دانش مشتری کاوی برای بررسی رفتار خرید گروهی آنلاین
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27520||2012||9 صفحه PDF||سفارش دهید||5694 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 39, Issue 3, 15 February 2012, Pages 3708–3716
Online group buying is an effective marketing method. By using online group buying, customers get unbelievable discounts on premium products and services. This not only meets customer demand, but also helps sellers to find new ways to sell products sales and open up new business models, all parties benefit in these transactions. During these bleak economic times, group buying has become extremely popular. Therefore, this study proposes a data mining approach for exploring online group buying behavior in Taiwan. Thus, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining, which is implemented for mining customer knowledge among online group buying customers in Taiwan. The results of knowledge extraction from data mining are illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to online group buying firms for future development.
In these tough economic times, it is not only important to find new sources of income but also to cut down on expenditures. By using online group buying, it is easy to find more people in a short period of time to share freight costs and to buy in bulk so as to lower prices. It is also easier to get bigger discounts when more people take part in a group purchase. On the other hand, online group buying is a model in which multiple buyers cooperate and buy the same good/service in order to bargain with the proprietor, if there are enough buyers, they may aggregate buyer power to get volume discounts. All parties benefit in these transactions. In the past, group buyers were limited to members of companies, friends, families or communities. They filled out the types of goods and quantity of items to be purchased on the flyers to order goods. As the internet develops, it is becoming an increasingly prosperous network for many types of commerce. In addition, the large shopping sites are familiar with online shopping markets and the follow-up group buying platforms are learning this market. They have created group buying discount zones and provided some incentives and discounts to attract more users to visit and buy. The more visitors that come, the more goods are exhibited and sold. Thus, the consumption patterns of group buying have become more active in online shopping markets. According to pollster online survey of group buying, the most preferred goods are foodstuffs, making up to 33%. The group buying shares of other items are: clothing and accessories 11%, cosmetics 8%, articles of daily use 5%, others 3% and home appliances 2%. However, 36% of people in Taiwan have yet to experience group buying (Market Intelligence & Consulting Institute in Taiwan, 2010). As online group buying (OGB) market increases and a great variety commodity stats become available, consumer demand is changing fast, implicitly shortening the product life cycle. Clear assessment of the overall sales strategies of internet group buying has a positive effect. However over-reliance can result in the cart coming before the horse. In 2009, as many as 50.3% of online stores did not break even, while 5.3% increased sales compared to last year in Taiwan. The reason is that business operation was impacted by the overall economy. More and more intense price wars led to descending overall profits (Market Intelligence & Consulting Institute in Taiwan, 2010). In addition, in electronic marketplaces, group buying is seen as an effective form of electronic commerce and a promising field. For example, Tokuro and Takayuki (2004) proposed using decision support systems for buyers in group buying. Their system supports buyers’ decision making by using the Analytic Hierarchy Process with three methods for group integration. First, buyers trade in simple group buying. Second, all buyers are integrated. Third, some buyers are integrated. Thus, buyers’ multi-attribute utilities are effectively expressed in group integration and buyers can purchase goods at a lower price. Buyers’ payments are decided based on their degree of compromise. Software agents can be useful in forming buyers’ groups since humans have considerable difficulties in finding Pareto-optimal deals (no buyer can be better without another being worse) in negotiation situations (Frederick & Brahim, 2006). The above study developed a negotiation protocol for software agents, which evaluated whether or not the problem is difficult on average and why. This protocol is probably able to find a Pareto-optimal solution and, furthermore, minimize the worst distance to the ideal among all software agents given strict preference ordering. In addition, Miguel and María (2009) explored the circumstances under which the retailers’ use of the buying group’s brand name may benefit them. Their research findings show that the retailer’s use of the buying group’s brand name is more capable of improving the retailer’s economic satisfaction with the buying group when differentiation is perceived to be a source of competitive advantage, when the environment is perceived as more dynamic and when the retailer is strategically integrated in the relationship with the buying group. However, only a few studies have explored online group buying behavior patterns and segments from customers. On the other hand, customers play an important role as business assets. Most of the parties involved in sales, such as the commercial web sites, retailers and channels, are aware of the need for businesses to acquire better customer knowledge. However, this is easier said 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. Inefficient utilization renders the data collected useless, causing databases to become ‘data dumps’ (Keim, Pansea, Sipsa, & Northb, 2004). Thus, finding ways to effectively process and use data is an artificial issue that 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 a 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). Customer knowledge extracted through data mining can be integrated with products and marketing knowledge from research and can be provided to up stream suppliers as well as downstream retailers. Thus, it can serve as a reference for product development, product promotion and customer relationship management. When effectively utilized, such knowledge extraction can enable enterprises to gain a competitive edge by producing customer-oriented goods that increase consumer satisfaction (Arie and Sterling, 2006, Liao et al., 2009, Liao et al., 2010, Liao et al., 2009, Liao et al., 2009, Liao et al., 2010 and Liao et al., 2008). Accordingly, this study investigates online group buying behavior, and implements data mining approach to analyze Taiwanese customers. There are two data mining stages implemented in this study. First, this study employs the k-means algorithm to cluster the customers into potential customers and target customers, and uses the Apriori algorithm to generate association rules for each cluster. The rules are proposed to the group buying firms to help them attain possible new customers, services and sales. The rest of this paper is organized as follows. Section 2 introduces the proposed data mining system, which includes the system framework, system design, and physical database design. Section 3 introduces the data mining approach, including the association rules and cluster analysis. Section 4 presents the data mining process and the analyzed results. Section 5 describes research findings, managerial implications. Finally, a brief conclusion is presented in Section 6.
نتیجه گیری انگلیسی
There are many stores investing in the online group buying market. However, only the minority really succeed. Only by creating unique commodities, having the competitive advantage in regard to price and quality and so on, can stores meet the customers’ needs and strive for each customer to repeat buying. In this way, stores can stand out and find the key to success. Thus, this study finds some online group buying behavior patterns, including customer purchase preferences and customer purchase demands, in order to generate different online group buying marketing alternatives for merchants. These research results will provide owners with some useful references to discover potential customers, develop latent business possibilities, maintain the loyalty of target customers, and earn higher profits with online group buying.