Review of CRM, KDD and Relationship Marketing

Satisfying customers and surviving in today's fast moving marketplaces requires: a brain to understand, learn, and analyze customers; a heart to pump and flow out the customeroriented blood throughout the organizational arteries; and hands and feet to deliver the services to the customers. Accordingly, three main themes for serving customers have been introduced in the business world: CRM Technology, KDD, and Relationship Marketing. KDD provides the processes to collect, analyze, and interpret data about customers - it constitutes the 'brain' of the customer service process. Relationship Marketing highlights the importance of customer orientation and a good relationship with customers - it constitutes the empathic 'heart' of the service activities. CRM technology provides the muscular hands and feet, the ' limbs ' that can help a firm understand customers and improve both the process related to serve customers and the interaction with customers. Some aspects of CRM technology also add cerebral muscle to the 'customer service brain.' Following are reviews of the trio in some detail.

CRM: Technologies and Approaches

What is CRM?

To define CRM, we need to ask first what the C - the customer part of CRM - represents. Broadly viewed , the notion of a customer includes suppliers, buyers , consumers, and even employees as 'internal customers' - as in the case of call center agents in My Twin (Gamble, 1999). For the purposes of this chapter, however, the definition of customer is limited to buyers of the products and services of the firm. Having narrowed the focus of the term 'customer' to the product/service buyer, understanding what is CRM and what elements constitute CRM are the next steps.

CRM connotes various things to different people in the varied contexts (Goodhue, Wixom, and Watson, 2002; Winer, 2001; Wright, Stone, and Abbott, 2002). CRM systems, therefore, are implemented in a multiple ways. In some cases, CRM simply entails direct emails or database marketing. In other contexts, CRM refers to OLAP (online analytical processing) and CICs (customer interaction centers). Wright (2002) argued that the understanding of definitions such as 'customer retention' and ' cross-selling ' and their application in practice is often weak (Wright, Stone, and Abbott, 2002). Even though the definition of CRM is not consistent across researchers, based on the review of previous frameworks of CRM, three core dimensions characterize a buyer-focused CRM system:

• * Customers are at the center of any CRM system (CMO, 2002; Gamble, Stone and Woodcock, 2002; Greenberg, 2002; Newell, 2003)

• * Management's articulation and tracking of customer relationship goals, plans, and metrics is an essential CRM component (Ang and Buttle, 2002; Greenberg, 2002)

• * Technologies for facilitating collaborative, operational, and analytical CRM activities are the visible 'limbs' of CRM (Goodhue, Wixom, and Watson, 2002)

First, the raison d' tre of any CRM system is the customer. This was obviously the case at My Twin, American Airlines, and Wells Fargo. Customer service and related issues must be included in the design, implementation, and operation of any CRM system. Davids (1999) emphasized that viewing CRM solely as a sales or customer service solution is a recipe for failure: organizations benefit from CRM only when they benefit their customers. CRM software needs to pay attention to not only internal users within the implementing organization, but also to the end customer (Earl, 2003). While enhancing the operational efficiency of the organization is an important goal of CRM technology, servicing and delighting the customers are the ultimate end-goals as well as the ultimate determinants of success. The narratives about My Twin, American Airlines, and Wells Fargo showed that customer- facing agents (as well as backend people such as Web designers, data analysts, and order fulfillment personnel) gained efficiencies and at the same time increased their prospects of delighting the customers.

Second, since CRM is usually seen as an organization-wide strategy (Ang and Buttle, 2002), various management levels should be included in any understanding of CRM. Starting from the corporate level goals, strategies should be established to accomplish such goals. Additionally, these strategies have to be followed by specific plans and the performance of these plans has to be tracked and evaluated thoroughly. CRM projects have to take organizational-level goals and strategies as the starting points because it is these goals, strategies, and plans that reflect the corporate philosophy regarding customer orientation and inculcate a customer- responsive corporate culture among the front as well as backend employees.

Third and finally, in terms of its technological structure, CRM contains analytical CRM systems, operational CRM systems, and collaborative CRM systems. We turn to these aspects of the technological structure next.

