Uber’s new pricing structure

So Uber has changed its pricing structure in Bangalore. Earlier they nominally charged Rs. 50 fixed, Rs. 15 per kilometer and Rs. 1 per minute, and then slapped a 35% discount on the whole amount. From today onwards the new fare structure is Rs. 30 fixed, Rs. 8 per kilometer and Rs. 1 per minute, without any further discounts. They’re marketing it using the Rs. 8 per kilometre number.

I took a ride this afternoon under the new fare structure, and the bill was Rs. 152, about the same as it would have been under the old fare structure. In that sense, I guess this was an “average ride”, in terms of the distance by time covered. This was the kind of ride where their assumption on distance travelled per unit time (in coming up with their new formula) was exactly obeyed!

So how do we compare the old and new formulae? We can start by applying the discount on the nominal numbers of the old formula. That gives us a fixed cost of Rs. 32.5, a per kilometer cost of Rs. 9.75 and a per minute cost per 65 paise. We can neglect the difference in fixed cost. Comparing this to the new cost structure, we find that the passenger now gets charged a lesser amount per kilometre, but a higher amount per minute.

In face, taking the “slope” between the old and new rates, the per kilometre cost has come down by Rs. 1.75 while the per minute cost has risen by 35 paise. Taking slope, this implies that Uber has assumed a pace of a kilometre per five minutes, or twelve kilometre per hour.

So if your journey is going to go slower than twelve kilometres per hour, on average, you will end up paying more than you used to earlier. If your journey is faster than twelve kilometres per hour, then you pay less than you did under the previous regime.

A few implications of the new fare structure are:

1. Peak hour journeys are going to cost more, for they are definitely going to go slower than twelve kilometres an hour
2. Your trips back from the pub should now be cheaper, for late nights when the roads are empty you’ll travel significantly faster than twelve kilometres an hour
3. What does this imply for the surge pricing in the above two cases? I think the odds of a surge during peak office hours will come down (since the “base price” of such a trip goes up, which will push down demand), and  the odds of a surge late on a Friday or Saturday night might go up (since base fare has been pushed down for that).
4. The Rs. 30 fixed cost implies that if a driver travels at 12 km/hr when looking for a new ride, the gap between rides for a driver is 11.5 minutes (if the driver spends X minutes, he will travel X * 12/ 60 kilometres in that time. The compensation for this combination is X + X*12/60 * 8, which we can equate to 30. This gives us X = 11.54).
5. Trips to/from the airport will now be cheaper, for you can travel much faster than 12 km/hr on that route. So Uber will become even more competitive for airport runs. Again this might increase probability of a surge at peak flight times.

I continue to maintain that Uber has the most rational price structure among all on-demand taxi companies, since the fare structure fairly accurately mirrors drivers’ opportunity costs. Ola doesn’t charge for the easily measurable time, and instead charges for “waiting time”, which is not well defined. Ola also has a very high minimum fare (Rs. 150). I wonder how they’ll play it if their planned acquisition of TaxiForSure goes through, since TaxiForSure was playing on the short trip model (with minimum fares going as low as Rs. 49). Given the driver approval before a ride, though, I doubt if anyone actually manages to get a Rs. 49 ride from TaxiForSure.

Times continue to be interesting in the on-demand taxi market. We need to see how Ola responds to this pricing challenge by Uber!

Cancellation charges in the airline industry

So seat 2B in flight number I5 1322, Air Asia flight from Bangalore to Goa this morning, went unfilled. I wasn’t on this flight, but perhaps for that precise reason I can assure you that the above statement is true. For that seat belonged to me, as I had booked my tickets to go to the Goa Project.

So I initially decided to go to the Goa Project, and booked my tickets for it (on AirAsia). And then work and other things meant that I decided against going, but when I went to the AirAsia website to cancel my ticket, I realised that I wasn’t able to do it! Essentially AirAsia has no concept of cancelling a flight!

This is option pricing taken to one extreme, where the entire price is taken in the form of an option premium! The airline industry was at the other extreme not so long ago – where options weren’t priced at all. In other words, until a decade or a bit more back, you could change or cancel your bookings at little extra cost, though this optionality (and other things such as regulation) meant that the ticket was  quite expensive!

