Tag: elephant

Why VCs continue to fund me-too startups

In a previous post, I had written about how a large number of startups in India are “me-too” companies, and that a sector, once it becomes hot, gets overcrowded. I had also expressed incredulity at the fact that Venture Capitalists continue to fund such “me-too” startups despite knowing that they are copies of companies that exist.

Thinking about it, however, there is one reason that makes the decisions by VCs to fund me-too startups worthwhile – mergers and acquisitions. And this hypothesis is based on M&A activity in the “hyperlocal delivery” (one of those “hot” buzzphrases) space.

Nowadays, due to activity in the sector, the hyperlocal delivery sector has become the equivalent of Pets.com from the turn of the millennium. At a conversation a month ago, for example, a bunch of us weren’t able to fathom how something like Swiggy is valued at what it is, given its decidedly low-tech business of taking packed food from restaurants and delivering it to customers. A couple of months before that, TinyOwl, which is in a very similar business, had raised similar money.

But then two events in the recent (and maybe not-so-recent) past have indicated why VCs continue to invest (and heavily ) in such sectors. Firstly, in February, Foodpanda acquired the Indian operations of Justeat. Both companies are in the business of delivering packed foods from restaurants to people’s homes. And last week, grocery retailer BigBasket acquired Delyver, yet another company in the business of transporting packed food from restaurants to homes.

There is this Panchatantra story about a Jackal and a dead elephant. Basically a jackal comes across a dead elephant, and wants to eat it. But for this, he has to fight off other competitors, and also get the elephant’s skin torn in the process. The story involves how he uses different strategies to outwit different animals. Here is a youtube video, not very well made, of this story:

This is the cover of the  Amar Chitra Katha edition where I first came across this story.

And this link has a good summary of the story, all you need to know. Exactly like how it’s in the Amar Chitra Katha story.

The moral I derive from this story in this context is that there are different ways to deal with opponents/competitors. Some opponents you just fight off and finish. Others you learn to coexist with. Yet other you simply “swallow” or acquire. Each of them has its own set of payoffs.

Based on the deals described above, what we notice in the “transport-of-packed-food-from-restaurant-to-homes” business is that companies are preferring to swallow each other (and coexisting with some others) rather than fighting. And when one company acquires another, investors in the target company get a “soft landing”, and don’t lose all of their investment (though it is well possible that the acquisition happens at a valuation lower than that when the investors invested, but ratchets might take care of that).

Apart from investors not losing too much, the advantage of acquisitions is that existing infrastructure of an erstwhile competitor can be leveraged. And when companies are in growth mode and profit and cash are not as important as growth, an acquisition works really well in generating significant inorganic growth. It is a win-win for multiple reasons.

The fact that mergers are the preferred way of getting rid of competition in the startup world puts a cap on the losses an investor might have to bear on an investment (and there are ratchets in any case). And since the downside is now limited, the risk of investing in a me-too startup is significantly lower. In other words, investors invest in a me-too startup since they believe that in the near-worst case it will get acquired rather than shut down. And as a further consequence, there is more incentive for entrepreneurs to set up me-too startups (assuming they can get funded) rather than venturing into virgin territory.

Arranged Scissors 13 – Pruning

Q: How do you carve an elephant?
A: Take a large stone and remove from it all that doesn’t look like an elephant
– Ancient Indian proverb, as told to us by Prof C Pandu Rangan during the Design of Algorithms course

As I had explained in a post a long time ago, this whole business of louvvu and marriage and all such follows a “Monte Carlo approach“. When you ask yourself the question “Do I want a long-term gene-propagating relationship with her?” , the answer is one of “No” or “Maybe”. Irrespective of how decisive you are, or how perceptive you are, it is impossible for you to answer that question with a “Yes” with 100% confidence.

Now, in Computer Science, the way this is tackled is by running the algorithm a large number of times. If you run the algo several times, and the answer is “Maybe” in each iteration, then you can put an upper bound on the probability that the answer is “No”. And with high confidence (though not 100%) you can say “Probably yes”. This is reflected in louvvu also – you meet several times, implicitly evaluate each other on several counts, and keep asking yourselves this question. And when both of you have asked yourselves this question enough times, and both have gotten consistent maybes, you go ahead and marry (of course, there is the measurement aspect also that is involved).

Now, the deal with the arranged marriage market is that you aren’t allowed to have too many meetings. In fact, in the traditional model, the “darshan” lasts only for some 10-15 mins. In extreme cases it’s just a photo but let’s leave that out of the analysis. In modern times, people have been pushing to get more time, and to get more opportunities to run iterations of the algo. Even then, the number of iterations you are allowed is bounded, which puts an upper bound on the confidence with which you can say yes, and also gives fewer opportunity for “noes”.

Management is about finding a creative solution to a system of contradictory constraints
– Prof Ramnath Narayanswamy, IIMB

So one way to deal with this situation I’ve described is by what can be approximately called “pruning”. In each meeting, you will need to maximize the opportunity of detecting a “no”. Suppose that in a normal “louvvu date”, the probability of a “no” is 50% (random number pulled out of thin air). What you will need to do in order to maximize information out of an “arranged date” (yes, that concept exists now) is to raise this probability of a “no” to a higher number, say 60% (again pulled out of thing air).

If you can design your interaction so as to increase the probability of detecting a no, then you will be able to extract more information out of a limited number of meetings. When the a priori rejection rate per date is 50%, you will need at least 5 meetings with consistent “maybes” in order to say “yes” with a confidence of over 50% (I’m too lazy to explain the math here), and this is assuming that the information you gather in one particular iteration is independent of all information gathered in previous iterations.

(In fact, considering that the amount of incremental information gathered in each subsequent iteration is a decreasing function, the actual number of meetings required is much more)

Now, if you raise the a priori probability of rejection in one particular iteration to 60%, then you will need only 4 independent iterations in order to say “yes” with a confidence of over 95% (and this again is by assuming independence).

Ignore all the numbers I’ve put, none of them make sense. I’ve only given them to illustrate my point. The basic idea is that in an “arranged date”, you will need to design the interaction in order to “prune” as much as possible in one particular iteration. Yes, this same thing can be argued for normal louvvu also, but there I suppose the pleasure in the process compensates for larger number of iterations, and there is no external party putting constraints.