This is the first post in our Data Visualization Spotlight series, where we showcase how different organizations are using data visualization and analytics to solve their day-to-day problems.

Each week, more than 245 million customers visit their 10,900 stores and ten websites worldwide. Walmart has sales of approximately $466 billion in the fiscal year 2013 and employs 2.2 million associates. It is clearly a name to reckon with in the retail arena.

Walmart

At Walmart, data-driven decisions are more like a norm than an exception.  A big part of their data endeavors is based on social data—tweets, blogs, pins, comments, shares, etc. And the task of mining all that data to generate retail-related insights rests on the team at WalmartLabs.

Capturing the Social Retail Pulse Through Data Visualization

As Arun Prasath, Principal Engineer, WalmartLabs, points out in an article, “Social Media Analytics is all about mining retail-related insights from social channels, a perilous and personally exciting task to us. When our team spent the 22nd of November feverishly following the social retail pulse on Black Friday, we knew the world wasn’t preparing for an apocalypse.”

Walmart blackfriday trendFig: By using real-time data visualization, the team observed a clear upswing in Walmart-related social buzz on 22nd November 2012, which gently reminded them of the promise that lay hidden deep within the treasure of the social data goldmine. Image Source: @WalmartLabs blog

In an age where information-sharing is easy, thanks to social media, such social buzz typically precedes all-important product launches. People are frequently expressing their views about the latest smartphone or the coolest video game hitting the shelf. WalmartLabs taps this social buzz and helps buyers plan their inventory and assortment.

Arun Prasath cites the following example. A few days ahead of its launch, Sony’s Android phone Xperia Z showed a similar spike in social activity.

Walmart Xperia Z trendFig: Such insights gathered through data visualization and social media analytics help its buyers make smarter decisions ahead of time. Image Source: @WalmartLabs blog

WalmartLabs uses such spikes in social network chatter to predict demand for out-of-the-ordinary products, too. In 2011, the team correctly anticipated heightened customer interest in cake-pop makers. They did so based on social media conversations on Facebook and Twitter. A few months later, it noticed a growing interest in electric juicers. This interest was linked to the popularity of the juice-crazy documentary Fat, Sick, and Nearly Dead. The team sends these data to Walmart’s buyers, who then use it to make their purchasing decisions.

walmart social media analytics dashboardFig: The Social Media Analytics dashboard for buyers gives them better insight into consumers’ thoughts on products. Image Source: Gigaom.com

Walmart’s buyers also get a sense of what they should stock online and in stores by checking out pins on Pinterest. Top pins feed into a social-media analytics dashboard for buyers. So do the reports from Twitter that engineers have created by visualizing and analyzing Twitter feeds. Buyers can see when the number of tweets on, say, gel nail polish peaked and see the most popular colors in which locations.

 “OMG!!! dis is sooo coool!  i luv ma new fone.”Challenges and the way forward

The language used in social forums is heavily unstructured, informal, and often ungrammatical. Mining petabytes of such social data to filter out relevant data points and then map them to meaningful retail products is difficult. Popular text analytics and natural language processing techniques based on standard language models do not suffice.

One of the several techniques WalmartLabs adopts to overcome this challenge is looking for several hand-verified n-grams [Related read: n-gram] around brands in a significant time window.

As Prasath points out, there are several such techniques in the offing.  “It is only after conquering all of these multifold challenges that meaningful recommendation can be made….Our social media analytics project operates on top of a searchable index of 60 billion social documents and helps merchants at Walmart monitor sentiments and popular interests real-time, or inquire into trends in the past. One can also see geographical variations of social sentiments and buzz levels. There are also tools that marry search trends on walmart.com, sales trends in our brick-and-mortar stores and social buzz all in one place, to help make correlations. Together, these tools provide powerful social insights.”

Final Thoughts

People are constantly talking about products on social media. A retailer must transform this humongous amount of social data into meaningful information and make it available in a form that their merchandisers can understand and use for assortment and inventory planning.

The secret to successful retailing is the delivery of the right product at the right place and time. And social media analytics coupled with data visualization can help the merchandiser achieve the same with remarkable results.

In our next post of the Data Visualization Spotlight series, read how Netflix plans to improve its operational visibility with dynamic data visualization.

Reference

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5 responses on “How Walmart Uses Data Visualization to Convert Real-Time Social Conversations into Inventory

  1. Is there a specific social media listening tool that you used to monitor this data? Or was it through a combination of tools? If yes, what tools/software programs?

    • Hi Trudy,

      As I mention in my article, the “task of mining all that data to generate retail-related insights rests on the team at WalmartLabs.” Walmart uses Social Genome, a gigantic knowledge graph that helps Walmart reach its customers based on semantic analysis of real time social media streams. The Social Genome provides Walmart with a layer of social metadata containing customers, topics, products, locations and events.

      Thanks

  2. It’s impressive that you are getting ideas from this paragraph as well as
    from our discussion made at this time.

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