FEATURES
Leveraging “Big Data” for Precision In-Store Marketing
Turning Real-Time Data Into Big-Time Insights BJOERN GOLLER*
STEFAN HOFFMANN**
Abstract: Transforming ever-multiplying retail data into information that supports real-time decisions is the key to better addressing consumer demand, outperforming the competition and improving margins. Demand changes not monthly or weekly, but by the day, hour and minute. In-memory computing provides a reliable and rapid system to strategically leverage real-time information.
At six o’clock on a Friday evening, a commuter hops on
a bus that takes her from her job. The phone buzzes in her pocket. She is receiving a promotion from a local supermarket through an app that recognizes that she is heading home. “Have you thought about supper?” it asks. As it happens, she has not. It turns out that those whole roasted chickens she loves are half price for the next 60 minutes. She can get off a few stops early to pick it up or order it online for delivery to her house shortly after she arrives. Think of this scenario as impulse buying on steroids. It
combines big data with location-based technology to accelerate and influence customer behavior at the point of influence instead of at the point of purchase. In order to thrive in today’s increasingly complex
environment, retailers must maximize the profit potential of each interaction, transaction and customer contact. This means they must reach the right customers at the right time with the right offer, reacting to events as they happen—which means not only having and using the right information, but also accelerating insight, acting decisively, and influencing performance as it happens. Fortunately, in-memory technology now allows some
complex queries to be answered in seconds that previously took days. This technology enables true real- time analysis, altering how everyone—from executives to store personnel—makes decisions and affects outcomes. Harnessing data for a radically new approach requires
retailers to:
collect, manage and analyze data, regardless of amount or source;
turn big data into big insights; and
act on that information to affect current transactions and events.
Retailers can use in-memory computing to manage and
utilize internal and external data for a significant advantage. They can better understand and fulfill customer needs, optimize the supply chain and competitively differentiate their retail experience through price and product assortment—all in the moment the transaction happens. The introductory scenario is not science fiction—a
major North American city’s transportation bureau is partnering with local retailers to send special offers to commuters who download a mobile application that pairs with their monthly transit pass. After completing a brief user profile and consenting to share their personal information in exchange for the service, commuters will be exposed to offers from local retailers, restaurants and event organizers. These offers will be personalized, location-based, and in real time, appealing to every shopper’s deep-seated need for an exclusive deal. Based on the SAP Precision Retailing solution, this
scenario appeals to retailers looking to improve their margins, market share, loyalty rates, and customer satisfaction. All this is made possible through SAP’s investment in in-memory computing. (See Box 6-1 for an explanation of the methodology.)
* Retail Principal, SAP ** Principal Technology Consultant, SAP INTERNATIONAL COUNCIL OF SHOPPING CENTERS 30 1 RETAIL PROPERTY INSIGHTS VOL. 20, NO. 1, 2013
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