The foundation of eCRM is the application of traditional CRM methodologies, techniques, and tools to data that is garnered via electronic commerce as opposed to traditional channels of distribu-
tion. Traditional CRM may be defined as a process that balances the use of corporate resources with the satisfaction of customer needs. Traditional CRM looks at outputs in terms of revenues and profits while taking customer value and motivation into account (Shaw 1999; Gebert et al. 2003).
Information technology is integral to successful application of CRM, and the definition of CRM may be extended to incorporate the significance of Internet-based technology in manag- ing customer relationships. Plakoyiannaki and Tzokas (2002) formulate a model of the CRM process that revolves around this extended definition. They define the following tasks for the CRM process:
- Creating a corporate culture conducive to customer orientation, learning, and innovations
- Making customer value a key component of the corporate strategy and planning process
- Collecting and transforming customer data to aid strategic and operational decision making
- Appreciating, identifying, and nurturing knowledge creation, dissemination, and use within the organization
- Developing clear market segments and customer portfolios
- Defining, developing, and delivering the value proposition
- Using campaign and channel management as part of the value proposition
- Measuring performance at each stage of the process to navigate decision
eCRM accomplishes these same tasks with the benefit of electronically gathered information, and in such a way as to tailor the service level specific to each customer (Romano and Fjermestad 2003). For example, logistical services may be tailored to better meet customer needs in profitable fashion, as outlined in Fuller, O’Conor, and Rawlinson (1993). In this pre-Internet article, the authors address the challenges of providing a level of service appropriate to the need of “logisti- cally distinct businesses,” which serve to cluster customers into categories so that the service creation can be provided most efficiently. In pre-Internet days, customers would be segmented by logistics requirements, followed by the establishment of a service standard for each segment, and a reconfiguration of the logistics pipelines so that each segment could be served efficiently, ac- cording to its newly identified and specially tailored level of service. Fuller et al. (1993) provide a telecom equipment manufacturer example. It highlights customers with distinctly different needs, including one who needs components for new system installation to be delivered as a complete order. The telecom manufacturer’s use of a series of eight variables for segmenting products may
disaggregate its customers into some 384 market “buckets.”
The steps identified previously encompass a vision of the CRM process and represent a foun- dation on which to define eCRM. Gurau et al. (2003) propose descriptions of the transition from traditional forms of media associated with CRM to eCRM-based ones. eCRM involves the collec- tion and mining of data involving online purchases and relationships. In many ways it can be thought of as a necessary tool for conducting SCM, as will be detailed in the next section. eCRM also involves using the knowledge gained to improve customer loyalty, expand sales, and im- prove customer service.
CRM and eCRM represent “relationship marketing,” in direct contrast to traditional “market- ing mix” approaches known as “transaction marketing.” Gonroos (1994) defines transaction mar- keting to include minimal customer contact, while relationship marketing utilizes a broader customer interface. At the heart of traditional CRM is the collection of customer satisfaction data and other key business performance-related data. Business decisions must be supported by knowl- edge gleaned from processed sales transaction and related data. Benefits from CRM are realized only when key decisions are influenced by this knowledge store. Bose and Sugumaran (2003) put
forth that true CRM is possible only through the integration of knowledge management systems and traditional customer tracking systems. “We observe in practice that customer relationship management and knowledge management have a considerable synergy potential. . . . While KM acts as a service provider for CRM, the interdependencies and mutual benefits between the two approaches result in a merger of equals” (Gebert et al. 2002).
Two main types of e-CRM exist: operational and analytical (Dyche 2001; Fjermestad and Romano 2003). Operational eCRM involves actual contact with the customer through electronic means such as an online Web form or fax. The processing of data collected through operational eCRM is analytical eCRM. This involves many of the same techniques as traditional CRM, such as data mining, to glean valuable information about current and potential customers. The defini- tion of the “e” in eCRM does not limit the data collection and processing to the Internet. By definition, any electronic contact with a customer through which data can be gathered for further analysis can be considered a form of eCRM.
eCRM has the potential to take a quantum leap forward with the tremendous expansion of wireless networking across the globe. Third-generation cellular networks promise high-speed Internet connections, text messaging, and a host of other services such as location-based services which inform wireless users about goods and services available in their immediate area. Knowing the purchasing habits of customers and their cellular/wireless device numbers/network IDs can only lead to developing a more intimate relationship as they travel about. The ability to integrate location-based information about a company’s goods or services with traditional e-CRM prin- ciples has the potential to even create a new form of eCRM.
The notion of a personal network (Niemegeers and Heemstra de Groot 2002) that surrounds and moves with each wireless user may become reality in the not-so-distant future. Such a net- work revolves around the location of the user and his/her wants and needs for information and support services. A personal network is just an extension of the notion of a personal area network, where a wireless user interacts in an ad hoc fashion, using very short distance networking tech- nologies such as Bluetooth with local devices. The evolution of personal area networks into more generalized “personal networks” is an ongoing process, as more and more location-based ser- vices are introduced and as third-generation cellular networks and related wireless technologies proliferate. The ability of a company to conduct eCRM using personal networks is the challenge of tomorrow. A piece of information as simple as the location of a wireless user may provide an opportunity to market additional goods or services using online coupons or other means. There will be a delicate balance between conducting effective eCRM and creating Internet-like spam in this new form of eCRM. Shen and Lee (2000) suggest that people will feel unhappy if they get too many advertisements or useless messages after paying for these services; so transferring the proper message is the most important concern in this emerging area. Hackney, Ranchhod, and Hackney (2004) characterized the shift to wireless technologies for e-commerce as “U-commerce” or “ul- timate commerce.” They caution that such systems are impotent to the challenges of customer behavior, where technology alone will not provide for the perceived value proposition in com- mercial activities. Determining what kind of “message” to send wireless users will hinge on the type of data that is collected.
Once data is collected, the analytical part of eCRM must be undertaken in order to put it to use. Although data mining is not the focus of this chapter, its importance does warrant mention, since it provides the basis for the analytical part of eCRM. Without the ability to determine the wants and needs of customers (or potential customers), collected data is worthless in the context of CRM. Data mining involves the use of statistical software tools in order to determine patterns in data. These patterns are used to develop knowledge about customers and also provide the founda-
tion for the ultimate use of CRM: developing better relationships with customers. The use of such knowledge must be well planned and follow a set of predetermined process steps aimed at en- hancing customer interaction and sales.