数据挖掘技术在CRM中应用外文原文和译文

Customer Relationship Management And Applications of Data Mining Techniques In Business-to-Business Industry

Customer Relationship Management (CRM): The Concept

Firms today are becoming more aware of the fundamental changes of customer relationships and the need to implement new solutions and strategies that address these changes (Rygielski et el. 2002). And thus the concept of CRM has been introduced.

Definition of CRM:

CRM is an enterprise-wide business strategy designed to optimize profitability, revenue and customer satisfaction by organizing an enterprise around customer segments, fostering customer-satisfying behaviours and linking processes from customers through suppliers. (Collins 2001)

Functions of CRM:

CRM requires the firm to know and understand its markets and customers. This involves detailed customer intelligence in order to select the most profitable customers and identify those no longer worth targeting. CRM also entails development of the offer: which products to sell to which customers and through which channel. In selling, firms use campaign management to increase the marketing department’s effectiveness. Finally, CRM seeks to retain its customers through services such as call centres and help desks. (Rygielski et el. 2002)

Many practitioners recognise that keeping customers is more profitable than attracting new customers (Bitran and Mondschein, 1997). According to Srivastava et el. (2002), to acquire a new customer costs five to seven times more than to retain an existing one. Hence, many companies are adopting CRM as a means to develop and maintain successful customer relationship (Verhoef and Donkers, 2001). This generally accepted view on the motive of adopting CRM focuses more on maintaining the relationship of existing customers, not on acquiring new customers. However, acquiring new customers, which can be viewed as Customer Relationship Establishment (CRE), should form a part of Customer Relationship Management. My arguments are a) all the CRM activities are based on the acquisition of new customers, it is the premise of the CRM activities onwards, and b) to understand a potential customer’s need is as strategically important as to understand a current customer’s in terms of product design as well as after-sales service, and furthermore, c) the same theory and practise of CRM activities on a current customer can also be applied to a prospect, e.g. marketing segmentation on differentiating profitable (potential) customers from those non-profitable. Thus, marketing activities involving converting prospects to customers should also be included into the CRM domain.

CRM in Business-to-Business (B2B) Industry: The Necessity

CRM is not only applicable for managing relationships between businesses and consumers, but even more crucial for business customers. In B2B environments, transactions are more numerous, custom contracts are more diverse, and pricing schemes are more complicated. CRM strategies, such as customised catalogues, personalised business portals, and targeted product offers, can help smooth this process and improve efficiencies for both companies. (Rygielski et el. 2002)

From the respective of customer behaviour, Bush (2002) suggests that B2B buyers choose a supplier with whom they can develop a relationship; one they can go back to as required and one on which they feel they can depend. Once they have chosen a supplier, having invested this time and effort, they are more likely to stay with that supplier for longer. This invokes the equal importance of deploying CRM in both recruiting new customers and maintaining existing customers.

Data Mining Techniques: The Tool

CRM can be viewed from two perspectives. Operational CRM refers to the business strategy that focuses on the day-to-day management of the customer relationship across all points of customer contact and is enabled by sales and service technologies. Analytical CRM is the part of the CRM business strategy that drives increased customer intelligence and makes information actionable across all touchpoints. (Collins 2001) It encompasses a host of data mining applications (e.g., marketing, forecasting and budgeting) that enable companies to develop greater customer intelligence and accordingly customer-specific strategies. Analytical CRM will be the main theme running throughout the research/project.

The essence of CRM is understanding customer needs and leveraging that knowledge to improve a company’s long term profitability. It requires the alignment of three building blocks: insight into customer decision-making, information about customers, and information-processing capability. (Stringfellow, et el. 2004)

Recent developments in Information Technology (IT) have improved the information-processing capability dramatically. This along with the increasing availability of customer information, collected internally with continuous transaction records or bought from external sources, has created opportunities as well as challenges for companies to leverage the data and gain competitive advantage. Large amount of customer information is accessible in the databases, however, the knowledge hidden behind the data is not explicit and ready at hand. With respects to these conditions, the need to use data mining tools, which can help uncover the hidden insight of customer behaviours, has been raised.

Data mining is the process of searching and analysing data in order to find implicit, but potentially useful, information. It involves selecting, exploring and modelling large amounts of data to uncover previously unknown patterns, and ultimately comprehensive information from large databases (Shaw 2001). Data mining can be easily fitted into various business functions. Lets take my MSc summer project for example...... Based on the interplay between potential value and realised value, CRM/marketing managers can devise customer-specific strategies.

Reference:

Bitran, G.R. & Mondschein, S.V. (1997), ‘A Comparative Analysis of Decision Making

Procedures in The Catalog Sales Industry’, European Management Journal, 15 (2).

Bush, R. (2002), The Interactive and Direct Marketing Guide, Chapter 3.6, The Institute of Direct Marketing, Middlesex.

Chang, J. (2002), The Customer Relationship Management Solutions Guide, Chapter 1, https://www.360docs.net/doc/578666000.html,.

Collins, K.(2001), ‘Analytical CRM: Driving Profitable Customer Relationships’, Strategic Planning, SPA-12-7120

Regielski, C., Wang, J.C. & Yen, D.C. (2002), ‘Data Mining Techniques for Customer Relationship Management’, Technology in Society, 24, pp. 483-502.

Shaw, M. et el. (2001), ‘Knowledge Management and Data Mining in Marketing’, Decision Support Systems, 31, pp.127-137.

Srivastava. J., Wang, J.H., Lim, E.P. & Hwang, S.Y. (2002), ‘A Case for Analytical Customer Relationship Management’, PAKDD 2002, pp. 14-27.

Stringfellow, A., Nie, W. & Bowen, D.E. (2004), ‘CRM: Profiting From Understanding Customer Needs’, Business Horizons, 47/5, pp. 45-52.

