Category Archives: platforms

Market-making in two-sided on-demand markets

Every on-demand market has “liquidity providers” and “liquidity takers”. In fact, the market is “on demand” only for the liquidity takers, for when they arrive at the market there are liquidity providers who are providing this liquidity. In other words, the liquidity takers’ demand is instantaneously supplied by the liquidity providers, who make sure that the market is on demand.

Now, providing liquidity is risky business. Let us look at it from the perspective of stock markets, where the concept of market making is most well established. Market makers in a stock market are responsible for keeping “live quotes” (on both the buy and sell side) at all points in time, so that whenever a trader enters the market, there is ready liquidity available for the trader to execute his trade “on demand”.

Let’s say I’m a market maker and have put out a bid (offer to buy) on a particular stock. Now, if there is a massive downward price movement in the market, my bid gets “taken out” (i.e. a counterparty takes me up on my offer to buy and sells me the stock at the price I’ve quoted), and the market continues hurtling down. Notice now that the moment my bid got “taken out”, I have a long position in the market (since I have now purchased the stock), and the continued downward movement of the market leads to a loss. It is similar if I’m making a market on the ask side and there’s a massive upward price movement.

To put it another way, the market makers (i.e. liquidity providers) are effectively “short optionality”, as they constantly need to write the option of trading at a particular price at any given instant. The “liquidity taking” side is effectively “long optionality”, since they have the option to trade at that particular instant at the price quoted by the market maker.

The question is how I as a “liquidity provider” (as a market maker I’m providing liquidity) need to be compensated for the liquidity I’m thus providing. In the American stock exchanges, for example, market makers are charged a much lower exchange fees than the liquidity takers (the opposite side which trades with the liquidity provided by the market maker). Sometimes the exchange even provides a fee to the market maker in exchange for “always being there” in the market. This is a recognition of the costs being borne by the market maker in order to provide liquidity. The question is how this can be replicated in other “on demand” markets.

It is well known that taxi providers such as Ola and Uber provide attractive “non-linear” (not directly tied to rides) incentives to drivers for being “switched on”. The economic rationale behind that is to compensate these drivers for the cost and risk associated with being “switched on” (there is opportunity cost in being switched on in terms of the driver’s time, risk of getting an unattractive ride, etc.). These incentives, are in other words, compensation to the drivers for “providing liquidity” to the market. In the long run, this gets paid out of the fees charged from the liquidity takers.

Similarly, in the electricity market (though we seldom look at it that way, electricity is the classic “on demand” market, since electricity cannot be stored and needs to be generated in response to demand), grids typically pay power producers a fixed cost to just “be there”. This is again in addition to the per unit cost of power that the producers are paid when power is actually drawn from them (this is the classic “two part tariff” that is prevalent in the electricity sector). On similar lines, think of a lower power tariff to customers who are willing to accept frequent power cuts!

In “traditional, one-sided” markets, where the seller of the good/service is also the owner of the “market” where the sale takes place, the seller usually absorbs this cost of providing liquidity. For example, your neighbourhood grocer (note that this is again “on demand” since the grocer sells you groceries whenever you demand it) provides liquidity by way of keeping his shop open (even when there are no customers) and maintaining inventory. A car mechanic provides “on demand” service by (again) keeping his shop open and having spare mechanics who can service spot demand immediately.

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.

Thus, the key to the success of an “on demand” marketplace is to be able to incentivise enough market participants who are willing to provide liquidity – who are willing to forego instantaneous matching, and take risk of not being matched so that other market participants can enjoy the liquidity thus provided. This process also involves the clever structuring of incentives such that such compensation to the liquidity provider is paid for by the liquidity taker – this is nontrivial since this may not be consistent with the way in which the marketplace actually charges participants!

Cross posted from LinkedIN

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!

Meru’s pricing strategy

Let’s assume I’m writing this post two weeks back when Uber, Ola and TaxiForSure were still running successfully in most places in India. Since then, they’ve been banned to various degrees and it’s gotten harder for customers to get them and for drivers there to find customers leading to a sharp drop in volumes.

