Data Mining_ Concepts and Techniques - Jiawei Han [365]
13.3.2. Data Mining for Retail and Telecommunication Industries
The retail industry is a well-fit application area for data mining, since it collects huge amounts of data on sales, customer shopping history, goods transportation, consumption, and service. The quantity of data collected continues to expand rapidly, especially due to the increasing availability, ease, and popularity of business conducted on the Web, or e-commerce. Today, most major chain stores also have web sites where customers can make purchases online. Some businesses, such as Amazon.com (www.amazon.com), exist solely online, without any brick-and-mortar (i.e., physical) store locations. Retail data provide a rich source for data mining.
Retail data mining can help identify customer buying behaviors, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, enhance goods consumption ratios, design more effective goods transportation and distribution policies, and reduce the cost of business.
A few examples of data mining in the retail industry are outlined as follows:
■ Design and construction of data warehouses: Because retail data cover a wide spectrum (including sales, customers, employees, goods transportation, consumption, and services), there can be many ways to design a data warehouse for this industry. The levels of detail to include can vary substantially. The outcome of preliminary data mining exercises can be used to help guide the design and development of data warehouse structures. This involves deciding which dimensions and levels to include and what preprocessing to perform to facilitate effective data mining.
■ Multidimensional analysis of sales, customers, products, time, and region: The retail industry requires timely information regarding customer needs, product sales, trends, and fashions, as well as the quality, cost, profit, and service of commodities. It is therefore important to provide powerful multidimensional analysis and visualization tools, including the construction of sophisticated data cubes according to the needs of data analysis. The advanced data cube structures introduced in Chapter 5 are useful in retail data analysis because facilitate analysis on multidimensional aggregates with complex conditions.
■ Analysis of the effectiveness of sales campaigns: The retail industry conducts sales campaigns using advertisements, coupons, and various kinds of discounts and bonuses to promote products and attract customers. Careful analysis of the effectiveness of sales campaigns can help improve company profits. Multidimensional analysis can be used for this purpose by comparing the amount of sales and the number of transactions containing the sales items during the sales period versus those containing the same items before or after the sales campaign. Moreover, association analysis may disclose which items are likely to be purchased together with the items on sale, especially in comparison with the sales before or after the campaign.
■ Customer retention—analysis of customer loyalty: We can use customer loyalty card information to register sequences of purchases of particular customers. Customer loyalty and purchase trends can be analyzed systematically. Goods purchased at different periods by the same customers can be grouped into sequences. Sequential pattern mining can then be used to investigate changes in customer consumption or loyalty and suggest adjustments on the pricing and variety of goods to help retain customers and attract new ones.
■ Product recommendation and cross-referencing of items: By mining associations from sales records, we may discover that a customer who buys a digital camera is likely to buy another