Now it seems like some of the “extreme low cost carrier” (such as AirAsia or RyanAir) have moved to the other extreme – where there is no concept of cancellation. And I’m not sure of the wisdom of this strategy. For I believe that this strategy does not maximise either the social utility or the airline’s profits.

The social utility bit is easy to see – I ended up paying full price for a ticket that I didn’t travel on, so I’m hurt. The marginal passenger who wanted to travel on today’s flight to Goa but didn’t find a ticket was hurt (this person could’ve flown had I cancelled my ticket). And the airline missed an opportunity to resell my ticket to someone else (potentially at a much higher price) and make money on it! So it’s a lose-lose situation all round.

The commercial aspect follows from the above – in case demand for this flight was low, then it perhaps made sense to not refund any of my money, for now the airline would not be able to sell the ticket to another passenger. However, if demand were higher (a very probable event), the airline missed an opportunity to make much higher revenue on this seat than what they did by making me not cancel it. I wave my hand here a bit, but it is easy to see that the airline is letting go of potential profits by not letting me cancel my ticket!

I’m not saying that the airline refund my full amount, or anything close to that – that doesn’t make sense for them since they’ve sold me an option. All I’m saying is that the price of zero (between total cost and option cost) doesn’t make sense. Even if the airline were to refund a thousand rupees (I paid around 6000 for the two-way fare) if I cancelled it, and the cancellation procedure were smooth, I would have cancelled it. And the expected revenues on this one seat would definitely exceed this kind of refund, you would expect?

Possibly it’s time for airlines to indulge in dynamic pricing for cancellations also. If the flight is near full, the airline can resell my cancelled ticket for a fairly high amount, so they can induce me to cancel by offering me a decent refund. If at the time I want to cancel, however, demand is low, then they need not offer me much. These things are not at all hard to price!

So by going extreme on the cancellation charges Air Asia and its ilk are leaving money on the table! If only someone were to tell them to pick it up!

Airline delays in India

So DNA put out a news report proclaiming “Air India, IndiGo flyers worst hit by flight delays in January: DGCA“. The way the headline has been written, it appears as if Air India and Indigo are equally bad in terms of delayed flights. And an innumerate reader or journalist would actually believe that number, since the article states that 96,000 people were inconvenienced by Air India’s delays, and 75,000 odd by Indigo’s delays – both are of the same order of magnitude.

However, by comparing raw numbers thus, an important point that this news report misses out is that Indigo flies twice as many passengers as Air India. For the same period as the above data (January 2015), DGCA data (it’s all in this one big clunky PDF) shows that while about 11.65 lakh passengers flew Air India, about 22.76 lakh passengers flew Indigo – almost twice the number. So on a percentage basis, Indigo is only half as bad as Air India.

airlinedelays

The graph above shows the number of passengers delayed as a proportion of the number of passengers flown, and this indicates that Indigo is in clear second place as an offender (joined by tiny AirAsia). Yet, to bracket it with Air India (by not taking proportions) indicates sheer innumeracy on the part of the journalist (unnamed in the article)!

I’m not surprised by the numbers, though. The thing with Indigo (and AirAsia) is that the business model depends upon quick turnaround of planes, and thus there is little slack between flights. In winters, morning flights (especially from North India) get delayed because of fog and the lack of slack means the delays cascade leading to massive delays. Hence there is good reason to not fly Indigo in winter (and for Indigo to build slack into its winter schedules). Interestingly, the passenger load factor (number of passengers carried as a function of capacity) for Indigo is 85%, which is interestingly lower than Jet Airways (a so-called “full service carrier”)’ s 87%. And newly launched full service Vistara operated at only 45% in January!

We are in for interesting times in the Indian aviation industry.

Matching problem in the Indian Dating Market

And no, this has nothing to do with Hall’s Marriage Problem.

As the more perceptive of you might have noticed, about a year and a bit back the wife started this initiative called “Marriage Broker Auntie”. Basically she thinks she is a good judge of people and a good judge of “matches” between people. As a consequence of this, there were many friends and relatives who would approach her to “set them up”. Having (successfully or unsuccessfully) made a few matches among such applicants, she decided to institutionalise it, and thus Marriage Broker Auntie was born.

