Data Mining Demographic Information and Transaction Data of a Large Retail Company – Business Plan Example

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The paper “ Data Mining Demographic Information and Transaction Data of a Large Retail Company” is an excellent example of a business plan on information technology. This document is a proposal presentation of the electronic business cases using data mining techniques to obtain insights on how a company can identify and support loyal customers. From the loyalty card scheme, it is possible to collect large amounts of customer shopping history and data on sales of various products sold by the company. The source of data is demographic information and the number and type of transactions undertaken by customers.

In this proposal, a suitable data mining technique and scheduling have been implemented to showcase the use of customer purchase trends and customer loyalty to build a need for funding of the program. 1.1 Aims, objectives and possible outcomesThe aim of this report is to data-mine ways of attracting new customers and retaining existing customers at a retail supermarket. Objectives; To investigate changes in customer loyalty or consumption of a retail company To suggest adjustments in a variety of goods and pricing at the retail supermarket The use of customer loyalty card information provides a way of observing the sequences of purchases from customers to analyze purchase trends and customer loyalty.

Sequential pattern mining groups similar consumers into sequences at different periods based on the goods they purchase. 1.2 BackgroundLarge retail stores competing among small stores have literally forced the later to close down while gaining immense power within the supply chain. They have invented new ways to attract customers and dictate terms to the retailers on an increasing basis. Although it is an elusive dream to hold onto the customer’ s imagination for long, customer disloyalty has been blamed on demographic shifts, increasing competition, and changing tastes and preferences among customers.

This is the reason why retailers are going on an extra mile to understand and reach the customer. Business Intelligence (BI) gives retailers the chance to meet the ever-changing desires and needs of customers through the use of tools like data mining and data warehousing. Customer analytics helps to accurately predict customer behavior and unravel new trends and patterns in data. This, in turn, will help retain customers, churn prediction, and identify valuable customers as shown in the customer analytics life cycle below. (Source: Isakka, 2006) Figure 1: Customer analytics lifecycle Isakka (2006) argues that different algorithms and data mining techniques have helped organizations to collect appropriate information on customers to develop new products, decide future plans, and expand marketing.

Data marketing techniques support marketing decisions and extract marketing knowledge (Bose & Mahapatra, 2001). Sumathi and Sivanandam (2014) observe that analyzing the reasons for customer attrition is critical in effective customer retention programs. As one drill down to individual transactions that result in loyalty change, business intelligence provides a greater understanding of customer attrition and factor that influence customer behavior. Although loyalty programs are needed by customers, their proliferation, and customer willingness to join is diluting the actual intentions.

For example, according to Maritz's report, overall growth rate (27 percent) of membership showed active membership growth at 21 percent, while actual active membership reduced to 44 percent from 46 percent in two years (International Institute of Analytics, 2014). This implies that despite loyalty programs acquiring members, many are left unengaged and could be a result of loyalty card overload.

There is a correlation between highly effective retention programs and analytics, especially those using a customer-focused lens like segmentation and customer experience. High performing organizations create business impact and drive business strategy by employing customer analytics (Grabmeier & Rudolph, 2002). Moreover, many focus on customer needs, enable organizations to offer personalized rewards to effectively target customers, and develop long-term relationships across the customer experience.

References

Berry, M.J. & Linoff, G.S. (2009) Data mining techniques (2nd Edition). John Wiley and Sons. http://www.huaat.com/wp-content/themes/LS/download/2009091Marketing.pdf.

Bose, I. & Mahapatra, R.K. (2001) Business data mining-a machine learning perspective. Information and Management, Vol. 39, pp 211-225.

Grabmeier, J. & Rudolph, A. (2002) Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery, Vol. 6, pp 303-360.

International Institute for Analytics (2014) Keeping customers: Successful loyalty through analytics. III. http://www.sas.com/content/dam/SAS/en_us/doc/research2/iia-keeping-customers-107145.pdf.

Isakki, P. (2015) Purpose of data mining for analyzing customer data. International Journal of advance research in computer science and management studies, Vol. 3, No. 4, pp 199- 203.

Kim, Yong Seog, & Street, W. Nick (2004) An intelligent system for customer targeting: A data mining approach. Decision Support Systems, Vol. 37, pp 215–228.

Rajagopal, S. (2011) Customer data clustering using data mining technique. International Journal of Database Management Systems, Vol. 3, No. 4, pp 1-9.

Sumathi, S. & Sivanandam, S.N. (2006) Introduction to data mining and its applications. Springer science and business media.

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