Pertinent observations on liquidity in startup markets

“Liquidity” was one of those words Wall Street people threw around when they wanted the conversation to end, and for brains to go dead, and for all questioning to cease

– Michael Lewis in Flash Boys

The quote that begins this blog post is also the quote that begins my book, which was released exactly a year ago. Despite its utility in everyday markets and economics, the concept of liquidity has not been explored too much outside of financial markets. In fact, one reason I wrote my book was that it appeared as if there was a gap in the market for material using the concept of liquidity to analyse everyday markets.

From this perspective, I was pleasantly surprised to come across a bunch of blog posts written by investors and tech analysts and startup fellows about the concept of “liquidity”. Most of these posts I came across by way of this excellent blog post by Andrew Chen of Andreessen Horowitz. It is always good to see others analysing topics in the same way as you are, so I thought I’ll share some insights from these posts here – some quotes, some pertinent observations. This is best done in bullet points. If you want to know more, I urge you to click through and read the blog posts in full. They’re all excellent.

  • You wonder why some startups make a big deal of how many cities they are in. This is because they usually function as within-city marketplaces, and so they need to be launched one city at a time. Uber famously started operations in San Francisco and remained there for a while.
  • “The best way to measure liquidity in the marketplace is to track the % of items or services that get sold/booked, and within what period of time. The higher the % and shorter period of time, the more sellers are making money and buyers are becoming loyal customers” – from here
  • “Where absolute pricing management makes most sense (i.e., where the marketplace operator sets prices) is where there isn’t a proper barometer for what the supply side should be charging and when the software can leverage systems should to optimize for liquidity” – from this excellent post
  • “In a zero sum game there, it’s most likely the marketplace with the most demand wins”. This was in the context of delivery marketplaces, and why Uber was likely to win that game (though it’s not clear if they’ve “won” it yet)
  • Trust is critical in building marketplaces. Both sides of the market need to trust the intermediary, and this can make marketplaces fragile. I had a recent incident where I appreciated the value of AirBnB landlord insurance (a lamp at a property I stayed at broke just after my stay, and the landlord wanted compensation). This post talks about how this insurance was critical to AirBnB’s growth
  • The same post talks about why even early stage businesses often make acquisitions – usually earlier stage businesses. “Marketplaces are normally winner-take-all markets. If we had lost ground to European competitors in 2012, we may have never gotten it back”
  • Ratings are a critical measure to build trust in a marketplace. And two-way ratings can help establish trust on both sides of the market
  • During the book launch function last year, there was a question on how marketplaces should build liquidity. I had given an example of the Practo/OpenTable model where you first sell a standalone service to one side of the market and then develop a marketplace. Another method (something I helped put in place for one of my current clients) is for the marketplace itself to become a “proprietary supplier”. The third, as this blog post describes, is about building markets where buyers are also sellers and the other way round (classic financial markets, for example).

For more on liquidity, and how it affects just about every market that you participate in on a daily basis, read my book!

Revenue management and transaction costs

So I just sent off a letter to India. To be precise, it is a document I had to sign and send to my accountant there – who sends regular “letters” any more?

The process at the post office (which, in my suburb, is located inside a large bookstore) was simple. In the first screen of the touch screen kiosk, there was an option for “worldwide < 20 grams”. A conveniently placed scale told me my letter weighed 18 grams, and one touch and one touch of my debit card later, I had my stamp. Within a minute, my letter was in the letterbox.

The story of how we pay the same amount for sending mail over large areas (“worldwide” in my case today) is interesting. Earlier, mail rates were based on distance, but as new roads kept being built in the 19th century America, and distances kept changing, figuring out how much to charge for a letter became “expensive”. A bright fellow figured out that the cost (in terms of time) of figuring out how much to charge for mail was of the same order of magnitude as the cost of the mail itself. And so the flat rate scheme for mail, that is prevalent worldwide today, was born.

Putting it in technical terms, transaction costs trumped price discrimination in this case. Price discrimination is the art (yes, it’s an art) of charging different amounts to different people based on their differential willingness to pay. Uber surge pricing is one example (I have a chapter in my book on this). Airline fares are another common example.

Until the late 18th century (well after mail prices had gone “flat”), price discrimination was rather common everywhere, a concept I have devoted a chapter to in the book. In fact, the initial motivation for fixed price retail was religious – Quakers, who owned many departmental stores in the US North-East, thought “all men are created equal before God” and so it was incorrect to charge different amounts to different people.

Soon other benefits of fixed prices became apparent (faster billing; less training for staff; in fact it was fixed prices that permitted the now prevalent supermarket format), and it took off. The concept is the same as stamps – the transaction cost of figuring out how much to charge whom is higher than the additional revenue you can make with such price differentiation (not counting possible loss of reputation, and fairness issues). Price discrimination at the shop is now confined to high value high margin businesses such as cars.

