Data Mining in Franchising

Data Mining in Franchising

Ye-Sho Chen (Louisiana State University, USA), Grace Hua (Louisiana State University, USA) and Bob Justis (Louisiana State University, USA)
DOI: 10.4018/978-1-60566-026-4.ch148
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Abstract

Franchising has been a popular approach given the high rate of business failures (Justis & Judd, 2002; Thomas & Seid, 2000). Its popularity continues to increase, as we witness an emergence of a new business model, Netchising, which is the combination power of the Internet for global demand-andsupply processes and the international franchising arrangement for local responsiveness (Chen, Justis, & Yang, 2004). For example, Entrepreneur magazine—well known for its Franchise 500 listing—in 2001 included Tech Businesses into its Franchise Zone that contains Internet Businesses, Tech Training, and Miscellaneous Tech Businesses. At the time of this writing, 40 companies are on its list. Netchising is an effective global e-business growth strategy (Chen, Chen, & Wu, 2006), since it can “offer potentially huge benefits over traditional exporting or foreign direct investment approaches to globalization” and is “a powerful concept with potentially broad applications” (Davenport, 2000, p. 52). In his best seller, Business @ the Speed of Thought, Bill Gates (1999) wrote, “Information technology and business are becoming inextricably interwoven. I don’t think anybody can talk meaningfully about one without talking about the other” (p. 6). Gates’ point is quite true when one talks about data mining in franchise organizations. Despite its popularity as a global e-business growth strategy, there is no guarantee that the franchising business model will render continuous success in the hypercompetitive environment. This can be evidenced from the constant up-and-down ranking of the Franchise 500. Thus, to see how data mining can be “meaningfully” used in franchise organizations, one needs to know how franchising really works. In the next section, we show that (1) building up a good “family” relationship between the franchisor and the franchisee is the real essence of franchising, and (2) proven working knowledge is the foundation of the “family” relationship. We then discuss in the following three sections the process of how to make data mining “meaningful” in franchising. Finally, future trends of data mining in Netchising are briefly described.
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Franchising: The Franchisor/Franchisee Relationship

Franchising is “a business opportunity by which the owner (producer or distributor) of a service or a trademarked product grants exclusive rights to an individual for the local distribution and/or sale of the service or product, and in return receives a payment or royalty and conformance to quality standards. The individual or business granting the business rights is called the franchisor, and the individual or business granted the right to operate in accordance with the chosen method to produce or sell the product or service is called the franchisee” (Justis & Judd, 2002, pp. 1-3). Developing a good “family” relationship between the franchisor and the franchisee is the key aspect of a successful franchise (Justis & Judd, 2002). Figure 1 describes how such a “family” relationship is built in the franchise community.

Figure 1.

Understanding how the franchisor/franchisee “family” relationship works

Key Terms in this Chapter

Franchising: A business opportunity based on granting the business rights and collecting royalties in return.

Franchisor: The individual or business that grants the business rights.

Franchisor/Franchisee Learning Process: The stages of learning, including Beginner, Novice, Advanced, Master, and Professional.

Data Warehouse: A database that is subject-oriented, integrated, time-variant, and non-volatile.

Data Mart: A small database with data derived from a data warehouse.

Franchisee Life Cycle: The stages a franchisee goes through in the franchise system: courting, “we,” “me,” rebel, renewal.

Customer Service Life Cycle (CSLC): Serving customers based on a process of four stages: requirements, acquisition, ownership, and retirement. Many companies are using the approach to harness the Internet to serve the customers.

Franchisor/Franchisee Relationship Management: The vital factor for the success of a franchise, including knowledge, attitude, motivation, individual behavior, and group behavior.

Data Mining: Analytical techniques used to find out the hidden relationships or patterns residing in the organizational data.

Franchisee: The individual or business that receives the business rights and pays the royalties for using the rights.

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