Thanks to the entry of app-based taxi booking services such as Uber, Ola and TaxiForSure, entrenched players such as Meru Cabs and Easy Cabs started losing business. This is not unexpected, for the former operated at around Rs. 13-15 per km range (depending on discounts, time of day, etc.) while the latter operated around the Rs. 20 per km price point. This meant that for immediate trips and mostly intra-city movement consumers eschewed the likes of Meru and embraced the likes of Ola.

In the last few weeks I’ve spoken to taxi drivers (mostly Uber; Ola drivers don’t inspire much confidence and so I don’t indulge them in conversation; and I’ve never got a cab via TaxiForSure) who have been affiliated to more than one aggregator, and from that I get what the problem with Meru’s pricing is.

What sets apart Meru, KSTDC and Mega Cabs is that the three are the only operators with a license to pick up passengers from the taxi rank at the Bangalore Airport. Any other taxi that you might book (Ola or Uber or a local cabwallah) don’t have the rights to pick up passengers there and park in the airport’s taxi parking zone. They instead have to park in the space allocated to private cars, paying the parking fees there, and  there is usually a delay from the time when the driver meets the customer at the arrival gate to the customer actually getting into the car. This distinction means that the likes of Meru and Mega offer superior service to the other operators at the airport and thus can command a premium price. Getting into anecdata territory but I always prefer to get a cab from the taxi rank (though the queue occasionally gets long) than to book a cab for which I’ve to wait.

At the city end, the difference between Meru and Uber (Ola is in an intermediate state) is that you can pre-book a Meru, while Uber only accepts “spot bookings”. This difference in service levels means that you can never be assured of getting an Uber at the time you want to leave for the airport – there is a statistically high chance of getting one but you don’t want to take the risk, and thus prefer to pre-book a Meru or a Mega, which lets you know at the time of booking if they are able to service you.

Now, this guarantee from a Meru or a Mega comes at a cost. An Uber cabbie who also drove for Easycabs told me that Easycabs would allocate his trip an hour before it was scheduled to start. Since Easycabs would have assured the customer of a cab reaching his place at the appointed time, this means that they need to account for a sufficient buffer to ensure that the cab does reach on time. Thus the allocation an hour in advance. This cabbie told me that from his point of view that was inefficient, for in the one hour of buffer that EasyCabs would add, he could complete one additional trip through Uber!

So it is clear as to why Meru is more expensive than Uber/Ola – their pre-booking provision means that they have to potentially ground your cab for an hour before pickup, and there is a license fee they have paid the airport for the right to pick up passengers from the taxi rank there. Notice that both these factors also result in increased convenience for passengers. So effectively, Meru is justified in charging a premium. The question is if the current structure is optimal.

The problem with Meru is that their fare structure doesn’t appropriately represent cost. A pre-booked taxi costs as much as a taxi hailed at the time of demand. A taxi from the airport (where they have paid license fee) costs as much as a taxi from anywhere else. So while their cost structure might be optimal for travel to and from the airport, the structure simply doesn’t work out for other rides. And they are getting priced out of non-airport rides.

Assuming that they want to get more non-airport rides for their fleet, how do they do it? The answer is rather simple – let the fare structure reflect cost. Rather than tacking on every piece of cost to the per kilometer fare, they can have a multi-part fare structure which is possibly more “fair”.

A typical trip from the airport to the city is about 40 km, and costs around Rs. 800 (excluding service tax). Instead of charging Rs. 20 per trip, how about charging Rs. 16 (Ola’s rate) per kilometer and an additional Rs. 200 “airport charge”? At the other end, how about charging an additional Rs. 100 or Rs. 200 as pre-booking charge in order to account for driver’s idle time on account of the pre-booking? If they were to charge this way, they will both make as much money as they currently do on airport trips, and also compete with Ola and Uber on intra-city immediate-ride trips.

To take an extreme analogy, this is like asset-liability management – prudent banking dictates that the term structure of your assets reflects that of your liabilities. Similarly, prudent pricing (to the extent it is practically implementable) dictates that your price structure reflects on your cost structure!

Fragility of two-sided markets

Two-sided markets are inherently fragile for participation of each side depends on a certain degree of confidence in participation on the other side. Thus, small negative shocks can lead to quick downward spirals.

