribution is used without qualification checking, then the distribution speed and coverage are improved, but the offer quality and consumer satisfaction deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-screening consumers against qualification criteria before presenting offers. The system performs preliminary checks on consumer attributes (such as creditworthiness, demographic fit, purchase history) to determine eligibility and likelihood of acceptance, ensuring that only qualified consumers receive specific offers, thereby maintaining both speed and quality.
Solution Approach 2:
The patent replaces manual or simple mechanical offer distribution methods with automated machine learning-based matching systems. This substitution enables rapid, large-scale offer distribution while simultaneously performing complex qualification assessments and relevance scoring, achieving both high productivity and high precision that would be impossible with traditional methods.
Data Source
AI summary
Systems and methods for generating recommended offers are disclosed. An example method may be performed by one or more processors of a recommendation system and include correlating attributes of users with attributes of offers based on historical data associated with the users and offers, training a machine learning model to predict a user's interest in an offer based on the correlating, obtaining current user data, obtaining current offer data, providing the current user data and the current offer data to the trained machine learning model, generating, using the trained machine learning model, a predicted level of interest that the current user has in each respective current offer of the number of current offers, identifying, among the number of current offers, at least one current offer having a predicted level of interest for the current user greater than a value, and generating one or more recommended offers for the current user.