{"id":4641,"date":"2019-06-13T06:20:11","date_gmt":"2019-06-13T11:20:11","guid":{"rendered":"https:\/\/reformingretail.com\/?p=4641"},"modified":"2019-06-13T06:20:17","modified_gmt":"2019-06-13T11:20:17","slug":"whats-my-data-worth-a-history-of-restaurant-data-markets-part-1","status":"publish","type":"post","link":"https:\/\/reformingretail.com\/index.php\/2019\/06\/13\/whats-my-data-worth-a-history-of-restaurant-data-markets-part-1\/","title":{"rendered":"What’s My Data Worth: A History of Restaurant Data Markets (Part 1)"},"content":{"rendered":"\n

This is part 1 of a 2-part series.<\/em><\/p>\n\n\n\n

This industry wrongfully assumes that there are myriad buyers of restaurant and retail data. That whatever data they can find, in whatever format and scale, is instantly transformed into a Scrooge McDuck vault.<\/p>\n\n\n\n

This couldn\u2019t be further from the truth.<\/p>\n\n\n\n

Since we\u2019re tired of explaining how these data markets work, what follows is a relatively complete, modern history of on-premise (this is the colloquial usage even though it should be on-premises, grammatically speaking) data markets in food and beverage. (Note: on-premise<\/a> is the term used for offline brick-and-mortar sales that don\u2019t occur in big box grocery and mass merchandising, which is known as off-premise.)<\/p>\n\n\n\n

The first attempt to monetize on-premise data\nwas Gazelle Systems Inc., a dot-com era startup founded in 1999 and shuttered\nin 2003. The company developed a suite of customer-facing applications (loyalty,\ngift, online ordering, etc.) designed to be integrated within that era\u2019s POS\nsystems (you guessed it, a strategy doomed to failure) that had access to full\ntransaction level detail. Gazelle was approached by IRI, who along with Nielsen\nremains one of the two biggest players in so-called syndicated store data, with\nthe objective of permissioning, collecting, and cleansing beverage-alcohol\nsales for IRI\u2019s largest client, Anheuser-Busch. Gazelle offered the restaurants\nfree use of the applications and detailed analytics of their customer base in\nexchange for blinded and aggregated use of the data. Nevertheless, after four\nyears and over $10MM in venture funding, Gazelle\u2019s traction was limited to\nv.1.0 Micros and Positouch integrations, had little adoption in the POS\nchannel, and only 1,500 restaurants nationwide to show for it. Yet another\ndot-com had bitten the dust. <\/p>\n\n\n\n

In 2004 another company called GuestMetrics\nentered the on-premise data market. The sole purpose of the company was to\ncollect item level (or SKU, stock keeping unit) data from food and beverage\nestablishments and sell it to beverage suppliers and distributors. This\nbusiness, in theory, could be very lucrative: roughly 25% of all food and\nbeverage sales occur on-premise and the off-premise data market is worth\nbillions annually once you count the multiple layers of data products.<\/p>\n\n\n\n

To gain access to the data, which was much\nharder to acquire because there were no such things as APIs or cloud POS back\nthen, GuestMetrics developed numerous relationships with POS resellers and POS\ncompanies. In exchange for a very simple reporting product, GuestMetrics would\nacquire the rights to sell the SKU data.<\/p>\n\n\n\n

This was a very costly initiative for\nGuestMetrics on several fronts.<\/p>\n\n\n\n

First, convincing POS companies and their\nresellers to partner took extremely long if they would even reply to the\nopportunity to begin with; as we\u2019ve shown, the majority of legacy POS companies\ncould never even bother to return a simple email. Part of this could be chalked\nup to the fact that data was a completely novel concept to this industry:\nremember, at this time POS companies and their channels didn\u2019t even participate\nin credit card residuals. It\u2019s also fair to note that the reporting product\nGuestMetrics offered was very basic and faced stiff competition from a fairly\nwide spectrum of providers. <\/p>\n\n\n\n

