The Unit Economics of On-Demand Startups Explained
Revenue growth! Revenue growth! Revenue growth! Get to as many geographies as possible, and do it fast. Let’s see that top-line revenue! I have been running a venture-backed, on-demand logistics startup targeting the restaurant space for the last 2 years, and we grew top line revenue over 8000% in 2015. While this may sound great, I can tell you first hand, it wasn’t. In fact, we were losing money fast at one point, until we fully wrapped our heads around unit economics. Here is what I learned.
Even with Uber and Amazon making moves, we could be on the verge of seeing a massive collapse in the on-demand space. Over the last year, companies have begun to realize the flaw in the revenue growth logic when it comes to on-demand startups; and, it all comes back to this little problem of unit economics.
First, you have to realize that no matter the app or system you are using to order your Chinese food, we are still in an age where a human being has to go pick something up and bring it to you… We have not automated fulfillment yet, and we are at least 5–10 years away from a drone or autonomous vehicle dropping off your food. Second, you need to look at everything on an hourly basis, because that is the same time scale that the supply-side labor (the people running your delivery or completing your on-demand task) thinks about his or her wages.
Here is the “Unit Economic Theory ” equation:
Basically, a company needs to ensure that they are bringing in more revenue per hour than it costs them to incentivize a person to pickup and run your deliveries, complete your tasks, etc. If this gets out of whack, the company is losing money every hour they operate. Now, let’s examine each variable and discuss the implications and where many of these on-demand companies are in trouble.
Revenue / Task: This is the cost the company is charging for the task. In some cases it is a food delivery, in other cases it might be a task like cleaning. This is NOT the price of the goods. It is the delivery fee, the service fee, etc. that you, the consumer, are paying the company to bring the goods to your door or complete some task.
# of Tasks Completed / Hour: This is the single most important variable in this equation. This is the average utilization of the company’s labor fleet. I say “fleet” because you should think about this number as an average across all of the company’s personnel executing that task in a given area. For example in delivery on-demand, it is the average number of deliveries per hour that the employee or contractor makes when working for the company.
Incentivization Cost / Hour: This is the amount per hour that the company has to pay the person running deliveries or completing tasks. It is called “incentivization” because it is the amount an employee or contractor needs to earn in order to keep showing up to work everyday and not explore other job opportunities. While bringing you pizza might sound thrilling, the bulk of the labor pool running these deliveries have other employment opportunities: work in retail stores, restaurants, construction, etc.; so, this is the cost of retaining that individual to run the company’s deliveries instead of them saying, “F-this! I can go make money elsewhere”.
Now, the full story:
The Revenue/Task is fixed by the cost of the good or service delivered. In the food delivery space, for example, the average cost of the food being delivered is $25. That means the delivery charge that a customer is willing to pay is somewhere in the $5-$10 area (at best). Very few consumers are going to pay more than 50% of the cost of goods sold for the convenience of on-demand delivery, especially for low price point items.
The Incentivization Cost/Hour is also fixed. In developed countries, there are standards on hourly pay rates, and expectations from people filling these jobs. If the incentivization cost is not being met, the company will have massive churn in their contractor/employee workforce. This leads to an endless, and expensive, recruiting cycle (advertising on craigslist, interviewing, screening, background checks, etc). There is also a theoretical limit to the supply side labor pool that at some point will be exhausted.
That means the only variable in this equation is # of Tasks Completed / Hour, or what I call the utilization. This is the magic number. You can use this equation to assess almost every on-demand company’s unit economics, and determine what efficiency, or utilization, they need to operate at to stay in business. And that is exactly what you find: Efficiency is king. Utilization is the number one influence of whether or not the unit economics of an on-demand business make any sense.
It doesn’t matter if a company is in 25 cities or 1 city, if utilization isn’t at the correct level, the company is losing money every hour it operates. In fact, the company in 25 cities is losing money much faster than the company in just one city. Therefore, we should all enjoy the free delivery or cheap cleanings while we can, because I anticipate a massive geographical constriction of these apps and a severe price increase for their usage. Ultimately, the on-demand boom is going bust in most areas where utilizations don’t pan out, and I think the majority of consumers will just decide to get off the couch.
