Do Data Category Killers Exist?
A chance meeting with Danny Wegman had me thinking about category killers. What makes a business a category killer? And do they exist in today’s data industry?
Recently, I met a food retail icon while doing my usual grocery shopping trip with my 5-year-old twins.
I spent many years covering the publicly listed staple retailers like Walmart, Kroger, Costco, Whole Foods, Safeway, and more. I had countless professional conversations with the C-Suite of those companies. But I was definitely a bit star struck when I got to briefly chat with Danny Wegman, the creator of Wegmans Food Markets. He just happened to be there doing a store visit while I was there.
And as you can see, I definitely nerdily asked for a photo with him. My kids faces say, “Who is this and why are we taking a picture with him in the grocery store?” And, meanwhile, I basically had rolled out of bed to get to the grocery store. Quite the contrast with those formal conversations with the industry titans as a sell-side analyst.
I shared some positive feedback about his company and our local Wegmans in Westchester, NY. We talked a little bit of shop after I shared my experience as a sell-side analyst benchmarking all food retailers to Wegmans as the gold standard. I briefly met his family and the local store manager as well. Really friendly, positive conversation!
On the drive home, I found myself thinking about the data-driven analysis I did back then as a sell-side analyst. I credit that data-driven approach for how I got on the right side of the industry fundamentals at a time of major changes in the industry. Would I have had the right framework for the analytics if I didn’t know the Wegmans “category killer” business model intuitively from my time at Syracuse University? Would I have been able to properly understand the data if I hadn’t had the grassroots experience of how Wegmans was able to “yes, and” where other chains only saw tradeoffs?
Lately, I’ve been thinking about the parallel between the food retail sector and the data industry. The lessons from Wegmans extend beyond food retail. Just as Wegmans disrupted grocery markets by combining quality, price, and customer loyalty, data companies face similar opportunities to redefine their industries. Who are the data category killers that can achieve a similar “yes-and” breakthrough?
Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. After moving on from UBS Evidence Lab, I’ve remained active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser, user, and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.
Grocery Store Category Killers
A "category killer” is a company that dominates its segment by breaking through trade-offs—offering unmatched quality, scale, and price where competitors struggle to do so.
Wegmans is a category killer in the US grocery market, combining multiple store formats under one roof. It offers premium and budget options, organic and traditional brands, prepared foods, and bulk items—making it worth the trade-off of size and travel for loyal customers. The trade-off to achieve this is the massive size of the store. For its loyal customers, it is a worthwhile destination to travel further to (compared to the local grocery store) or to spend more time in (compared to a quick grocery trip to a smaller store format).
Market impact
When Wegmans enters a market, it significantly shifts local market share by appealing to both high-end shoppers with its premium experience and budget-conscious consumers with competitive pricing. This comes at a high cost that many food retailers are unwilling to make: large stores and substantial staffing expenses.
Wegmans won’t be the biggest grocery store chain in the US, but in the local markets they are a category killer.
More anecdotal proof points of its category killer status:
Invests in human capital: Wegmans is always a top 10 best company to work for (https://fortune.com/ranking/best-companies/). There’s a high connection between employee satisfaction in retail and the revenue per square foot productivity of the store because of the dependency on human capital to execute high service and merchandise presentation.
Exceptional brand power: Back when I covered the food retail sector (a decade ago), I learned that the Wegmans-branded sports drink outsold Gatorade head-to-head in Wegmans stores. This outsized success of Wegmans’ private-label sports drink over a powerhouse brand like Gatorade demonstrates a category killer’s ability to disrupt even entrenched brands in commoditized markets.
Respective of competition: Harris Teeter, a regional high-end grocery store I covered before it was acquired by Kroger, told me that when I said they were like “Wegmans of the South,” they said they aspired to that, but Wegmans was a 10 and they felt like they were an 8 or 9.
By combining premium quality, competitive pricing, and strong customer loyalty, Wegmans shifts the competitive dynamic—exactly the kind of “yes-and” strategy that category killers use to redefine the game.
More anecdotal evidence? The reaction from upstate NY when they saw this sign at a Syracuse at Duke basketball game.
The parallels of food retail to investment data product companies
The data industry parallels retail: few levers for differentiation, intense commoditization, and a need for strategic positioning to stand out. The challenges faced by food retail—commoditization, the fight for market share, and disruption through distribution—mirror those of the investment data industry. Here’s how the two industries align across key dynamics.
Food retail and data industry common characteristics:
Commoditization
Food retail is highly commoditized, with companies offering nearly identical products and services. Most retailers balance selling other brands’ products with private label or “own label” items1.
Many data companies sell data that is the same as other data companies, offering similar services as well. There is a need to differentiate but have limited levers to pull.
