In our years of dealing with restaurants we are always 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 – at least – the 20th century.
First, we should examine the sorts of things the restaurant industry is missing. There are a myriad of solutions and progressions that we could point to, but we limited our analysis to 4 core areas, which we will discuss 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 credit card authorization clearing houses. This means, for the first time, sales data and consumer spend data were available in quasi-electronic format. Around that time, Experian began analyzing this data alongside retailers to better understand who their core customers were, what they were buying, and how audiences were responding to promotions.
A few years later, in grocery, Nielsen started providing grocers with free scanning hardware. The goal was to collect intelligence about the kinds of items being sold at grocers around the world. With access to this information, suppliers would help low-margin grocers better manage their supply chain and analyze opportunities for revenue and margin growth. Since the inception of Nielsen’s Scantrack, grocers have divested from the G&A associated with managing categories as suppliers foot the bill for all things supply chain.
If there were ever a sister to the restaurant industry, it would be the hotel industry. The hotel industry maintains 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. Since markets respond to local stimuli, it was important to create a tool that would be relevant for local markets. Since its inception, it’s estimated that nearly 80% of the hotels use the STAR report.
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 spectacularly was i2 Technologies, later acquired by JDA. As the pervasiveness of point of sale 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 to perfection.
Now that we’ve detailed some specifics that other verticals have leveraged over the past 40 years, where are restaurants relative to this? In a simple answer: the same place they’ve been since the 1800′s.
Restaurants are unable to accurately build customer segmentation databases. Even large, public restaurant chains are incapable of pulling data across multiple locations to understand who their customers are, what they’re buying, and what promotions they’re responding to. Restaurants are also unable to leverage the intelligence of their suppliers to maximize revenue and margin through the supply chain, and suppliers have abandoned hope of restaurants becoming as sophisticated as most other verticals.
The restaurant industry also does not maintain a benchmarking tool relevant in local markets. Small hoteliers have grasped this value from the 1980′s even though large restaurant chains do not yet understand the importance of benchmarking. Restaurants have developed simple rules for how stores should operate, irrespective of location. For instance, stores that do between $1-2M in revenue will all be staffed the same on Thursdays. On its face, this is absurd.
With ample opportunity to improve performance, why have solutions not yet been developed? We sought to understand this conundrum with both empirical data and anecdotal feedback. The results, now that we understand them, are not that surprising.
We first looked at other industries. Using data from Pacific Crest and Marketing Sherpa, we found sales cycle, contract size, and number of touches that SaaS providers went through in other verticals. We then broke that data down by customer size, as seen below.
We then looked at this data compared to SaaS companies in the restaurant vertical serving operators with 1000+ employees. This quick juxtaposition shows precisely why no providers have bothered to help the restaurant vertical.
By serving the restaurant vertical, SaaS providers are taking an astounding 8x revenue risk. Restaurants pay roughly ~30% of the contract value you’d get in other verticals, and they take more than twice as long to make a decision. The result is a non-starter for innovation; investors do not want promising ideas spending time with restaurants, as the time it would take to prove value and gain traction could be sufficiently long to kill the bank account and motivation of many startups.
The good news is that any of these solutions would have massive impacts on a profit-poor operator, and they’ve been proven in other industries over 20+ years. To attain these solutions, restaurants will need to make themselves known as a friend to business and innovation. The two ways of doing this are either increasing contract sizes or decreasing sales cycles commensurate with the small amount of money they spend. Until there is a mental shift, however, we don’t expect innovation to come to the restaurant vertical.