Customer Value Optimization via Predictive Segmentation

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Solution Overview

Problem

Financial institutions lack the capability to effectively allocate resources to enhance customer value and improve customer relationships, as they struggle to identify and target customers for products or services that increase customer value or improve their relationship with the institution.

Innovation Solution

A method and system that analyze customer data, including financial transaction and account data, to segment customers, calculate current and future values, and determine eligible recommended actions such as new products or service modifications, using predictive modeling scores and computational values to optimize customer value.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If financial institutions manually analyze customer data to identify target customers, then they can understand customer needs, but the process is time-consuming and resource-intensive

Engineering Contradiction:

Improvecustomer value assessment accuracyVSAvoidresource allocation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical analysis of customer data with automated computer-based systems that use algorithms and models to calculate customer current value and future value, segment customers, and generate recommended actions, thereby eliminating time-consuming manual processes while maintaining or improving assessment accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables financial institutions to automatically self-assess their customer base by computing customer values, identifying target segments, and generating recommended actions without external intervention, allowing continuous automated optimization of resource allocation

Inventive Principle:

Principle #25Self-service

2Productivity

If financial institutions offer products to all customers, then they maximize potential revenue, but they waste resources on customers unlikely to purchase

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidmarketing resource waste

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The patent segments the customer base into distinct groups based on calculated current value and future value metrics, enabling targeted marketing efforts focused on high-potential segments while reducing or eliminating resources allocated to low-potential segments, thereby improving resource allocation efficiency and reducing waste

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies different marketing strategies and resource allocation levels to different customer segments based on their specific characteristics and potential, rather than applying a uniform approach to all customers, optimizing resource distribution to match local customer needs and potential

Inventive Principle:

Principle #3Local quality

3Measurement precision

If financial institutions use complex predictive models to calculate customer future value, then they improve targeting accuracy, but the system complexity increases

Engineering Contradiction:

Improvefuture value prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent implements a universal computer-based system that performs multiple functions including data collection, customer segmentation, current value calculation, future value prediction, and recommended action generation within a single integrated platform, managing complexity through consolidation while maintaining predictive accuracy

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS8688557B2Systems and methods for customer value optimization involving relationship optimization

Publication Date: 2014.04.01 FISERV INC

AI summary

Systems and methods can provide for customer value optimization. The customer value optimization can include analyzing certain transaction and/or non-transaction data of customers with one or more predictive models to determine predictive modeling scores, values, or indicators. These one or more predictive modeling scores, values, or indicators can be used with other transaction or non-transaction data of customers, either alone or with other derived values/calculations, to provide certain optimizations relating to relationship optimization.