The explicit objective of the initiative was to broker marriages (as the name clearly suggests. The wife was the “auntie”. The plural in the title was because briefly there was one more (mad) auntie involved but she’s since moved on). The methodology was to “know the customers” and then use an intelligent human process in order to find pairs who might be interested in each other and then set them up for a date. As simple and basic as it gets. The quant in me had already started dreaming up of an expanded business where I could use “big data” and “analytics” and whatnot to “understand” large sets of people and match them up.

But there was a small problem – ok the fundamental problem was that the number of people who had signed up was not very large, but let’s assume that can be solved through marketing – the problem was that the sex ratio on the website was skewed. Heavily. At one point in time I remember there being 20 girls and 5 boys being registered on the website (all heterosexual, perhaps a consequence of “marriage” in the title)! The ratio remained thus as long as the initiative was in existence.

Now this contrasts heavily with other “dating” sites that are operational in India, primarily global sites such as OkCupid and Tinder. The gender ratio on these sites is heavily skewed, too, except that it is heavily skewed in the opposite direction (too many boys, hardly any girls). For example, check out this piece in Man’s World on Tinder, which talks about users who think there is a “permanent bug” in the site that doesn’t allow matches, and of “all girls on the site being bots”.

The problem with most dating sites in India is that there are way too many boys and way too few girls (I should add Orkut also to this list, and should mention that I met my wife through a combination of Orkut and LiveJournal). This leads to girls on the site feeling like they’re “being stalked”, and getting freaked out and getting out of the site. A girl I know signed up on OkCupid India, just on a whim, only to find a hundred “interests” from boys within a few minutes of logging on.

Reading stories like this, you might be bound to imagine that there are no girls in India interested in dating, or getting married. But if you were to look at sites like Marriage Broker Auntie (small sample, I know, but significant gender bias) you know that this is simply untrue. There are girls out there who are looking for flings and relationships, to date and to get married. And they are short of ideas on how to meet such men. “Traditional” dating sites such as OkCupid or Tinder intimidate them, and shaadi.com and bharatmatrimony.com are in a completely different business altogether – they make matches on a different set of variables such as caste and gotra and stars and so forth.

So what we have here is classic market failure – of the Indian dating market (this, however, is NOT a call for government regulation! :P ). The market is surely fertile (no pun intended), and there is plenty of opportunity to make fat profits if someone can get the matching right. There are a number of players looking to enter the market as I write this (I’ve spoken to some of them), but none look particularly promising.

Oh, and you might want to know why Marriage Broker Aunties gets all the chicks – it’s because of a complicated sign up process (a five page google docs form if i remember right) which puts off any non-serious players. Also there is the promise that until matched, potential counterparties cannot see your profile, and there is a “trusted third party” (in the MBA case, my wife, but an algorithm should do reasonably well to scale) who does the matchings.

The most important bit here is the anonymity – the ground reality in India is that online dating is still seen as a “last resort” – to be resorted to only if you can’t find a match through your network. With Tinder and OkCupid being exclusively dating sites (unlike Orkut which was fundamentally a social network), signing up on one of these two sites is an admission of a degree of desperation (in the eyes of most people), and there is a chance people might see you differently after they know that you’re a member on such sites.

While this explains reasonably well why chicks flock to Marriage Broker Auntie, why is there a shortage of guys on the site? It can’t be that there are no serious guys around for whom the 5-page form is a massive transaction cost. The wife’s (and my) perception is that fundamentally guys want to “check out a girl” (i.e. know well what she looks like, etc.) before agreeing to meet her on a date (I remember scouring Orkut and Facebook for all possible pictures of my then-future-wife and “checking her out” before meeting for the first time). And in an anonymised matching site, this experience is not there. So men don’t like this!

It’s a hard problem but not intractable. There are many companies that are coming into this space now. Hopefully someone will get it right!

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}

 

The end of experience

While it might have turned out that the stories about TCS laying of tens of thousands of IT workers in India are simply not true, the fact remains that the Indian IT sector is bloated around the middle. There are way too many employees in the middle management level who have few skills apart from project management, and who are essentially dispensable to their employers. The question is what the change is at the industry level that is putting to peril careers of so many people in their 40s.

Back in my parents’ generation, you could choose two paths, especially in a government job. If you were ambitious, you could choose to be an officer, for which you had to write (and pass) exams and be prepared to work demanding hours (unlike what people usually expect from a “government job”). In return you got advancement in your career, get promoted and get a chance to be part of your company’s top management.

