Predictive Customer Quality Model Using Machine Learning

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Solution Overview

Problem

Businesses face challenges in identifying and prioritizing high-quality customers effectively, leading to increased costs and reduced profitability due to serving lower-quality customers over extended periods.

Innovation Solution

The development of a predictive analytic model using artificial intelligence and machine learning to determine historical customer quality scores and predict future customer quality, enabling businesses to identify and target high-quality customers by recognizing patterns in multi-source data and adapting to real-time updates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Duration of action of stationary object

If businesses serve customers with various product or service offerings over an extended period of time, then customer relationships are maintained, but servicing costs accumulate significantly due to lower quality customers

Engineering Contradiction:

Improvecustomer relationship durationVSAvoidservicing cost

Core Design Contradiction:

Duration of action of stationary objectVSLoss of energy

Solution Approach 1:

The system performs preliminary assessment of customer quality before fully engaging in extended service relationships. By evaluating customer characteristics, payment history, and quality indicators upfront, businesses can identify high-quality customers worth long-term investment while avoiding costly relationships with lower-quality customers from the start

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The predictive analytics system automatically evaluates and segments customers based on quality metrics, enabling the business to self-select which customers deserve extended service relationships. This automated customer quality assessment reduces manual evaluation efforts and ensures consistent application of quality criteria across all customer interactions

Inventive Principle:

Principle #25Self-service

2Loss of energy

If businesses expend significant resources assessing customer quality, then profitability increases by focusing on higher quality customers, but assessment costs increase

Engineering Contradiction:

ImproveprofitabilityVSAvoidassessment cost

Core Design Contradiction:

Loss of energyVSUse of energy by moving object

Solution Approach 1:

The system replaces manual, resource-intensive customer quality assessment with automated machine learning models and predictive analytics. These algorithms process customer data, payment history, and behavioral patterns to generate quality scores automatically, eliminating the need for expensive human evaluation while maintaining or improving assessment accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms qualitative customer quality assessment into quantitative metrics through predictive analytics. By converting subjective quality judgments into objective numerical scores based on multiple data parameters, the system enables efficient automated comparison and ranking of customers, reducing assessment resources while improving decision accuracy

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If businesses manually locate and evaluate potential customers, then customer selection is made, but time and effort are significantly consumed

Engineering Contradiction:

Improvecustomer selection processVSAvoidlocating and evaluating customers

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The predictive analytics system automatically performs customer quality assessment and ranking without manual intervention. The machine learning models independently evaluate potential customers, generate quality scores, and present prioritized lists to business users, eliminating time-consuming manual evaluation while improving selection accuracy through data-driven insights

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system extracts and isolates the time-consuming manual evaluation tasks from the customer selection process. By automating data collection, analysis, and scoring functions, the system separates the analytical workload from human decision-makers, allowing them to focus only on final selection decisions based on pre-processed quality assessments

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20230196392A1System and methods for customer quality prediction

Publication Date: 2023.06.22 TGRES LLC

AI summary

Apparatus and associated methods relate to determining scores rating historical customer quality, training a predictive analytic model to recognize historical customer quality determined as a function of ranking the scores, and predicting future customer quality based on the model. In an illustrative example, quality may be a vector quantity representing multi-source data. In some examples, the predictive analytic model may be trained to recognize a historical customer as a member of a subset of customers. For example, the model may be trained to recognize a customer subset selected based on a quality threshold characterizing the subset as good. In various embodiments, the predictive analytic model may be a neural network, permitting prediction based on weights adapted by machine learning techniques to learn which data sources are optimal predictors. Various examples may advantageously predict a customer quality trend as a function of time, permitting decisions based on predicted future customer quality.