Abstract
Article history: Received February 5, 2016 Received in revised format April 15, 2016 Accepted May 7, 2016 Available online May 9, 2016 Customer Relationship Management (CRM) has been an important part of enterprise decisionmaking and management. In this regard, Decision Tree (DT) models are the most common tools for investigating CRM and providing an appropriate support for the implementation of CRM systems. Yet, this method does not yield any estimate of the degree of separation of different subgroups involved in analysis. In this research, we compute three decision-making models in SMEs, analyzing different decision tree methods (C&RT, C4.5 and ID3). The methods are then used to compute ME and VoE for the models and they were then used to calculate the Mean Errors (ME) and Variance of Errors (VoE) estimates to investigate the predictive power of these methods. These decision tree methods were used to analyze smalland medium-sized enterprises (SME’s) datasets. The paper proposes a powerful technical support for better directing market tends and mining in CRM. According to the findings, C&RT shows a better degree of separation. As a result, we recommend using decision tree methods together with ME and VoE to determine CRM factors. © 2016 Growing Science Ltd. All rights reserved.
Highlights
In recent decades, Customer Relationship Management (CRM) has reflected the crucial role of the customer as a factor that helps a company’s profitability and operation meet
Customer Relationship Management (CRM) is an enterprise management strategy which concentrates on customers (Stringfellow et al, 2004)
CRM applies modern information technology (IT) to increase the capability of enterprises to maintain and recognize their customers through Business Process Reengineering (BPR), and to maximize the profitability
Summary
Customer Relationship Management (CRM) has reflected the crucial role of the customer as a factor that helps a company’s profitability and operation meet. Considering the increase in the complexity and accumulation of customer information, most organizations may succeed only by performing critical activities such as analyzing complex customer data, identifying customers’ values, detecting the trend of customers’ behaviors, appreciating the real value of customers, and analyzing customers from a lifecycle marketing perspective All of these factors depend on data mining (DM), and the more the data are accumulated in a database, the more useful DM techniques are. Customer data and information technology tools shape the foundation to build successful strategy (Ngai et al, 2009) With this trend, technologies such as data mining and data warehousing have changed CRM as a new area in which most organizations may gain competitive advantage.
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