Last year we tipped our hat to Maple, a startup kitchen providing delivery-only service, as an example of a restaurant concept deftly using data. Look no further than this example of theirs:
The current gold standard for zipping patrons through a lunch line (what the industry calls “throughput”) is Chipotle. According to its 2014 annual report, Chipotle manages to serve 300 meals per hour—a transaction every 12 seconds—at its best-performing locations, and the chain is so obsessed with its productivity that it assigns employees efficiency roles with names like “linebacker.”
When Maple launched its first location in April, it served around 50 meals per hour at peak times. Less than a year later, on average it is now serving 800 meals per hour from each of its four kitchens. A few days before I visited in February, it had set a new record: 1,100 meals cooked and delivered in one hour.
Maple was able to generate such impressive operational numbers because it was run by people from outside the restaurant industry who understood and valued data. Former investment bankers, consultants, and a team of engineers.
We recently read the news that Maple is suspending service after raising over $50MM.
How could such a high flier fall so far, so fast? Were we wrong in our praise?
Luckily leaked financials will lead us to some answers.
Here’s a chart of Maple’s expenses for 2015 and early 2016. You can see that they were barely breaking even in March (the “now”) of 2016.
But there were still nagging issues. Look at Maple’s financial projections to see why:
Food waste was nearly 45% of food costs in 2015 and is still projected to be 20% of food costs looking into the future. Labor is also exceptionally high when you add in corporate payroll, hub-level labor costs and another slush fund that might hide labor dollars: city level operating costs. Marketing is also a staggering expense, running at 20% of revenue.
Maple was offering its meals at a fixed price, which included tax, tip and delivery, but eventually changed course and added a $1.95 delivery fee to each order. Was this a harbinger for their underlying problems?
Foodservice businesses have thin margins as it is. It’s also an incredibly saturated landscape where consumer loyalty is fickle, and an endless list of substitutes sits a few taps away on your smartphone.
Maple’s biggest mistake, however, was taking too many investor dollars. Their aggressive marketing spend and rapid growth made forecasted demand impossible, even with their advanced machine learning capabilities. This uncomfortable fact cascaded through their operations, manifesting itself as higher labor costs and unsustainable food waste. Add to this that delivery services are still not economic (and probably won’t be until you can use third world rickshaw drivers or autonomous vehicles) and you have a time bomb on your hands.
Maple’s swan song was heavily influenced by market maturity because they had the talent and available capital: they just put them to work on the wrong things at the wrong time, making customer acquisition costs too high to turn a profit. If delivery services were economic (ie autonomous vehicles were widely available) would we still be having this discussion?
Life is about timing, and Maple may have only been guilty of getting that wrong.