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Related Experiment Video

Updated: Apr 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

A CBR-based and MAHP-based customer value prediction model for new product development.

Yu-Jie Zhao¹, Xin-xing Luo¹, Li Deng¹

¹Business School, Central South University, Changsha 410083, China.

Thescientificworldjournal

|August 28, 2014

Summary

This study introduces a new customer value prediction model for new product development, enhancing accuracy with case-based reasoning (CBR) and multiplicative analytic hierarchy process (MAHP). The model improves upon previous methods by incorporating dynamic customer transition probabilities for better market insights.

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Last Updated: Apr 25, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

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Published on: May 24, 2019

Area of Science:

Business and Management
Decision Science
Marketing

Background:

Enterprises need new product development to meet customer needs and increase market share.
Existing customer lifetime value (CLV) prediction models have limitations in dynamic environments.
Previous research by Chan et al. proposed a dynamic decision support system for CLV in new product development.

Purpose of the Study:

To propose an improved customer value prediction model for new product development.
To address deficiencies in existing models for better customer need fulfillment.
To introduce a novel model integrating case-based reasoning and multiplicative analytic hierarchy process.

Main Methods:

Development of a CBR-based and MAHP-based customer value prediction model (C&M-CVPM).
Utilizing case-based reasoning (CBR) to reduce expert workload and evaluation time.
Employing multiplicative analytic hierarchy process (MAHP) for effective influencing factor analysis.
Incorporating dynamic customer transition probabilities for enhanced realism.

Main Results:

The proposed C&M-CVPM model demonstrates a sensible and convincing application.
Simulation experiments validate the model's effectiveness in predicting customer value.
The integration of CBR and MAHP provides a more robust prediction framework.

Conclusions:

The C&M-CVPM offers a significant advancement in customer value prediction for new product development.
The model's dynamic nature and integrated methods provide more accurate and realistic market insights.
This approach supports enterprises in better meeting customer needs and achieving market success.