Once upon a time there was a brilliant startup founder who hit upon a great idea, built a product, immediately hit exponential growth and went on to become a billionaire icon.
Who was the lucky founder? Facebook’s Zuckerberg? Groupon’s Mason? AirBnB’s Blecharczyk?
The answer is none of the above. This account is a fairy tale, imbued with as much fantasy as a Grimm’s bedtime story, yet arguably a tale that is at least partially responsible for the vast majority of startup failures—via unrealistic expectations.
We can be forgiven for crafting compelling narratives: As humans, our neurobiology demands it. Tens of thousands of years of evolution developed stories into the vehicles by which our brains derive meaning from the world. If the cavemen had shared data around the campfire, things might have turned out differently.
The problem happens when all the soft nuances are shaved off the story and it hardens into myth. Fine distinctions get warped into broad generalizations and real meaning is distilled to a simplified headline. Currently, myths such as these encourage founders to attempt growth before their business is ready, leading as many as 74% of high growth internet startups to fail due to premature scaling.
This number is so large it can initially be challenging to believe, yet one ultimately finds this incredibly common mistake goes a long way to explaining the dismal 90% failure rate of startups. The lower one’s burn rate, the longer one can survive, using every opportunity to pivot as necessary to achieve a fit between product and market. The higher burn rates of startups pushing for scale take away the safety net.
Of course, there is nothing wrong with growth. The primary distinction between a small business and a startup is the expectation of high growth—or as Paul Graham put it in one of his essays: Startup=Growth—but data shows that attempts to scale must be appropriately timed. Serial entrepreneur Jim Pitkow defined the concept very succinctly: “Premature scaling is growth in anticipation of demand instead of demand-driven growth.”
So the billion dollar question—literally—is when is a startup ready to scale? Until recently, founders have had no objective way to know.
Over the 20 years I’ve spent in marketing, one of my most memorable leadership experiences came while working for a startup that eventually achieved a successful IPO. Sitting down one afternoon with the CEO, I thought I knew our data inside and out: customer acquisition cost, lifetime value, retention, spend, headcount. In fact, I was feeling particularly proud that we’d achieved a user growth figure significantly better than prior years. Yet when I mentioned the number he asked the best possible question, a simple one that left me utterly stumped: “Is that good?”
In one of those lightning bolt moments, I realized that all my detailed internal analysis may have been useful for managing our marketing department, but not leading it. I only knew our growth rate was better than last year, but did that put us in the the 90th percentile of our peers or in the 10th? Did I deserve a pat on the back or a kick in the behind? I had the data, but was missing the most important element: context by which to make sense of it.
I was not alone. For mature industries, growth benchmarks are widely available, but for small and medium businesses trying to set targets or plug figures into a business model, finding a good benchmark for growth has been next to impossible.
In place of relevant benchmarks, founders and investors often encounter myths presented as fact: 22% week-on-week growth for Facebook, 20% for Groupon and 17% for AirBnB. What’s extraordinarily difficult to find are the nuances behind those stories. AirBnB founders spent years getting themselves into credit card debt (then selling political-themed cereal to get themselves out) before their storied growth curve began. Groupon was a struggling activism engine that tested coupons as a skunkworks project to keep the lights on before they pivoted to real growth. Facebook was a side project for Zuckerberg who initially accepted ad revenue to offset the $85 per month server space he rented.
Why does myth and uncertainty lead to failure? Founders are consistently under intense pressure to scale, but have few tools beyond their own instincts to decide if they’re ready. Investors want to see a growth curve. The head of sales wants to hire a team of five. Marketing needs more budget to beat AdWords competitors. Without benchmarks to provide an objective perspective, it’s no wonder so many startups scale too fast. But what if founders weren’t blind to their performance relative to peers? What if they could see an abnormally low retention rate that identified a product issue needing to be solved before the big ad campaign began? Or a lower than average close rate pointing to a valid need for more sales staff? Or that peers relying more heavily on PR achieved better results than pay-per-click anyway?
Startups that wait for the right time to scale have much higher rates of both survival and success, whereas those likely to fail overspend on customer acquisition, hiring, product development and several other key metrics before they’re ready.
The graph below demonstrates that those that those who start strongest are not necessarily those who finish strongest. Here we can finally bring in a fairy tale that is valid for comparison: that of the Tortoise and the Hare.
Startup failure rates aren’t just a problem for entrepreneurs or Silicon Valley. The Kauffman Foundation Study showed that net job growth in the US was driven entirely by technology startups. Thus, it is not a stretch to say that if we could improve the success rate of startups by giving founders context about when to scale, the economic future of the country could be significantly improved. Even the globe.
To meet that need, Compass has built a benchmarking engine to allow startup founders to compare themselves to relevant peers, based on multiple criteria. Our mission is no less than to shine the light of transparency into the dark corners of myth and uncertainty, to provide a platform to allow founders and investors to access the critical context they need to make effective strategic decisions. Our 30,000+ CEOs now have a simple tool that helps answer the most fundamental question—“Is that good?”—with real-time benchmarks against a customized group of peers.
Outside the tool, we can provide aggregate figures which are less uniquely relevant, but still provide critical transparency into an otherwise opaque world.
The median user growth rate for startups is 9% per month and those in the 90th percentile hit at least 65.2%.
Having this clear range is a good first step, but to make decisions with figures aggregated from thousands of startups with different customers, business models, products, acquisition channels, user bases and levels of funding would be about as effective as blending all the food in your fridge together and calling it soup. You can forget talking about the nuance of flavors when you’ve got mustard, peanut butter and last week’s burrito in the same bowl.
So here is a more refined breakdown.
User growth by user base
In the graph below, we broke down the growth rates of software businesses by the size of their existing user base and displayed both the median and 90th percentile values. What can we learn from this? First is a 5-10x difference between the median and the fastest growers, which shows significant variation in acceptable results. We can also see some trending, where median growth rates tend to peak around 1000 users but the fastest growers keep getting bigger until at least a million.
User growth by acquisition channel
Here we see data from the ten most popular acquisition channels for startups (listed in order of popularity). Again, we see significant variation from the median to the fastest growers, as well as among channels.
User growth by funding level
Another interesting perspective on user growth is to look at the data broken out by funding levels. For those who’ve received some level of funding, the following breakdown represents the user growth they are experiencing.
The most heartening thing about this graph is how similar it looks to those above. The median growers are around 9% per month and the fastest growers are 50-60%, showing that plenty of investors are looking at other factors in addition to growth rate when funding companies.
Of course, we are still looking at aggregates. You may be an enterprise software company for whom 10 corporate users generates significant revenue, comparing yourself to a freemium mobile app who needs a million users before they break-even, or vice-versa. What you really want is not data from thousands of startups but the 50-100 that are relevant to you via your customized Compass peer group.
Using Compass, I can finally answer that our growth rate at that previous company was in the 80th percentile of our relevant peer group and 3x the median of our peers, so yes—pretty darned good. I wish I’d had access to that information back when it would have been helpful with decision-making.
Luckily, leaders today have more tools at their disposal. Finally, we can move past the fairy tales and embrace the nuance by providing context to our data. Welcome to the new world of growth: measured through the lens of real-time, relevant benchmarks.