Customer Relationship Management

And

Applications of Data Mining Techniques

In

Business-to-Business Industry

A Literature Review

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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.  

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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.

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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, CRMGuru.com.

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.