مدیریت ارتباط با مشتری در خرده فروشی: تجزیه و تحلیل محتوای مجلات خرده فروشی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|875||2007||6 صفحه PDF||سفارش دهید||1 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Retailing and Consumer Services, Volume 14, Issue 6, November 2007, Pages 394–399
The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement customer relationship management (CRM) in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify CRM strategies used by retail companies. A keyword search within business databases using CRM and CRM identified publications with CRM content. Content analysis was used on articles (N=149) drawn from Stores, Chain Store Age, Harvard Business Review, and Retail Forward over a 5 year period (2000–2005). Selected articles were stored as text files in QDA Miner, a computerized qualitative analysis tool. Key organization/infrastructure needs emerged focusing on data structure, organizational systems, technology structure, and data accessibility. Retailers goals/objectives and strategies focused on marketing, customer service, understanding customers through data analysis and increasing acquisition and retention through customer loyalty programs. Data mining tools identified supported marketing and customer analysis efforts. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.
Retailers face a dynamic and competitive retail environment. With increased globalization, market saturation, and increased competitiveness through mergers and acquisitions, retailers are seeking competitive advantages by better managing customer relations through database management. This is not a new concept but seeking competitive advantage through improving relationships with customers has taken on new life. “Companies recognize that customer relationships are the underlying tool for building customer value, and they are finally realizing that growing customer value is the key to increasing enterprise value” (Rogers, 2005, p. 262). Retail companies seek to maximize relationships with customers. Thus, a shift in organizational thinking is necessary as retailers embrace a “customer-centric” focus and implement strategies to support this focus. This shift in organizational culture challenges retailers to revise organizational systems and processes, identify customer-related metrics, and identify areas of strategic advantage. Organizational systems and processes, especially those related to data and information management, are changing to respond to this shift toward “customer-centric” retailing. To address this customer focus, discussions of data management and availability, data warehousing, and data mining are occurring at various levels within retail companies from the boardroom to the store management level. A clear shift toward data-based decision making is evident. In tandem with this shift toward customer focus and data-based decision making, customer relationship management (CRM) has emerged to allow retail companies to respond to shifting customer needs and wants using analytical tools in conjunction with their enterprise-wide databases. In a survey of 708 global executives, 82% of those surveyed planned to employ CRM in their companies (Rigby & Ledingham, 2004). A recent CRM retail survey conducted for the national retail federation (NRF) by Gartner Dataquest reported that “nearly two-thirds of retail companies expect to increase their spending for CRM technology during the next 2 years” ( p. 94). It was projected that by the year 2005, 92% of those surveyed would have CRM plans in place (Reda, 2003). Data mining technology can consolidate retail data, analyze and distribute data to users, capture data across multiple retail channels, and create “one view” of the customer. With the use of data mining tools, the plethora of data currently gathered and stored by retailers can be leveraged to gain customer and company insight to support CRM. With the dramatic changes in retail today, taking a customer-centric approach is necessary to stay competitive. Data mining has been defined as a statistical process of analyzing data stored in a data warehouse (Decker, 1998). A data warehouse is an extensive data repository consisting of information from all facets of an organization that is maintained to support decision making. Through data mining technology large databases can be explored to find relationships and trends previously unknown, to provide support for complex decisions. Retail databases often include such information as consumer shopping patterns and behavior, sales history, promotional information, inventory information, and pricing data. Empirical research on data mining applications in the retail industry is limited. Studies have focused primarily on the e-commerce sector. Lee et al. (2001) analyzed click stream data to study online shopping behavior as well as visualization and data mining analysis techniques to analyze the movement of customers through websites as a means to better understand online merchandising. Path analysis has been used to study web traffic (Berkin et al., 2001). Data mining has been explored in optimizing inventory levels for electronic commerce, to analyze product performance of online stores and to analyze web-based shopping systems (Dhond et al., 2000; Lee and Podlaseck, 2000; Arlitt et al., 2001). Data mining research related to “bricks and mortar” or store-based retailing is limited. Two store-based retail studies identified were focused on product selection and assortment (Brijs et al., 2000). Clearly, the research on the use of data mining to implement CRM in retailing is limited. Recent work in marketing suggests that paying attention to CRM can enhance firm performance. Cao and Gruca (2005) developed a cost-effective method for reducing adverse customer selection through CRM. The study resulted in a model for improved accuracy in new customer acquisition and more effective target marketing to increase customer lifetime value. Gustafsson et al. (2005) studied telecommunication services to examine the effects of customer satisfaction and behavior on customer retention. Results indicated a need for CRM managers to more accurately determine customer satisfaction in order to reduce customer churn. Jayachandran et al. (2005) conceptualized and measured organizational routines that are critical for CRM. Results identified ways to improve the use of CRM technologies to enhance firm performance. Lewis (2005) identified new measures for a more accurate assessment of customer lifetime value. Mithas et al. (2005) studied the effects of CRM initiatives showing that CRM efforts improve a firm's knowledge of their customers and in turn, improved customer satisfaction. They also determined that sharing CRM information with suppliers created gains in customer knowledge. Ryals (2005) found that CRM increases firm performance through the analysis of customer lifetime scores in two longitudinal case studies. Srinivasan and Morman (2005) analyzed the link between a firm's strategic commitments and the rewards of CRM initiatives. Thomas and Sullivan (2005) used case study analysis to develop an initial marketing communications strategy for the multi-channel retailer. Despite its apparent value, data mining and its application to CRM has not been systematically studied in the retail environment. Research on CRM and the use of data mining to support CRM is limited. The academic literature is virtually silent on this topic. In this emerging research area, current practice can provide insight for research and theory development. Thus, trade publications were chosen as a primary source of data since current retail practice is frequently reported in the trade literature. The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement CRM in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify customer relationship strategies used by retail companies. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.
