رویکرد مبتنی بر هستی شناسی داده کاوی اجراشده برای بازاریابی ورزشی

Since sport marketing is a commercial activity, precise customer and marketing segmentation must be investigated frequently and it would help to know the sport market after a specific customer profile, segmentation, or pattern come with marketing activities has found. Such knowledge would not only help sport firms, but would also contribute to the broader field of sport customer behavior and marketing. This paper proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer knowledge from the database. Knowledge extracted from data mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to the case firm, Taiwan Adidas, for possible product promotion and sport marketing.

Sport marketing involves all activities that purport to satisfy the demand and desire of sport customers through the procedure of exchange (Mullin, Hardy, & Sutton, 1993). Owing to increased advertising, media broadcasting, promotion and endorsements, organized sport is no longer only a sport but a business as well. Sport marketing is the most obvious form of commercialization in sport and yet sport events and marketing patterns have attracted comparatively little academic attention (Polley, 1998). Although sport marketing research has been carried out on areas such as sport tourism (Funk and Bruun, 2007, Morgan, 2007 and Kim and Chalip, 2004), sport Internet marketing (Beech, Chadwick, & Tapp, 2000), sport event sponsorship (Mason & Cochetel, 2006), and hedonic consumption (Hightower, Michael, & Thomas, 2002), however; a few research has been found on the specific segmentation of customer profile of sport products, events, and endorsers with media broadcasting for possible sport marketing. Since sport marketing is a commercial activity, precise customer and marketing segmentation must be investigated frequently and it would help to know the sport market after a specific customer profile, segmentation, or pattern come with marketing activities has found. Such knowledge would not only help sport firms, but would also contribute to the broader field of sport customer behavior and marketing. On the other hand, ontology was first developed in Artificial Intelligence (AI) to facilitate knowledge sharing and reuse, and had gained wide popularity in the early 1990s in several field applications, such as knowledge engineering, natural language processing, and knowledge representation. Since then, its popularity has extended to more and more research fields. Nowadays, ontology is also a popular research topic in knowledge management, cooperative information systems, electronic commerce, information retrieval, intelligent information integration and medicine, among others (Corcho et al., 2003, Davies and Fensel, 2003 and Fensel, 2001). The term ontology in philosophy refers to the theory about the nature of existence, while in computer science; it is a term referring to all the core concepts, including their terms, attributes, values, and relationships that belong to a specified knowledge domain. Ontology has become increasingly popular because it promises a shared and common understanding of knowledge domains that can be communicated between people and application systems. This article investigates the potential contextual relationships between customers, purchase behavior, sport events, and marketing activities using ontology. To achieve such, this study designs the questionnaire and constructs a data base for further data mining. In addition, as an enterprise asset, the customer occupies an important position. Most of the parties involved in marketing such as customer profile, advertising, media broadcasting, promotion, and endorsements are aware of the importance and need for marketers to acquire and share better knowledge of their customers. However, this is easier said than done since customer knowledge is concealed in customers. It is available but difficult to access, and there is little possibility to explore the full volume of data that should be collected for its potential value. Thus, how to effectively process and use data is becoming increasingly important. This calls for new techniques to help analyze, understand or even visualize the huge amounts of stored data gathered from business and scientific applications (Liao et al., 2004). Among the new techniques developed, data mining is the process of discovering significant knowledge, such as patterns, associations, changes, anomalies and significant structures from large amounts of data stored in databases, data warehouses, or other information repositories (Keim et al., 2004 and Liao and Chen et al., 2008). In the literature, there are many data mining models such as classification, estimation, predictive modeling, clustering/segmentation, affinity grouping or association rules, description and visualization, as well as sequential modeling. Similarly, there are also many application methods, including association rule, sequential pattern, grouping analysis, classification analysis and probability heuristic analysis (Anita and Dirk, 2005, Arie and Sterling, 2006, Goodwin et al., 2003, Liao, 2005, Liao and Hsieh et al., 2008, Mehta and Bhattacharyya, 2004 and Musaev, 2004). Knowledge of customers extracted through data mining can be integrated with customer profile, sport events, and marketing knowledge from research and then provided to sport firms. Accordingly, this paper investigates the following research issues on sport marketing of Adidas in Taiwan. They are (1) generating an sport ontology about sports products, consumers, the case firm – Adidas in Taiwan, advertising media, and endorsers; and at the same time, constructing a relational database and design a questionnaire according to this ontology, (2) collecting information using questionnaire to construct a physical database for recording consumer basic data, consumer behavior, media preference, brand awareness and endorser data, (3) implementing ontology-based data mining approach to acquire customer information, and (4) investigating possible customer profile and marketing segmentation using association rules and cluster analysis, and (5) understanding customer knowledge and making useful suggestions for sport marketing of the case firm. The rest of this paper is organized as follows. In Section 2, we present the background of the case firm – Taiwan Adidas. Section 3 describes the methodology, including research framework, ontology design and development, sport marketing ontology, system architecture, database design, questionnaire design and collection, association rules and cluster analysis, and data mining tool – SPSS Clementine. Section 4 illustrates the initial analysis results. Section 5 presents the marketing strategy analysis, including associations of different customer clusters. Section 6 discusses research findings, including knowledge map for sport marketing, marketing map for media and endorser, and possible marketing patterns. Finally, Section 7 contains a brief conclusion.

Customers’ needs and wants are sensitive and complicated. If an enterprise can understand them, makes efforts to fulfill their wants and provides friendly service, then the customers will be more supportive to the enterprise. In addition, as a result, sport firms have the responsibility to develop products that fulfill the customers’ needs and wants and find the customer profile and segmentation of sport events and marketing activities, as this will increase competitiveness and lead to higher profits and customer loyalty as the brand equity. In addition, once customer knowledge is extracted from market, sport marketing can identify the specific needs of groups of customers and develop the right offer for one or more market segments through promotion and advertising. By doing so, sport business can respond to differing preferences, attributes, desires, media, and endorser of customers for more precise satisfaction of their varying wants with marketing activities. This paper proposes using the Apriori algorithm of association rules, and clustering analysis based on an ontology-based data mining approach, for mining customer knowledge from the database. Knowledge extracted from data mining results is illustrated as knowledge patterns, rules, and maps in order to propose suggestions and solutions to Taiwan Adidas for possible product promotion and sport marketing.