Through data mining, one can use detailed sales history to pinpoint where to target the customer and hence retain the patron. Data mining is used to improve revenue generation and reduce the costs of business. The retail sector is no exception. The first case study, Predicting Algae Blooms, provides instruction regarding the many useful, unique data mining functions contained in the R software 'DMwR' package. Following these steps a target dataset for the analysis has been generated. In the long-term view, some of the consumers might be potentially very highly profitable or unprofitable at all. Further research for the business includes: conducting association analysis to establish customer buying patterns with regard to which products have been purchased together frequently by which customers and which customer groups; enhancing the merchant's web site to enable a consumer's shopping activities to be captured and tracked instantaneously and accurately; and predicting each customer's lifecycle value to quantify the level of diversity of each customer. Data mining is not only used in the retail industry, but it has a wide range of applications in many other industries also. This allows different transactions created by the same consumer on the same day but at different times to be treated separately. What are customers’ purchase behaviour patterns? In this article we present a practical case study for building an AI model and deploying it in a mobile application, all in less than an hour. The knowledge gained from the data is required to be organized and presented in such a way so that it can be easily understood and used by its users. In which sequence the products have been purchased? Compared with clusters 4 and 5, this group of customers has a lower frequency throughout the year and a significantly smaller average value of monetary, indicating that a much smaller amount of spending per consumer. Overall there were totally 73 instances were excluded by the Filter node, and the summary of the resultant filtered target dataset is given in Table 5. These printers are especially beneficial for brand managers when they are not sure which customer to target for the products of their brand. As such, these variables should be normalized before the clustering analysis. Department of Informatics, Faculty of Business, London South Bank University, London, UK, You can also search for this author in Data mining is the process of exploration and analysis of a large pool of information by total automatic or semiautomatic means. Let's stay in touch :), Your email address will not be published. By studying the past purchase history of customers, they can prepare strategies to target customers and obtain business from them, and knowledge from data mining can also be used to stop customers from moving to their competitors. Overall the business seems to be quite healthy in terms of profitability. This knowledge can help retailers to make better business-related decisions. There are some 459 consumers in cluster 2. Correspondence to Corresponding to these transactions, there are 406 830 instances (record rows) in the dataset, each for a particular item contained in a transaction. In the subsequent section the k-means clustering analysis is performed and a set of meaningful clusters and segments of the target dataset has been identified. This group, although the smallest (only composed of 5.05 per cent of the whole population), seems to be the most profitable group. You can follow me on Facebook. (c) Distribution of monetary by cluster. This group seems to be the least profitable group as none of the customers in this group purchased anything in the second half of the year. Data mining methods are used by retail organizations to determine which products are vulnerable at competitive risks or varying customers buying pattern. Data mining is a concept of computer science, but it has played a significant role in the retail industry as it helps retailers to learn about the behavior and buying a pattern of their customers. Thompson, W. (2008) Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry. The most valuable consumers of the business have contributed more than 60 per cent of the total sales in year 2011, whereas the least valuable ones only made up 4 per cent of the total sales. Over that particular period, there were 22 190 valid transactions in total, associated with 4381 valid distinct postcodes. Data preparation phase refers to the phase where all kind of activities takes place to construct final dataset using initial raw data. The rise of omni-channel retail that integrates marketing, customer relationship management, and inventory… The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. Retailers are now looking up to Big Data Analytics to have that extra competitive edge over others. Cluster 1 relates to some 527 consumers, composed of 14.4 per cent of the whole population. Since data mining is about finding patterns, the exponential growth of data in the present era is both a boon and a nightmare. Chen, D., Sain, S. & Guo, K. Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. The whole purpose of designed and creating a database is to increase knowledge obtained from the obtained data. (2007) Introduction to Data Mining Using SAS Enterprise Miner. On the basis of the initial insight into the dataset, a project diagram has been set up in SAS Enterprise Miner for the clustering analysis as depicted in Figure 3. In addition to this, you will also encourage similar behavior in your loyal customers and can establish a long-lasting relationship with them. (d) Distribution of first purchase by cluster. The customer transaction dataset held by the merchant has 11 variables as shown in Table 1, and it contains all the transactions occurring in years 2010 and 2011. Interpreting and understanding each cluster identified is crucial in generating customer-centric business intelligence. There is a general concept of BI solution Since then the company has maintained a steady and healthy number of customers from all parts of the United Kingdom and Europe, and has accumulated a huge amount of data about many customers. The first example of Data Mining and Business Intelligence comes from service providers in the mobile phone and utilities industries. As the first ever pilot study for the business to generate sensible customer intelligence, only the transactions created from 1 January 2011 to 31 December 2011 are explored in this article.
2020 data mining in retail industry case study