نتیجه گیری انگلیسی
4.1. Linkage of CRM goals, objectives, and strategies It appears that the goals and objectives for CRM identified by retailers in the trade press were articulated into actionable strategies. Retailers indicated a desire to improve marketing effectiveness through the use of CRM as the most prevalent goal/objective. The strategy that emerged as most prevalent was the use of CRM for marketing. Retailers also indicated a desire to enhance customer loyalty through improving customer service as another goal/objective. An emerging strategy was using the results of CRM to identify and implement special customer services. Increasing customer acquisition and retention was also linked to increased customer loyalty. Customer loyalty programs were a major CRM strategy implemented by retailers. Finally, retailers indicated a desire to better understand their customers through customer analysis and the resulting strategy identified were the use of customer data analysis to facilitate this goal. 4.2. Data mining tools linked to CRM strategies It appears that a majority of the identified data mining tools could support marketing and customer analysis, the top two CRM strategies identified by retailers. Results in market basket and affinity analysis could allow for more targeted marketing efforts and help facilitate the identification of appropriate recipients of e-mail and e-coupon promotions. Customer profitability ratings allow retailers to identify their best and worst customers, better understanding customer segments and aligning strategies to enhance service options and marketing efforts to specific customer segments. Click stream analysis facilitates the understanding of online shopping behavior, a prominent customer analysis strategy (Dyche, 2002). Data mining tools to perform advanced customer analytics and predictive analytics also allow retailers to fulfill this strategy. It is difficult to determine what the outcome of using key performance indictors may be without speculation since no detail was given as to the specific indicators that were analyzed. Although mentioned only once in the articles analyzed, other data mining tools can be linked to strategic efforts. Abandoned shopping baskets, decision tree use for online shopping behavior analysis, and the use of conjoint analysis to gauge customers buying patterns are all ways to perform customer analysis. Studying customer migration patterns may provide some insight into customer loyalty, a CRM strategy, and linking transaction data to customer exit surveys may provide information to enhance the customer experience, also identified as a CRM strategy. 4.3. Gaps in the identified use of CRM in retailing Although it is interesting to discover CRM applications in goals/objectives, strategies, and the data mining tools retailers are using to facilitate these, uncovering areas where CRM is not reported as being used provides added value. Few retailers suggested CRM as a tool to look at segmentation of customers. Of the articles reviewed, only 7% identified segmentation as a goal/objective and 3% identified segmentation as a current CRM strategy. Product-related decisions using CRM information was also mentioned infrequently (2% of goals/objectives and 3% of strategies), pricing strategies and business planning issues were also lacking from the data. Retailers mentioned strategies involving pricing-related decisions in only 1% of the articles and business planning strategies in 3%. This indicates a lack of evidence of higher-level strategic uses reported in the retail trade press. Similarly, the data mining tools identified by retailers seem to focus on marketing and customer analytics, but there was limited evidence of use for creating and maintaining customer loyalty programs and special customer services, two prominent strategies identified by retailers. It appears that retailers are applying CRM to a limited range of problems and a limited range of strategies. 4.4. Emerging trends in retail data mining, CRM implementation, and future directions Based on data from four retail trade publications, this research identified emerging trends in organization/infrastructure requirements, goals/objectives, retail strategies and data mining tools for effective CRM. Four major organization/infrastructure concepts emerged from the data addressing the issues of data structure, organizational systems, technology structure, and data accessibility. Five major concepts emerged as prominent based on the content analysis results relating to retail industry goals/objectives. Those were improving marketing effectiveness, improving customer service, customer analysis, increasing acquisition and retention strategies, and improving tactics to grow or drive business. In the area of retail industry, strategies for CRM use, four major categories emerged; the use of marketing, customer data analysis efforts, customer loyalty programs, and special customer services. Overlap between goals and objectives and strategies exist in four areas, marketing, customer analysis, customer loyalty, and customer service. The identification of data mining tools to support these strategies was limited and focused on the categories of marketing and customer analysis. To further study CRM in retailing, empirical research is needed to identify how retailers of various types and sizes are actually using CRM. An evaluation of current retailers through surveys and in-depth case studies is suggested to determine if the CRM approaches identified in this study are representative of retail practice on a broader scale.