At the prodding of executives in the restaurant industry, I’ve consolidated several past blog posts. What follows is what I believe to be an objective, outside appraisal of today’s restaurant industry. It is geared towards chain operators, because independents do not have the economies of scale to employ middle management decision makers. It’s meant to spur questions and challenge the status quo. Some might prefer comforting lies, but nobody can improve unless they face the hard truth – me included.
This will be our last post in 2016 (barring some crazy news story we can’t resist). In 2017 we will commit more time to similar analysis of the retailer data we access.
In our years of dealing with restaurants, we have been amazed at the lack of basic progress the industry has made relative to its peers. We thought it would be interesting to investigate why this might be occurring, and see what plausible changes could be made to bring the restaurant industry into our modern age.
To start, we examined the sorts of things the restaurant industry is missing. Although there are a myriad of solutions and progressions that we could point to, we limited our analysis to four core areas, which we discuss below chronologically.
Credit cards made their real debut in the 1960′s. By the 1970′s, forward-looking companies had implemented electronic cash registers and more advanced clearing houses for credit card authorization. For the first time, sales and consumer spend data were available in quasi-electronic format. Consulting companies began analyzing this data alongside retailers to better understand core customers, their purchase patterns, and promotional efficacy.
A few years later, in grocery, Nielsen provided grocers with scanning hardware to collect intelligence about items sold. With access to this information, suppliers would help low-margin grocers better manage their supply chain and analyze opportunities for revenue and margin growth. Many times suppliers came armed with increased promotional dollars. Since the inception of Nielsen’s Scantrack, grocers have divested much of the G&A associated with managing categories as suppliers (and distributors) foot the bill.
If there were ever a sister to the restaurant industry it would be the hotel industry. Hotels maintain fixed assets (rooms) that become very expensive when not in use. That’s why Smith’s Travel Research created the industry’s standard benchmarking tool (STAR report) in the mid 80′s. Because markets respond to local stimuli, it was important to create a tool that would be relevant for local markets. Since its inception, STR tells me that nearly 75% of US hotels use the STAR report (thanks to Annie and Nick of STR for helping me curate the data).
Lastly, it should be common sense that each location – even under the umbrella of the same brand – operates differently. This is why retailers and third party providers started performing analyses for each location as if each were a different entity. A company which did this well was i2 Technologies, later acquired by JDA. As the pervasiveness of point of sale systems and computers came into its own in the 80′s, companies started cheaply putting data to work to better understand how to operate each location.
Now that we’ve detailed some specifics advances other verticals have leveraged over the past 40 years, what have restaurants been up to?
Restaurants have been unable to accurately build customer databases. Even large, public restaurant chains have floundered at pulling data across multiple locations to understand who their customers are, what they’re buying, and what promotions they’re responding to. A friend who started a company to do this for restaurants has since left the vertical and has seen remarkably faster success with Consumer Packaged Goods (CPG) companies – companies that are exponentially larger than restaurants and should be expected to move slower!
Restaurants have also been unable to leverage the intelligence of their suppliers to maximize revenue and margin through supply chain, marketing and other facets of their operations. For instance, did you know that, in Australia, suppliers are responsible for the 2.5% profit increase food retailers have seen over the past five years? Chain restaurants still haven’t tapped into the value of their suppliers, much to their suppliers’ dismay.
The restaurant industry has not maintained a benchmarking tool relevant in national markets – let alone local markets. It’s not as if someone hasn’t tried: BlackBox Intelligence, started by an ex-Carlson CEO, has spent 20+ years building a data pool and has only tapped 2% of the restaurant market; over that same 20-year period STR penetrated ~70% of US hotels. Even then, Blackbox does not have access to the granular data that small hotel operators have understood to provide value since the mid-80’s.
The majority of restaurants have done no appreciable forecasting. They might use some basic tools – like the average of the last four Mondays – to determine demand. Sadly, even most large restaurants have developed simple rules for how stores should operate, irrespective of location. Contrast this with Maple, a tech-forward startup that does 100% delivery. Maple can output 1,100 meals per hour, slaying Chipotle’s 300 meal per hour efficiency – all in a 3,000 square foot box. How? In a single world: data. With the use of data models and machine learning to predict demand and product mix, Maple can align scarce resources to maximize quality and output.
Similarly, we are currently providing small operators outrageously better data science and outcomes than large restaurant chains have even considered. That shouldn’t happen. How embarrassing that $500k/year operators have better management tools than the largest casual and quick service chains on the planet.
With ample opportunity to improve performance, why have solutions not yet migrated to enterprise restaurants? We sought to understand this conundrum with both empirical data and anecdotal feedback. The results, now that we’ve collected data, are not that surprising.
We first looked at other industries. Using data from Pacific Crest and Marketing Sherpa, we found sales cycles, contract sizes, and the number of touches that SaaS providers went through in other verticals. We then broke that data down by customer employee size, as seen below.
Next, we looked at this data compared to SaaS companies in the restaurant vertical serving operators with 1000+ employees. The data was curated from our restaurant SaaS friends – thanks to those who shared! This quick juxtaposition shows precisely why innovation has not come to the restaurant vertical.
By serving the restaurant vertical, SaaS providers are taking an astounding amount of business risk. Restaurants pay much less than other verticals, and they take incredibly long to make purchasing decisions.
