The Expert System Designed to Improve Customer Satisfaction

TL;DR

This paper presents an expert system utilizing Artificial Neural Networks to enhance customer satisfaction by optimizing product colors and design choices based on customer data and expert knowledge.

Contribution

It introduces a novel expert system that combines neural networks and rule-based decision making to improve product design selection tailored to customer preferences.

Findings

The system effectively classifies customer preferences for colors and designs.

It successfully ranks product options based on customer data.

Validation shows high accuracy across different customer types.

Abstract

Customer Relationship Management becomes a leading business strategy in highly competitive business environment. It aims to enhance the performance of the businesses by improving the customer satisfaction and loyalty. The objective of this paper is to improve customer satisfaction on product's colors and design with the help of the expert system developed by using Artificial Neural Networks. The expert system's role is to capture the knowledge of the experts and the data from the customer requirements, and then, process the collected data and form the appropriate rules for choosing product's colors and design. In order to identify the hidden pattern of the customer's needs, the Artificial Neural Networks technique has been applied to classify the colors and design based upon a list of selected information. Moreover, the expert system has the capability to make decisions in ranking the scores of the colors and design presented in the selection. In addition, the expert system has been validated with a different customer types.