Category: platforms

Cabs to airports

Early yesterday morning I had a minor scare when Mega Cabs stood me up. I had a flight to catch at 7 am to Mumbai, and had booked a Mega Cab for 5am. This was after consulting a few friends who are frequent travellers on Monday mornings, who advised that finding an Uber or Ola at 5am is not particularly straightforward. I must mention that I haven’t done business trips for a while, which means I haven’t had to catch 7am flights, so the last time I took one such flight was before Ola/Uber became big in Bangalore (October 2014). And I’ve always preferred Mega to Meru since their cabs are relatively better maintained and more prompt.
And then Mega stood me up. The assigned driver Nagesh N never called me, and when I called him, didn’t pick up. I didn’t panic, since I knew I could get a cab on Uber or Ola, except that neither had any cabs available. I called Mega customer care, who promised an alternate cab at 5:15 (still leaving enough time to get to the airport and catch my flight). But then I received an SMS saying that I’ll get a cab at 6:15. Rather than arguing with Mega, I tried Uber once again, and this time I was in luck, finding a cab that would take me to the airport at a surge of 1.8X (80% more than the “normal” fare).
So on the way to the airport I got talking to Kumar, my Uber driver, about the economics of cab rides in Bangalore, and airport trips. As I had mentioned in my earlier post on Uber’s new pricing model, the reduction in per kilometer fare and increase in per rupee fare has meant that an airport run is normally not remunerative for an Uber driver. Add to this the fact that Uber’s bonus payments to drivers are on a “per trip” basis rather than a percentage or distance basis, that a driver reaching the airport at around 6am has to wait for at least a couple of hours to get a passenger to ride back to the city, and that Uber’s new bonus structures that began today not paying much incentives for trips before 7 am (this was told to me by Kumar), drivers have responded by simply not switching on their Uber systems at 5 in the morning, when the likelihood that any trip is an airport run approaches 1.
This is clearly inefficient, and  consequence of bad pricing on behalf of Uber. On the one hand, drivers are denied opportunities to carry customers over long distances, which is an airport run. On the other, customers are inconvenienced thanks to the lack of cabs, and have to rely on the otherwise rather unreliable and mostly unused Meru or Mega cabs, whose cars are of poor quality and drivers unresponsive. A lose-lose situation. All thanks to bad regulation (read my post in Pragati on how Uber is like a parallel regulator).
The solution is rather simple – an airport surcharge. Any trips to or from the airport on Uber can be slapped a further surcharge (of Rs. 200, perhaps). Such a surcharge will make the ride remunerative for drivers, while at the same time still keeping Uber much cheaper than the likes of Meru or Mega. In fact, this morning’s trip, after the 1.8X surge, cost me Rs. 780, which is cheaper than what it would have cost me if Nagesh N of Mega Cabs had not ditched me, and I could pay in a “cashless” manner, directly from my Paytm account. It’s a surprise that Uber hasn’t yet figured this out, given all their “data science” prowess!
Update: 
A friend who I met on the flight told me that in his town (Whitefield) it’s not hard to find an Uber/Ola cab at 5am on Mondays, except that the drivers cancel rides once they figure out it’s for an airport drop. Again pointing to the fact that incentives are not aligned for maximum throughput

Pipes, Platforms, the Internet and Zero Rating

My friend Sangeet Paul Chaudary, who runs Platform Thinking Labs, likes to describe the world in terms of “pipes” and “platforms”. One of the themes of his work is that we are moving away from a situation of “dumb pipes”, which simply connect things without intelligence, to that of “smart platforms”. Read the entire Wired piece (liked above) to appreciate it fully.

So I was reading this excellent paper on Two-Sided Markets by Jean-Charles Rochet and Jean Tirole (both associated with the Toulouse School of Economics) earlier today, and I found their definition of two-sided markets (the same as platform business) striking. This is something I’d struggled with in the past (I admit to saying things like “every market is two-sided. There’s a buyer and a seller”), especially given the buzzword status accorded to the phrase, but it is unlikely I’ll struggle again. The paper says:

A necessary condition for a market to be two-sided is that the Coase theorem does not apply to the relation between the two sides of the markets: The gain from trade between the two parties generated by the interaction depends only on the total charge levied by the platform, and so in a Coase (1960) world the price structure is neutral.