Following the ill-advised ban on Uber and other taxi aggregators in four Indian states (Delhi, Karnataka, Andhra Pradesh, Telangana), business for drivers who ply their services via such apps has dropped significantly. While on first inspection you might expect it to go to zero (given their services have been banned), the fact that enforcement is tough (there is nothing to identify a cab as “belonging to Uber”) means that apart from Delhi (where Uber has pulled its services) these cabs continue to ply.

In the days after the ban, various news reports have interviewed drivers who ply for Uber who complain about drastically reduced services. While numbers vary from report to report, the general sense is that so far the number of trips per driver per day has fallen by half. And I expect this to fall further unless drastic steps are taken – such as issuance of new regulations or removal of the ban.

In a “normal” market (where the owner of the market is also a participant), when demand for a particular good drops, price is expected to fall and availability is expected to increase. If demand for a particular item that you have in stock drops, you need to take steps to get rid of the excess inventory that you have. You are most likely to indulge in discounting or other such promotional activities, in order to make it more attractive for the buyers to buy, and thus take the inventory off your shelves.

In a “two-sided market” (one where the owner of the market is not a participant), however, things work differently. It is a popular saying that in such markets “demand creates its own supply”. A corollary to that is that “lack of demand creates lack of supply”. Let us take the case of Uber itself. Over the last few days, irrespective of whether the ban on the service is official or not, legal or not, the number of people who have been requesting for the service has dropped.

Now, if you are a driver using the app, you realise that your potential revenues and profits from continuing to use the app are not as high as they used to be. Thus, if there are other avenues for you to make money, you are now more likely to take those avenues rather than logging on to Uber (since the “hurdle rate” for such a switch is now lower thanks to lower Uber revenues). As many of you take the same route, the availability of cabs on Uber also drops – something that I’ve seen anecdotally over the last few days. And when availability of Uber cabs drops beyond a point, I start questioning my trust in the service – a week ago I would be confident that I would be able to hail an Uber from anywhere in Bangalore with very high confidence; that confidence has now dropped. And when my trust in the service drops, I start using it less, and when many of us do that, drivers see less demand and more of them pull away from the market. And this results in a vicious cycle.

Notice that things would work very differently had Uber been a “traditional” taxi service which owned its cabs and employed its drivers. In that case, falling demand would have been met with a response that would have made it easier for customers to buy – price cuts, perks, etc.

The point is that platforms or two-sided markets are inherently fragile, and highly dependent on confident in the system. I leave my car at home only if I have enough trust in the taxi platforms that I’ll be able to get a cab when I need one. A driver will forsake other trips and switch on his Uber app only if he is confident that he can get enough rides through the app.

The same network effects that can lead to a rapid ramp-up in two-sided markets can also lead to its downfall. All it takes is a small trigger that leads to loss of confidence in the service from one side. Unless that loss of confidence is quickly addressed, the “positive feedback” from it can quickly escalate and the market grinds to a halt!

Another good example of lack of confidence killing two-sided markets is in the market for CDOs and associated derivatives in 2007-08. There were standardised pricing models for such products and a vibrant market existed (between sophisticated financial institutions) in 2007. When house prices started coming down, some people started expressing doubts in such models. Soon, this led to massive loss of trust in the pricing models that underpinned such markets and people stopped trading. This meant companies were unable to mark their securities to market or rationalise their portfolios, and this led to the full-blown 2008 financial crisis!

So when you build a platform, you need to make sure that both sides of the market retain confidence in your platform. For in the platforms business loss of confidence can lead to a much quicker fall than in “traditional” markets. This dependence on confidence thus makes such markets fragile.

Practo and rating systems

The lack of a rating system means Practo is unlikely to take off like other similar platforms

So yesterday I found a dermatologist via Practo, a website that provides listing services for doctors in India. I visited him today and have been thoroughly disappointed with the quality of service (he subjected me to a random battery of blood tests – to be done in his own lab; and seemed more intent on cross-selling moisturising liquid soap rather than looking at the rash on my hand). Hoping to leave a bad review I went back to the Practo website but there seems to be no such mechanism.

This is not surprising since doctors won’t want bad reviews about them to be public information. In the medical profession, reputational risk is massive and if bad word gets around about you, your career is doomed. Thus even if Practo were to implement a rating system, any doctors who were to get bad ratings (even the best doctors have off-days and that can lead to nasty ratings) would want to delist from the service for such ratings would do them much harm. This would in turn affect Practo’s business (since the more the doctors listed the more the searches and appointments), so they don’t have a rating system.

