Category: platforms

How much surge is too much surge?

I had gone for a wedding in far-off Yelahanka and hailed an Uber on the way back. The driver was bragging about how it’s easy to find an Uber at any time anywhere in Bangalore, when I pointed out to him that earlier in the evening when I was on my way to the wedding I’d failed to find one, and had taken an Ola instead.

He was surprised that an Uber wasn’t available in Jayanagar when I told him that there were cars available but at a 1.7X surge, and given the distance I was to travel I found it more economical to take an Ola which was offering a ride at a flat Rs. 50 premium. To this, the driver said that he had also noticed that demand sharply dropped off once the level of surge went beyond 1.5X, and at such surges supply would easily outstrip demand.

Now I’m no fan of Ola’s pricing – I think the flat Rs. 50 premium during peak hours is unscientific, but I wonder if the level of Uber’s surges makes sense. From a pure microeconomic standpoint, it is easy to see where Uber is coming from – raise price until quantity demanded matches quantity supplied and let the market clear. The question, however, is if this kind of a surge makes sense from a behavioural standpoint.

The point is that the “base fare” (“1X”) is “anchored” in the customer’s mind, and thus any decision he takes in terms of willingness to pay is made keeping this “anchor” in mind. And when the quoted price moves too far from the anchor (beyond 1.5X, say), the customer deems that it is “too expensive”, and decides that waiting for a few minutes for fares to drop (or using a competing app) is superior to paying the massive premium.

I suppose that Uber would have noticed this. That there is a “cliff” surge price beyond which there is a massive drop off in volume of matchings. The problem is that if they restrict their surges to this “cliff value” they might be leaving money on the table by not being able to match the market. On the other side, though, if the surge is so high that the volume of transactions drops sharply, it results in much lower commissions for Uber! I’m assuming that a solution to this problem is on the way!

And I’ve found that it’s always harder to find a taxi on a Sunday. The problem is that because demand is lower, supply is also lower (this is a unique characteristic of “two-sided markets”) because of which the chances of finding a match are harder, and transaction costs are higher. I wonder if it makes sense for taxi aggregators to levy a “Sunday premium” (perhaps with Uber holding a day-long minimum of 1.2X surge or something) to compensate for this lack of liquidity!

Why the proposed Ola-TaxiForSure merger is bad news

While a merger intuitively makes economic sense, it’s not good for the customers. The industry is consolidating way too fast, and hopefully new challengers will arise soon

Today’s Economic Times reports that Ola Cabs is in the process of buying out competitor TaxiForSure. As a regular user of such services, I’m not happy, and I think this is a bad move. I must mention upfront, though, that I don’t use either of these two services much. I’ve never used TaxiForSure (mostly because I never find a cab using its service), and have used Ola sparingly (it’s my second choice after Uber, so use it only when Uber is not available).

Now, intuitively, consolidation in a platform industry is a good thing. This means that more customers and more drivers are on the same platform, and that implies that the possibility of finding a real-time match between a customer who wants a ride and a driver who wants to offer one is enhanced. The two-sided network effects that are inherent in markets like this imply super-linear returns to scale, and so such models work only at scale. This is perhaps the reason as to why this sector has drawn such massive investments.

While it is true that consolidation will mean lower matching cost for both customers and drivers, my view on this is that it’s happening too soon. The on-demand taxi market in India is still very young (it effectively took off less than a year back when Uber made its entry here. Prior to that, TaxiForSure was not “on demand” and Ola was too niche), and is still going through the process of experimentation that a young industry should.

For starters, the licensing norms for this industry are not clear (and it is unlikely they will be for a long time, considering how disruptive this industry is). Secondly, pricing models are still fluid and firms are experimenting significantly with them. As a corollary to that, driver incentive schemes (especially to prevent them from “multihoming”) are also  rather fluid. The process of finding a match (the process a customer and a driver have to go through in order to “match” with each other), is also being experimented with, though the deal indicates that the verdict on this is clear. Essentially there are too many things in the industry that are still fluid.

The problem with consolidation at a time when paradigms and procedures are still fluid is that current paradigms (which may not be optimal) will get “frozen”, and customers (and drivers) will have to live with the inefficiencies and suboptimalities that are part of the current paradigms. It looks as if after this consolidation the industry will settle into a comfortable duopoly, and comfortable duopolies are never great for innovation and for finding more optimal solutions.

Apart from the network effects, the reasons for the merger are clear, though – in the mad funding cycle unleashed by investors into this industry, TaxiForSure was a clear loser and was finding itself unable to compete against the larger better-funded rivals. Thus, it was a rational decision for the company to get acquired at this point in time. From Ola’s point of view, too, it is rational to do the deal, for it would give them substantial inorganic growth and undisputed number one position in the industry. For customers and drivers, though, now faced with lower choice, it is not a great deal.

