Walmart's Analytics Help Them Help You [Shop]

Walmart, the nation’s most prolific retailer, has been collecting customer data for years.  Walmart saves an estimated 2.5 petabytes of data accumulated from more than 1 million transactions per hour; an increasing amount of these happening online (Lutts , 2012).

Most of this customer data is being collected from in-store and online transactions and also from the content of social media conversations.  In addition to Walmart and Sam’s Club, Walmrt has partnerships with banks, vision centers and pharmacies from which they can also glean additional customer information. Customers can cash payroll checks at Walmart, providing yet another layer of personal information including highly private and unique data like driver’s license and social security numbers.  In many cases, Walmart’s customer information database is chock full of information customers willingly provided directly to the retailer. Many retailers offer a loyalty card which tracks information, but Walmart does not. However, they do offer a reloadable prepaid “Money Card” that looks like a branded credit card for which shopping behavior data may be collected from in store and online transactions.

Venky Harinaryan, Walmart’s senior vice president of global ecommerce, said “we see social commerce fueling the next generation of e-commerce where online and retail stores bring a continuous shopping experience to millions of users” (Walmart, 2011). Social commerce allows a retailer to tap into the current customers, but also potential customers who are in the social circles of their friends or followers, who share similar lifestyle typologies and who may be predisposed to purchasing the same items or similar from Walmart.

Walmart’s research division, @WalmartLabs, is dedicated to the convergence of online shopping and social media. The company started employing a platform known as Mupd8 which has the capability of analyzing incoming social media data and issuing an immediate response, such as a product recommendation. According to Accenture’s 2011 global consumer survey, more than two-thirds of consumers search for and read about brands on social sites (Accenture, 2011) so data mining in the social space is a prudent strategy.

Walmart also employs Shopycat, a Facebook app, which analyzes Facebook statuses including Likes,  Interests and post content (such as a friend making a recommendation to another friend) to make a socially-based product recommendation. Social media analytics gives insight into purchase habits and also sentiment analysis and reputation monitoring. It can also identify key influencers who are brand advocates in their social spheres.  

Here’s a snapshot of what came up for me without putting any personal information into the site. I recently did an online search for Ina Garten recipes on Google and on FoodNetwork.com so Shopycat demonstrated to me that it can be eerily accurate.

Wal-Mart is using these applications to analyze more that 300 million status updates a day (Dusto, 2012)! In late 2012, Walmart made Mupd8 open source to developers, a rare move for the dominant retailer.

In addition to exploiting the latest technology to develop customer profiles, Walmart uses Omniture’s SiteCatalyst product to look closely at the performance of their online shopping portal walmart.com. Walmart is interested in knowing how merchandising across this site directly effects conversions by “mining data in real time to quantify and visually reflect the effectiveness of walmart.com and its marketing objectives,” (Khan, 2004). Valuable observations can include things like product placement, campaign duration, price variables and design elements (such as the color of banners) and how these elements contribute to conversion. By reviewing the functionality of their ecommerce portal, Walmart can respond to the online behaviors they observe and adjust elements affecting the clickstream, purchase funnel and conversion rate of the site.

Due to the massive scale of Walmart’s distribution processes, predictive analytics data can also help the company measure inventory and supply chain systems and improve overall operational efficiency. In 2004 when a series of hurricanes hit the southeastern coast of the US, Walmart mined data for shopping patterns that occurred during previous storms and learned that things like strawberry Pop-Tarts and beer have higher sales in advance of a hurricane (Hays, 2004). When a company is able to identify the most popular items in a region or particular store, and manage down to the number of items in stock, the company can deliver better customer service and sell more.

In April 2012, Walmart allowed customers to order online, but pick up and pay for their orders in cash at their neighborhood store after identifying two groups of customers for whom this service would be appealing – those without credit cards, and those who prefer to keep their financial information private (Clifford, 2012). Knowing the buying patterns of these customers is important, since the individual stores actually act as distribution centers, and need to have the product in as soon as the customer is ready to have it, often the same day.