The question is if the lack of a rating system is going to hinder Practo’s growth as a platform. One of the reasons I would go to a website like Practo is when I don’t know any reliable doctors of the specialisation that I’m looking for. Now, Practo puts out some “objective” statistics about every doctor on its website – like their qualifications, number of years of experience and for some, the number of people who clicked through (like the doctor I went to today was a “most clicked” doctor, whatever that means), but none of them are really correlated with quality.

And healthcare is a sector where as Sangeet Paul Chaudary of Platform Thinking puts it, “sampling costs are high”. To quote him:

There are scenarios where sampling costs can be so high as to discourage sampling. Healthcare, for example, has extremely high sampling costs. Going to the wrong doctor could cost you your life. In such cases, some form of expert or editorial discretion needs to add the first layer of input to a curation system.

So the lack of a rating system means that Practo will end up at best as a directory listing service rather than as a recommendation service. Every time people find a “sub-optimal” doctor via Practo, their faith in the “platform” goes down and they become less likely to use the platform in the future for recommendation and curation. I expect Practo to reach the asymptotic state as a software platform for doctors to manage their appointments, where you can go to request an appointment after you’ve decided which doctor you want to visit!

Potential investors would do well to keep this in mind.

Update

Today I got an SMS from Practo asking me if I was happy with my experience. I voted by giving a missed call to one of the two given numbers. I don’t know how they’ll use it, though. The page only says how many upvotes each doctor got (for my search it was all in the low single digits), so is again of little use to the user.

Privacy and network effects

It is intuitive that some people are more concerned about their privacy than others. These people usually connect to the internet via a VPN (to prevent snooping), do not use popular applications because they rank marginally lower on privacy (not using Facebook, for example), and are strict about using only those apps on their phones that don’t ask for too much privacy-revealing information.

The vast  majority, however, is not particularly concerned about privacy – as long as a reasonable amount of privacy exists, and their basic transactions are safe, they are happy to use any service that is of value to them.

Now, with the purchase of WhatsApp by Facebook, the former (more concerned about privacy) brand of people are concerned that WhatsApp, which famously refused to collect user data, did not store messages and did not show advertisements, is now going to move to the “dark side”. Facebook, in the opinion of some of these people, is notorious for its constant changing of privacy terms (making it harder for you to truly secure your data there), and they suspect that WhatsApp will go the same way sooner rather than later. And they have begun their search to move away from WhatsApp to an alternate messenger service.

The problem, however, is that WhatsApp is a network effect based service. A messenger service is of no use to you if your friends don’t use it. Blackberry messenger, for example, was limited in its growth because only users with blackberries used it (before they belatedly released an android app). With people moving away from Blackberries (in favour of iOS and Android), BBM essentially died.

I see posts on my facebook and twitter timelines asking people to move to this messenger service called Telegraph, which is supposedly superior to Facebook in its privacy settings. i also see posts that show that Telegraph is not all that better, and you are better off sticking to WhatsApp. Based on these posts, it seems likely that some people might want to move away from WhatsApp.  The question is if network effects will allow them to do so.

Email is not a network effect based service. I can use my GMail to email anyone with a valid email address, irrespective of who their provider is. This allows for people with more esoteric preferences to choose an email provider of their choice without compromising on connectivity. The problem is the same doesn’t apply to messenger service – which are app-locked. You can use WhatsApp to only message friends who also have WhatsApp. Thus, the success (or lack of it) of messenger services will be primarily driven by network effects.

For whatever reasons, WhatsApp has got a significant market share in messenger applications, and going by network effects, their fast pace of growth is expected to continue. The problem for people concerned about privacy is that it is useless for them to move to a different service, because their less privacy conscious friends are unlikely to make the move along with them. Unless they want to stop using messenger services altogether, they are going to be locked in to WhatsApp thanks to network effects!

There is one upside to this for those of us who are normally not so worried about privacy. That these privacy conscious people are locked in to WhatsApp (thanks to network effects) implies that there will always be this section of WhatsApp users who are conscious about privacy, and vocal about it. Their activism is going to put pressure on the company to not dilute its privacy standards. And this is going to benefit all users of the service!