So, how do you really measure if a VC is an early adopter versus a late adopter? (lets keep it simple and only put them into 2 categories).
My thinking is the only way you can do that is to look at their investments (portfolio companies) and find out the categories of companies they invested in. Then find out if any other VC’s invested in another company in that category after the “first” VC did. There are other ways to do that, like ask entrepreneurs who responded the fastest when they were looking for funds, but those dont evaluate who puts their money where their mouth is.
Why is this question useful to answer?
For entrepreneurs who are innovating in a new area, this list of early adopters will help you determine who you should go to first versus who should you expect will fund a possible competitor.
Lets define our methodology and assumptions:
1. We will look at all their websites and make a list of the Indian VC portfolio. Fortunately we have that list of over 50 VC’s in India.
Flaw: Many dont update their website as frequently so there may be a 20% (or higher) error, but I have tried to be comprehensive.
2. We will then categorize their investment into 5 buckets – Media and content, eCommerce, Business to Business, Mobile and other (Education, Healthcare, etc). This is important so we know not only which VC’s are early adopters but we can also try to find that out by sector.
3. Then we will look at the announcement dates of their funded companies from press releases, Unpluggd, YourStory, ET and VCCircle. We will give them 2 points for every investment done in a sector before any other VC did.
Flaw: Most (I suspect over 50%) of companies report their funding 3-6 months after they have raised the money, so this will be a large flaw, but lets do the analysis anyway.
4. Finally look at stage of investment. If a VC puts money in the series A, I would give them two points in the early adopter bucket. If, however they participated in series B or later, they get one point in the late adopter bucket.
First let me give you the results (not in any order other than early adopters vs. late adopters).
Early adopters VC’s.
- Accel (eCommerce, B2B) – 78 points
- Indo US Venture Partners (B2B) – 56 points
- Saif partners (Mobile, eCommerce), but they are late adopters in B2B – 49 points
- Venture East (B2B) – 45 points
- Sequoia (Media) – 46 points
- Seedfund (Scored enough, but dont have a clear winning category) 42 points
In the middle
- Blume ventures – 40 points
- Nexus Venture partners – 36 points
- Helion – 36 points
- Ojas ventures – 34 points
Later adopter VC’s – all scored less than 30
- Bessemer Venture Partners
- Cannan partners
- India Innovation fund
- Inventus Capital
- Footprint ventures
- IDG ventures
- India Internet Fund
- Lightspeed partners (but have done well in Education)
What I hope this list will do?
1. Make Indian VC’s think about being innovation catalysts rather than ambulance chasers. I understand you have a responsibility to provide returns, but you also have a responsibility to grow the Indian startup ecosystem. Might I suggest a 5-10% of your portfolio towards risky, “first time this is going to happen” investments?
2. Make Indian company founders announce their funding. Unlike the US, here entrepreneurs are loathe to do so. I can understand the competitive pressures, but not doing any announcement is just lame.
3. Educate Indian entrepreneurs on their target VC list. Depending on the opportunity you are trying to pursue, please target the right VC firm. The only thing you have (and dont have) on your side is time. Use it judiciously.
P.S. I have confidence in the methodology but I would be the first to admit its neither comprehensive nor scientific. If you are an eager MBA / Engineer / analyst and would like to help make this methodology and analysis more robust, I’d love your help. You can take all the credit. In fact, I can convince many publications to give you credit for the work if you desire and if you keep it updated every 3-6 months.
P.P.S. If you are a VC and not in the early adopter list, or you are not happy with the analysis I’d also welcome your associate’s help in making this analysis robust.