This is an absolutely brilliant way to define two-sided markets. The paper elaborates:

Definition 1: Consider a platform charging per-interaction charges a^B and a^S to the buyer and seller sides. The market for interactions between the two sides is one-sided if the volume V of transactions realized on the platform depends only on the aggregate price level

a=a^B +a^S

i.e., is insensitive to reallocations of this total price a between the buyer and the seller. If by contrast V varies with a^B while a is kept constant, the market is said to be two-sided.

So for a market to be two-sided, i.e. for it to be intermediated by an “intelligent platform” rather than a “dumb pipe”, the volume of transactions should depend not only on the sum of prices paid by the buyer and seller, but on each price independently.

The “traditional” neutral internet, by this definition, is a platform. The amount of content I consume on Youtube, for example, is a function of my internet plan – the agreement between my internet service provider and me on how much I get charged as a function of what I consume. It doesn’t depend on the total cost of transmitting that content from Youtube to me. In other words, I don’t care what Youtube pays its internet service provider for the content it streams. Transaction costs (large number of small transactions) also mean that it is not practically possible for Youtube to subsidise my use of their service in this model.

Note that if buyers and sellers on a platform can make deals “on the side”, it ceases to be a platform, for now only the total price charged to the two matters (side deals can take care of any “adjustments”). The reason this can’t take place for a Youtube like scenario is that you have a large number of small transactions, accounting for which imposes massive transaction costs.

The example that Rochet and Tirole take while explaining this concept in their paper is very interesting (note that the paper was written in 2004):

…As the variable charge for outgoing traffic increases, websites would like to pass this cost increase through to the users who request content downloads…

..an increase in their cost of Internet traffic could induce websites that post content for the convenience of other users or that are cash-strapped, to not produce or else reduce the amount of content posted on the web, as they are unable to pass the cost increase onto the other side.

Note how nicely this argument mirrors what Indian telecom companies are saying on the Zero Rating issue. That a general increase in cost of internet access for consumers can result in small “poor” consumers to not consume on the internet at all, as they are unable to pass on the cost to the other side!

Fascinating stuff!

On Sony Six telecasting Pacquiao-Mayweather

Summarising the blog post:

1. Having paid for the rights to the fight, the incremental cost of showing the fight to a customer is negligible, making this a great case for “revenue management”.
2. Each television market is independent, and in each the holder of the rights indulges in “monopoly pricing”. The monopoly price for the US is $~100. For India, it is close to zero. 
3. Television is a two sided market, and by offering the content at Zero rupees in India, the rights holders are maximising the sum total of what they can earn from viewers (subscription fees) and what they can earn from advertisers. 

Now for the harikathe:

So the much-awaited bout between Manny Pacquiao and Floyd Mayweather is going to be telecast on Sony Six tomorrow, as per this tweet:

Some people are surprised that this fight is being telecast on a “normal” sports channel in India, considering that elsewhere in the world it is being mostly telecast on pay-per-view channels, with the payment for one connection running close to a hundred dollars. Yet, in India, we will get to see this without shelling out any incremental cost over what we have already shelled out to receive Sony Six (and most people who are interested in the fight are likely to have already subscribed to the channel since it telecasts the ongoing IPL. The difference between {people who want to watch Pacquiao-Mayweather} and {people who want to watch IPL} is infinitesimal and can be ignored).

So why is it that a fight that is being sold at an exorbitant premium in most places in the world, and billed as the most sought after boxing bout in over twenty years, is being shown at a throwaway price (close to zero) in India? The answer is simple – revenue management.

For the holder of the telecast rights of this fight, having paid for global telecast rights, any further costs of telecasting to an additional television set are marginal. In that sense, any marginal revenue that they make from the further sale of these rights goes directly to their bottom line. Hence, this is a classic case for “revenue management”, where they will try to maximise the revenues from the rights they hold.

Given that they hold monopoly rights over telecast of the bout, we can expect them to follow “monopoly pricing” to price their product. Monopoly pricing, as the name says, is how a monopoly would price a product, which is literally true in this case. For every price point, there is a certain demand, and monopoly pricing prices the product at a level that maximises revenue (price x quantity). And considering that television rights are usually at a national (or even sub-national) level, monopoly pricing can mean that there are different prices in different markets.

