Two Ways GPU Databases Are Transforming the Retail Industry

In today’s retail market, defined by brutal competition, narrow margins and well-informed customers, the survivors will be those companies that can combine operational excellence with world-class customer engagement. Data management mastery is a must.

GPU databases apply the thousands of processing cores of GPUs in parallel to change the game for retail in both operational excellence and customer experience – breaking the bottleneck of today’s CPU-bound systems.

Operational Excellence

Shortages and outages are lost revenue, so integrating point-of-sale data with inventory and supply chain information is a retailing must. GPU databases can support analytical queries across billions of rows of data with sub-second response rates, giving the retailer the ability to get an up-to-the-minute picture of its business at any point. Companies with multiple locations can create visualizations of sales across stores, regions and product lines to move stock to areas of greatest anticipated demand.

Predictive analytics on GPU databases enables retailers to combine real-time transaction data with historical trends to forecast future purchasing behavior. These insights reduce the risk of product shortages, permit promotions to be synced with inventory forecasts, and even adjust prices on the fly.

Predictive analytics improves the supply chain as well. GPU databases can churn through historical data to forecast seasonal and event-based changes in demand. These projections can ripple back through the supply chain to adjust orders in real time to meet anticipated demand.

Improve Customer Journey

Having product on hand is table stakes. The bigger challenge is to deliver a customer experience that is both memorable and delightful.

That starts with creating rich customer profiles. By combining demographics and buying history, retailers can classify customers into “buckets” such as bargain hunters, enthusiasts and trendsetters. Marketing campaigns can be overlaid on each of these profiles to deliver offers and coupons that precisely match buyer behavior.

GPU databases have parallel processing ability and compute power to combine, correlate, and analyze data from multiple sources, including point-of-sale systems, social media streams, weather forecasts, news stories, and even wearable devices. Knowing that a clothing style is trending or that a prominent TV personality recently recommended a brand of footwear enables retailers to instantly notify its supply chain, prepare email blasts and notify its ad agency to create a promotional radio spot.

E-retailers can realize even greater value from GPU databases. Recommendation engines are one of the most valuable tools retailers have to improve total shopping basket value, accounting for 35 percent of Amazon’s retail sales.

These engines require high-speed database queries that look at the contents of a shopping cart and correlate it with purchases other customers have made. Recommendations need to be delivered in a split second, which is far too fast for standard database management systems. By harnessing parallel processing with GPUs, retailers can scan millions of previous transactions to deliver a result and the time it takes to load a web page.

Online shopping cart abandonment rates run between 60%, and 80%, costing e-retailers more than $4.5 trillion every year. Predictive analytics can identify customer behavior that presages an abandoned transaction and offer a pop-up code for a discount or an instant follow-up email with an incentive to return and check out.

Retail is an unforgiving business. Companies armed with the fastest decision-making technology can not only serve customers better but delight them as well.