Market concentration2:
The food retail market is highly fragmented and commoditized. The market leaders may have pockets of highly concentrated share at the local level, but nationally, the leaders like Walmart and Kroger have ~20% and ~10% market share, but the long tail of market share declines rapidly from there. We need to consider the category beyond the traditional “grocery store” format because of the wide range of options available to buy food, beverage, household and personal care products. https://www.supermarketnews.com/independents-regional-grocers/walmart-kroger-costco-make-top-three-grocery-retailers-list
Data companies are in a highly fragmented market with a handful of market leaders. The market leaders have not consolidated the market enough to be considered an oligopoly. There is a long tail of data companies. Bloomberg, S&P, Factset, and Refinitiv are the big players, but if we take a broad definition of the data industry, there is a very long tail of data competition. If anyone has good data on the market shares, please feel free to share in the comments.
Market share drives profitability
Retailers have high operating leverage3, with high fixed costs. A niche strategy—premium or private-label products—can boost margins, but limited household consumption requires a minimum market share for success. So a minimum threshold of market share needs to be achieved even for the niche strategy to succeed. Therefore, market share is the primary hurdle to profitability.
Data products are a high-fixed-cost business; raw data isn’t going to cleanse and wrangle itself. Many of the data categories face price competition, forcing gross margin4 compression. Market share is the primary driver of profitability. There are niche strategies where one premium data set can generate high margins, but there are limited data budgets, so a minimum market share is needed to be profitable.
Network effects are critical
Grocery stores depend on a network effect5 for the fresh groceries and prepared foods to be profitable. The faster the fresh food turns over, the fresher the inventory is for customers, attracting more customers for fresh quality produce. By contrast, the grocery store with fewer customers sees the produce spoil, which puts the grocer in a tough spot: throw out the food and take the margin hit, throw out the food and raise prices to avoid a margin hit, leave the less fresh inventory on the shelves for longer, and stock fewer items in the category. Other than taking the margin hit, it leads to negative network effects.
Data companies depend on network effects, though unlike expiring fresh produce, there is a network effect around the alpha or beta of the dataset, which attracts the network effect of consumption. If others are benefiting from the data, it attracts more buyers. Making lots of data available after the “expiration date” of when the data is needed hurts the network effect.
Changes in distribution are disrupting the market
Changes in distribution are disrupting the food retail market with lower-cost or more convenient ways to purchase grocery categories.
Changes in distribution technology are changing the way data is consumed, with the new efficient approaches disrupting legacy methods.
Category Killer Evolution
Food retail has seen many category killer formats over the last 100 years
The US grocery market has seen many category killers over the decades. A&P was the first, consolidating market share when local mom-and-pop stores dominated. However, after many decades of success, they failed to adapt to new competitors. Eventually, they failed to adapt to the next big category killer: Walmart Superstore, which combined low prices with a vast assortment of discretionary and staple goods. And of course the latest category killer—Amazon did the same for assortment and prices, leveraging the e-commerce format, which further disrupted the staple retail category.
Do data category killers exist?
Food retail category killers redefined their markets by adapting to new formats and breaking trade-offs. The data industry, facing similar disruptions, may be on the cusp of its own evolution. The question remains: who will lead it?
In the data industry, companies like Bloomberg or S&P Global have achieved leading market share in specific segments, much like Wegmans in local markets. Today, however, the data industry faces a unique challenge: an explosion of datasets coming to market. The data industry is now highly fragmented, with Eagle Alpha profiling ~2,000 data providers (https://www.eaglealpha.com/platform/data-buyer/) and aMass Insights cataloging ~20,000 datasets(https://amassinsights.com/#/unproviders), the market is highly fragmented. Has any company truly broken through all trade-offs to redefine the industry on a broader scale?
Category killers redefine their industries by solving trade-offs with “yes-and” strategies. The data industry faces the same pressures and opportunities as food retail. Which data company will break through with unmatched quality, scale, and accessibility? Share your thoughts—who do you think will lead the next evolution of data category killers?
- Jason DeRise, CFA
Private Label / Own Brand: Products manufactured to be sold by a retailer, sold under a retailer’s brand name, often positioned as lower-cost or premium alternatives to national brands.
Market Concentration: A measure of how much market share is controlled by a few companies. A highly concentrated market has dominant players, while a fragmented one has many small competitors.
Operating Leverage: One way to think about operating leverage is the sensitivity of operating profit growth (profit before interest and taxes) to changes in volume or revenue growth of the business. The primary driver of operating leverage is the mix of variable costs, which change as volumes change, and fixed costs, which do not change as volumes change. The more fixed costs as a portion of the business, the more sensitive operating profit is to changes in volume and revenue growth (declines).
Gross Margins: A financial metric representing the percentage of revenue that exceeds the cost of goods sold (COGS). Higher gross margins indicate greater profitability.
Network Effects: A phenomenon where the value of a product or service increases as more people use it. In data, this could mean a dataset becomes more valuable as more participants benefit from its insights.