The US, for example, is a market that has an established model of pay-per-view, and the price they’ve arrived at there (of USD 90 per connection, or whatever) is a function of this history. Based on historical responses to such events, and what people have indicated as their willingness to pay, this rate has been arrived, and from what I notice on social media, it has probably been successful in terms of raising revenues.

In a market like India, however, firstly there is no established pay per view model, and no “channels” for exhibitors to show pay per view content (Tata Sky Showcase might be an exception but it’s too niche). Moreover, boxing is also not that big in India – while Indians (like me) might be interested in big fights like this one, it is not as big for us to actually pay money to watch. In that sense, even if the channels had offered this fight at a low (but non-zero) price, the uptake would have been small.

In other words, for an event like this one, the “monopoly price” that the owner of the content could charge in India would be extremely small, and even at that price, the number of people watching would have been small, leading to small revenues.

But then television is a “two-sided market”. The content is simply a platform to bring together the advertiser and the viewer, and the amount that an advertiser will be willing to pay for an advertisement can be considered to be proportional to the viewership. In India, where the volumes for a non-zero price will be low, the price that the broadcaster can command from the advertiser will also be similarly low, leading to low revenues all along.

Instead, by offering the rights to Sony Six, which will offer the content for “free” for all its currently existing viewers, the owners of the rights are ensuring that a significantly positive section of the population is going to watch the fight. Which in turn means that a significant premium can be extracted from advertisers, which will form strictly positive revenues for Sony Six, a part of which will go to the global rights holders. And these revenues are significantly greater than what the rights holders would have achieved in case the content had not been offered at all in India.

 

Why Google, Facebook, etc. are against Net Neutrality in India

I’ve been out of country for close to a month now, so haven’t really been following India news too closely (apart from via social media). But from my (biased 🙂 ) sources I understand that TRAI has put out a discussion paper in which they want to permit telecom companies to charge you based on the service that you use, thus violating Net Neutrality.

Now I’m yet to take a stand on this (this argument by Tim Harford against Net Neutrality is rather compelling, making me believe that well implemented competition regulations can mean we can make do without Net Neutrality, but I haven’t given it too much thought yet), but I have an idea as to why the likes of Google and Facebook, which in the past and in other geographies have come out strongly in favour of Net Neutrality, are okay with Net Neutrality violation in India.

The basic issue in India is with “over the top” services such as WhatsApp and Viber which the likes of Airtel and Vodafone see as a threat for it competes with their rather lucrative voice and SMS business. I’ve mentioned in the past that there’s a quality issue here which the telecom companies can differentiate on (packet switching doesn’t work that well for voice), but given costs it is hard to make a compelling case for using circuit switching for international calls.

So the likes of Airtel and Vodafone are threatened by such services and want to charge users more for using WhatsApp and Viber compared to other applications. Net Neutrality supporters, who argue that internet infrastructure should just be a set of neutral pipes (rather than a “two-sided platform”, as Harford argues), argue that this is unfair, and that Airtel and Vodafone are exploiting their positions as gatekeepers (literally) to defend their own related business.

Coming to the point of this post, entities such as Google and Facebook are coming out on the “wrong” side of the net neutrality debate here in India, arguing that internet companies should be looked at as two-sided platform markets rather than neutral pipes (resisted the urge to use the phrase “information superhighway” there!). Considering that they’re proponents of Net Neutrality elsewhere, why are they taking this stance in India?

Assuming that final regulations come out in favour of net neutrality (treating internet as infrastructure, and not a platform), how should the likes of Airtel and Vodafone react? Clearly their data business is cannibalising their voice business, so they should logically increase their prices for data plans (no brainer). Given that they will not be allowed (in this situation) to charge differential rates based on the service, they will have to uniformly jack up data rates.

This can be troublesome for Google and Facebook on two counts. Firstly, the telecom providers may not get their pricing right, and rather than having a ramp (charging heavy users heavily, since only such people will be using WhatsApp or Viber), they might increase data rates across the board. This will result in a drop in mobile internet penetration (one reason it’s so high now is that it’s cheap), and considering that Google and Facebook are services that pretty much every who uses the internet in India uses, it will result in loss of user base, traffic and revenue (possibly) for them.