Technological Structure of CRM

1. Analytical CRM: Analytical CRM systems help a firm to analyze the huge amount of customer data in order to detect valuable patterns of customers' purchasing behavior. A basic element of analytical CRM systems is a data warehouse or a customer database. A data warehouse typically maintains historical data that supports generic applications such as reporting, queries, online analytical processing (OLAP), and data mining as well as specific applications such as campaign management, churn analysis, propensity scoring, and customer profitability (Goodhue, Wixom, and Watson, 2002).

   As a tool to analyze CRM-related customer data, data mining and knowledge discovery in databases (KDD) have received considerable attention (Mackinnon, 1999; Fayyad, Piatetsky-Shapiro, and Smyth, 1996). Systematic combining of data mining and knowledge management techniques can be the basis for advantageous customer relationships (Shaw et al., 2001). Data mining is often defined as the process of searching and analyzing data to unearth deeply embedded but potentially valuable information (Shaw et al., 2001).

   Data mining methods allow marketers to understand better their customers from the growing volumes of data. Kim, Kim, and Lee (2002) point out that companies are eager to learn about their customers by using data mining technologies, but because of diverse situations of such companies, it is very difficult to choose the most effective algorithm for the given problems. Shaw et al. (2001) introduced three major areas of application of data mining for knowledge-based marketing: (1) customer profiling, (2) deviation analysis, and (3) trend analysis.

2. Operational CRM systems: Operational CRM technology refers to the systems that start from ordering and go up to the step of delivering the product to the customers. Data must be captured from the inbound and outbound touchpoints, including the Web site, call centers, and stores (Goodhue, Wixom, and Watson, 2002). CRM vendors have developed products that enable automation of selling, marketing, and service functions.

   Integration of diverse business functions is critical for operational CRM systems. Since the sales process depends on the cooperation of multiple departments performing different functions, the systems to support the business process must be configurable to meet the needs of each department (Earl, 2003; Greenberg, 2002). It is important, therefore, for the organization to review its business process before installing such systems.

3. Collaborative CRM: Collaborative CRM systems refer to any CRM function that provides a point of interaction between the customer and the channel itself (Greenberg, 2002). The Web, call centers, stores, and ATMs can be seen as inbound touchpoints while e-mail, direct mail, telemarketing, and mobile devices can be seen as outbound touchpoints (Goodhue, Wixom, and Watson, 2002).

   Even before the Internet arrived, companies were under pressure to serve their customers with multiple and varied channels. With the advent of the Web-based Internet, Web sites became popular channels to touch the customers. Johnson (2002), however, cautions that companies should employ the Internet creatively to ensure that the technology enhances all their other channels. Companies also have to skillfully manage potential channel conflict in ways that allow electronic and physical channels to complement one another (Johnson, 2002).

KDD: Techniques and Challenges

With improving technologies of information collection, transmission, processing and storage, companies have access to timely , valid, and reliable information for solving important customer relationship problems (Moorman, 1992). Reliable and inexpensive hardware and database technologies allow efficient data storage and access (Fayyad, Piatetsky-Shapiro, and Smyth, 1996). The Web has become an important and convenient new channel for promotion, transactions, and business process coordination; as well as a source of customer data (Shaw et al., 2001). Huge warehouses of customer data exist.

Competitive pressures are compelling companies to seek insightful customer knowledge (Kim, Kim, and Lee, 2002) for CRM purposes and e-commerce.

Valuable marketing insights about customer characteristics and their purchase patterns, however, are often hidden and untapped (Shaw et al., 2001). New computational techniques and tools are emerging for extraction of such valuable knowledge from the rapidly growing volumes of data. It is increasingly critical for companies to be acquainted with what, when, and how to use such data and tools.

As tools for analyzing CRM-related customer data, data mining and knowledge discovery in databases (KDD) have received the most attention (Mackinnon, 1999; Fayyad, Piatetsky-Shapiro, and Smyth, 1996). As the American Airlines case study showed, the new CRM system with KDD capabilities allowed the identification of small groups of promising prospects and allowed the company to try out test offers.

