Whether companies are engaged in B2B or B2C transactions, they need to understand their customers. Once customer data are captured and stored in ODSs or DWs, they are then subject to further customer-centric intelligence processing in a manner that facilitates the execution of complex query performance and the competition on ‘analytics’ (Davenport, 2006). It is certainly not true that companies with the most data always win; the success lies in processing the existing data to learn about trends and attitudes of customers. This chapter, as well as the coming chapter, discusses the strategic, or analytical, side of CKM. The term ‘analytical CKM’ is used in this book to refer to both information and knowledge discovery tools. The views presented in this chapter are from the ‘information management’ perspective, whereas the coming chapter adopts a ‘knowledge management’ perspective.
The new millennium has witnessed several turbulent and discontinuous environmental changes on one hand, and a proliferation of information/knowledge seeking organizations on the other, in the search for achieving SCA. This section provides discussion of the role of customers in the new economy, the concept of ‘customer information’, customer-centric information discovery process and systems, and an example of setting customer profiling and up-selling rules.
The Information Age: All Power to the Customer
National economies are engaged in a competition for a larger share of global markets and economic wealth. In the 21st century, the information age, the wealth of nations depends on how well a society can organize information and knowledge. The national wealth of an information-based economy will depend upon the efficiency and effectiveness of information workers and strategic utilization of ICT systems.
The increasing demand of customers for higher quality, innovative, and customized products and services puts companies under pressure. Effective design and development of CKM enable organizations to ‘do right things right’ by leveraging DCCs, investing heavily in CK to understand customers better, and by adding value for specific segments of customers in the search for achieving SCA. Discovering best-target customers requires extensive, comprehensive, and reliable customer-profile information to customize effectively marketing programs. Planning and implementing a particular campaign for one segment of customers, or a specific customer, requires understanding of the demographic characteristics, lifestyle behaviors, product preferences, and channel preferences that drive their buying decisions. Customer-profile information helps companies find new customers for their businesses by extracting prospective customers that match the profile for current customers, and who are more inclined to buy certain products or services.
The Concept of ‘Customer Information’
Compiling profiles for customers takes place by converting customer data into customer information. Information is a descriptive entity that relates to past and present events (Camerer and Johnson, 1991; Dubin, 1996). It is a set of data in context that is relevant to one or more entities at a particular point in time or for a period of time. Information is data in context with respect to giving meaning to facts. It is data filled with meaning, relevance and purpose. A set of data in context is a message that only becomes information when one or more persons are ready to accept that message as relevant to their needs (Brackett, 1999).
Customer information may represent the number of residential or business customers (age groups, living areas, etc.); products which may represent the number of mobile or fixed telephone lines; traffic that may relate to the usage behavior of customers (in terms of volume, duration, and time of calls per each category of customers, products, age groups, or living areas); or revenue that may refer to the amount of money generated per category by customers, products, age groups, or living areas.
Information refers to data, plus meaning and understanding of patterns and relationships that take place through the following five major functions (5Cs) identified by Awad and Ghaziri (2004):
Condensation: summarizing in more concise form and unnecessary depth is eliminated.
Contextualization: knowing why the data were collected.
Calculation: analyzing data.
Categorization: grouping of data; the unit of analysis is known, such as customer value.
Correction: errors have been removed.