Predictive Model for Customer Interaction Probability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for targeting marketing efforts to prospective customers are inefficient due to reliance on generalized assumptions, leading to improper identification of interested individuals and increased waste from irrelevant communications, as they struggle to accurately predict customer behavior amidst vast data sets.
Innovation Solution
A system utilizing a machine learning program with a neural network to predict customer interaction probabilities by correlating personal data sets, generating predictive models that adjust based on changes in user interactions, and sending tailored communications based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generalized assumptions are used to target marketing efforts, then the complexity of data analysis is reduced, but the accuracy of identifying interested customers deteriorates
Solution Approach 1:
The patent transforms the approach by changing the parameters of analysis from generalized assumptions to specific data-driven parameters. The system analyzes multiple variables including demographic data, transactional data, and behavioral patterns to create precise customer profiles, thereby improving identification accuracy while managing complexity through systematic parameter evaluation
Solution Approach 2:
The patent creates simplified representations (copies) of complex customer data through predictive models and scoring systems. These models copy the essential patterns from vast datasets into manageable formats that indicate customer interest levels, allowing accurate identification without directly managing the full complexity of raw data
2Reliability
If marketing materials are sent to all prospective customers, then the risk of missing potential customers is reduced, but the waste of resources on irrelevant communications increases
Solution Approach 1:
The patent applies local quality by tailoring marketing communications to specific customer segments based on their predicted interest levels. Instead of uniform marketing to all customers, the system customizes the approach for each individual or segment, sending targeted communications only to those with high predicted interest, thereby maintaining coverage of potential customers while eliminating waste on uninterested prospects
Solution Approach 2:
The patent uses partial action by sending marketing materials only to a selected portion of the customer base—specifically those with high predicted interest scores. This partial targeting approach ensures that resources are concentrated on the most promising leads rather than being distributed excessively to all prospects, optimizing the balance between coverage and resource efficiency
3Adaptability or versatility
If the number of variables analyzed for customer targeting is increased, then the comprehensiveness of customer profiling is improved, but the difficulty of finding relevant relationships increases
Solution Approach 1:
The patent introduces intermediary elements in the form of predictive models and algorithms that mediate between the vast number of variables and the final customer interest assessment. These intermediaries process and synthesize multiple variables including demographic, transactional, and behavioral data, making the relationships between variables manageable and interpretable while maintaining comprehensive profiling capability
Solution Approach 2:
The patent segments the complex set of variables into distinct categories such as demographic data, transactional data, and behavioral patterns. By organizing variables into manageable segments, the system can analyze relationships within each category more effectively while maintaining the comprehensiveness of the overall customer profile through integration of all segments
Data Source
AI summary
A system for guiding interactions with a user device includes a computer generating a predictive model during training of a machine learning program. A training data set includes a personal data set of each of a plurality of first users. The predictive model predicts a first probability of a second user associated with the user device interacting with a first product and/or service as well as a test probability of the second user interacting with the first product and/or service based on a modified personal data set corresponding to a change in the relationship between the second user and a first entity. The computer sends a communication to the user device of the second user including content relating to a change in the relationship between the second user and the first entity when the test probability exceeds the first probability.