In the context of the Web, Jackson (2002) divides data mining approaches into three distinct categories: Web content mining, Web structure mining, and Web usage mining. Web usage mining is also referred to as clickstream analysis (Edelstein, 2001). Hidden in the clickstream data available to many e-commerce sites is precious information that can provide sharp diagnostics and accurate forecasts, allowing e-commerce sites to profitably target and market to their customers (Moe, 2001). Online analytical processing (OLAP) refers to the various types of query-driven analysis for analyzing stored data (Berry & Linoff, 1997). Data mining and OLAP can be seen as complementary tools (Jackson, 2002).

Shaw et al. (2001) pointed out, however, that the multiple data formats and distributed nature of the knowledge on the Web make it a challenge to collect, discover, organize and manage such knowledge. They proposed knowledge integration as the solution. They argued that ownership and access to the marketing knowledge, standards of knowledge interchange, and sharing of applications become critical success factors. Especially for effective customer-centric marketing strategies, the discovered knowledge has to be managed in a systematic manner.

Relationship Marketing: Concepts and Modes

The importance and benefits of a customer relationship have been long recognized (Kotler, 1997; Reichheld and Sasser, 1990). Customer retention costs are lower than customer acquisition costs by factors of five to seven (Kotler, 1997), and a company can improve profits by 25% to 85% simply by reducing customer defections by a mere 5% (Reichheld and Sasser, 1990). By assessing each customer individually and making a determination of whether to serve that customer directly or via a third party, and whether to create an offering that customizes the product or standardizes the offering, customer- centric marketing could lead to customers and firms co-creating products, pricing, and distribution (Sheth, Sisodia, and Sharma, 2000). Furthermore, in interactive marketing contexts, customers have the ability to 'block out,' mentally or technologically, those marketing actions that they deem as intrusive and unwarranted. Therefore, customer- centric marketing in such contexts has to be driven by appropriate depth and width of 'permissions' obtained from the customers (Godin, 1999; Krishnamurthy, 2001).

The main idea of relationship marketing is for companies to get to know their customers more intimately by understanding their preferences and thus increasing the chances of retaining them (Dyche, 2002).

The advent of new information technologies, including the Internet, offers amazing possibilities for creating and sustaining ideal, highly satisfying customer relationships (Goodhue, Wixom, & Watson, 2002; Ives, 1990). With help of such technologies, database marketing and one-to-one marketing methods have come to the fore. The strategic goal of database marketing is to use collected information to identify customers and prospects as individuals and build continuingly personalized relationships with them, leading to greater benefits for the individuals and greater profits for the corporation (Kahan, 1998).

Database marketing anticipates customer behavior over time and reacts to changes in the customer's behavior. Database marketing identifies unique segments in the database reacting to specific stimuli such as promotions (McKim, 2002).

One-to-one marketing has received increasing attention from academics and practitioners . It represents the ultimate expression of target marketing - market segments with just one member each - or at least one at a time (Pitta, 1998). One-to-one means not only communicating with customers as individuals, but also developing custom products and tailored messages based on customers' unspoken needs. It relies on two-way communications between a company and its customers to enhance a true relationship and allows customers to articulate the desires that the company can help fulfill (Dyche, 2002).

A promising solution to implementing one-to-one marketing is the application of data mining techniques aided by information technology. Data mining allows organizations to find patterns within their internal customer data. Whatever patterns are uncovered can lead to target segmentations. Armed with such information, organizations can refine their targets and develop their technology to achieve true one-to-one marketing (Pitta, 1998).

As an extension of one-to-one marketing, the concept of permission marketing is focused on seeking customers' agreement about desired marketing methods (Godin, 1999; Krishnamurthy, 2001). Customers not only need to be approached as individuals, but they themselves should be able to stipulate how and when they wish to be approached (Newell, 2003). Permission marketing is focused on such methods of communication (Godin, 1999; Krishnamurthy, 2001). Along with the idea of permission marketing, customer-centric 'co-creation' marketing (Sheth, Sisodia, and Sharma, 2000) can be viewed as a larger concept that emphasizes the empowerment of customers to aid in product creation, pricing, distribution and fulfillment, and communication.

One-to-one marketing, permission marketing, and co-creation marketing rely heavily on information technology to track individual customers, understand their differences, and acknowledge their interaction preferences (Dyche, 2002). Such methods are intertwining increasingly with KDD and data mining techniques.