Tag: uber

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!

Uber and the narrative bias

Following the alleged rape of a Delhi woman by a cab driver who she’d engaged via the Uber app, the Delhi government has banned Uber. Union home minister rajnath Singh has issued a notification to other state governments to do the same though union transport minister Nitin Gadkari has rightly called it a silly idea.

Irrespective of whether the service gets banned, fewer people are likely to use it. A survey conducted by Mint newspaper has shown that nearly half the people surveyed will not use an Uber following the incident (the survey doesn’t mention how many of those surveyed are existing users of Uber).

About a year back, two buses of the Volvo make (one travelling from Bangalore to Hyderabad and the other from Bangalore to Pune) caught fire, resulting in passenger deaths. While the government of Karnataka mercifully didn’t ban Volvo buses (instead simply subjecting them to safety checks and insisting on emergency exits), there was a large backlash from the public who eschewed travel by Volvos in favour of travel by other means of transport.

In 2001, following the 9/11 attacks, Americans eschewed air travel in favour of driving. Gerd Gigerenzer, a specialist in risk, has estimated that 1595 additional people died in the year following 9/11 on account of driving rather than taking flights.

The question that arises is what those current users of Uber who don’t want to use the service any more are going to do – surely they must resort to alternate means of transport to commute? The question they need to ask themselves is If the new chosen means of transport is safer than Uber!

People abandoning Uber in droves following last weekend’s incident is due to what I can the “narrative bias”. Last weekend’s incident has introduced the narrative that Uber is not necessarily safe – at least it is not as safe as people assumed it to be prior to the incident. And this narrative is likely to lead to people reacting, and in a direction that is not necessarily better for them!

So if people abandon Uber, or if it gets banned (the proposal is to ban other app based cab services too ), what is the alternative, and is it safer than Uber? Extremely unlikely, If the answer is auto rickshaws for example. We might as well end up in a situation like what happened on the highways in the US after 9/11.

News by definition is spectacular and spectacular incidents are much more likely to be reported than unspectacular ones (a favourite example I use is – how many times do we see a headline that says ” Ashok Leyland bus catches fire. Passengers dead “? The fact that we seldom see such headlines doesn’t mean that Ashok Leyland buses never catch fire). This, however, doesn’t mean that policymaking, too, be based on spectacular events only.

Any regulation, and decisions by people, should be based on rational expectations and not be biased by narratives and the spectacular. There is always pressure on the policymaker to ” do something “. This however doesn’t mean that anything will do. Decisions need to be based on reason and not narratives!

PostScript: I’ve written this post sitting in the back of an Uber taxi in Bangalore

Why app based taxi services should not be banned

The move towards banning Uber and other app-based taxi services is devoid of logic on several counts

Writing during the Takshashila Hudson conference on India’s growth I had argued that an easy way to increase the level of business activity in the country, and thus GDP was by means of reducing transaction costs. Transaction costs are costs borne by buyers of a good or Service which don’t accrue to the seller.

The thing with transaction costs is that they introduce friction in the market – the cost ends up reducing both the market clearing price (as it accrues to the seller) and the market clearing quantity. And transaction costs are usually to no ones gain and thus reducing them is a quick and pareto optimal method of boosting GDP.

In this regard, the government must encourage all means that result in reduction of transaction costs. For example better road and rail network significantly reduce the transaction cost of moving goods and people. Removal of interstate taxes on goods and services results in more optimal setups of warehouses and plants.

Similarly apps such as Uber play an important role in reducing transaction costs in the local taxi market. By reducing the distance and time to be traveled by the driver, and by reducing the amount of the the passenger has to wait for the cab, these services significantly reduce the cost of local transport and benefit drive and users alike.

Thus moves such as banning such services are utterly brainless and devoid of logic. Moreover such moves will dampen investor sentiment in India and kill off any positive vibes that have been generated ever since the current government came to power.

I hope better logic prevails and the government focuses on improving law and order (a public good that can further reduce transaction costs) rather than knee jerk actions like banning taxi services which seek to reduce transaction costs.

Pricing markets in cabs and beer

Earlier this evening Udhay and I shared a cab back after beer and biryani. We don’t stay particularly close by (using the place we met as point of reference), but I think it was pareto optimal for us to share the cab rather than take two cabs. I got off first at my place and Udhay went on to his place. We used Uber’s fare splitting feature for the trip.

I just got the bill and saw that I’ve been charged exactly half of the total bill. Given the distance from our meeting point to my place and Udhay’s place it perhaps was pareto optimal but had we met any closer it may not have been a fair split – if the place we met was closer to my place than my place is to Udhay’s, then splitting the fare equally would have been unfair to me – for I would have paid more for sharing the ride than I would have had I taken a cab by myself! Can Uber do better?

