Know your data 36: the what and the why

In the unlikely place of a food blog (link), there appeared a feature article about restaurant data science, which provides a rare glimpse of the ins and outs of the data collection business.

The article describes pretty basic elements of data science that have been used by other industries for a while; evidently, restaurants have been reluctant to join this game until now.

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Right from the start, the featured company is described as "stealthy." This description has a double meaning – it's a "stealth startup" although given the number of important restaurant groups mentioned in the article that already use its services, it's unclear whether this sense of "stealth" was what the reporter intended; the other meaning is that the company collects data "stealthily" and works behind the scenes with restauranteurs. I have never understood the secrecy in the data collection business when the industry leaders insist that they utilize the collected data strictly for the benefit of their customers – recall the recent fallout over Wendy's announcement to adopt adaptive pricing (link).

Perhaps these data collectors won't voice what they know, which is that raw data aren't all that useful. There is a question that lingers in my head as I read through the article: can we have at least one clear statement of the benefits arising from the collected data?

The article begins with what must be the elevator pitch of SevenRooms, which is portrayed as a leader in data collection and analytics in the restaurant space.

"Let's say you're dining at Lure Fishbar in Soho on a weeknight for dinner... kicking things off with a bottle of Champagne. As it turns out, without even going over to your table, owner... and managers know what you and and your tablemates are spending in real time, as you're spending it."

This leading paragraph is a bit anticlimactic. Restaurant managers have always known which table ordered what, and whether the table is a big spender – even without deliberate data collection. And the reporter didn't answer the question: How does SevenRooms utilize such knowledge? How do these actions benefit the diners?

"Based on your previous visits to any restaurant in the Mercer Street Hospitality group... they know your dietary restrictions and your wine preferences; how often you've visited...; how much you spend on average, per night, and per year."

This sentence also focuses on the what, but not the so what? Let's think this through.

What extra service do frequent diners get? Do the waiters come by more often because I'm a frequent patron? Compared to the prior state, does total service effort increase because more service is rendered to frequent patrons? Or is total service level fixed, implying that the waiters visit low-frequency diners less? If total service effort goes up, are waiters paid more? If so, does the manager expect that the higher service effort will induce frequent patrons to spend more money, thus paying for itself?

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Next, let's consider the nature of data such as "wine preference". What SevenRooms actually possesses is the history of wine orders for each patron at the subset of restaurants that use SevenRooms, from which the diner's preference is deduced. The accuracy of this deduction depends on the individual. Some people may like only a single type of grapes but others may want a particular vintage from a particular winery of one type of grape if they order a particular dish at a particular restaurant but another vintage from another winery of a different grape when eating some other meal, and so on. If all those factors are included in the model of wine preferences, and we desire accurate predictions at the individual level, then we'd need quite a lot of data for each diner, beyond the amount that SevenRooms could have amassed.

From this discussion, you should realize that "wine preference" information is only useful if (a) the restaurant group is relatively large so it can capture most of the diner's wine drinking; and (b) the diner attains some threshold of loyalty to the restaurant group.

We have encountered this data science application before. Your Netflix movie recommendations won't hit the target if (a) you do a good chunk of your movie watching on other platforms; and (b) you've watched (and rated) only a small number of movies on Netflix.

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Let's pretend that SevenRooms actually has a good model of people's "wine preference".

For a one-grape patron, it's easy to get "wine preference" right, and I'm sure the managers would have gotten it right, with or without data collection.

In the other extreme, a good wine list may have many wines that fit with a diner's history, and should the restauranteur impress the diner by sending the top recommendation - in lieu of the wine list - to the table? "Here's your wine for the evening. (And don't you dare say no)." If the restaurant doesn't send the wine directly, but arms the sommelier with the recommendation as an ice-breaker, is this materially different from the status quo?

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The reporter gets more and more confused about the benefits of SevenRooms's data as she issued more words. The data could seemingly solve world hunger. The following miracles were mentioned:

"data can be key in the battle for luring diners"

"using customer data to tailor menus"

"should you choose to splurge on that $3,000 bottle... all hands on deck, with every manager getting an alert"

"can parse seating arrangements so that some tables are earmarked for Resy, some for OpenTable, ..."

"steps up service for VIPs"

"using it to plug and play among various reservation platforms quickly and on the fly"

"stitching together historical information on... needs and guidances"

"plotting tables on the floor plan to optimize the flow of service"

"tracking a table's stage in their dining experience"

"quote wait time"

"automated email campaigns"

keep track of which restaurant reservation channels are responsible for sending restaurants what numbers of diners

let restaurants send automated Priority Alerts to their most valuable guests about availability

"helps restaurants reward their most valuable guests to get them through their doors most often"

run a loyalty program "behind the scenes"

"improves customers' experience"

"and much more"

For each of these presumed benefits, one should consider how it changes the revenues and costs of the restaurants, whether the collected data really provide reliable insights to drive these actions, and how will the restauranteurs learn whether they yield the expected incremental gains.