And it works in other high gross margin businesses such as airlines, hotels and telecom. These are all businesses with high fixed costs and low marginal costs for the suppliers. Low marginal costs has meant that price discrimination ha been termed as “revenue management” in the airline industry.

During the launch function of my book last year, I got asked if Uber’s practice of personalising fares for passengers is fair (I had given a long lecture on how Uber’s surge pricing is a necessary component of keeping average prices low and boosting liquidity in the taxi market). I had answered that a marketplace needs to ensure that its pricing is perceived as being “fair”, else they might lose customers to competitors. But what if all players in a market practice extreme price discrimination?

Thinking about it, transaction costs will take care of price discrimination before businesses and marketplaces start thinking of fairness. Beyond a point (the point varies by industry), the marginal revenues from price discrimination will fall below the transaction cost of executing this discrimination. And that poses a natural limit to how much price discrimination a business can practice.

Waiting for Kumaraswamy’s Tiger

Finally, last week Softbank announced that it has closed its $9.3 Billion investment in Uber. Since the deal was in the making for a long time, the deal itself is not news. What is news is what Softbank’s Rajeev Misra told Uber – to “focus on its core markets in US, Europe and Latin America”.

One way of reading this message is to see it as “keep off from competing with our other investments in Didi, Grab and Ola“. If Uber takes Misra’s words seriously (they better do, since Softbank is now probably Uber’s second biggest shareholder, after Travis Kalanick), it is likely that they’ll go less aggressive in Asian markets, including India. This is not going to be good for customers (both drivers and passengers) of taxi marketplaces in India.

Until 2014, the Indian market had three vibrant cab marketplaces – Uber, Ola and TaxiForSure. Then in early 2015, TaxiForSure was unable to raise further funding and sold itself to Ola, turning the market into a duopoly. Back then I’d written about why it was a bad deal for Indian customers, and hoped that another company would take TaxiForSure’s place.

Three years later, that has not come to be and the Indian market continues to be a duopoly. When I visited Bangalore in December, I noticed service levels in both Uber and Ola being significantly inferior to what I’d seen a year earlier when I was living there. Now, if Uber were to cede ground to Ola in India (as Softbank implicitly wishes), things will get further worse.

Back in 2015, when TaxiForSure was shutting down, I had assumed that another corporate entity, perhaps Meru (which runs call taxis) would take its place. And for a really long time now there have been rumours of Reliance entering into the cab marketplace business. Neither has come to be.

So this time my hopes have moved from corporates to politicians. The word on the street in Bangalore when I visited in December was that former Karnataka Chief Minister HD Kumaraswamy had partnered with cab driver associations to start a new cab marketplace, supposedly called “Tygr” (sic). The point of this marketplace, I was informed during my book launch event in Bangalore in December, was that it was going to be a “driver oriented app”.

This marketplace, too, has been coming for a long time now, but with the Softbank deal, it can’t come sooner. Yes, it is likely that it will not be a great app (if it is “too driver oriented”, it won’t get passengers and the drivers will also subsequently disappear), but at least it will bring in a sense of competition into the market and keep Ola honest. And hopefully there will also similar competition in other cities in India, though it is unlikely that it will be Kumaraswamy who will disrupt those markets.

A lot is made of the fact that investors like Warren Buffett own stocks in all major airlines in the US. Now, Softbank seems to be occupying that space in the cab marketplace market. It can’t be good either for drivers or passengers.

Ratings revisited

Sometimes I get a bit narcissistic, and check how my book is doing. I log on to the seller portal to see how many copies have been sold. I go to the Amazon page and see what are the other books that people who have bought my book are buying (on the US store it’s Ray Dalio’s Principles, as of now. On the UK and India stores, Sidin’s Bombay Fever is the beer to my book’s diapers).

And then I check if there are new reviews of my book. When friends write them, they notify me, so it’s easy to track. What I discover when I visit my Amazon page are the reviews written by people I don’t know. And so far, most of them have been good.

So today was one of those narcissistic days, and I was initially a bit disappointed to see a new four-star review. I started wondering what this person found wrong with my book. And then I read through the review and found it to be wholly positive.

A quick conversation with the wife followed, and she pointed out that this reviewer perhaps reserves five stars for the exceptional. And then my mind went back to this topic that I’d blogged about way back in 2015 – about rating systems.

The “4.8” score that Amazon gives as an average of all the ratings on my book so far is a rather crude measure – since one reviewer’s 4* rating might differ significantly from another reviewer’s.

For example, my “default rating” for a book might be 5/5, with 4/5 reserved for books I don’t like and 3/5 for atrocious books. On the other hand, you might use the “full scale” and use 3/5 as your average rating, giving 4 for books you really like and very rarely giving a 5.