This consolidation doesn’t mean the end, though. The strength of a robust industry is one where weak firms go out of business and new firms spring up in their place in their attempt to make a profit. That three has become two doesn’t mean that it should remain at two. There is room in the short term for a number three and even possibly a number four, as the Indian taxi aggregation industry tries to find its most efficient level.

I would posit that the most likely candidates to emerge as new challengers are companies such as Meru or EasyCabs, which are already in the cab provider business but only need to tweak their model to include an on-demand component. A wholly new venture to take up the place that is being vacated by TaxiForSure, however, cannot be ruled out. The only problem is that most major venture capitalists are in on either Uber or Ola, so it’s going to be a challenge for the new challenger to raise finances.

\begin{shameless plug}
I’m game for such a venture, and come on board to provide services in pricing, revenue management, availability management and driver incentive optimisation. 🙂
\end{shameless plug}

 

Finally some sensible Uber regulation

Ever since Uber launched, regulators worldwide haven’t had a clue as to how to regulate it – it has been such a big disruptor in the taxicab market. Some countries and cities have taken to banning it outright (the list is too long to post links here). Others (such as some states in India) have tried to get Uber to register itself as a “taxicab company”.

The problem with all these regulations is that the Uber model (being replicated by firms such as Ola and TaxiForSure in India) is a fundamental gamechanger. As I have written in this earlier post, the on-demand model propagated by Uber implies that a number of the inefficiencies in the taxicab market don’t exist any more. In this context, trying to regulate it by moving it back to the earlier (extremely inefficient) model is extremely regressive. The right way to regulate is to create a level playing field for taxicab aggregators (which includes Uber) and move the market to a regime where the new technology-enabled efficiencies are made good use of.

And that is precisely what Los Angeles has done. In a rather progressive move (which ought to be copied by other states and cities and countries), the city has decreed that all city-based cab operators need to offer app-based booking services. The interesting bit in the regulation (see link above) is that drivers who fail to install the e-hail app are actually going to be fined.

What this will lead to is that the local taxi market is itself going to become more efficient which should definitely increase both profitability for the local cab industry and also availability of local cabs to the people of LA. What this will also do is to give people of LA a choice between using Uber and the traditional taxi app, which will lead to an improvement in Uber’s service levels. As things stand now I don’t see any downside from this LA regulation.

I hope the model succeeds in LA and other cities see the brilliance of the model and accept the efficiencies brought into the market thanks to this model and adopt similar regulation. I see this kind of regulation coming into the Bangalore market though the backdoor though. Ola already helps match auto rickshaws to customers and now TaxiForSure is also getting into that market. Will this mean that autos won’t have to line up for hours together in front of Lalbagh gate for passengers arriving in the city by bus?

Oh, and LA is not the first city to implement regulations requiring taxis to be “hailable” via an app. When I visited Singapore in November 2013, I found that cabs in the city worked the same way. Locals had an app which they would use to call taxis. The problem there though was that the app was only available to locals (your android/iOS had to be registered in Singapore for you to be able to even install the app), which made it a nightmare for us tourists to move around.

Oh, and while on the topic, a good revenue source for companies such as Ola or TaxiForSure would be to provide the technology backbone to cities that are seeking to use app-based hailing services for their cabs.

 

On getting fired

On Capital Mind, Deepak Shenoy has a great post out on the TCS layoffs. TL;DR: TCS could have handled it better, but getting fired is a part of corporate life. And 3 months’ severance is generous. He also adds that we should hedge – build your brand, build savings, build skills so that getting fired won’t hit you so hard.

An argument that is being bandied about in relation to the TCS layoffs that if you need job mobility, then job insecurity is a related price you have to pay. For example, check out these tweets from Raj:

So the basic argument here (which I completely agree with) is that you can’t have one-way optionality. A generation ago, there was almost no optionality. You couldn’t get sacked, and it was very difficult for you to leave. That was the way the world worked back then.

Soon, the economy expanded, and you started seeing mobility. You started seeing optionality – the job was a one-way option. You could choose when you wanted to leave, but given the high growth and general shortage of skilled talent back in the days, companies couldn’t sack you. That sweet spot existed for a short while.

In the last decade or so, though, this has started changing. Companies realised that keeping deadwood on the books is a lot more expensive than their financial cost-to-company. A “no firing” policy sends out the wrong incentives – people without motivation are more likely to stick around than the ambitious. And that can never be good for the company. So now companies want optionality both ways. And as the TCS episode illustrates, people are not liking that the optionality exists both ways now. It seems like they were used to the one-way optionality street that existed for a short while during the rapid expansion of the IT sector.

