Big Data for Grocers: what is the buzz or benchmark?

What do grocers have to learn from web giants? Before embarking upon mindless imitation and buying into snake-oil pitches, grocers must have a clear understanding of what Big Data is  and, more importantly, how it fits within the company.

What is Big Data, then? Data scientists in IBM have identified the four dimensions that make Big Data: Volume, Velocity, Variety, Veracity(1). To put it more succinctly, if it doesn’t fit in an Excel spreadsheet, it’s Big Data(2). It combines large volumes of data from inside the company (e.g. transaction information, inventory data, costumer feedback, in store sensors) and data from outside the company (e.g. macroeconomic data, social media content and mobile devices). By using this kind of data, grocers around the world have achieved amazing results: Safeway uses personalised ads to reach 45% of its sales base(3), Kroger has improved its coupon redemption rate to 60% (4), TESCO’s redemption rate shot up from 3% to 70% (5)!

Big Data Analytics, though, are no longer reserved to industry giants.  Cloud computing has enabled companies to offer services like these to small and medium businesses. Established behemoths, like IBM, Google and SAS, offer integrated and personalised services; there are also grocer-specific platforms, like mywebgrocer(6).

To formulate a Big Data strategy, and not get swept away in the sea of options, grocers must have a clear understanding of their needs and questions. Big Data is not a magical beast that feeds on data, obscurely tamed by obscure data-oracles; Big Data’s answers will be as good as the questions you ask(7). Only then can one know what is worth measuring, collecting and analysing. Some of the problems most frequently approached through Big Data are:

  • Recommendation engines and affinity analysis,
  • Digital coupon targeting,
  • Adjusting inventory to changing environment conditions (weather, competitors, events),
  • Tracking word-of-mouth buzz to understand paths of influence and sentiment analysis(8).

The implementation of an analytics program must go beyond mere projects. As Marcus Shingles, analyst for Deloitte, puts it “The emphasis is on the need to think beyond data tools and techniques, and focus on analytical talent models, decision processes and cultural shifts”(9). The analytical insights of Big Data will only come to those who wield it mindfully.

-The Sutti Team







(6) Here is a list that may help you get started:




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