The second problem is that even if telecom operators get their pricing right (maintain current pricing for basic plans, but jack up rates for high data usage) it spells trouble for Google and Facebook. One of Google’s widely used services is the video streaming application Youtube, and Youtube consumes high bandwidth. Facebook is getting into native video in a big way, and it is estimated that it might be more successful than Youtube in terms of advertising. And with correct internet pricing under net neutrality, demand for services such as Youtube and Facebook Video will go down significantly, which is not good for those services.

So the simple answer is that the reason Google and Facebook are coming out against Net Neutrality is that they are coming out on the right side of the new proposed (anti neutrality) regulations. Like WhatsApp and Viber, they too are high bandwidth applications, but unlike WhatsApp or Viber they don’t compete directly with the owners of the pipes. Thus, they want providers to have the ability to impose differential pricing, since that will mean that subscribers can access their content for cheaper, and this allows them to make more advertising revenues.

In my view (again note that I’m yet to take a stand on this net neutrality business), this move by Google and Facebook to support the anti-neutrality regulations is extremely short-sighted since it can hit them back at a later point in time. There is no guarantee that in the long term their services will not compete with that of telecom providers (Hangouts? Facebook voice calling?) and the regulations that they are currently supporting can come back to hit them at a later point in time.

It seems that Google and Facebook are working on an assumption that there will not be other high-bandwidth applications that will compete less with pipe-owners (telecom operators) than them (Google & Facebook). They are very likely to be in for a surprise, and end up as the cranes in this Panchatantra story.

Useless LinkedIn

I’m not a big fan of LinkedIn. I mean, I use it, and fairly regularly at that (check it at least once a day), and I think conceptually it’s quite useful. However, in practice, I think there are a number of sticking points about the service, which makes it quite useless.

For starters its apps (iPad and Android) are quite lousy, and offer nowhere close to the kind of experience that the web interface offers. Things are extremely unintuitive (down to the tabbing order – you compose message, hit tab and enter, and you don’t send the message. It takes you to the profile of the person you’re messaging instead) on the website. Sometimes the apps show notifications even after you’ve checked them on the web, and so on.

In other words it’s an extremely poorly engineered product, but which is surviving (and thriving) thanks to network effects!

I might have commented on this in the past but there is this thing on endorsements. This was something that coincided with the time when LinkedIn went public (if I’m not wrong), and you could endorse people for their “skills” on LinkedIn. For a while I played along with the game. But then I completely lost it when a distant uncle who I’m sure has never traded derivatives endorsed me for “derivatives”. I quickly deleted my skills.

Then there are the LinkedIn recommendations, which has inherent selection bias and hence adds no value. And then you have the “say goncrats” feature, where LinkedIn prompts you to “say congrats” on people changing jobs or hitting job anniversaries. I’ve found this mildly useful (dropping a note when someone switches jobs is a good way to stay in touch), but there are the bugs in terms ofjob downgrades and people getting fired.

And of late, there has been serious spam in terms of people’s status updates. I don’t know when it became popular to post silly puzzles on professional networking sites, yet I find several of them popping up on my timeline every day, and the number of people who have shared each is not funny. Then you have these cartoons (Dilbert and the copycats), and “guru quotes” that appear in the form of images that further spam your timelines! The only way I can think of these being useful is that they act as a negative indicator when you’re checking out the profile of someone you are looking to hire or do business with!

To summarise, LinkedIn seems to be an extremely badly engineered product on several counts, but thanks to network effects (so many people are already on it that entry barriers for competitors are really high) the site still manages to do well! I wonder what it will take to disrupt it. Facebook for business is not the answer for sure – the potential havoc caused by a breach in chinese walls there will scare people enough to not sign up.

What do you think? Here is their stock price movement for reference:

 

 

Uber’s new pricing structure

So Uber has changed its pricing structure in Bangalore. Earlier they nominally charged Rs. 50 fixed, Rs. 15 per kilometer and Rs. 1 per minute, and then slapped a 35% discount on the whole amount. From today onwards the new fare structure is Rs. 30 fixed, Rs. 8 per kilometer and Rs. 1 per minute, without any further discounts. They’re marketing it using the Rs. 8 per kilometre number.

I took a ride this afternoon under the new fare structure, and the bill was Rs. 152, about the same as it would have been under the old fare structure. In that sense, I guess this was an “average ride”, in terms of the distance by time covered. This was the kind of ride where their assumption on distance travelled per unit time (in coming up with their new formula) was exactly obeyed!