Once we have enabled the ride sharing and splitting thing, Uber knows who all are travelling, and Uber knows where each of us gets off (if our phones are on, that is). Based on where we break off from the cab, can Uber estimate where each of us got off and split the fare accordingly? Given how good their app has been so far, I would expect them to tweak their ride splitting algorithm and introduce this measure soon.

Going a little back in the day, Udhay and I were at Punjabi By Nature in SuddgunTepALya. We were there during the restaurant’s “happy hours” where they have a buy-one-get-one-free offer on beer. However, it was after we had ordered a “tray” of samplers that we were told that the Bogof didn’t apply to the tray. We also ordered another glass of beer, which duly arrived with a “partner”.

There are two things about Punjabi By Nature’s pricing that I found interesting. The first bit was the non-applicability of “happy hours” to the tray. Is it a measure by them to reward their regular customers who know what to drink at the cost of first-timers who invariably ask for the sampler set? Any other explanation for happy hours not applying to the tray?

The second interesting bit is about the pricing of the drinks itself. A 500ml glass of beer was priced at Rs. 240 plus taxes, which is par for the course for a microbrewery in Bangalore. In most other microbreweries, the sampler trays are priced “reasonably”, approximately at the same per-ml price as the glasses of beer. Here, though, the tray (on which we didn’t get Bogof, remember) was prices at Rs. 625 per taxes! Of course, there were six beers that were sampled in the tray and the quantity was also significantly more than at other sampler trays (here it was at least 150ml per glass if I’m not wrong; in other microbreweries in Bangalore it’s more like 100ml), yet the premium in pricing for the samplers was significant!

I wonder what makes other microbreweries price their samplers at about the same per-ml cost as their glasses – given that the standard practice is to incentivise customers to buy in larger units. I also wonder what makes Punjabi By Nature impose a “penal” price (assuming it was 150ml per sample, it works out to about 70 paisa per ml. The glass of beer (not accounting for happy hours) costs 240/500 = 48 paisa per ml, so the sampler is 50% more expensive) on its samplers. For now that I know how it’s priced, the next time I go to Punjabi By Nature I’m going to order glasses of beer (hopefully in happy hours) and not the sampler!

Pricing is a funny game, I tell you!

Uber’s surge pricing, Urban Ladder’s Diwali sales and clearing marketplace transactions

