Tag: network effects

Who do you subsidise?

One basic rule of pricing is that it is impossible for all buyers to have the same consumer surplus (the difference between what a buyer values the item at and what he paid). This is because each buyer values the item differently, and is thus willing to pay a different price for it. People who value the item more end up having a higher consumer surplus than those who value it less (and are still able to afford it).

Dynamic pricing systems (such as what we commonly see for air travel and hotels) try to price such that such a surplus is the same for all consumers, and equal to zero, but they never reach this ideal. While the variation in consumer surplus under such systems is lower, it is impossible for it to come to zero for all, or even a reasonable share of, customers.

So what effectively happens is that customers with a lower consumer surplus end up subsidising those with a higher consumer surplus. If the former customers didn’t exist, for example, the clearing price would’ve been higher, resulting in a lower consumer surplus for those who currently have a higher consumer surplus.

Sometimes the high surplus customer and the low surplus customer need not be different people – it could be the same person at different times. When I’m pressed for time, for example, my willingness to pay for a taxi is really high, and I’m highly likely to gain a significant consumer surplus by taking a standard taxi or ride-hailing marketplace ride then. At a more leisurely time, travelling on a route with plenty of bus service, I’d be willing to pay less, resulting in a lower consumer surplus. It is important to note, however, that my low surplus journey resulted in a further subsidy to my higher surplus journey.

When it comes to markets with network effects (whether direct, such as telecommunications, or indirect, like any two-sided marketplace), this surplus transfer effect is further exacerbated – not only do low-surplus customers subsidise high-surplus customers by keeping clearing price low, but network effects mean that by becoming customers they also add direct value to the high surplus customers.

So when you are pleasantly surprised to find that Uber is priced low, the low price is partly because of other customers who are paying close to their willingness to pay for the service. When you pay an amount close to the value you place on the service, you are in turn subsidising another customer whose willingness to pay is much higher.

This transfer of consumer surplus can be seen as an instance of bundling, but from the seller’s side. Since a seller cannot discriminate effectively among customers (even with dynamic pricing algorithms such as Uber’s surge pricing), the high-surplus customers come bundled with the low-surplus customers. And from the seller’s perspective, this bundling is optimal (see this post by Chris Dixon on why bundling works, and invert it).

So the reason I thought up this post is that there has been some uncertainty about ride-hailing marketplaces in Bangalore recently. First, drivers went on strike alleging that they weren’t being paid fairly by the marketplaces. Then, a regulator decided to take the rulebook too literally and banned pooled rides. As i write this, a bunch of young women I know are having a party, and it’s likely that they’ll need these ride-hailing services for getting home.

Given late night transport options in Bangalore, and the fact that the city sleeps early, their willingness to pay for a safe ride home will be high. If markets work normally, they’re guaranteed a high consumer surplus. And this will be made possible by someone, somewhere else, who stretched their budget to be able to afford an Uber ride.

Think about it!

Cross-posted at RQ

Characterising network effects

Met a bunch of people for drinks this evening. Most of the conversation was just okay. But there was this little bit about network effects. Where I figured out how to calculate whether network effects are present in an industry. It all came out of Kingsley claiming that the age of network effects is over, and there are no more network effects left.

The discussion presently moved to how you discover whether there exist network effects in different industries. Does the fact that  Amazon’s marketshare is nowhere close to that of a monopoly mean that there are no network effects in e-commerce marketplaces? Doesn’t Google have network effects in that given the larger number of people searching on the platform, there are more clicks and more opportunity for learning (for Google) and hence better results?

At a point of time in the conversation, I made the statement that Google (in particular and search engines in general) has “partial network effects”, in that more users means more learning and hence more results. And that for this reason Bing or any other competitor can’t match up.

So how can we characterise whether an industry has network effects, and if so, to what degree? Thinking about it, it’s rather simple. In a “normal” non-networked industry, the value of the user base is directly proportional to the number of users. Going by Metcalfe’s Law, in a fully networked industry, the value of the user base is directly proportional to the square of the number of users. An industry with “partial network effects” should surely have its value a power between 1 and 2 of the number of users?

Here’s how we figure out how networked an industry is. Take all the players in the industry and tabulate the size of the user base and the value of each of the players (excluding very small players). Plot them on a log-log plot, and measure the slope. If the slope of this log-log plot is close to 1, it means that the industry is not networked at all. If the slope is close to 2, it means it has “full network effects”. And the numbers in between represent the spectrum of possible values.

Rather simple, isn’t it? This is why I love drinking sessions, for they allow you to unleash such thoughts. Oh, and I “recorded” this thought by sending a WhatsApp voice message with the gist of the above content to Hariba. He replied with “keep them coming” or some such thing, but this was all it was for this evening.

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}

 

Guarantees in meetings

There are some events/meetings which involve strong network effects. People want to attend such events if and only if a certain number of other people are going to attend it. But then they don’t know before hand as to who else is coming, and hence are not sure whether to accept the invitation. These are events such as school reunions, for example, where if only a few people come, there isn’t much value. And it’s hard to coordinate.