So how do we compare the old and new formulae? We can start by applying the discount on the nominal numbers of the old formula. That gives us a fixed cost of Rs. 32.5, a per kilometer cost of Rs. 9.75 and a per minute cost per 65 paise. We can neglect the difference in fixed cost. Comparing this to the new cost structure, we find that the passenger now gets charged a lesser amount per kilometre, but a higher amount per minute.

In face, taking the “slope” between the old and new rates, the per kilometre cost has come down by Rs. 1.75 while the per minute cost has risen by 35 paise. Taking slope, this implies that Uber has assumed a pace of a kilometre per five minutes, or twelve kilometre per hour.

So if your journey is going to go slower than twelve kilometres per hour, on average, you will end up paying more than you used to earlier. If your journey is faster than twelve kilometres per hour, then you pay less than you did under the previous regime.

A few implications of the new fare structure are:

1. Peak hour journeys are going to cost more, for they are definitely going to go slower than twelve kilometres an hour
2. Your trips back from the pub should now be cheaper, for late nights when the roads are empty you’ll travel significantly faster than twelve kilometres an hour
3. What does this imply for the surge pricing in the above two cases? I think the odds of a surge during peak office hours will come down (since the “base price” of such a trip goes up, which will push down demand), and  the odds of a surge late on a Friday or Saturday night might go up (since base fare has been pushed down for that).
4. The Rs. 30 fixed cost implies that if a driver travels at 12 km/hr when looking for a new ride, the gap between rides for a driver is 11.5 minutes (if the driver spends X minutes, he will travel X * 12/ 60 kilometres in that time. The compensation for this combination is X + X*12/60 * 8, which we can equate to 30. This gives us X = 11.54).
5. Trips to/from the airport will now be cheaper, for you can travel much faster than 12 km/hr on that route. So Uber will become even more competitive for airport runs. Again this might increase probability of a surge at peak flight times.

I continue to maintain that Uber has the most rational price structure among all on-demand taxi companies, since the fare structure fairly accurately mirrors drivers’ opportunity costs. Ola doesn’t charge for the easily measurable time, and instead charges for “waiting time”, which is not well defined. Ola also has a very high minimum fare (Rs. 150). I wonder how they’ll play it if their planned acquisition of TaxiForSure goes through, since TaxiForSure was playing on the short trip model (with minimum fares going as low as Rs. 49). Given the driver approval before a ride, though, I doubt if anyone actually manages to get a Rs. 49 ride from TaxiForSure.

Times continue to be interesting in the on-demand taxi market. We need to see how Ola responds to this pricing challenge by Uber!

Market-making in on-demand markets

I’ve written a post on LinkedIn about the need for market-making in on-demand markets. I argue that for a market to be on-demand for one side, you require the other side to be able to provide liquidity. This liquidity comes at a cost and the side needs to get compensated for it. Driver incentive schemes at Ola/Uber and two-part electricity tariffs are examples of such incentives.

An excerpt:

In a platform business (or “two sided market”, or a market where the owner of the marketplace is not a participant), however, the owner of the market cannot provide liquidity himself since he is not a participant. Thus, in order to maintain it “on demand”, he should be able to incentivise a set of participants who are willing to provide liquidity in the market. And in return for such liquidity provided, these providers need to be paid a fee in exchange for the liquidity thus provided.

Read the whole thing! 🙂

What sets Uber apart from other marketplaces

While at the gym this evening I was thinking of marketplaces.  To give some context, the reason I went there was that there were too many thoughts running around my head, so I needed to focus on something mindless or something that required so much concentration that I could only hold one other thought in my mind at that point in time. In fact, when you go “under the bar” (do a back squat),  even that one thought will vanish – you need all your physical and mental energy to complete the squat.

Anyway so I was thinking of marketplaces, and marvelling at the kind of impact companies like Uber and Ola have had. They have been an absolute gamechanger in their business in that it has completely changed the way that people and cabs get matched to each other. This was a matching that had been extremely inefficient in the past, but with these apps, they have become better by an order of magnitude. And it is this order of magnitude that sets apart Uber/Ola from other marketplace businesses.

