توزیع و گسترش مدیریت اطلاعات ارتباط با مشتری (CRM) در محیط کسب و کار به کسب و کار
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|21125||2013||7 صفحه PDF||سفارش دهید||6150 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Industrial Marketing Management, Volume 42, Issue 6, August 2013, Pages 855–861
CRM data is among the most important and comprehensive information available to management in many organizations. This is particularly the case in business-to-business marketing, where the firm's extended working relationship with its customers is frequently crucial for the maintenance of a healthy business. However, in many instances management has treated CRM data as highly specific to its client relationships and has therefore neglected to analyze this information across market segments, customer categories, and customer–firm relationship forms in order to draw meaningful conclusions for driving business decisions. The authors present a method for effectively classifying CRM information in ways that may help guide management decisions. This can lead to an improved understanding of the in-forming process in the firm's dealings with its clients, the directionality of customer–firm decision-making, the key decision drivers across deals, and the historical record of the firm's relationship with its customers. The end result will be an improved use of valuable historical information for establishing competitive strategies and the related programs and policies for retaining and growing the firm's customer base and other elements of the firm's value delivery network.
The past four decades have seen a rapid increase in the volatility and mutability of many markets (Achrol, 1991). This is reflected in shorter product life-cycles, lower barriers to competitor entry, and globalized markets (Crotts, Dickson, & Ford, 2005). These developments have spurred similarly dramatic changes in the ways managers have sought to understand changing demand conditions and competitive dynamics. One major area of focus is data mining, the search for patterns in existing customer and financial databases (Hand, Mannila, & Smyth, 2001). Many organizations struggle with the strategic application of customer databases, particularly in business-to-business (B2B) settings. Although customer data contains potentially valuable information, it appears that few firms have the skills and resources to consistently exploit this value. The rapid acceptance and growth of customer relationship management (CRM) systems during the past two decades raises the opportunity within many firms to utilize this data consistently over time to secure competitive advantages (Eichorn, 2004). In particular, B2B CRM data, the information collected for the express purpose of guiding sales and sales support personnel in creating value for commercial and industrial customers, may be applied across the organization for informing the entire value creation process from marketing research to design to logistics and through distribution and sales (Berger & Nasr, 1998). Several strategies are available to the firm for improving its internal knowledge management by consistently utilizing its CRM databases to inform management decisions and to continually control marketing processes. Unfortunately, few firms consistently and effectively apply their CRM data to create value for decision-making at the executive level of the organization. Rather, CRM data is structured and developed to provide tactical guidance for managing individual customers and individual sales opportunities. This is entirely different from the cross-sectional and longitudinal data required by senior management to guide the development of new value propositions and the tailoring of marketing programs for key target market segments. However, there are ways in which the extensive granular data provided by CRM systems can be effectively utilized to provide managers with continuously updated and actionable information for introducing new customer solutions and maintaining the firm's long-term marketing effectiveness.