In the past 3 years we have been tracking the progress of many startup closely, because we organised mentoring across multiple startup accelerators and programs.
This weekend I decided to aggregate and slice the data of our 528 log reports of coaching 111 startups in the past 2 years across 7 different programs. Most of these startups were really young when they entered these programs.
I found some useful insights while digging through all this data thoroughly:
Below is a detailed description of the data we got, how I analysed in and came to the above conclusions — including some examples. Also, we have some recommendations for accelerators to improve their program.
The log reports are mostly filed by mentors and program managers. We tracked the following:
I enriched the data with a couple of public data points per startup:
We use milestone categories which startups can associate to actions. This is not entirely the same as the business model focus, though.
On aggregate level, it’s clear that most early-stage companies struggle to find the right target customers and articulate their value proposition. It’s also interesting to compare that to the milestones these startups set regarding funding and the concerns the mentors have regarding costs.
Funding is not the main topic of discussion in most of the checkins and since most startups in the program are effectively only the founders, it means their runway is probably long enough to make it until the end of the program.
Hence, the aggregate data doesn’t teach us something we wouldn’t already know. Most startups fail, because of ‘No market need’. This is what CB Insights has proved already and if you look further down the list you’ll find more failure causes in the same category, such as ‘Ignoring customers’ or ‘Product without a business model’.
Since most accelerator program managers are well aware of these failures, they craft curriculums, which train the founders to avoid these problems. In some cases they do that so well that these founders can skip the follow-on funding stage in favour of paying customers.
In order to get to insights beyond what’s known in the industry, I had to dig deeper and look for patterns. Also, it’s hard to strictly draw conclusions from the data by itself. Here’s what I started doing:
However, when analysing the patterns across batches and pair that with my personal experience and those of the mentors I’ve worked with closely, here is what I found.
The pattern across these startups is that the mentor checks off a different part of the business model after each session every few weeks. They move from Value Proposition and Customer Segment to Channel, Revenue and other things.
Basically it means that the founders have identified potential customers and have a clear understanding what value they bring to these customers. They do so by following through on engaging with customers with the goal to produce learnings and insights. This only works if their customers are interested enough to also appreciate the company’s efforts.
From personal experience I also know that I had a good relationship with the startups who followed this pattern. The founders tend to be good listeners who follow through on advice. This doesn’t mean that they’ll blindly accept advice I’d give them, but they do process it often by triangulating it.
We all know the classical examples of how startups proved purchase intent through landing pages. The most known two examples are:
However, for many startups it is not feasible to copy these online experiments. For example, many business-to-business startups have a hard time to get potential customers to come to their website or landing page in the first place.
What remains is to simply go back to these potential customers and keep hoping that they’ll buy the product. The risk of that is that these customers will not buy and are simply pushing the conversation to learn from you just as much as you want to learn from them. What to do to get real commitment in case you can’t sell them a product yet?
Here are some examples out of Tristan Kromer’s Real Startup Book.
The principle behind these experiments is to match the customer’s words with actual behaviour. Rob Fitzpatrick, author of The Mom Test calls this Learn & Confirm. It’s an indication of** applying Lean Startup as a mindset.**
A few startups have a very interesting pattern, in which desirability (basically the right side of the canvas) was not in question at all. Basically, the big challenge for these startups are related to their Key Activities.
Here are three examples:
Lots of marketplace startups have this particular challenge, where the hardest part is building up inventory instead of finding interested customers.
I wrote a lot about the behaviour of successful founders, but lest we not forget that 21 startups went out of business and at least 66 did not get past the small team setup by now, which might indicate that they too are bound for failure. What makes them fail?
The data shows that many of these startups are wheel-spinning and do not get past the point of the right value proposition and the right customers willing to pay for that. During the accelerator programs the workshop and coaches do advise them on this, but yet it doesn’t happen. There are three main reasons for this:
Accelerator programs can address this in various ways:
Yet, many teams who come in have nothing to accelerate in the first place. They’re still figuring out what their business is about, who their customers are and what problem they could solve. Startups who get past that point do benefit from accelerators as they now need help to focus on growth and can follow through on advice from the coaches, network introductions etc.
However, this is currently the exception, not the rule, unfortunately.