By simply taking an arithmetic average of ratings, it is possible to overstate the quality of a product that has for whatever reason been rated mostly by people with high default ratings (such a correlation is plausible). Similarly a low average rating for a product might mask the fact that it was rated by people who inherently give low ratings.

As I argue in the penultimate chapter of my book (or maybe the chapter before that – it’s been a while since I finished it), one way that platforms foster transactions is by increasing information flow between the buyer and the seller (this is one thing I’ve gotten good at – plugging my book’s name in random sentences), and one way to do this is by sharing reviews and ratings.

From this perspective, for a platform’s judgment on a product or seller (usually it’s the seller, but for products such as AirBnb, information about buyers also matters) to be credible, it is important that they be aggregated in the right manner.

One way to do this is to use some kind of a Z-score (relative to other ratings that the rater has given) and then come up with a normalised rating. But then this needs to be readjusted for the quality of the other items that this rater has rated. So you can think of some kind of a Singular Value Decomposition you can perform on ratings to find out the “true value” of a product (ok this is an achievement – using a linear algebra reference given how badly I suck in the topic).

I mean – it need not be THAT complicated, but the basic point is that it is important that platforms aggregate ratings in the right manner in order to convey accurate information about counterparties.

Thaler and Uber and surge pricing

I’m writing about Uber after a really long time on this blog. Basically I’d gotten tired of writing about the company and its ideas, and once I wrote a chapter about dynamic pricing in cabs in my book, there was simply nothing more to say.

Now, the Nobel Prize to Richard Thaler and his comments sometime back about Uber’s surge pricing has given me reason to revisit this topic, though I’ll keep it short.

Basically Thaler makes the point that when businesses are greedy and seen to be gouging customers in times of high demand, they might lose future demand from the same customers. In his 2015 book Misbehaving (which I borrowed from the local library a few months ago but never got down to reading), he talks specifically about Uber, and about how price gouging isn’t a great idea.

This has been reported across both mainstream and social media over the last couple of days as if Thaler is completely against the concept of surge pricing itself. For example, in this piece about Thaler, Pramit Bhattacharya of Mint introduces the concept of surge pricing and says:

Thaler was an early critic of this model. In his 2015 book Misbehaving: The Making of Behavioral Economics, Thaler argues that temporary spikes in demand, “from blizzards to rock star deaths, are an especially bad time for any business to appear greedy”. He argues that to build long-term relationships with customers, firms must be seen as “fair” and not just efficient, and that this often involves giving up on short-term profits even if customers may be willing to pay more at that point to avail themselves of its product or service.

At first sight, it is puzzling that an economist would be against the principle of dynamic pricing, since it helps the marketplace allocate resources more effectively and more importantly, use price as an information mechanism to massively improve liquidity in the system. But Thaler’s views on the topic are more nuanced. To continue to quote from Pramit’s piece:

“I love Uber as a service,” writes Thaler. “But if I were their consultant, or a shareholder, I would suggest that they simply cap surges to something like a multiple of three times the usual fare. You might wonder where the number three came from. That is my vague impression of the range of prices that one normally sees for products such as hotel rooms and plane tickets that have prices dependent on supply and demand. Furthermore, these services sell out at the most popular times, meaning that the owners are intentionally setting the prices too low during the peak season.

Thaler is NOT suggesting that Uber not use dynamic pricing – the information and liquidity effects of that are too massive to compensate for occasionally pissing off passengers. What he suggests, however, is that the surge be CAPPED, perhaps at a multiple of three.

There is a point after which dynamic pricing ceases to serve any value in terms of information and liquidity, and its sole purpose is to ensure efficient allocation of resources at that particular instant in time. At such levels, though, the cost of pissing off customers is also rather high. And Thaler suggests that 3 is the multiple at which the benefits of allocation start getting weighed down by the costs of pissing off passengers.

This is exactly what I’ve been proposing in terms of cab regulation for a couple of years now, though I don’t think I’ve put it down in writing anywhere. That rather than banning these services from not using dynamic pricing at all, a second best solution for a regulator who wants to prevent “price gouging” is to have a fare cap, and to set the cap high enough that there is enough room for the marketplaces to manoeuvre and use price as a mechanism to exchange information and boost liquidity.

Also, the price cap should be set in a way that marketplaces have flexibility in how they will arrive at the final price as long as it is within the cap – regulators might say that the total fare may not exceed a certain multiple of the distance and time or whatever, but they should not dictate how the marketplace precisely arrives at the price – since calculation of transaction cost in taxi pricing has historically been a hard problem and one of the main ways in which marketplaces such as Uber bring efficiency is in solving this problem in an innovative manner using technology.

For more on this topic, listen to my podcast with Amit Varma about how taxi marketplaces such as Uber use surge pricing to improve liquidity.

