Which types of customers are more likely to respond to a certain promotion mailing? Interesting Case Studies of Data Analytics in Retail Industry As one of the major global industries, retail sector represents 31% of the world’s GDP. Through data mining, one can use detailed sales history to pinpoint where to target the customer and hence retain the patron. For each of these consumer groups, it is essential to further find out which products the customers in each group have purchased, which products have been purchased together most frequently and in which sequence the products have been purchased. and. Examining Table 7 and Figures 4 and 5, it is interesting to see that each cluster indeed contains a group of consumers that have certain distinct and intrinsic features as detailed below. Previous purchase history of customers is used to determine their loyalty for the brand. The rest of this article is organized as follows. Which products/items have customers purchased together often? Retailers and shop owners have been mining data for years to improve business. Data mining can help a retailer to understand the behavior of customers to survive the cut-throat competition in the market. What are the sales patterns in terms of various perspectives such as products/items, regions and time (weekly, monthly, quarterly, yearly and seasonally), and so on? Daqing Chen. Journal of Database Marketing & Customer Strategy Management Data mining can be used in the field of risk management in the retail industry. It makes the use of information about the products already bought by customers to determine what kind of products they are likely to buy when given social offers or by simply making them aware about the existence of the products. As such, these variables should be normalized before the clustering analysis. Various techniques such as regression analysis, association, and clustering, classification, and outlier analysis are applied to data to identify useful outcomes. Accordingly a set of recommendations is further provided to the business on consumer-centric marketing. Nowadays data proves to be a powerful pushing force of the industry. Project diagram in SAS Enterprise Miner 6.2. For years in the past, the merchant relied heavily on direct mailing catalogues, and orders were taken over phone calls. According to the study, it is found that 38% loss of retail business occurs because of the dishonesty of retail employees and one-fourth of these frauds can be detected at Point-of-Sale. In fact, those 188 consumers contributed 25.5 per cent of the total sales in the year. but the response from mass advertising is dropping day after day as people get annoyed by the continuous advertising on their face. The jump will be almost four times in the very short period of 2019-2026. Data mining is proved to be one of the most important tools to identify useful information from the large pool of information collected over time. This is possible with the help of data mining only. There are some 459 consumers in cluster 2. Datamine has done a significant amount of work in New Zealand and Australia's retail and B2C industry, ranging from marketing automation consulting to predictive modelling. Even for the first half of the year, the consumers didn’t shop often, and the average value of frequency was only 1.3. Paper 154-2008, SAS Global Forum, 16–19 March, San Antonio, TX. (2006) Customer Relationship Management: A Databased Approach, Hoboken, NJ: John Wiley & Sons. After that, the knowledge from the collected data is used to establish data mining definition of the problem and preparing a preliminary plan to achieve desired objectives. For example, out of the total 3799 instances, there was only one instance taking a monetary value of more than £87 684, and therefore, that instance was extended from the analysis. 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