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 10 websites worldwide. With sales of approximately $466 billion in fiscal year 2013 and employing 2.2 million associates, Walmart is clearly a name to reckon with in the retail arena.
At Walmart, data-driven decisions are more like a norm than an exception. A big part of their data endeavors are based on social data—tweets, blogs, pins, comments, shares, and so on. 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.”
Fig: 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 sharing of information has been made 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 to be hitting the shelf. WalmartLabs taps this social buzz and helps buyers plan their inventory and assortment.
Arun Prasath cites the following example. Few days ahead of its launch, Sony’s Android phone Xperia Z showed a similar spike in social activity.
Fig: Such insights gathered through data visualization and social media analytics helps 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 based on social media conversations on Facebook and Twitter. A few months later, it noticed growing interest in electric juicers, linked in part 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.
Fig: 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 in to 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 which colors were the most popular in which locations.
“OMG!!! dis is sooo coool! i luv ma new fone.”— Challenges & 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 what is relevant and then mapping it to meaningful retail products is an uphill task. Popular text analytics and natural language processing techniques based on standard language models do not suffice.
One of the several techniques which WalmartLabs adopts to overcome this challenge is to look for the several hand-verified n-grams [Related read: n-gram] around brands in a large 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.”
People are constantly talking about products on social media. It is crucial for a retailer to transform this humongous amount of social data into meaningful information and make it available in a form which their merchandisers can understand and use for assortment and inventory planning.
The secret to successful retailing lies in delivering the right product at the right place and at the right 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.
What to read next?
Check out our 5-part series on Behind the scenes of dashboard design where we speak to Product Managers, Developers and Designers of software products with kick-ass information dashboards to help you get an insider’s view into their making.