In such events it’s always useful to provide a guarantee. For example, a friend from (B) school was in town last week and expressed an interest in meeting other batchmates in Bangalore. A mail thread was promptly started but until the morning of the event, people remained mostly noncommittal. Not many of us knew this guy particularly well, though he is generally well-liked. So none of us really wanted to land up and be among only one or two people along with this guy.

And then there was a guarantee. One other guy sent a mail saying he’d booked a table at a bar, and this sent a strong signal that this guy was going to be there too. Then there were a couple of other very positive replies and the guarantee having been set, some seven or eight people turned up and the meeting can be called a “success”.

Sometimes when you’re trying to organise an event, it makes sense to get unconditional attendance guarantees from a couple of people before you send out the invite to the wider world. So you tell people that “X and Y” (the early guarantors) are definitely coming, and that will pull in more people, and that can be the trigger in making the event a success! In certain circles, X and Y need to be celebrities. In smaller circles, they can be common men (or women), but people whose guarantees of attendance are generally trusted (i.e. people who don’t have a history of standing up people)!

Another small reunion of my B-school batch happened last month and in the run-up to that I realised another thing about RSVPs – yeses should be public and noes private. One guy took initiative and mailed a bunch of us proposing we meet. I hit reply all on purpose to say that it was a great idea and confirm my attendance. Soon there was another public reply confirming attendance and this snowballed to give us a successful event. There were a few invitees we didn’t hear from, who didn’t attend, and I assume they had replied privately to the invite in the negative.

The problem with events on Facebook is that your RSVP is public irrespective of your reply – so even if you say no, everyone knows you’ve said “no”. And so you think it’s rude to say “no”, and say “yes” just out of politeness, even though you have no intentions of attending.

I’ve attended a few events where the hosts estimated attendance based on a Facebook invite and grossly overestimated attendance – too many people had hit “yes” out of sheer politeness.

So the ideal protocol should be “public yes, private no”. Facebook should consider giving this as an option to event creators so that people reveal their true preferences in the RSVP rather than saying “yes” out of sheer politeness.

In that sense it’s like a Vickery auction whose basic design principle is that people reveal their true willingness to pay and not underbid to avoid the winner’s curse!

Extension of nightlife in Bangalore

Finally the Government of Karnataka has bitten the bullet and announced that restaurants will be allowed to open till 1 am every night, and bars will be allowed to open until 1 am on Fridays and Saturdays. The catch, however, is that this is just a pilot move, and the government will take a decision on making this extension permanent based on feedback from various stakeholders after the pilot period.

The problems with having a three-month pilot period are several. For starters, the Bangalore city police is already over-stressed, and in three months (with only a pilot scheme) it is impossible to recruit and train more policemen who will be required to maintain law and order at the late hours. The result of this will be that the existing policemen will get over-stressed, with extended working hours, which cannot be good for policing in general. And as my colleague Saurabh points out, the police might vote to not extend the deadline beyond the pilot period.

The next problem is with the businesses themselves. A number of restaurants, I’m given to understand, were not in favour of the extension of deadline since they did not think the extended hours of working would be adequately compensated in terms of revenues. Take for example a typical “Family restaurant” (like Shanti Sagar). If most of the restaurant’s regular clientile is families, they are unlikely to provide any incremental business in the extended hours, and thus it doesn’t make sense for such restaurants to be open late in the night.

One constituency that should normally welcome later opening hours are the offshored IT and BPO workers, a large number of whom reside in Bangalore. However, in response to the early shutdown of the city, IT and BPO firms have adapted over the years, and arranged for in-house food and transport for their employees. While life should become theoretically easier for these workers with the extended hours (giving them wider choice for food and transport), three months, and that too a pilot scheme, is not enough for them to change their behaviour. So it is unlikely that these people will take advantage of extended opening hours.

Then, to be open for two additional hours in the night, bars and restaurants will need to make further investments in terms of personnel. However, if the extension in deadline is only for three months, there is no way any bar can realistically invest in the necessary personnel and infrastructure to be open in the late hours. This is likely to further mute the response to the pilot.

Finally, it needs to be noted that there are strong network effects involved in maintaining a night life. The streets of a city will be safe late at night only if they are busy. The streets of residential areas are unlikely to be busy at that hour (in fact, they are empty as early as 9:30 pm in some areas), but what we need for successful night life is a cluster of bars, restaurants, theaters and other “happening” places in small geographical areas that ensure large human traffic in those areas, which makes them safe. You wouldn’t, for example, feel safe traveling back on entirely empty streets from the pub to your home. What this implies is the need for organic growth of night-life. Abruptly shifting the deadline, and that too on a temporary basis, is unlikely to have an impact.

It appears that the three-month pilot for extension of deadlines is a policy that is designed to fail. Three months is too short a time for any of the stakeholders to make any realistic investments or behavioural changes, and given the network effects involved, it is unlikely that we are going to have a critical mass of establishments and people who will take advantage of the extended deadline for the policy change to be made permanent.

Expect the extension in deadline to be rolled back as soon as the three months of pilot are over.

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!