Intelligent profitable customers segmentation system based on business intelligence tools

doi:10.1016/j.eswa.2005.01.013

Expert Systems with Applications

Volume 29, Issue 1, July 2005, Pages 145-152

https://doi.org/10.1016/j.eswa.2005.01.013 Get rights and content

Abstract

For the success of CRM, it is important to target the most profitable customers of a company. Many CRM researches have been performed to calculate customer profitability and develop a comprehensive model of it. Most of them, however, had some limitations and accordingly the customer segmentation based on the customer profitability model is still underutilized. This paper aims at providing an easy, efficient and more practical alternative approach based on the customer satisfaction survey for the profitable customers segmentation. We present a multi-agent-based system, called the survey-based profitable customers segmentation system that executes the customer satisfaction survey and conducts the mining of customer satisfaction survey, socio-demographic and accounting database through the integrated uses of business intelligence tools such as DEA (Data Envelopment Analysis), Self-Organizing Map (SOM) neural network and C4.5 for the profitable customers segmentation. A case study on a Motor company's profitable customer segmentation is illustrated.

Introduction

In today's competitive business environment, the ability to identify profitable customers, build their long-term loyalty and steadily expand existing relationships is key competitive factors to a company. To meet these factors, companies across a wide range of industries have made Customer Relationship Management (CRM) one of the leading business strategies, integrating sales, marketing and service across multiple business units and customer contact points.

CRM helps companies understand the value of customers, target their most profitable customers, cultivate and maintain high-quality relationships that increase loyalty and profits. Precise evaluation of customer profitability and targeting the most profitable customers are crucial elements for the success of CRM.

Many CRM researches have been performed to calculate customer profitability based on customer lifetime value and develop a comprehensive model of it. Most of them, however, had some limitations by not considering such as the change of profit contribution resulted from the customer defection (Berger and Nasr, 1998, Gupta and Lehmann, 2003). They need further extensions considering additional factors such as customer reactivation possibility, attracting/service cost and causes of customer defection.

On the other hand, the customer segmentation based on their profitability to a company is still an underutilized approach. This study aims at providing an easy, efficient and more practical alternative approach based on the customer satisfaction survey for the profitable customers segmentation instead of using a customer profitability model, which is an important tool for marketing and managing customer relationships by providing the information of overall satisfaction level, repurchase intentions, word-of-mouth intentions, etc.

In our approach, we use intelligent tools such as Data Envelopment Analysis (DEA), Self-Organizing Map (SOM) neural network and C4.5 to segment profitable customers. DEA evaluates efficiency through the relation analysis between the company's input costs for a customer (e.g. marketing cost, production cost, inventory cost, delivery cost, service cost and relationship management cost) and the output (e.g. his/her satisfaction level, repurchase intentions and word-of-mouth intentions in the customer satisfaction survey and his/her profit contribution to it).

Through the successive mining of customer satisfaction survey and socio-demographic data by SOM and C4.5, we segment profitable customers among all the surveyed customers.

We present a survey-based profitable customers segmentation system (SPCSS) that designs, executes (on-line, e-mail, etc.) the customer satisfaction survey for all customers in customer database of a company and conducts those mining works for the profitable customers segmentation. SPCSS has an architecture based on intelligent agent technology and also the integration of those mining process into decision support system framework by means of applying that technology.

This paper is organized as follows. Section 2 presents a review of literature in the profitable customer segmentation and customer satisfaction survey. In Section 3, we introduce our research methodology for profitable customer segmentation based on customer satisfaction survey and the basic structure of proposed system incorporated that methodology is presented. A case study on a Motor company's profitable customer segmentation in South Korea is illustrated in Section 4 and the concluding remarks are presented in Section 5.

Section snippets

Profitable customer segmentation and customer satisfaction survey

Traditional customer segmentation models were based on demographic, attitudinal, and psychographic attributes of a customer (Griffin, 2003). They gave too simple results and poor accuracy for today's complicated business environment. Recently, the customer segmentation based on customer transactional and behavioral data (e.g. purchases type, volume and history, call center complaints, claims, web activity data, etc.) collected by various information systems is commonly used. However, the

Profitable customers segmentation based on customer satisfaction survey

We propose a survey-based profitable customers segmentation system (SPCSS) based on data mining and agent technology that designs, executes (on-line, e-mail, etc.) customer satisfaction survey and conducts predefined mining processes for the profitable customers segmentation. SPCSS has a multi-agent based architecture and the integration of predefined mining processes into decision support system framework (Fig. 1).

There are three types of intelligent agents within the SPCSS architecture:

A case study on a motor company's profitable customers segmentation

We implemented a web-based SPCSS prototype and validated the effectiveness of our approach through the customer satisfaction survey data of T Motor company.

The Survey management (SM) agent in SPCSS conducted a customer satisfaction survey of T company via e-mail and 491 customers responded to this survey. SM agent selected the survey customers from the customer database of T company. All respondents were asked to rate 24 questions addressing factors such as product quality, customer service,

Conclusion

To intelligently segment profitable customers of a company in terms of their profitability, we present an easy and efficient alternative approach based on the mining of customer satisfaction survey, socio-demographic and accounting database instead of using a complicated customer profitability model.

First, the presented approach uses DEA to find out the customers with higher cost efficiency, High Efficiency Customer Group (HECG), among all the surveyed ones about their output from a company's

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