By David Dorf-Oracle on Mar 27, 2015
This is the second post in a 3-Part Series gearing up to our Thursday April 2, 2 PM EDT, Webinar with RSR Research, IBM and Oracle. Register here to join us!
Everyone acknowledges that retail growth is a business imperative. What’s less clear-cut are the most intelligent ways to achieve that growth. After all, a retailer could spend many millions to build new stores, with more devoted to advertising to woo new customers, but such growth is purchased at a high price.
A smarter and ultimately more cost-effective path to growth involves applying more rigorous science to everyday decision-making. Better, more customer-focused choices about merchandising and product assortments, allocation decisions, promotions and price markdowns do more than just improve operational efficiencies; they create the conditions necessary for growth.
“Retailers must implement BI and Analytical technologies to populate operational KPIs and management dashboards with usable and timely insights,” write RSR Research Managing Partners Brian Kilcourse and Paula Rosenblum in their Retail Growth Strategies benchmark report. “But modern technologies also enable retailers to move beyond a transactional mindset, and move toward being a ‘sense-and-respond’ business.”
Retailers have a head start in this area because they are well versed in big data, which is characterized by its variety, velocity and volume. Big data is nothing new to retailers, who routinely collect enormous amounts of transactional, product and customer data. However, without retail-specific algorithms to turn that data into actionable insight it’s useless – that’s where big science comes in.
Take a very basic example: category management. A grocer with 30 SKUs of yogurt (including various brands, sizes, and flavors) wants to open up shelf space in the always crowded refrigerated section. But which SKUs should go? Simply dropping the five slowest sellers could easily disappoint customers loyal to those brands. Experienced retailers know that seemingly unprofitable SKUs may be acting as traffic builders, drawing key shoppers to a highly profitable area of the store.
Understanding the science of demand transference can optimize this decision. Applying algorithms to extensive sales transaction history data, Oracle Retail applications can show that if the grocer drops the cherry flavored yogurt, an acceptable percentage of customers will simply transfer their demand to another flavor. However, if the grocer stops carrying six-pack cups of yogurt, an unacceptable number of shoppers will not purchase any yogurt product at all. These customers will not only leave the store disappointed; some will seek the product at a competing store.
Applications powered by big science allow retailers to optimize each of these thousands upon thousands of small (but significant) decisions, and to tailor each one to the assortment and customer profile of each store in the chain. When retailers can smarten up their everyday operations and do so at an enterprise-wide scale, they create a highly scientific formula for growth.
To read more on smart growth for retailers, look at my earlier post on growing by connecting with customers. Register here to join RSR Research Managing Partners Paula Rosenblum and Brian Kilcourse, Cor Hoekstra, IBM Global Business Services and myself to discuss retail growth strategies on a live webinar, Thursday April 2.