Mining data to discover customer segments
- PDF / 154,526 Bytes
- 8 Pages / 556 x 782 pts Page_size
- 77 Downloads / 199 Views
ords: data mining, customer segmentation, CRM, mass customisation, clustering, direct marketing
Mining data to discover customer segments Sea´n Kelly Received (in revised form): 30 October 2002
Abstract Increasingly companies realise that consumers differ in their needs, preferences, sensitivities, opinions and behaviours. The transition from mass marketing to target marketing that is currently in progress creates demands for more sophisticated customer segmentation techniques. The key enabler of any segmentation strategy is customer data. Customer data are the raw material that must be captured, integrated and effectively analysed in order to achieve the goal of profiling customers. Effective segmentation requires both static customer profile data (eg demographic, lifestyle, preferences) and customer behaviour data (eg usage, loyalty, profitability). Once these data are integrated they can be interrogated to discover groups of customers sharing the same characteristics and needs. Market segmentation is the process of partitioning the heterogeneous market into separate and distinct homogeneous segments. A segment consists of a group of consumers who react in a similar way to a given set of marketing stimuli. Traditionally, the enterprise would define a segmentation matrix and then, based on the data, would allocate customers to segments. This a priori approach to segmentation defines, in advance, a framework or system that describes characteristics of customers or prospects based on information that is known about those individuals. What data mining makes possible is a different approach to segmentation — namely cluster segmentation. The cluster segmentation approach, in direct contrast to the a priori method, seeks to discover naturally occurring clusters of customers who share common characteristics or behave in the same way.
Introduction
Sea´n Kelly Comhra Ltd PO Box 7 Skibbereen Co Cork Ireland Tel: +353 2838483 Fax: +353 2838485 E-mail: [email protected]
Businesses have become increasingly sophisticated in their efforts to capture consumer information, but the process of exploiting consumer information remains relatively immature. Data mining is a process that applies the techniques of artificial intelligence to the task of discovering useful patterns in data, and is proving particularly powerful in the identification of customers sharing the same characteristics. This segmentation of customers into affinity clusters presents new possibilities for customer segmentation, and it is this particular application of datamining techniques that is the subject of this paper. Data mining is a term that describes the automatic, or semi-automatic, analysis of large datasets in order to discover meaningful patterns and rules. Data mining differs from conventional data analysis techniques in
& H E N R Y S T E W A R T P U B L I C AT I O N S 1 4 7 8 - 0 8 4 4 . I n t e r a c t i v e M a r k e t i n g . V O L . 4 N O . 3 . PP 235–242. JANUARY/MARCH 2 0 0 3
235
Kelly
Role of reporting tools
Acquiring customer data
so f
Data Loading...