And because today’s consumer wants retailers to respond on their schedule, Walmart stores in some cities are offering same-day delivery  for holiday season orders placed online (Evans, 2012) which appeals to a segment of their customer who prefers the convenience of online shopping, but does not want to wait for an item to ship. More than half of Walmart’s online shoppers are now picking up items at their 4,700+ U.S. stores (Clifford.) Real time analytics would play a key role in making sure that stores are ready for the customer demand.

So, we know that Walmart is already developing and utilizing its own cutting edge data management and analytics technology to:

·         Build accurate customer profiles

·         Improve all aspects of the customer experience, both in store and online

·         Increase efficiency in their supply chain and distribution systems

·         Develop predictive online experiences that increase conversions

·         Integrate data across multiple customer channels

So What Else Can The Retailing Behemoth Do?

Walmart can continue to customize the shopper‘s online experience, playing to different segments and subsegments.  Overlaying customer profile data with online site usage data could help Walmart design different online shopping experiences based on factors like the age of the user. For instance, should a Walmart.com designed for person aged 50+ look different than the one offered up to a 21 year old shopper? Variations between sites might not only include merchandise options, but also in the layout and architecture of the site based on visitor flow and on-site engagement data.

I would say another big opportunity for Walmart is in the area of customer acquisition. Walmart predicts this will be a tough holiday season for the majority of its shoppers and therefore the company’s fourth-quarter earnings could fall below estimates. Target, catering to a more affluent customer base, expects to exceed Wall Street projections (D’Innocenzio, 2012).

The average household income for a Walmart shopper is in the range of $30,000-60,000, while the median household income of a Target customer is $64,000 (D’Innocenzio). Walmart already outperforms Target in the area of social media engagement. Continuing to play to this strength would be a good strategy. By mining both the social engagement data and shopping habits of their highest income customers, Walmart could identify their top tier income customers and those in their social circles. Walmart could choose to engage in meaningful ways with these prospects and, quite possibly, adapt their messaging from “deep discount” or “extreme value” to “convenience and selection” or something else that would be appealing to this type of customer.

Also of paramount importance to Walmart, and any online retailer for that matter, are Key Performance Indicators such as site abandonment, cart abandonment, and checkout abandonment rate.  These metrics equate to revenue so they deserve close analysis. These metrics also give a clear indication of some design elements such as how things are merchandised, arranged and communicated. The Walmart site is not for the clutter-averse; there is a LOT going on there. To a new customer, it could be overwhelming. Comparing the figures on these metrics for new versus returning (familiar) shoppers might suggest some site changes with new shoppers in mind such as a Welcome Page, or a quick video tutorial on how to best use the site.

The mounds of personal and behavioral data that is obtained across multiple channels  including online, social communities, call centers, mobile applications,  is transformed into actionable insights that can affect customer behaviors in real time. Multi-channel data integration is key for Walmart in doing what they do best – making it easy for us to consume.

References:

Accenture. (2012). The new realities of dating in the digital age: Are customers really cheating, or are you just not paying enough attention? 2011 Global Consumer Research study. Retrieved from http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Global-Consumer-Research-New-Realities.pdf

Clifford, S. (2012, July 4). Luring online shoppers offline. Retrieved on December 9, 2012 from http://www.nytimes.com/2012/07/05/business/retailers-lure-online-shoppers-offline.html

D’Innocenzio, A. (2012, November 18). Walmart and Target: A tale of two discount chains. Retrieved on December 10, 2012 from http://www.cbsnews.com/8301-505145_162-57551520/walmart-and-target-a-tale-of-two-discount-chains/

Dusto, A. (2012, October 31). Walmart’s online marketing technology gets a ‘mup-date.’ Internet Retailer.  Retrieved on December 8, 2012 from http://www.internetretailer.com/2012/10/31/wal-marts-online-marketing-technology-gets-mup-date

Evans, B. (2012, October 12).Walmart, Amazon, eBay: Who’s the speed king of retail? Retrieved on December 9, 2012 from http://www.nytimes.com/2012/10/10/business/wal-mart-tests-same-day-delivery.html?_r=0

Hays, C. (2004, November 14, 2004). What Walmart knows about customers’ habits. Retrieved on December 9, 2012 from http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html?_r=1&pagewanted=print&position=&