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What Walmart’s Data Monetization Strategy Means for CPGs

Near the end of 2020, Walmart began discreetly promoting its new data arm, “Walmart Data Ventures (WDV).” Focused on productizing Walmart’s data assets, its suite of products is said to eventually “deliver actionable, customer-centric insights” in order to “help merchants and brands make better business decisions.” By monetizing a once free offering to CPG brands, however, it has many anxious about what that will mean for their own bottom lines and competitive edge upon the — currently undisclosed — launch. 

While the monetization of retail data is certainly not a new concept — just look at the likes of IRI, Albertson’s, and 84.51° from Kroger — Walmart had been one of the biggest proponents of free access to rich intelligence for years. With more data, the thought has been, CPGs and suppliers can drive greater efficiencies, stronger merchandising, and more impactful marketing plans that increase overall sales.

By productizing that data though, Walmart is making a smart move to boost its revenue. In fact, according to AdAge, this venture is projected to generate $1 million in data and analytics for every $1 billion of sales, and $400 million projected annually for the U.S alone. It would be an understatement to say it’s a massive opportunity in today’s market. Not to mention that once monetized, this could help with faster access to cleaner, standardized, and more well-managed data sets.

But this strategy has created concerns from CPGs and suppliers with regard to budget allocations, planning capabilities, and competition. Data budgets for many companies have grown over the years, but the pace of monetized data has simultaneously increased alongside it — and this move from Walmart could catalyze others to price against them with uncertain results.

The retailer/manufacturer relationship has also historically been a complex one to begin with. While inherently dependent upon each other, the two are often at odds with conflicting agendas and their own profitability targets. It remains to be seen how much of their valuable data Walmart will actually be willing to share with each CPG, given that this data could, presumably, help them ‘win’ against the retailers’ own store brand products. In a bit of a Catch 22, the ability for CPGs to gain retailer trust and demonstrate category captaincy – or the position for them to make decisions for all category products – is dependent upon solid data and sound decision making that they otherwise might not have.

The cost of this data is of particular concern to small- and mid-sized brands (generally $1-10 billion in revenue). Often lacking the deeper pockets of some of their big-named peers that may have five times that revenue or more, the burning questions on their minds now are, “Do we earmark funds solely for an influential and valuable retailer like Walmart or do we split between the myriad of other companies to maximize our access to hard data?” This question of course is then promptly followed by, “Can we afford either?” 

While these are important questions to ask, an even bigger one should be whether or not CPG companies are getting high quality insights out of their ‘data subscriptions’ at all or are they paying substantial amounts of money for a lot of extraneous information. There’s no denying the power of the Walmart Big Data machine, their 245 million customers and 2.5 petabytes of unstructured data generated from over one million customers each hour. Nor is it easy to overlook the extensive analysis of point-of-sale trends that come with that data. But for many CPGs that utilize these insights, the potential that just a fraction of this narrower, more-targeted intelligence could enable stronger results than what is currently derived from existing, super broad datasets is very appealing.

To succeed, companies need to place their bets on the data that will make the most impact. For decades, CPGs have relied heavily on off-the-shelf software, historical insights, manual calculations, and even gut instinct to drive critical decision-making. While a brand might sustain 5% year-over-year growth through these techniques and deem it a success, if they knew a different approach could secure 35% and take a bigger slice out of the market pie against competitors – they might not maintain that approach.

Considering today’s unprecedented product landscape, global supply chain disruption, inflation, and evolving consumer preferences – new technologies and strategies that incorporate data-backed forecasting and predictive analytics have become increasingly essential for category growth management. In order to enable visibility around sensitivity analyses, incremental demand transfer, and price elasticities, CPGs need to know the exact shape of data needed to help them optimize.

Before brands are required to pay for any data from Walmart and others in the future then, it’s important that CPG category and revenue growth managers take a hard look at the data they currently have access to in their own systems, match it against what they are getting from those big retailers, reduce any redundancies, and implement the right tech to make sense of it all now and in the future. To start, they should:  

  • Prioritize their data: CPGs should consider the most important elements of data they have access to and where they’re getting the most bang for their buck today
  • Rationalize their data: Of that data, they should understand what info will prove most meaningful towards making their business decisions and more accurately and reliably predict key KPI’s against sales, volume, loyalty expansion, customer conversions, etc. and scrap anything that won’t.
  • Hyperlocalize their data:  CPGs should consider if there is an opportunity to further subsegment data and only purchase a snapshot that will empower their decision-making (i.e., subcategories, store level insights, points of distribution, pricing, etc.) 

With a more strategic approach, CPGs can unlock stronger insights that help them understand their category challenges and opportunities. By forging their proprietary, operational IP with the right data from those like Walmart, brands will be much more agile, continuously innovative, and empowered to win across everything from assortments, trade promotions, and pricing to help them realize long-term success.

About the Authors

Henrique Aveiro is director of ML/AI at Insite AI, where he’s primarily responsible for solving customers’ business problems through machine learning algorithms. With extensive industry experience, Henrique has a proven track record of leading teams of data scientists, ML engineers and data engineers, delivering solutions across product innovation, category management and marketing in CPG. Prior to his role at Insite AI, Henrique was the manager of machine learning/AI at Procter & Gamble, where he worked on modeling simulation applications across R&D and IT organizations.

Ryan Powell is VP of Retail Strategy & Consulting for Insite AI. With nearly 20 years of experience solving complex business and technology challenges for consumer goods and retail brands, Ryan plays a key role in helping consumer product brands and partners enhance, streamline and support positive growth strategies. Prior to his role at Insite AI, Ryan served as Vice President of Merchandising and Category Management at Symphony RetailAI Solutions.

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Comments

  1. If Walmart replaces Retail Link with Data Ventures and begins charging suppliers for access to their account data, it will result in either higher costs passed on the Walmart by the suppliers (and thus, higher prices to the consumer) OR a consolidation of suppliers, since small- to mid-size suppliers simply don’t have the budget for this type of thing.
    This theory was put into action when Sam’s Club began charging suppliers for access to their data in the MADRID program. Most suppliers simply couldn’t afford it, so they simply don’t have access to valuable insights that (prior to MADRID) benefited both Walmart/Sam’s and the suppliers.
    It’s a short sighted view with unknown consequences.

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