The retail industry is a major application area for data mining because it collects huge amounts of records on sales, users shopping history, goods transportation, consumption, and service. The quantity of data collected continues to expand promptly, especially because of the increasing ease, accessibility, and popularity of business conducted on the internet, or e-commerce.
Today, multiple stores also have websites where users can create purchases online. Some businesses, including Amazon.com (www.amazon.com), exist solely online, without any brick-and-mortar (i.e., physical) store areas. Retail data support a rich source for data mining.
Retail data mining can help identify user buying behaviors, find user shopping patterns and trends, enhance the quality of user service, achieve better user retention and satisfaction, increase goods consumption ratios, design more effective goods transportation and distribution policies, and decrease the cost of business.
There are a few examples of data mining in the retail industry are as follows −
Design and construction of data warehouses based on the benefits of data mining − Because retail data cover a broad spectrum (such as sales, customers, employees,goods transportation, consumption, and services), there can be several methods to design a data warehouse for this market.
The levels of detail to contain can also vary substantially. The results of preliminary data mining exercises can be used to support guide the design and development of data warehouse architecture. This contains deciding which dimensions and levels to contain and what pre-processing to implement in order to facilitate effective data mining.
Multidimensional analysis of sales, customers, products, time, and region − The retail market needed timely data regarding customer requirements, product sales, trends, and fashions, and the quality, cost, profit, and service of commodities. It is essential to provide dynamic multidimensional analysis and visualization tools, such as the construction of sophisticated data cubes according to the requirement of data analysis.
Analysis of the effectiveness of sales campaigns − The retail market conducts sales campaigns using advertisements, coupons, and several types of discounts and bonuses to promote products and attract users. Careful analysis of the efficiency of sales campaigns can support improve company profits.
Multidimensional analysis can be used for these goals by comparing the number of sales and the multiple transactions including the sales items during the sales period versus those including the same items before or after the sales campaign.
Furthermore, association analysis can disclose which items are likely to be bought together with the items on sale, specifically in comparison with the sales before or after the campaign.