Of course given pyramidal structures of organisations things wouldn’t have worked out so well for everyone had everyone chosen to go along this path (growth would’ve been painfully slow) so there was a parallel track – you could choose to not become an officer. While this meant that beyond a point you would stop getting promoted, you continued to get paid quite well (my parents’ “senior assistant” friends made almost as much as my officer parents did), and you retire with a comfortable pension. It worked well for everyone. Or so it seemed.

As Deepak Shenoy explains so well in this excellent post (same link as above), back in the days when IT exporters made big margins, they could afford to pay their employees well. And they gave them fat raises every year irrespective of their performance. Employees went to middle management. They stopped coding. And the only skills they developed was “project management”, and perhaps people management. And they continued to get fat raises each year. Until margins started thinning down.

Now, as Deepak explains, IT exporters are facing diminishing margins, and they need to cut cost. When you are cutting costs, the first person on the block is one that is drawing a fat salary for not doing too much. And in the Indian IT sector, it’s these mid-level project management guys, who don’t code, are not key to management and have no specific skills. And so, sooner or later, as margins thin out, their jobs are going to be in trouble.

The problem with this particular cohort of workers is that they haven’t developed enough skills as they have gone along, and the skills that they have are easily replaceable with someone much younger (and thus drawing a much lower salary). In something as generic as project management, you are not going to lose too much by replacing a project manager with 15 years experience with one with 10 years experience, especially if the one with 10 years experience will get paid much lower than the other guy.

From a company’s perspective, it should not matter how long a particular employee has been there in its compensation decision. So if an employee with 10 years’ experience is offering the same value as one with 15 years’ experience, they ought to be paid similar salaries. Except that given the massive raises in salaries back in good times and the power of compound interest, the employee with 15 years’ experience is getting paid much more than the one with 10 years’ experience. And that is what makes him dispensable.

The big lesson from this story is that you should continue developing and never “settle”. With 15 years’ experience, you get paid more than someone with 10 years’ experience, but you should also demonstrate sufficient skill sets that show you as being significantly superior to the other guy. Experience, to put it in one way, is a proxy for measuring how much you’ve learned in your job, and if you stop learning there is no point in attributing value to that part of your experience where you’ve not learnt much!

Selection bias and recommendation systems

Yesterday I was watching a video on youtube, and at the end of it it recommended another (the “top recommendation” at that point in time). This video floored me – it was a superb rendition of Endaro Mahaanubhaavulu by Mandolin U Shrinivas. Listen and enjoy as you read the rest of the post.

I was immediately bowled over by youtube’s recommendation system. I had searched for both Shrinivas and Endaro … in the not-so-distant past so Youtube had put two and two together and served me up an awesome rendition! I was so happy that I went to town twitter about it.

It was then that I realised that this was the firs time ever that I had noticed the top recommendation of Youtube. In other words, every time I use youtube, it recommends a video to me, but I seldom notice it. And I seldom notice it for a reason – they’re usually irrelevant and crap. The one time I like the video it throws up, though, I feel really happy and go gaga over the algorithm!

In other words, there’s a bias which I don’t know what its exactly called – the bias that when event happens in a certain direction, you tend to notice it and give credit where you think it’s due. And when it doesn’t happen that way, you simply ignore it!

In terms of larger implications, this is similar to how legends such as “lucky shirts” are born. When something spectacular happens, you notice everything that is associated with that spectacular event and give credit where you think it’s due (lucky shirt, lucky pen, etc.). But when things don’t go your way you think it’s despite the lucky shirt, not because the shirt has become unlucky.

It’s the same thing with belief in “god”. When you pray and something good happens to you after that, you believe that your prayers have been answered. However, when you pray and something good doesn’t happen, you ignore the fact that you prayed.

Coming back to recommendation systems such as Youtube’s, the problem is that it is impossible for a recommendation system to get recommendations right all the time. There will be times when you get it wrong. In fact, going by my personal experience with Youtube, Amazon, etc. most of the time you will get your recommendation wrong.

The key to building a recommendation system, thus, is to build it such that you maximise the chances of getting it right. Going one step further I can say that you should maximise the chances of getting it spectacularly right, in which case the customer will notice and give you credit for understanding her. Getting it “partly right” most of the time is not enough to catch the customer’s attention.

Putting marketing jargon on it, what you should focus on is delighting the customer some of the time rather than keeping her merely happy most of the time!