How big data gives businesses the nose for smelling what’s selling

How big data gives businesses the nose for smelling what’s selling

The sheer scale of businesses such as Sainsbury’s allows for an incredibly detailed picture of consumer habits, to transform and personalise shopping

As data starts to permeate every nook and cranny of the retail trade, an incredibly detailed picture of consumer habits is slowly building. Big data is becoming big business. For a supermarket like Sainsbury’s, with data harvested from millions of customers and billions of transactions, this means an opportunity to differentiate.

Sainsbury’s has 16% of the UK grocery market, according to recent figures from Kantar Worldwide, but it’s looking to data analytics to help it compete more strongly in an increasingly competitive space. According to Andrew Day, chief data officer at their newly established data analytics centre of excellence (DACE), improving customer satisfaction, increasing revenues and enthusing staff in-store are at the heart of the plan.

“We have huge amounts of data so we felt we had to try and bring it to life,” says Day. “DACE is the result of that thinking and is a collection of highly skilled people in data management, visualisation, reporting and solution finding.”

DACE is asking big questions and seeing if the data can find solutions to problems. One of those is around stocking stores with products suited to specific demographics. Day admits that matching customers with products is a huge challenge, given the business has around 90,000 products across Sainsbury’s Group; but data analytics helps to identify patterns, and react accordingly.

Another challenge is how to get the best out of the supply chain and logistics. Can data help create a plan which improves efficiency of supply and delivery, reducing costs and time?

Day explains: “The data is helping us look at the way we work with farmers, for example. Are we buying and growing the right crops? The idea is to be efficient and sustainable from farm to fork, using data analytics to help us model scenarios with both suppliers and customers.”

Previously this would have been done through intuition and Experience, which perhaps wasn’t always accurate.

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