For even more on the topic, read my book Between the buyer and the seller which has a long chapter dedicated to the topic,

Incredible stupidity in taxi marketplaces

So it’s nearly a week since Uber and Ola drivers in Bangalore went on strike, and there’s no sign of it (the strike) ending. The longer the strike goes on for, the more incredibly stupid all parties involve look.

The blame for the strike should first fall on Uber and Ola, who in some hare-brained madness, forgot that running a platform means that both sides of the market are customers and need to be taken care of. They took good care of passengers, providing discounts and growing their market, but rather quickly pulled the plug on drivers, and there is no surprise that drivers are a rather pissed off lot.

The root cause of driver dissatisfaction has been falling bonus payments, and consequently, incomes. This is a result of Uber and Ola providing too great a subsidy during the time they built up the market.

I don’t fault them for providing those bonuses – when you are building a two-sided market, you need to subsidise one side to solve the chicken-and-egg problem. Where I have the problem is with the extent of bonuses, which gave drivers an income far in excess of what they could make in steady state. This meant that as the market approached steady state and incentives were withdrawn, once side of the market started getting pissed off, undermining the market (Disclosure: I’d once proposed to Ola that they hire me to help them with pricing and incentive structuring. the conversation didn’t go too far).

With Uber and Ola having done their stupid things, the next round has gone to the drivers. In a misguided attempt that a long strike will help them get better deals from the platforms, they are prolonging the strike. They’ve even ransacked Uber’s offices, and gone to the government for help.

What they don’t realise is that having invested what they have in their cars to drive on these marketplaces, their success is inextricably tied to the success of the marketplaces. And the more the jeopardise the marketplaces, the less their incomes in future.

A long strike reduces market size on two counts – it gives people time to adjust to the absence of service and get adjusted to alternate arrangements, and it decreases the reliability of the marketplaces in the eyes of the passengers. Thus, the longer and more frequent the strikers by the drivers, the less that passengers will look to use these services in the future.

A strike can work when the striking employees are protected by some form of labour laws, and there is no way ahead for their employers apart from a negotiated settlement. In case of a marketplace, the platform has absolutely no obligation to the drivers, and Uber and Ola can simply do what Uber and Lyft did in Austin, TX – pack up and move on. And if they do that in Bangalore, the drivers with their shiny new cars will be significantly worse off than they were before the strike.

The other act of stupidity on the drivers’ part has been to involve the government, which, as expected, has responded in a nandelliDLi (“where do I keep mine?”) fashion. The recent ban on shared rides (UberPool/OlaShare) came after a regulator read the rulebook after the last strike by the drivers. Given the complex economics of platform markets, any further regulation can only hurt the drivers.

All in all, the drivers’ stupidity can be traced back to not understanding platform markets, and protesting the way protests used to be done in highly unionised industries. Drivers, whose main skill is in driving cars, cannot be faulted so much for not understanding platform markets. Uber and Ola, on the other hand, have no such excuse!

Aswath Damodaran, Uber’s Valuation and Ratchets

The last time I’d written about Aswath Damodaran’s comments on Uber’s valuation, it was regarding his “fight” with Uber investor Bill Gurley, and whether his valuation was actually newsworthy.

Now, his latest valuation of Uber, which he concludes is worth about USD 28 Billion, has once again caught the attention of mainstream media, with Mint writing an editorial about it (Disclosure: I write regularly for Mint).

I continue to maintain that Damodaran’s latest valuation is also an academic exercise, and the first rule of valuation is that “valuation is always wrong”, and that we should ignore it.

However, in the context of my recent piece on investor protection clauses in venture investments (mainly ratchets), it is useful to look at Damodaran’s valuation of Uber, and how it compares to Uber’s valuation if we were to account for investor protection clauses.

“True value” of Indian unicorns after accounting for investor protection. Source: Mint

When Uber raised $3.5 Billion from Saudi Arabia’s Public Investment Fund earlier this year, the headline valuation number was $62.5 Billion. Given the late stage of investment, it is unlikely that the investor would have done so without sufficient downside protection – at the very least, they would want a “full ratchet” (if the next investment happens at a lower valuation, then they get additional shares to compensate for their loss). This is a conservative assumption since late stage (“pre-IPO”) investments usually have clauses more friendly to the investor, usually incorporating a minimum “guaranteed return”.

Plugging these numbers into the model I’ve built (pre-money valuation of $59 Billion and post-money valuation of $62.5 billion), the valuation of the put option written by existing investors in favour of Uber comes to around $1.28 Billion. Accounting for this option, the total value of the company comes out to $39.6 Billion.

Damodaran’s valuation, based on his views, principles and numbers, is $28 Billion. Assuming that investors and management of Uber are aware of the downside protection clauses and its impact on the company’s valuation, Damodaran’s valuation is not that much of a discount on Uber’s true valuation!