And as I was moving between weights, I had another thought – the trick with Uber/Ola as a marketplace is that it is near impossible to do “side deals”. The ultimate nightmare for a platform/marketplace provider is to let the two sides “discover each other” and conduct further deals “offline”. This can be the bane of services such as dating services, where once you go on your first date (as recommended by OkCupid or Tinder), the dating service need not know anything about your relationship after that! They’ve “lost” you. In fact, talking to someone from the industry recently, I learnt that they do dating rather than marriage since in the former there is the hope of “repeat happy customers”.

It is similar with a service such as Airbnb, where once you’ve located a B&B you like, you can cut airbnb out of the deal from the next time on. Of course availability and stuff matter, but given how much in advance you book, a quick call to check availability is a small cost vis-a-vis the benefit of cutting out the middle man.

The beauty of Uber/Ola, however, is that it is impossible to do deals offline. Yes, after a ride, the driver and the passenger have each other’s numbers. But the next time the passenger wants a ride, the probability that the same driver is in the vicinity and free to give a ride is infinitesimal. So the passenger has to go to the app again. Moreover, it is the app that takes care of the pricing (using GPS, etc.) – something that is impossible to estimate if you try to cut out the app.

So when people say that they are building the “Uber for <some other service>”, in most cases it is not exactly the case – not all marketplace transactions are like Uber. For to be like Uber, you need to be an instant matching mechanism that changes the way the industry fundamentally operates; and you need a mechanism that keeps deals “online” by force.

Chew on it!

Rating systems need to be designed carefully

Different people use the same rating scale in different ways. Hence, nuance is required while aggregating ratings taking decisions based on them

During the recent Times Lit Fest in Bangalore, I was talking to some acquaintances regarding the recent Uber rape case (where a car driver hired though the Uber app in Delhi allegedly raped a woman). We were talking about what Uber can potentially do to prevent bad behaviour from drivers (which results in loss of reputation, and consequently business, for Uber), when one of them mentioned that the driver accused of rape had an earlier complaint against him within the Uber system, but because the complainant in that case had given him “three stars”, Uber had not pulled him up.

Now, Uber has a system of rating both drivers and passengers after each ride – you are prompted to give the rating as soon as the ride is done, and you are unable to proceed to your next booking unless you’ve rated the previous ride. What this ensures is that there is no selection bias in rating – typically you leave a rating only when the product/service has been exceptionally good or bad, leading to skewed ratings. Uber’s prompts imply that there is no opportunity for such bias and ratings are usually fair.

Except for one problem – different people have different norms for rating. For example, i believe that there is nothing “exceptional” that an Uber driver can do for me, and hence my default rating for all “satisfactory” rides is a 5, with lower scores being used progressively for different levels of infractions. For another user, for example, the default might be 1, with 2 to 5 being used for various levels of good service. Yet another user might use only half the provided scale, with 3 being “pathetic”, for example. I once worked for a firm where annual employee ratings came out on a similar five-point scale. Over the years so much “rating inflation” had happened that back when I worked there anything marginally lower than 4 on 5 was enough to get you sacked.

What this means is that arithmetically averaging ratings across raters, and devising policies based on particular levels of ratings is clearly wrong. For example, when in the earlier case (as mentioned by my acquaintance) a user rated the offending driver a 3, Uber should not have looked at the rating in isolation, but in relation to other ratings given by that particular user (assuming she had used the service before).

It is a similar case with any other rating system – a rating looked at in isolation tells you nothing. What you need to do is to look at it in relation to other ratings by the user. It is also not enough to look at a rating in relation to just the “average” rating given by a user – variance also matters. Consider, for example, two users. Ramu uses 3 for average service, 4 for exceptional and 2 for pathetic. Shamu also uses 3 for average, but he instead uses the “full scale”, using 5 for exceptional service and 1 for pathetic. Now, if a particular product/service is rated 1 by both Ramu and Shamu, it means different things – in Shamu’s case it is “simply pathetic”, for that is both the lowest score he has given in the past and the lowest he can give. In Ramu’s case, on the other hand, a rating of 1 can only be described as “exceptionally pathetic”, for his variance is low and hence he almost never rates someone below 2!

Thus, while a rating system is a necessity in ensuring good service in a two-sided market, it needs to be designed and implemented in a careful manner. Lack of nuance in designing a rating system can result in undermining the system and rendering it effectively useless!

