Big data for retail means a chance to see why a sale didn’t occur. Is it product selection? Pricing? Store display? Ineffective promotional material?
Before, this information was hard to track, but with the advent of big data and in-memory computing, two products ideally suited to collecting and analysing unstructured data types like that of retail, are poised to play a significant role in sales.
For example, web logs are not the typical financial data people relate with the term “big data“. This web information shows how consumers navigate through an internet storefront. The data can be combined with previous BI (Business Intelligence) apps and sales data, generating clear insight.
Retailers now have the opportunity to see website traffic for a particular product and compare it to the sales. Before, if a product wasn’t selling it would be removed from the line. Now, managers can readjust pricing; ensure there are enough colours and sizes, and any other aspects that take a look to a sale.
This analytical approach to customer decisions is not limited to the web; some retailers are now using technologies to analyse foot traffic throughout their physical stores. These maps, combined with sales data, make way for new applications focused on optimising store layout and product placement.
Retail relies heavily on in-store and online purchases, but they are not successful without making sure their product is delivered on time. Predictive analysis applications using the first day’s delivery, past delivery data, and real-time traffic data, provide revised delivery schedules, allowing retail managers to take immediate corrective action.
This is incredibly advantageous for retail managers, preparing them to better meet customer expectations and maintain high operational efficiency.
This operational efficiency is essential when retailers always want to know what their customers need before they even know they need it.
For example, using big data retailers now can see, through data from store cards, cashed-in coupons, and purchase history, when a customer may need a refill on a product. This data gives retail marketers the upper hand, sending the low stocked customers promotional material – urging them to buy the refill.
As Mark Ledbetter put it in his recent article, “Go Big or Go Home: How Big Data Can Bring Big Sales“, “How retailers use it to change their business, how they take advantage of it to grow sales…is only limited by their imagination.”
This article was written by Lindsay Nelson of SAP
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