基于B2B产业的数据挖掘技术在CRM中的应用

客户关系管理（CRM）的概念：

今天，企业更加意识到客户关系根本性的变化换和实施一些新的办法和策略来应对这些变化，并因此介绍了CRM的概念。

CRM的定义：

CRM是一个企业经营战略旨在优化获利能力、收入和通过组织一个企业市场细分，通过供应商培养客户满意度行为和连接过程。(2001科林斯)

CRM的作用：

CRM需要公司认识并且理解她的市场和客户。这涉及到具体用户信息,选择最有利可图的顾客和识别那些不具有长期价值的目标客户。CRM承担了产品的发展，哪个产品通过哪些渠道卖给哪些客户。在销售上，公司利用活动的管理提高销售部门的效率。最后，客户关系管理(CRM)寻求到通过一些比如呼叫中心、服务台等的服务来保持其客户 (Rygielski。2002年)。

许多从业者认识到：保持客户比吸引新客户更加有效益(Bitran and Mondschein, 1997)根据斯塔瓦2002年的说法，获取一个新客户要花费超过保持客户成本的5到7倍。因此，很多公司都采用CRM同时发展和保持成功的客户关系(Verhoef and Donkers, 2001)，一般公认的看法是采用CRM更注重维护现有的客户关系而不是获取新的客户的动机。然而，获取新的客户可以看做是客户关系的建立（CRE），应该形成客户关系管理的组成部分。我的论点是：A、所有的CRM活动都建立在收购新客户的基础上，它的前提是CRM活动的开始，

B、了解一个潜在客户需要与了解一个现在客户同样具有非常重要的战略意义，现阶段产品的设计与产品售后服务同样、甚至更加重要。

C、CRM对于现有客户活动的理论和实践同样也可以应用到一个有前景的，比如对于不同前景的客户从那些没有潜在价值的客户中进行市场细分，因此，市场营销活动涉及转换客户前景应该被列入CRM领域。

CRM用于B2B产业的必要性：

CRM不只是适用于管理企业和消费者之间的关系，对于商业客户具有更重要的意义。在B2B的交易环境中，交易更多，客户包含的种类也更加广泛，定价的方案也就更加复杂了。CRM策略，比如客户个性化定制、个性化的商业门户、更具目标性的产品报价都能帮助润滑这个过程和提高双方合作的效率(Rygielski et el. 2002)。

从具有代表性的顾客行为，布什（2002）建议B2B买家选择并与供应商发展关系，其一，他们可以要求到一个可以使他们觉得赖以生存的环境，一旦他们选择了供应商，并且投

入自己的时间和精力，他们就会更倾向于留在供应商那久一点，这与CRM获取新客户以及保持老客户一样重要。

数据挖掘技术的工具：

CRM可以从两个方面被观察到，运作型CRM指的是通过所有的客户联系和有效买卖和服务技术关注客户关系日常管理工作的经营策略，分析性CRM是CRM商业策略中使得客户智力增长和信息通过所有的接触点更加可控的一部分(Collins 2001)。它包括了一大堆数据挖掘的应用（例如：营销、预测和预算），确保了企业根据客户特殊策略发展更大客户智力相适应。分析型CRM将会是贯穿研究工作的主题。

CRM的本质是理解客户需求和获取提高公司取得长期利益的杠杆。他要求三个有序的部分：洞察用户决策、客户信息和信息处理能力(Stringfellow, et el. 2004)。

最近信息技术（IT）的发展都证明了信息的引人注目的处理能力，这个随着越来越多的层长的客户信息，手机和持续的交易记录或者内部资源购买，为公司在为公司杠杆数据和获取竞争优势带来了机遇与挑战，大量的客户信息数据的访问，但是，隐藏在数据中的已经拿到了的知识是不明确的。在这些条件下，就需要用到数据挖掘工具，它能帮助揭露和发现隐藏的客户行为，已经被提高了。

数据挖掘是为了发现隐藏的东西而寻找和分析数据，是潜在的、有用的信息。它包括选择、探索和建模大量的数据来揭开以前未知的东西，并最终从大量数据库中综合信息(Shaw 2001)，数据挖掘能够很容易的运用到各类不同的商业智能当中。我们从我的MSC夏季项目作为例子······，基于潜在价值和现实价值的交互，CRM/销售经理可以分别出特殊客户策略。

参考文献：

Bitran, G.R. & Mondschein, S.V. (1997), ‘A Comparative Analysis of Decision Making Procedures in The Catalog Sales Industry’, European Management Journal, 15 (2).

Bush, R. (2002), The Interactive and Direct Marketing Guide, Chapter 3.6, The Institute of Direct Marketing, Middlesex.

Chang, J. (2002), The Customer Relationship Management Solutions Guide, Chapter 1, https://www.360docs.net/doc/578666000.html,.

Collins, K.(2001), ‘Analytical CRM:Driving Profitable Customer Relationships’, Strategic Planning, SPA-12-7120

Regielski, C., Wang, J.C. & Yen, D.C. (2002), ‘Data Mining Techniques for Customer

Relationship Management’, Technology in Society, 24, pp. 483-502.

Shaw, M. et el. (2001), ‘Knowledge Management and Data Mining in Marketing’, Decision Support Systems, 31, pp.127-137.

Srivastava. J., Wang, J.H., Lim, E.P. & Hwang, S.Y. (2002), ‘A Case for Analytical Customer Relationship Management’, PAKDD 2002, pp. 14-27.

Stringfellow, A., Nie, W. & Bowen, D.E. (2004), ‘CRM: Profiting From Understanding Customer Needs’, Business Horizons, 47/5, pp. 45-52.