Applications of Data Warehousing and Data Mining in the Retail Industry

Applications of Data Warehousing and Data Mining in the Retail Industry

Nov 07 2015

Data Warehousing, Retail

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Huge amounts of data are generated as a result of daily business activities and transactions in the retail business. These massive databanks have little value if they remain in disconnected archives. The cross-functional data must be transferred, stored and cleansed in a centralized repository before any analytics can be conducted. This is where the data warehouse comes in. The data warehouses enable the collection of data from disparate sources, data cleansing and storage of data in some consistent format, so that they can be further utilized for complex data analysis and reporting.

A research paper by Professors C .M. Maran and Abdul Nazar, titled A Roadmap to Build Data Warehousing for Retail Industry suggests that when retail businesses have to make quick, real-time decisions on a busy day by extracting or comparing information from different operational databases, a data warehouse powered with data mining tools and equipped with an Online Analytical Processing (OLAP) system is the only answer. According to Data Mining in Retail Industry, data mining technology has the power to discover shopping patterns and trends, extract customer behavior data, enhance customer service and help, achieve improved customer loyalty, enhance buying, and improve the existing shipping and distribution policies.

Beye Network indicates that in the retail industry, data warehousing has moved from rudimentary reporting databases to mission-critical decision enablers in a decade. The retail business intelligence, which was once the domain of few “expert’ have now evolved into a enterprise-friendly system supporting managers, logistics staff, sales executives, and other personnel in business monitoring and decision making.

Moreover, with sophisticated front ends, today’s retail businesses are in a position to share powerful information with vendors, suppliers, store managers, or even customers. Here are in some benefits of combined data warehousing and data mining techniques to the retail merchant:

Locate sites for outlets: The data warehouses, packed with valuable information on customer demographics, location-wise traffic patterns, buying patterns, and sales performance, and successful marketing campaigns, retailers can accurately predict prime locations for future outlets.

Create store-specific product mix: With low storage costs and powerful database capabilities, the retailer can now develop unique product mix for each store instead of offering the same product lines for every store. This kind of sales data-enabled sales strategy ensures maximum sales and low volume of clearance items.

Develop targeted marketing campaigns: By studying historic price and trends, retailers can design highly targeted promotions and marketing campaigns. Instead of guessing the outcome of such promotions, the retailers know in advance the exact impact of such promotions on their margins.

Study customers behavior: The customer feedback data, loyalty programs data, and customer analysis—all contribute to the retailer’s understanding of business customers. This data can often be utilized to create effective marketing campaigns, alter product mix, or design store layouts.

Supply chain management: The retailers, suppliers, and distributors are encouraged to share ongoing sales, inventory, and logistics information well ahead of time, so that retailers can minimize bother overstocked or out-of-stock situations. Although advanced BI tools can help to optimize the supply chain, Retailers equipped with ERP systems are better positioned to manage the overall supply chain. During Deloitte’s interview with Alison Kenney Paul, vice chairman and US Retail and Distribution leader, Deloitte LLP, recorded in 2015 Retail Industry Outlook, Kelley pointed out that much of future success of global retailers will depend on their ability to manage the global supply chain.



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