استخراج دانش مشتری برای توسعه محصول جدید گردشگری و مدیریت ارتباط با مشتری

In recent years tourism has become one of the fastest growing sectors of the world economy and is widely recognized for its contribution to regional and national economic development. Tourism product design and development have become important activities in many areas/countries as a growing source of foreign and domestic earnings. On the other hand, customer relationship management is a competitive strategy that businesses need in order to stay focused on the needs of their customers and to integrate a customer-oriented approach throughout the organization. Thus, this paper uses the Apriori algorithm as a methodology for association rules and clustering analysis for data mining, which is implemented for mining customer knowledge from the case firm, Phoenix Tours International, in Taiwan. Knowledge extraction from data mining results is illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to the case firm for new product development and customer relationship management.

In recent years tourism has become one of the fastest growing sectors of the world economy and is widely recognized for its contribution to regional and national economic development. Tourism product design and development have become important activities in many areas/countries as a growing source of foreign and domestic earnings. In this regard, marketing decisions and strategic planning of tourism new product development require knowledge of factors, attributes, patterns of customer demand and market supply affecting destination choice, customer preference/capability, product characteristic, type of trips and forecast of tourism flows in the short and long term. Thus, it can be suggested that the purpose of the study of tourism new product development (NPD) is to improve the ability to estimate and/or forecast and understand travel behavior, traveler satisfaction, and tourism management (Bramwell, 1998 and Witt and Witt, 1995). In addition, an important objective of tourism product demand and development analysis is to improve the understanding of public behavior towards particular customer purchases profiles and patterns. It is, therefore, interesting to know how customers select their tourism products and investigates which factors and attributes are determining their choices become important sources not only understand the demand of tourism but also investigate the segmentation of possible tourism product development (Seddighi & Theocharous, 2002). Customer relationship management (CRM) is the key competitive strategy businesses need to stay focused on the needs of the customers and to integrate a customer facing approach throughout the organization. By using information and communication technology, businesses are trying to get closer to the customer so that they can create long-term relationships in tourism industry (Sevki & Rifat, 2006). Customer relationship management refers to all business activities directed towards initiating, establishing, maintaining, and developing successful long-term relational exchanges and it is the set of methodologies and tools that help an enterprise manage customer relationships in an organized way (Lawson-Body & Limayem, 2004). As customers and businesses interact more frequently, businesses will have to leverage CRM and related technologies to capture and analyze massive amounts of customer information. Because, information and communication technology allows customer data to be collected, consolidated, manipulated, and analyzed on an unprecedented scale. However, CRM demands more than information and communication technology. The customer must become the focal point of the organization. All members of the organization must understand and support the shared values required for CRM (Piccoli, O’connor, Capaccioli, & Alvarez, 2003). In addition, most of the parties involved in the product design and development, such as the tourism suppliers and retailers, are aware of the importance and need for tourism firms to acquire and share better customer knowledge. But this is easier to say than done since customers’ knowledge is concealed within the customers. It is available but not accessible, and there is little possibility of exploring the full volume of data that should be collected for its potential value. Therefore, how to effectively process and use customer 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 & Chen, 2004). Among the new techniques developed, data mining is the process of discovering significant customer knowledge, such as patterns, associations, changes, and significant structures from large amounts of data stored in databases (Liao et al., 2008 and Liao et al., 2008). Customer knowledge extracted through data mining can be integrated with product and marketing knowledge from research and can be provided to up tourism stream suppliers as well as downstream retailers. Thus it can serve as a reference for product development, product promotion and customer relationship management. In terms of integrating data mining approach and tourism, Wickramasinghe, Amarasiri, and Alahakoon (2004) proposed an approach that integrates traditional mathematical, data mining, and evolutionary techniques with a multi-agent system. It is implemented as a travel optimizer application for the e-tourism domain. Law, Bauer, Weber, and Tse (2006) developed a rough sets based model that can capture the essential information from business travelers. In addition, Junping, Min, and Xuyan (2008) introduced the concept of the holiday tourism information data mining, which improves a distributed sampling association rule mining algorithm: DS-ARM, define the realization process of the algorithm, test the capability of the algorithm, and use the algorithm in the analysis of the holiday traveler destination traveling behavior. However, a few research considered the integration of data mining and tourism problem on new product development and customer relationship management. On the other hand, map display is a powerful tool with the ability to convey a large amount of information in a limited space, and it also provides an interactive tool to allow the user to interact with the underlying information (Lin, 1997). Thus, the mapping approach, which focuses on the use of IT, can be used as a tool to support new product development. Holmlund and Strandvik (1999) proposed perception configuration as a new concept, and introduced configuration maps as tools for analyzing perceptions in business relationship studies. Tülin and Russell (1998) presented market maps with a probabilistic spatial panel data model that allows the positions of products sharing the same name to be correlated across product categories. In a business setting, the combination of perceptions by two parties (such as buyers and sellers) can be represented as a perception configuration. All the perceptions are depicted on the horizontal and vertical axes of the map. This map can be used to capture both the composition and the dynamics of perception configurations, and it is generically applicable to dyadic perception studies. Daniel, Wilson, and McDonald (2003) utilized a marketing map to represent the best practice in marketing and also used the process map to understand how IT can be deployed in order to support a marketing information system. In addition, marketing map and product map are designed and implemented on business alliances and new product development (Liao, Chang, & Lee, 2008). Thus, the map mainly illustrates the links between various stages of the planning and marketing process (Liao et al., 2009 and Liao and Wen, 2009). Based on this concept, this study implements a knowledge map to illustrate that new product development and customer relationship management are essentially the function that matches the customer profile and product segmentation. Accordingly, this paper investigates the following research issues in a Taiwan tourism firm: What exactly are the customers’ profiles for tourism market? Are tourism knowledge of the customers and the product itself reflected in the needs and wants of the market? Can tourism product design and planning for product mix be developed according to the knowledge of customers? Can the knowledge of customers be transformed into knowledge assets of the case firm for new product development and customer relationship management? In addition, regarding the marketing methods, the direct marketing model can also be considered to ensure that the products developed are customer-oriented after customer/product patterns and segmentations been found. Clustering analysis and the Apriori algorithm are methodologies for data mining, which is implemented to mine knowledge from customers for NPD and CRM. The knowledge extracted from data mining results is illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to the case firm. The rest of this paper is organized as follows. In Section 2, we present the background of the current life insurance market in Taiwan. Section 3 introduces the proposed data mining system, which includes system framework, and physical database design. Section 4 presents the data mining process, including clustering analysis, Apriori algorithm, knowledge extraction process, and result analysis for NPD and marketing. Managerial implications are presented in Section 5; and Section 6 presents a brief conclusion.

Customers’ needs and wants are a sensitive and complicated, if a firm can understand them and make efforts to fulfill their wants and provide friendly service then the customer will be more supportive to the enterprise. During the process of developing from the product concept to the actual product itself, the customer can only passively receive new information, and can only select from the products that are currently on sale in the market. No matter which type of product, the customer cannot individually come up with a product concept and then develop it. Furthermore, buying what is available on the market does not mean that customers are satisfied with the current product, because the customers’ preferences and experiences were not considered in developing the product so they can only accept the product as it is. As a result, tourism firm has responsibility to develop products that fulfill the customers’ needs and wants, as this will increase the tourism firm’s competitiveness and it is an essential criterion to earning higher profits. This paper proposes Apriori algorithm as a methodology of association rule and clustering analysis for data mining, which is implemented for mining customer knowledge from the case firm. Knowledge extraction from data mining results is illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to the case firm for NPD and CRM.