داده کاوی مشتریان برای قطعه بندی سبک زندگی

A good relationship between companies and customers is a crucial factor of competitiveness. Market segmentation is a key issue for companies to develop and maintain loyal relationships with customers as well as to promote the increase of company sales. This paper proposes a method for market segmentation in retailing based on customers’ lifestyle, supported by information extracted from a large transactional database. A set of typical shopping baskets are mined from the database, using a variable clustering algorithm, and these are used to infer customers lifestyle. Customers are assigned to a lifestyle segment based on their purchases history. This study is done in collaboration with an European retailing company.

The recent economic and social changes that occurred in Europe transformed the retailing sector. In particular, the relationship between companies and customers changed significantly. In the past, companies focused on selling products and services without searching detailed knowledge concerning the customers who bought the products and services. With the proliferation of competitors, it became more difficult to attract new customers, such that companies had to intensify efforts to keep current consumers. The evolution of social and economic conditions also changed lifestyles, and as a result customers are less inclined to absorb all the information they receive from the companies. This context led companies to evolve from product/service-centered strategies to customer-centered strategies. The establishment of loyalty relationships with customers also became a main strategic goal. Indeed, companies wishing to be at the leading edge have to continually improve the service levels in order to ensure a good business relationship with customers. Some companies invested in building databases that are able to collect a big amount of customer-related data. For each customer, millions of data objects are collected, allowing the analysis of the complete purchasing history. However, the information obtained is seldom integrated in the design of business functions such as marketing campaigns. In fact, in most companies the information available is not integrated in procedures to aid decision making. The overwhelming amounts of data have often resulted in the problem of information overload but knowledge starvation. Analysts are not being able to keep pace to study the data and turn it into useful knowledge for application purposes. Data mining (DM) techniques are rising as tools to analyze data resulting from customers’ activity, stored in large databases. They can be applied in order to detect significant patterns and rules underlying consumer behavior. However, the use of DM in marketing is still incipient and most companies still use mass strategies to instigate customers loyalty. The marketing segmentation of customers or the identification of customer groups with similar behavior patterns is often done in an ad-hoc way, which constitutes the basis for the definition of customized promotions. This paper proposes a method for customers segmentation, informed by the nature of the products purchased by customers. This method is based on clustering techniques, which enable segmenting customers according to their lifestyles. The structure of the remainder of the paper is as follows. Section 2 includes a review of segmentation approaches. Section 3 introduces the company used as case study. Section 4 includes a presentation of the methodology, and Section 5 presents the data and discusses the results. Section 6 suggests marketing actions based on the lifestyle segmentation. The paper finishes with the conclusion.

Customers segmentation can be used to support companies’ strategic actions and promote competitiveness. Recognizing customers’ differences can be the key to successful marketing, since it can lead to a more effective satisfaction of customers’ needs. This paper segmented customers of an European retailing company according to their lifestyle and proposed promotional policies tailored to customers from each segment, aiming to reinforce loyal relationships and increase sales. Data mining tools allowed to identify typical shopping baskets based on transactional records stored in the company loyalty card database. These typical shopping baskets were identified using a divisive cluster analysis technique, which considers the correlation between the products purchased. As a result, the products were grouped into six clusters. The methodology also involved the inference of the lifestyle corresponding to each cluster of products, by analyzing the type of products included in each cluster. In particular, it was analysed the business unit, the category and the position of the product brand concerning the value. Each customer was then assigned to the segment whose shopping basket is more similar to the customer’s past purchases. The research described in this paper also identified some marketing actions, such as customized promotional campaigns, adjustment of stores’ range of products and adjustment of stores’ layout, that can help to reinforce the relationship between companies and customers.