I recently bought a bed from Urban Ladder. Since I bought it during their Diwali sale, I got a 20% discount on it. It was supposed to be delivered in three weeks, but it took four. For the trouble caused by the delayed delivery, they gave me an additional 5% discount. As a lay customer, I would have been delighted. As someone with ideas on liquidity and two-sided markets, I’m still delighted by the customer service but intrigued as to why they had to offer the sale at all.
Before we proceed, a word on two-sided markets. By definition, all markets are two-sided, for there is a set of buyers and a set of sellers. The difference between a “traditional market” and a “two sided market” or “platform” as we understand it is that in the former case, the owner and designer of the market is also a participant. Rather the designer of the market is the only participant on the sell side. In a “platform” scenario, the designer of the market is not a participant. The designer makes money by enabling and facilitating transactions. For the rest of this post, however, we will return to the popular definitions and imply “two sided markets” to refer to markets where the market designer is not a participant.
In a two-sided market (by popular definition), the owner of the market usually makes money on a transaction basis. She either takes a fixed sum of money per transaction or more usually a proportion of the value transacted. For example, when you trade stocks, both buyer and seller pay small fees to the exchange (this is in addition to the fees paid to their respective brokers). When you ride an Uber, the marketplace (Uber) takes 20% of the ride proceeds (not currently the case in India, though). Thus, it is in the interest of the designer of the market to maximise the volume/value (usually the latter) of transactions on the market.
Now, two-sided markets have a virtuous cycle/positive feedback built in. The more the buyers you have, the more the sellers want to sell on your “exchange”. And the more the sellers you have, the more the buyers who want to buy. Thus, as a market designer, your job is to “seed” the exchange, to an extent that this virtuous cycle takes off, and then you can essentially relax as the market builds itself and more transactions are transacted.
This necessitates that in the initial stages of building the market, the marketplace will have to make some investments such that buyers and sellers find it profitable to transact. For example, you might choose to take the hit on most of the transaction costs that buyers and sellers face. For example, consider the cab companies in India such as Uber and Ola, which are subsidising both drivers and customers in the hope of building up their respective marketplaces. Once these marketplaces are built and the virtuous cycle kicks in, the platforms can then start making profits.
Building a marketplace is in a sense like climbing two ladders simultaneously, with one foot on each. You have to make constant efforts to beef up both demand and supply, for if at any point in time one goes too far ahead of the other, the market gets unbalanced and you will either have dissatisfied participants (because they could not find a counterparty) or you have to take a hit to ensure that the market gets cleared (to continue the metaphor you either strain your loins or you fall off the ladders 🙂 ). From this perspective, the recent Diwali sale on Urban Ladder doesn’t make too much sense.
I’m getting into anecdata territory here, but as a customer my main pain point regarding Urban Ladder has been their availability. Every time I’ve wanted to buy something it’s either been out of stock or the delivery cycle has been too long – never has the price been a problem to me. My understanding of their market, thus, has been that demand has been far outstripping supply, and at their current market clearing prices (notice that urban ladder sets the prices at which customers buy on the platform), quantity demanded far exceeds quantity supplied. The normal economic response to this would either be to jack up prices – to a level where the market clears, or to aggressively woo suppliers, such that the market clears at current prices. Instead, Urban Ladder made the problem worse by subsidising customers, which further pushed up the gap between quantities demanded and supplied.
Figure 1 illustrates this problem. In the face of the discount (effectively a subsidy) by Urban Ladder, the demand curve shifted right. There was already a gap between demand and supply at the undiscounted price (which was lower than the market clearing price), and the introduction of the discount only made this gap worse. (the Y axis of this graph refers to the price received by the seller).
In the face of the discount, demand moves downward along the curve, and the demand-supply gap increases as shown in the figure.
urbanladder2
During Diwali, Urban Ladder offered a 20% discount. It is unlikely that this discount would have been passed on to their suppliers, which means that the marketplace took a temporary hit in margins in order to grow their market. While it would have grown the market in terms of increasing orders from the buy side, it is unlikely that the market itself would have grown – since the problem with Urban Ladder is supply and not demand.
In the traditional inventory-led model, sales promotions and customer incentivisation are common techniques in order to grow sales – the incentives not only lead you to increase your sales, but also result in a clearing out on your inventory to make room (and working capital) for fresh stocks. In a marketplace model, however, where the bottleneck is clearly on the supply side, it is not clear how a sale results in growth. It seems like Urban Ladder got carried away by the traditional model of growing topline in an inventory-led model.
So does that mean that Urban Ladder’s Diwali sale was wrong? Not really, for they could have done it better. The way to do it would have been to first approach suppliers and lock in an increase in supply. This would have necessitated some subsidies on the supply side – like for example guaranteeing a certain amount of orders during the sale month. Supplies thus guaranteed, Urban Ladder could have then brought on a sale on the demand side to an extent that
1. it would be within their promotional budget and
2. the market would have been cleared.
In fact, it is not even necessary that the discount would have to be entirely monetary – for Urban Ladder could have structured the discount as “10% off sale price and 1 week delivery” or something.
The important thing to consider, thus, is that in a market place model, both demand and supply side are elastic – something that is not the case in an inventory led model where once you have the inventory the supply is largely inelastic. Thus, when demand exceeds supply, one way to clear the market is to actually raise the incentives for the supply side (rather than reducing incentives for the demand side). And this is something that Uber gets right with its surge pricing.
When there is a surge in demand on Uber, prices are jacked up, and more importantly, the jacked up prices are passed on to the drivers. Thus, the jacked up prices help clear the market from two sides – culling demand and increasing supply – for higher prices for a ride would mean that drivers who would otherwise be loathe to venture out into heavy traffic or rain (conditions when surge usually kicks in) would have more incentive to come in and help clear the market!
A market place such as Uber or Urban Ladder is basically a mechanism of matching supply to demand, and the key is in getting the pricing right. Constantly “listening” to both demand and supply helps you do that, and as Uber’s constantly updating surge prices show, adjustments are required. Of course such frequent adjustments are not prudent from the perspective of a company like Urban Ladder. But it is important for them to get at least the direction in the price movement right.
Errata
The original version of this piece indicated the change in price as effecting a shift in the demand curve itself. As those of you know Econ 101 better than I do know, this is simply wrong and the price change results in a movement along the curve. Thanks to Shruti Rajagopalan for pointing this out.

Perverse regulations

So Uber has tied up with PayTM to process its payments without a second factor of authentication in order to comply with RBI regulations. This is a major win-win for both companies. Uber can now gain access to the part of the relatively affluent Indian population that does not own a credit card (this is a significant segment). PayTM now has a compelling reason to sign up users for its Wallet solution, since all Uber customers now form a sort of a captive audience for this solution.

While discussing this on twitter, someone suggested that once the new Payment Bank regulation is brought in by RBI, wallet solution providers such as PayTM can then set themselves up as Payment Banks.

The problem with that is that if PayTM becomes a payment bank then it will have to comply with RBI regulations of second factor authentication and thus Uber users will not be able to use their PayTM wallets (now accounts) for seamless payment!

#Thatzwhy we need strong regulations.

The RBI does a Ramanamurthy

This is the second time in a few weeks I’m referring to this scene from Ganeshana Maduve. Please watch it first.