Matching problem in the Indian Dating Market

And no, this has nothing to do with Hall’s Marriage Problem.

As the more perceptive of you might have noticed, about a year and a bit back the wife started this initiative called “Marriage Broker Auntie”. Basically she thinks she is a good judge of people and a good judge of “matches” between people. As a consequence of this, there were many friends and relatives who would approach her to “set them up”. Having (successfully or unsuccessfully) made a few matches among such applicants, she decided to institutionalise it, and thus Marriage Broker Auntie was born.

The explicit objective of the initiative was to broker marriages (as the name clearly suggests. The wife was the “auntie”. The plural in the title was because briefly there was one more (mad) auntie involved but she’s since moved on). The methodology was to “know the customers” and then use an intelligent human process in order to find pairs who might be interested in each other and then set them up for a date. As simple and basic as it gets. The quant in me had already started dreaming up of an expanded business where I could use “big data” and “analytics” and whatnot to “understand” large sets of people and match them up.

But there was a small problem – ok the fundamental problem was that the number of people who had signed up was not very large, but let’s assume that can be solved through marketing – the problem was that the sex ratio on the website was skewed. Heavily. At one point in time I remember there being 20 girls and 5 boys being registered on the website (all heterosexual, perhaps a consequence of “marriage” in the title)! The ratio remained thus as long as the initiative was in existence.

Now this contrasts heavily with other “dating” sites that are operational in India, primarily global sites such as OkCupid and Tinder. The gender ratio on these sites is heavily skewed, too, except that it is heavily skewed in the opposite direction (too many boys, hardly any girls). For example, check out this piece in Man’s World on Tinder, which talks about users who think there is a “permanent bug” in the site that doesn’t allow matches, and of “all girls on the site being bots”.

The problem with most dating sites in India is that there are way too many boys and way too few girls (I should add Orkut also to this list, and should mention that I met my wife through a combination of Orkut and LiveJournal). This leads to girls on the site feeling like they’re “being stalked”, and getting freaked out and getting out of the site. A girl I know signed up on OkCupid India, just on a whim, only to find a hundred “interests” from boys within a few minutes of logging on.

Reading stories like this, you might be bound to imagine that there are no girls in India interested in dating, or getting married. But if you were to look at sites like Marriage Broker Auntie (small sample, I know, but significant gender bias) you know that this is simply untrue. There are girls out there who are looking for flings and relationships, to date and to get married. And they are short of ideas on how to meet such men. “Traditional” dating sites such as OkCupid or Tinder intimidate them, and shaadi.com and bharatmatrimony.com are in a completely different business altogether – they make matches on a different set of variables such as caste and gotra and stars and so forth.

So what we have here is classic market failure – of the Indian dating market (this, however, is NOT a call for government regulation! 😛 ). The market is surely fertile (no pun intended), and there is plenty of opportunity to make fat profits if someone can get the matching right. There are a number of players looking to enter the market as I write this (I’ve spoken to some of them), but none look particularly promising.

Oh, and you might want to know why Marriage Broker Aunties gets all the chicks – it’s because of a complicated sign up process (a five page google docs form if i remember right) which puts off any non-serious players. Also there is the promise that until matched, potential counterparties cannot see your profile, and there is a “trusted third party” (in the MBA case, my wife, but an algorithm should do reasonably well to scale) who does the matchings.

The most important bit here is the anonymity – the ground reality in India is that online dating is still seen as a “last resort” – to be resorted to only if you can’t find a match through your network. With Tinder and OkCupid being exclusively dating sites (unlike Orkut which was fundamentally a social network), signing up on one of these two sites is an admission of a degree of desperation (in the eyes of most people), and there is a chance people might see you differently after they know that you’re a member on such sites.

While this explains reasonably well why chicks flock to Marriage Broker Auntie, why is there a shortage of guys on the site? It can’t be that there are no serious guys around for whom the 5-page form is a massive transaction cost. The wife’s (and my) perception is that fundamentally guys want to “check out a girl” (i.e. know well what she looks like, etc.) before agreeing to meet her on a date (I remember scouring Orkut and Facebook for all possible pictures of my then-future-wife and “checking her out” before meeting for the first time). And in an anonymised matching site, this experience is not there. So men don’t like this!

It’s a hard problem but not intractable. There are many companies that are coming into this space now. Hopefully someone will get it right!