A Taxonomy of Customer Relationship Management

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A Taxonomy of Customer Relationship Management Analyses for Data Warehousing Colleen Cunningham and Il-Yeol Song
College of Information Science & Technology Drexel University Philadelphia, PA , 19104 USA cmc38@drexel.edu, song@drexel.edu

Customer Relationship Management (CRM) is a strategy that supports an organization’s decision-making process to retain long-term and profitable relationships with its customers. Effective CRM analyses require a detailed data warehouse model that can support various CRM analyses and deep understanding on CRM-related business questions. In this paper, we present a taxonomy of CRM analysis categories. Our CRM taxonomy includes CRM strategies, CRM category analyses, CRM business questions, their potential uses, and key performance indicators (KPIs) for those analysis types. Our CRM taxonomy can be used in selecting and evaluating a data schema for CRM analyses, CRM vendors, CRM strategies, and KPIs. Keywords: CRM, Customer Relationship Management, CRM Analyses, Taxonomy, Data Warehousing, KPIs, Key Performance Indicators.

In our earlier work, we defined a more complete definition of CRM as a data-driven strategy that utilizes organizational knowledge and technology in order to enable proactive and profitable long-term relationships with customers (Cunningham et al. 2003, Cunningham et al. 2004). It integrates the use of knowledge management, data warehousing, and data mining technologies to enable organizations to make decisions about, among other things, product offerings, marketing strategies, and customer interactions. This brings us to the business motivation for using CRM. Interestingly, repeat customers can generate more than twice as much gross income as new customers (Winer 2001). Additionally, acquiring new customers can cost five times more than it costs to retain current customers (Massey et al. 2001). As such companies need to develop and manage their relationships with their customers such that the relationships are long-term and profitable. Therefore, companies are turning to CRM techniques and CRM-supported technologies to differentiate between customers that are valuable (or potentially valuable) from those that are not. While companies realize that there are benefits of CRM, many have not actually achieved the full benefits of implementing CRM. In fact, recent statistics indicate that between 50% and 80% of CRM initiatives fail due to inappropriate or incomplete CRM processes, poor selection and design of supporting technologies (e.g. data warehouses), and the inability to utilize CRM technologies beyond the basic capacity due to the lack of understanding of business analyses (Gardner 1998, Pass and Kuijlen 2001, Myron & Ganeshram 2002, Panker 2002). Furthermore, analytical capabilities and the types of analyses used have been identified as essential components of CRM (Jackson 2005, Roberts et al. 2005, Alvarez 2006). These results show that a good understanding of various CRM analysis types, including a reference taxonomy on those analyses and business questions, and their impact on data warehouse design decisions could significantly improve the success of CRM processes. While it is clear that the design of the CRM data warehouse model contributes to the success or failure of CRM, there are no agreed upon standardized rules for how to design a data warehouse to support CRM and how to effectively use CRM technologies. Thus, the ultimate long-term purpose of our study is to systematically examine CRM factors that affect design decisions for CRM data warehouses and to build a taxonomy of CRM



Customer Relationship Management (CRM) is a strategy that supports an organization’s decision-making process to retain long-term and profitable relationships with its customers. Some define CRM as merely a business strategy (Jackson 2005), while others define it as a datadriven approach to assess customers’ current needs and profitability (Fitzgibbon &...
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