Second, cleansing the data was a nightmare. In\noff-premise, the market has matured enough to develop UPCs, or universal\nproduct codes. These codes, administered and maintained by an organizational\nbody named GS1<\/a>,\nmean there\u2019s complete traceability across the entire supply chain: so when\nPepsi creates Cherry Diet Pepsi in 12 ounce cans, everyone from Pepsi, to the\ndistributor, to the grocer, and even the consumer (what do you think that\nbarcode is on all the products you scan from the grocery store?) knows what\nthey\u2019re handling.<\/p>\n\n\n\n

In on-premise, there\u2019s no such thing as UPC,\nand there are barely even SKUs, at least in food service. That\u2019s because\nrestaurants are real-time manufacturing operations \u2013 nothing goes out the front\ndoor looking anything like it did coming in the back door. When you order a\nMike\u2019s Special from a restaurant, how do you know what\u2019s in it? You don\u2019t, and\nprobably neither does Mike despite making the damn thing. The results are POS\ndatabases fill with very<\/strong> dirty\nrecords that must be cleansed if there\u2019s any hope of suppliers and distributors\ntaking interest. Look at the below sample check and tell us if you know what\nthe hell these items are:<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Precisely. <\/p>\n\n\n\n

Even so, GuestMetrics made this harder on\nthemselves because they never used AI or even a technical approach to solving\nthe data cleansing problem. Whereas any decent software engineer would build a\nfairly rigorous canonical database and pair it with some form of save-and-train\nuser coding system, GuestMetrics employed a handful of bodies in New Orleans to\nmanually clean the data. The tragedy was that no machine was trained, so\nnothing was ever going to become automated. The result was a continuously high\ncost of goods.<\/p>\n\n\n\n

Third, and related to the second point,\nGuestMetrics had to convince suppliers and distributors that there was\nsufficient on-premise data at a high enough quality that it deserved their\nbudgets. When it wasn\u2019t, and it didn\u2019t. GuestMetrics was, like Gazelle before\nthem, barely adding 1,000 restaurants a year by being behind the POS and reseller\nbus instead of driving it.<\/p>\n\n\n\n

This really gets to two issues: statistical\nsampling size, and data cleanliness.<\/p>\n\n\n\n

There are nearly 700,000 commercial restaurant & bar establishments in the US, broken into a lot of crazy groups like full-serve vs quick-serve, bar vs club, independent vs chain, etc., and never mind geographic distribution. Even 5% sampling penetration \u2013 the bare minimum<\/strong> for reasonable projectability and accuracy \u2013 means one not only needs 35,000+ establishments supplying data, but they must be the \u201cright mix\u201d of establishments. (Note: in reality statistical confidence is reached when one hits > 20% of stores, or 140,000 restaurants.)<\/p>\n\n\n\n

Not easy to do. At all.<\/p>\n\n\n\n

That 35,000-establishment count translates into roughly 1.75 billion guest checks a year, each with an average of ten individual item purchases. So in order to make the business go, you have to organize and cleanse 17.5 billion individual item sales a year<\/em>, and you can\u2019t do that manually. These are restaurants, remember: they name, organize, and enter their menu items any which way they want, and they\u2019re all vastly different.<\/p>\n\n\n\n

The counterparts at suppliers and distributors\nhas been accustomed to relatively pristine data from off-premise data companies\nNielsen and IRI for literal decades; nearly nobody employed at Pepsi remembered\nwhat the initial set of data from Nielsen\u2019s Scantrack looked like in 1979. <\/p>\n\n\n\n

Additionally, Walmart represents about 25% of off-premise volume, with the next 5 largest grocers adding up to another 35%. Adding these together achieves a large sample size with high statistical accuracy for projections. How do you reach 60% of on-premise volume with 6 merchants? You don\u2019t.<\/strong><\/p>\n\n\n\n

The net result of these failures – some of\nwhich are GuestMetrics\u2019 own doing while some are simply endemic to the rat hole\nthat is on-premise – meant GuestMetrics burned through nearly $25M before\nmerging with Restaurant Sciences, the next topic of discussion.<\/p>\n\n\n\n

Restaurant Sciences took a wildly different tack than that of GuestMetrics, which we’ll discuss in part 2.<\/p>\n