Targeted Offer System Using Interaction Associations
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Solution Overview
Problem
Merchants often miss opportunities to offer relevant products or services to customers due to a lack of awareness about their specific needs, as they rely on customers expressing their needs directly or using general customer data, which can lead to irrelevant offers and missed sales opportunities.
Innovation Solution
A method and system that analyze customer data, including social network information, to generate interaction associations based on behavioral and demographic criteria, enabling targeted offers to be made to entities with similar characteristics and propensities, thereby predicting customer needs and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If merchants wait for customers to express their needs directly, then they can provide relevant products or services, but they miss opportunities to offer products for unexpressed needs
Solution Approach 1:
The system performs preliminary analysis of customer data, social network information, and behavioral patterns to predict customer needs before customers explicitly express them. This allows merchants to prepare and offer relevant products proactively, resolving the contradiction between waiting for explicit needs and capturing unexpressed opportunities
2Productivity
If merchants use general customer data for offers, then they can reach broader audiences, but the offers become irrelevant to specific customer needs
Solution Approach 1:
The system applies local quality by tailoring offers to specific customer segments based on their unique characteristics, behaviors, and social network data. Instead of uniform general offers, the system customizes marketing messages for different customer groups, achieving both broad reach and high relevance simultaneously
3Measurement precision
If merchants analyze detailed customer personal circumstances, then they can predict customer needs accurately, but the system complexity increases
Solution Approach 1:
The system introduces intermediary components including specialized data processing modules, prediction algorithms, and integration layers that handle the complexity of analyzing detailed customer data. These intermediaries transform raw data into actionable insights, enabling accurate predictions without exposing the entire system complexity to end users
4Measurement precision
If merchants access customer data from multiple sources including social networks, then they can identify unexpressed needs better, but data privacy concerns increase
Solution Approach 1:
The system extracts only the specific data elements necessary for predicting customer needs from multiple sources including social networks. By selectively extracting relevant information rather than collecting comprehensive data, the system achieves accurate customer need identification while minimizing privacy intrusion and data security risks
Data Source
AI summary
A method for making a targeted offer to an audience of a population of entities (e.g., social network). The method involves retrieving, from one or more databases, a first set of information including activities and characteristics attributable to a first plurality of entities; generating a plurality of interaction associations based on at least one of selected activities criteria and selected characteristics criteria from the first set of information; and conveying to a third party one or more interaction associations to enable the third party to identify a second set of information including activities and characteristics attributable to a second plurality of entities. The second set of information has matching activities and characteristics to the activities and characteristics of the interaction associations. The second plurality of entities has a propensity to carry out certain activities based on the activities criteria and/or characteristics criteria used in forming the interaction associations, to enable a targeted offer to be made to an audience of the second plurality of entities. A system for making a targeted offer to an audience of a population of entities (e.g., social network).