When it comes to big data, retail giant Walmart is a real pioneer. Since 2011, it’s been developing a number of tools at its @WalmartLabs and recently set up a Data Café (standing for “Collaborative Analytics Facilities for Enterprise”). With over 2.5 petabytes of data analysed an hour, Walmart – the world’s largest retailer – now has one of the world’s largest collections of data and can thus anticipate the needs of its 250 million weekly customers.
using big data to identify customerS’ expectations
Based in Silicon Valley, @WalmartLabs is a hive of innovation, with hundreds of data specialists who have joined the group through a serious of startup acquisitions, the most recent of which was Jet.com and its dynamic pricing platform.
As Cognitive Scientist Om Marwah explains in an interview with Forbes: “Traditionally, data science is merely statistics-driven. We also use psychology to make shopping easier, more enjoyable, and more convenient.” Marwah goes on: “Recommendations and targeting are the pillars of our personalization engine.”
One of the many innovations developed at the @WalmartLabs is the Social Genome project, a big data analytics solution that analyses millions and billions of Facebook messages, tweets, YouTube videos, blog postings etc., as well as giving customers product information and discounts if they mention a Walmart product on the social media. The group also has a gift-recommending app called Shopycat which is part of its Facebook page and was launched in 2011.
REAL-time data analysis
As well as being a key driver for the customer experience for Walmart, big data also enables the retailer to optimise the supply chain. And this is where real-time capabilities are essential, as Naveen Peddamail, who runs the Data Café, explains:
“If you can’t get insights until you’ve analysed your sales for a week or a month, then you’ve lost sales within that time. Our goal is always to get information to our business partners as fast as we can, so they can take action and cut down the turnaround time. It is proactive and reactive analytics.”
Peddamail gives an example of the retailer’s grocery team who were struggling to understand why sales of a particular product were declining. But thanks to data, the Cafe’s analysts were able to attribute the drop in sales to a pricing error. Once this was rectified, sales began to pick up again within days.
Peddamail also recalls monitoring sales of a new range of cookies that weren’t selling at all in certain outlets. An alert was sent to the team in charge of these stores, who soon realised that the products hadn’t even been put on the shelves. Thanks to real-time data analysis, this oversight was quickly addressed.
According to Walmart, the Data Café has led to a reduction in the time it takes to resolve problems from an average of two to three weeks to around twenty minutes.
Walmart are precursors in the field, but the retail sector as a whole is on the brink of a revolution thanks to big data, particularly where buying behaviour, industry trend analysis, predictive modelling and geographical segmentation are concerned. It’s crucial for retailers to seize these opportunities in order to stand out from the competition and offer a unique, innovative customer experience.
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