An Approach to Discovery of Customer Profiles
The goal of the paper is to present the opportunity of exploiting data analysis methods and semantic models to discover customer profiles from financial databases. The solution to the problem is illustrated by the example of credit cards promotion strateg
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zęstochowa University of Technology, Częstochowa, Poland [email protected] 2 Wrocław University of Economics, Wrocław, Poland [email protected]
Abstract. The goal of the paper is to present the opportunity of exploiting data analysis methods and semantic models to discover customer profiles from financial databases. The solution to the problem is illustrated by the example of credit cards promotion strategy on the basis of historical data coming from the bank’s databases. The database contains information, personal data, and transactions. The idea is founded on data exploration methods and sematic models. With this purpose in mind, multiple algorithms of clustering and classification were applied, the results of which were exploited to elaborate the ontology and to define the customer profile to be used in decision-making. Keywords: Customer profile Semantic models
Data mining
Ontology of marketing
1 Introduction In the age of personalization, one of the greatest challenges for marketers is eliciting and communicating customer requirements. A customer profile — also known as a customer persona — is a set of data describing a high-level abstraction model that depicts the key characteristics of a group of consumers who could be interested in a specific product. Personas are fictitious, specific, concrete representations of target users [1]. Construction of customer profiles promotes overall internal alignment and coordination of marketing strategy with product development which helps to drive down cost of promotion by reducing the number of useless marketing messages and ineffective contacts with customer. Although making use of customer feedback is an established method of gathering marketing intelligence, interpreting data obtained by traditional structured methods such as questionnaires, interviews and observation, is often too complex or too cumbersome to apply in practice [1, 2]. Today, a lot of data can be gathered in an automatic way from transactional systems. These data describe the features of customers (such as age, the place of residence, number of family members, etc.) as well as behaviors (such as time and purpose of transactions performed by the customer, amounts and frequency of expenditures). © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved A.M. Tjoa et al. (Eds.): CONFENIS 2016, LNBIP 268, pp. 88–99, 2016. DOI: 10.1007/978-3-319-49944-4_7
An Approach to Discovery of Customer Profiles
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The discovery of useful knowledge from databases is one of the major functions of analytical decision support systems [3, 4]. Various data mining algorithms can be used to discover and describe behavior patterns of individuals and to relate them with personal data collected in CRM data bases. Unfortunately, most of the systems lack semantics, which can be considered a key component for business knowledge management. Marketing databases are today very broad. The volume of information is so huge that the analysis b
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