The problem with the above argument (encapsulated in Raj’s tweets, which I agree with), however, is that it assumes that employees have a choice. When you say that “if you want mobility, you get insecurity as part of the package”, the subliminal message is that it there exist jobs where you can choose to forego your mobility in order to save yourself from insecurity. Unfortunately not too many such jobs exist. And it is a matter of liquidity.

Yes, there still exist plenty of jobs where there is strong two-way commitment. However, they are nowhere as numerous as jobs where there exist two-way optionality. The simple matter is that the “market has moved”. Most people are comfortable with the “latest” arrangement, where you can leave easily but also get sacked easily. Given that most people are comfortable with this arrangement, companies are also comfortable with this and have moved to this arrangement. And that has led to a virtuous cycle and the number of companies and number of people who like this arrangement have hit a critical mass.

In other words, if you want an “optionless” job, that is like living in the world until yesterday. But it is not enough that you want to live in that world. The world as we know it is social, and for us to live a certain way, we need other people to agree to live the same way. In other words, we can’t live our chosen lifestyle in isolation without counterparties living that way too.  And when most employers have moved on from the optionless regime to the two-way optionality regime, even if you want to live in yesterday’s world, there aren’t too many companies that still live that way. So you don’t have a choice!

So you need to learn to adapt to live and thrive in the new regime. And it is not that this regime will last forever. I’m sure people will innovate and other regimes might supersede this regime. Some people are slow to react to change, but liquidity makes the world ruthless, and punishes you badly for not adapting. That is the hard truth that some of these people who are cribbing about getting fired from TCS need to digest.

Inefficiencies in the auto rickshaw market and Uber

Taxi marketplaces such as Uber and Ola address inefficiencies and failures in the auto rickshaw / taxi market

Weary after a long cold night journey you get off the overnight bus from Chennai at Lalbagh’s Double Road gate, and look around for auto rickshaws. There are some ten of them around. The drivers are equally weary, having woken up early and left their homes to stand in the cold, hoping to find passengers alighting from buses. They want to get compensated for this, and quote you a fare that includes such compensation. All of them quote similar fares. You grudgingly bargain and agree, and conclude that Bangalore’s auto drivers are bastards.

Alternate scenario: as the bus reached Madivala, ten minutes away from Lalbagh Double Road gate at that time of the morning, you pull out your app and ask for a taxi to pick you up from Double Road gate in ten minutes’ time. The driver has been up, but resting at home. He leaves home now, just in time to be there at Double Road gate by the time you get off there. Off you get into the car and go.

You have to get to work and try catching an auto rickshaw. The guy asks for extra money for he has to take you through traffic-laden roads, which are a tax on his time, which the regulated fare doesn’t compensate him for. You bargain, get in, and conclude that auto drivers are bastards.

In an alternate scenario, you use an app-based taxi which calculates the fare as a linear combination of distance travelled and time taken, which means that the driver gets compensated for getting stuck in traffic without having to bargain for it. And without you having to think that the driver is a bastard.

In the evening you are trying to get an auto rickshaw from MG Road, and the guy asks for a premium. This premium is not reflective of costs, but the fact that demand for auto rickshaws in that area at that time is high, and that there will be customers willing to pay that premium. You conclude that the auto rickshaw driver is a bastard. Uber’s surge pricing (which can be steep at times) doesn’t evoke the same reaction from you. Uber has centralised knowledge of demand and supply so they can clear prices better, while the auto driver, lacking that knowledge, quotes a price that reflects his lack of market knowledge. And not having a good idea of what to charge, he might try to charge above market price.

What I’m trying to say here is that the local taxi/auto rickshaw market is inefficient, and ridden with failures. There is lack of information flow between demand and supply, which leads to inferior price negotiation, and the transaction cost of time and effort wasted on negotiation as opposed to using that time to travel! And when a market fails, the classic economic response is regulation, but in the case of taxi markets regulation is so poor (regulated prices do not reflect costs) that it enhances the market failure. The (badly) regulated prices anchor into people’s minds unrealistic expectations, and when auto drivers nudge them towards more realistic market prices, passengers assume that they (drivers) are bastards.

It is in this context that players like Uber and Ola (I’m not a fan of Ola’s pricing model, though) step in and try to resolve the market failure by improving flow of demand-supply information and setting “clearing prices” that compensate the driver in line with his costs. If you look closely, these companies are actually rescuing the local taxi market from its inherent inefficiencies and failures and bad regulation!

It is important, however, that no one market place ends up becoming a monopoly. As long as we have two or three different marketplaces, both customers and drivers have the choice of moving between one and the other, and this will ensure that these market places face market pressures from the two sides of the market, and if they “regulate” in an unfair manner, their participants will move to a competing marketplace, resulting in loss of business for the marketplace.

But then, considering the inherent network effects of the marketplace model, I don’t know how we can ensure that competition exists!

 

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.