We can look at a simple example within foodservice: restaurant innovation vs grocery innovation. Olo (whom I love) started providing mobile ordering services to restaurants. In their first decade they successfully penetrated 150 restaurant brands and raised $25M. McDonald’s JUST announced its commitment to rolling out online ordering 15 years after online ordering made its debut. Another restaurant ordering service, Tillster, announced a 2.5 year effort to bring mobile ordering to Burger King. In that same 2.5 year period, Instacart, who handles mobile ordering and delivery for grocers, not restaurants, raised $265M and counts names like Costco and WholeFoods as partners.
This simplistic comparison of grocery to restaurants highlights the major risks: even though restaurant and grocery markets are nearly the same size (~$700B/year in the US), investors have put 10x more money in 1/5th the time into a solution that serves grocery over one that serves restaurants. Another way to frame it is that investors think restaurants are 50x riskier than grocers as measured by the time it takes them to become educated on innovation – and they put their money where their mouth is.
The result is a non-starter for restaurant innovation; investors do not want promising ideas spending time with restaurants. The time it takes to prove value and gain traction could be sufficiently long to kill the bank account and motivation of many entrepreneurs. Thus innovative solutions are seriously advised to bypass the restaurant industry entirely.
How did restaurants – particularly chains – get to this point? Well, restaurants have a brick and mortar presence. Thus they feel ubiquitous and accessible to many people looking to sell something. The result, which many operators will tell you, is a high number of inbound solicitations. It is not uncommon for certain positions within a restaurant to receive 50 solicitations per week. At this rate, responding to sales people would likely consume most of a restaurant operator’s time.
However, the implicit misfortune is that the restaurant is now missing opportunities to be educated about solutions that could massively impact their business. It seems to us that while restaurants do receive a large number of inbounds, they are very bad at sorting out legitimate opportunities. In an effort to simplify their lives (which, by the way, stymies financial returns and misserves their shareholders), restaurant management is applying categorical filters to every inbound conversation.
We believe part of this comes from the makeup of the restaurant industry. In most cases you will find that senior executives have been in the industry for their entire careers. While there is nothing particularly wrong with this in the abstract, it poses a serious problem when the restaurant industry is decades behind; people who have only known restaurants are not unlike remote tribes who die prematurely from treatable infections while the larger world has seen the likes of the Renaissance and Industrial Revolution.
Remember Maple from earlier? The company is run by engineers and ivy league graduates whose professional experience is mostly in the upper echelons of finance. But it doesn’t matter: business fundamentals are business fundamentals. That’s the same reason 3G brought in young, sophisticated operators to run Burger King.
Here’s a well-documented case study (Who Says Elephants Can’t Dance?) demonstrating the value of outside experience. Lou Gerstner graduated from Harvard and spent time at McKinsey, RJR Nabisco, and American Express. His background does not scream IT pioneer. Yet he was hired as CEO of IBM during its flailing 1990′s, and is largely credited with turning the company around. Why? Because Lou had been exposed to processes and people outside of IT, he acquired the learnings and confidence to turn around a company which he knew little about. Remember, business fundamentals are business fundamental, irrespective of industry.
Contrast this to Clarence Otis, CEO of Darden, who was ousted by Starboard. Clarence studied at Stanford School of Law and held a lustrous career as an attorney and investment banker. Then he joined Darden. From 1995 until his departure in 2014, Clarence was solely focused on the restaurant industry. Taking this myopic view, Clarence did not learn how other industries had implemented more advanced tools and systems; if he did, it was not apparent in Darden’s culture or its operations. Instead, Clarence increased G&A to an industry high while consistently underperforming peers.
There is, it seems, a special variety of self-preservation that is trumping fiduciary responsibility in restaurants.
To quote Steve Jobs,
“Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things. And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people.
Unfortunately, that’s too rare a commodity. A lot of people […] haven’t had very diverse experiences. So they don’t have enough dots to connect, and they end up with very linear solutions without a broad perspective on the problem. The broader one’s understanding of the human experience, the better design we will have.”
The good news is that many of these solutions would have massive impacts on profit-poor restaurant operators, and they’ve been proven in other industries over 20+ years. Instead of talking hypothetical impact, I’ll provide a concrete example. Using machine learning, we see 20% profit boosts through labor optimization. Ignore all the other potential benefits to supply chain, marketing, and other business areas; just focus on overstaffing costs. Darden’s latest annual results show $873M in EBITDA. Assuming a 20% gain is possible, we’re talking about an additional ~$175 million annually. So there could be someone – likely an entire group – within Darden who’s preventing shareholders from $175M in annual value… and it’s been going on for years. And trust me: it’s not isolated to Darden.
I understand restaurants can’t afford to compete for solutions on check size alone. But they don’t have to! The prices for such solutions have fallen so much that even the smallest of operators are being served profitably. If chain restaurants can make faster decisions and become more educated, these types of solutions won’t skip over them entirely – and new solutions may even flock to them first.
Today, restaurants have an opening to optimize their operations and generate more free time to focus on their customers – a staple of any successful hospitality business. This is a great opportunity for management that wants to do right by their shareholders, employees and their own personal bank accounts.
When will chain restaurants come around? Their leadership should pray before their shareholders find out…