The future of grocery marketing lies in big data
Thursday, November 16, 2017
A recent Wall Street Journal article chronicled how grocery stores are racing to master big data and leverage that information to stay ahead in the grocery wars. Stores are competing with each other and also with online behemoths like Amazon and its smaller counterparts.
The digital age has made it possible for all businesses, big or small, to compete on an equal footing. Media and marketing are no longer just the domain of those with deeper pockets. Stores have realized that the future of grocery shopping lies in reading and leveraging information about shoppers — what and how they buy products.
The future belongs to high-tech services like augmented reality apps. One such app in the works would allow customers to check out items as they shop. Shoppers can see the prices and check out options as they stroll through the aisles. At the same time, it shows store managers how products are selling in each aisle, sales figures, projections and even inventory options.
Chains like Kroger are successfully implementing big data in their stores. Kroger data scientists, researchers and app developers focus on mining consumer information. They study customer buying habits over long periods, and they are developing and testing apps that take into account shoppers' eating habits and lifestyle preferences. The data is then used to suggest what to buy, where to look for it and what recipes to try.
Recent innovations include infrared sensors to track the number of customers in a store. It automates the checkout process and deploys clerks to the checkout stations when needed. This helps streamline resources and reduce customer wait times at once. Retailers hope that this would also make grocery shopping enjoyable for the new generation.
Kroger isn't alone in investing millions in emerging technologies. Other major food retailers like Walmart are racing to leverage this powerful weapon. Data analytics play a major role in determining product freshness, inventory management, shelf display and logistics, among others.
All these aspects tie in with customer preferences and the customized banner ads in the grocery store apps. Customers save time by zeroing in on the goods that they want instead of loitering along with aisles. They shop, scan and pay as they go through the apps, which are convenient and fast.
These features are making it possible for retailers to compete with Amazon. The latter has won customers over with its quick deliveries and competitive prices, and of course its robust data analytics. It has set the trend in using data successfully to target customers based on their buying and shopping habits.
Kroger and Walmart may have jumped onto the data mining bandwagon, but they have a long way to go. Research shows that Amazon spends over 12 percent of its revenue on technology, research and innovation. Compared to that, other food retailers are spending about 3 percent.
Mining customer data has become imperative to hold on and increase market share. Retailers need to ramp up on this if they expect to hold on to the $800 billion U.S. grocery market.
A study conducted by Dinesh Gauri and his team at the Sam M. Walton College of Business corroborates this theory. It shows that data-based promotions are 85 percent effective across grocery retail categories. Using data from multiple sources, they developed an efficient model to study shopper behavior and sales patterns. The information was culled from point-of-sales data and millions of transactions across major retail grocery categories.
They found that big data played a major role in shaping future grocery promotions and marketing. Things that contribute to this important database are:
- Store and category characteristics
- Geographical locations
- Inventory management
- Distance to competition
They also found out that the impact of promotions varied between segments like premium, economy and value. The above information provides businesses with relevant resources and strategies to maximize sales. They get real-time insights on product demand, leverage predictive analytics, enhance in-store stock management.
Big data can help them carve out a lean model for success without sacrificing customer experience.
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