To repeat the story:

Ramanamurthy the owner of the “vaTaara” (a kind of apartment that was popular in Bangalore till the 1980s, with lots of small houses in the same compound) wants to whitewash his house. The residents of the vaTaara  demand that if he whitewashes his house he should whitewash the entire vaTaara. After a long and protracted negotiation, Ramanamurthy agrees to their condition – he doesn’t whitewash his house!

It is a similar story with taxi operators in India. Uber (the Ramanamurthy) figured out a way to bypass RBI’s two factor authentication system and offer seamless payment options for their taxi services. Soon other taxi operators like TaxiForSure and Ola started crying foul saying they too wanted their houses painted, i.e. they too wanted to locate payment servers abroad to accept one factor authentication credit cards.

And now RBI, like the rent controller ubiquitous (in mention only) in movies of the late 80s has stepped in and stopped Ramanamurthy from painting his house, too – they’ve barred Uber from charging in US dollars for Indian rides. It would be interesting to see how the market will develop now.

My personal opinion is that RBI’s insistence on two factor authentication is half-assed. They should make every effort possible to increase the number of credit card (or account-to-account) transactions. On one hand it decreases flow of black money but more importantly it means that people will keep more cash within the banking system (rather than as hard cash) which will have a multiplier effect on money available for lending and all that.

It’s fine to have regulations in place such that credit card fraud is minimized but that doesn’t mean cutting credit card transactions altogether! Hopefully the RBI will see the light of day on this one soon.

Damodaran on Uber’s Valuation

It is fascinating to watch this back–and–forth between NYU Prof Aswath Damodaran and Uber board member Bill Gurley on the taxi company’s valuation.

To set the context, when the latest funding round for Uber was announced, valuing it at USD 17 billion, Damodaran – a guru in valuation – wrote his own analysis which valued the company at about a third of that value. While it was a typical Damodaran post – long, detailed and making and stating lots of assumptions – it was probably intended as an academic exercise (the way I see it).

Instead it seems to have really caught the fancy of the silicon valley investment community, and led to a response by Gurley (I admit I haven’t read his full response – it seemed to attack straw men in places). And Damodaran has responded to the response. Now that the Three Way Handshake is complete I don’t expect any more backs and forths, but I won’t rule it out either (it’s possible but not plausible, to use Damodaran’s terminology).

What fascinates me is why an academic’s academic post on valuation of a company has created so much of a flutter – so much to merit a long-winded response from the board member. I’m reminded of two things that my valuation professor had told me some 10 years back when I was in business school.

1. Valuation is always wrong
2. Value of a company is what the market thinks it’s valued at

The first of these is a bit of a motherhood statement and adds no value to this particular discussion so let’s not take that into account. It’s the second reason that has got the investors’ knickers in a twist.

In the past, I’ve seen Damodaran publish valuations of companies that are about to go public, or are already public – Tesla and Twitter for example. It is usually an academic exercise, and Damodaran’s valuations value these companies at lower than what the market values. However, given that these posts have appeared after there has been a broad consensus of a company’s valuation, it has not really impacted a company’s valuation, and thus have been treated as an academic exercise.

The problem with Uber is that it is a private company, and unlikely to go public for a very long time. The problem with a private company is that it is difficult for investors to agree on its valuation – there are very few trades and the stock is illiquid (by definition). And illiquidity means extremely high bid-ask spreads (to put a technical spin on it) and widely varying valuations.

Sometimes, when nobody knows what something is valued at (like Uber – which is creating a new category which no one has any experience in valuing), what people look for is some kind of a peg, or an “anchor”. When they see what they think is a reasonable and broadly reliable valuation, they tend to use that valuation as an “anchor” and if a large number of investors agree on one such anchor, the anchor ends up being the company’s valuation itself.

To reiterate, value of a company is what the market thinks it’s valued at. Nobody knows what Uber is valued at. Investors and existing shareholders agreed at a particular valuation, and did a deal at that valuation. However, this valuation is not “deep” – not too many people agree to this valuation.

It is in this context that an (very well renowned) academic’s valuation, which values the company at far less than the last transacted price, can act as an anchor. Damodaran is extremely widely respected in investing circles, and hence his valuation is likely to have received much attention. It might even be possible that his valuation becomes an “anchor” in investors’ minds of Uber’s valuation. And this is where the problem lies.

Even if you were to account for the consistent downward bias in Damodaran’s valuations and adjust Uber’s valuation accordingly, it is likely to lead  to a much lower anchor compared to the last transacted price. And this is not likely to be good for existing investors. Hence, they need to take steps to quickly debunk Damodaran’s valuation, to make sure it doesn’t end up as an anchor! And hence the long response by Gurley, and the silicon valley investor community in general!

To summarize, all that this entire brouhaha on Uber’s valuation shows is that its price discovery so far has been rather shallow.