This post is a follow-on to our recently released FinLab Snapshot, a report in which we identified industry insights, trends and analyses based on FinLab’s 356 applicants in 2016. In this post, we’ll share more of our funding related applicant data and call out what we think these data imply.
As we shared in our Snapshot report our applicant pool raised a total of $420M, which is spread across 351 applicants – five applicants did not report their funding and we could not find it publically. Here’s a chart of funding comparing this year’s applicants to last year’s. This was included in the original report.
An arithmetic mean of this year’s applicant funding data shows that the average applicant had raised $1.2M. But as is the case with most averages, this number is largely useless. We’ll break this data down across a few different applicant attributes: sector, geography, team size and product type.
For-profits compared to nonprofits
The first way to make this data more useful is to look at nonprofit and for-profit applicants separately. We had 36 nonprofit applicants and 320 for-profit applicants. Nonprofit applicants had raised an average of $2.3M while for-profit applicants had raised an average of about half of that at $1.1M. After removing outliers – 14 applicants had raised more than $5M – nonprofits had raised 27% more than for-profits: an average of $568K compared to the for-profit average of $447K, a number we reported in the original report.
This difference is skewed by the fact that fewer nonprofits are being started than for-profit startups in today’s market: 95% of applicants founded in the 12 months preceding our application are for-profit startups. So the nonprofits that did apply have been around longer than the for-profit applicants.
Nonprofit applicants also had a larger variance. That’s best conveyed by the graph below.
This larger spread is at least partially due to a smaller number of applicants – the law of large numbers applies more to the for-profit applications than nonprofits. We’ll focus on for-profit applicants for the rest of this post because we only had 36 nonprofit applicants and how nonprofits raise capital is structurally different from for-profit startups (ie., grant capital compared to venture capital funding).
Our Bay Area for-profit applicants this year had raised 2.7x the amount raised by companies based outside of the Bay Area – though they had only 33% larger teams and 45% more users. This Bay Area multiple jumps to 5.2x for pre-product applicants (ie., reporting 0 users), though the number of applicants in these groups is small. Both of these multiples represent significant increases from the multiples last year (1.8x and 2.1x).
We did a similar analysis for companies in NYC vs non NYC. While NYC companies had raised more, the multiples are seemingly more reasonable (1.4x overall and 1.8x for pre-product) than Bay Area multiples.
One way to normalize funding across applicants is to measure funding raised per team member. Most startups raise money when they need to hire. The average for-profit applicant had raised $103K per team member (inclusive of founders). Statistically, the coefficient of variance for funding is double that of funding per team member.
Interestingly, six out of eight for-profit companies that we ended up selecting for FinLab were above average. The two that were below the average were both raising capital around the time we selected them and had secured enough funding to move them above the average funding per team member. The average FinLab winner had raised $278K per team member when applying – 2.7x the average. While this was not part of our selection criteria, the core idea here is not surprising, but worth noting: capital is a proxy for product traction.
By product type
Insurance applicants had raised the least of any product type, perhaps because insurance innovation is still nascent in its development, relative to other verticals within fintech. On the other hand, Payments and Credit startups had raised the most capital – 4x and 3x more than Insurance startups – perhaps because entrepreneurs have been innovating for a few years in these verticals and because high-levels of capitalization are more advantageous in lending in payments than in planning and savings startups.