Tag: aggregation

Barriers to entry in cab aggregation

The news that Reliance might be getting into the cab aggregation game got me thinking about the barriers to entry in this business. Considering that it is fundamentally an unregulated industry, or rather an industry where players actively flout regulations, the regulatory barrier is not there.

Consequently, anyone who is able and willing to make the investment and set up the infrastructure will be able to enter the industry. The more important barrier to entry, however, is scale.

Recently I was talking to an Uber driver who had recently switched from TaxiForSure. The latter, he said had lost “liquidity” over the last couple of months (after the Ola takeover), with customers and drivers deserting the service successively in a vicious cycle. Given that cab aggregation is a two-sided market, with prominent cross-sided network effects (number of customers depends on number of cabs and vice versa), it is not possible to do business if you are small, and it takes scale.

For this reason, for a new player to enter the cab aggregation business, it takes significant investments. The cost of acquisition for drivers and passengers is still quite high, and this has to be borne by the new player. Given that a significant number of drivers have to be initially attracted, it takes deep pockets to be able to come in.

Industry players were probably banking on the fact that with the industry already seeing consolidation (when Ola bought TaxiForSure), Venture Capitalists might stop funding newer businesses in this segment, and for that reason Uber and Ola might have a free rein. Ola had even stopped subsidising passengers in the meantime, reasoning (correctly for the time) that with their only competition being Uber they might charge market rates.

From this perspective it is significant that the new player who is entering is an industrial powerhouse with both deep pockets and with a reputation of getting their way around in terms of regulation. The first ensures that they can make the requisite investment (without resorting to VC money) and the second gives the hope that the industry might get around the regulatory troubles it’s been facing so far.

I once again go back to this excellent blog post by Deepak Shenoy on the cab aggregation industry. He had mentioned that what Uber and Ola are doing is to lay down the groundwork for a new sector and more efficient urban transport services. That they may not survive but the ecosystem they create will continue to thrive and add value to urban transport. Reliance’s entry into this sector is a step in making this sector more sustainable.

Will I switch once they launch? Depends upon the quality of service. Currently I’m loyal to Uber primarily because of that factor, but if their service drops and Reliance can offer better service I will have no hesitation in switching.

The ET article linked above talks about drivers cribbing about falling incentives by Uber and Ola. It will be interesting to see how the market plays out once the market stabilises and incentives hit long-run market rates (at which aggregators need to make a profit). A number of drivers have invested in cabs now looking at the short-term profits at hand, but these will surely drop with incentives as the industry stabilises.

Reliance’s entry into cab aggregation is also ominous to other “new” sectors that have shown a semblance of settling down after exuberant VC activity – in the hope that VCs will stop funding that sector and hence competition won’t grow. After the entry into cab aggregation, I won’t be surprised if Reliance Retail were to move into online retail and do a good job of it. The likes of Flipkart beware.

Rating systems need to be designed carefully

Different people use the same rating scale in different ways. Hence, nuance is required while aggregating ratings taking decisions based on them

During the recent Times Lit Fest in Bangalore, I was talking to some acquaintances regarding the recent Uber rape case (where a car driver hired though the Uber app in Delhi allegedly raped a woman). We were talking about what Uber can potentially do to prevent bad behaviour from drivers (which results in loss of reputation, and consequently business, for Uber), when one of them mentioned that the driver accused of rape had an earlier complaint against him within the Uber system, but because the complainant in that case had given him “three stars”, Uber had not pulled him up.

Now, Uber has a system of rating both drivers and passengers after each ride – you are prompted to give the rating as soon as the ride is done, and you are unable to proceed to your next booking unless you’ve rated the previous ride. What this ensures is that there is no selection bias in rating – typically you leave a rating only when the product/service has been exceptionally good or bad, leading to skewed ratings. Uber’s prompts imply that there is no opportunity for such bias and ratings are usually fair.

Except for one problem – different people have different norms for rating. For example, i believe that there is nothing “exceptional” that an Uber driver can do for me, and hence my default rating for all “satisfactory” rides is a 5, with lower scores being used progressively for different levels of infractions. For another user, for example, the default might be 1, with 2 to 5 being used for various levels of good service. Yet another user might use only half the provided scale, with 3 being “pathetic”, for example. I once worked for a firm where annual employee ratings came out on a similar five-point scale. Over the years so much “rating inflation” had happened that back when I worked there anything marginally lower than 4 on 5 was enough to get you sacked.

What this means is that arithmetically averaging ratings across raters, and devising policies based on particular levels of ratings is clearly wrong. For example, when in the earlier case (as mentioned by my acquaintance) a user rated the offending driver a 3, Uber should not have looked at the rating in isolation, but in relation to other ratings given by that particular user (assuming she had used the service before).

It is a similar case with any other rating system – a rating looked at in isolation tells you nothing. What you need to do is to look at it in relation to other ratings by the user. It is also not enough to look at a rating in relation to just the “average” rating given by a user – variance also matters. Consider, for example, two users. Ramu uses 3 for average service, 4 for exceptional and 2 for pathetic. Shamu also uses 3 for average, but he instead uses the “full scale”, using 5 for exceptional service and 1 for pathetic. Now, if a particular product/service is rated 1 by both Ramu and Shamu, it means different things – in Shamu’s case it is “simply pathetic”, for that is both the lowest score he has given in the past and the lowest he can give. In Ramu’s case, on the other hand, a rating of 1 can only be described as “exceptionally pathetic”, for his variance is low and hence he almost never rates someone below 2!

Thus, while a rating system is a necessity in ensuring good service in a two-sided market, it needs to be designed and implemented in a careful manner. Lack of nuance in designing a rating system can result in undermining the system and rendering it effectively useless!

Discrete and continuous jobs

Earlier today, while contributing to a spectacular discussion about ambition on a mailing list that I’m part of, I realized that my CV basically translates to spectacular performance in entrance exams and certain other competitive exams, and not much otherwise. This made me think of the concept of a “discrete job” – where you are evaluated based on work that you do at certain discrete points in time, as opposed to a continuous job where you are evaluated based on all the work that you do all the time.

A good example of a discrete job is that of a sportsman. Yes, a sportsman needs to work hard all the time and train well and all that, but the point is that his performance is essentially evaluated based on his performance on the day of the game. His performance on these “big days” matter significantly more than his performance on non-match days. So you can have people like Ledley King who are unable to train (because of weak knees) but are still highly valued because they can play a damn good game when it matters.

In fact any performing artist does a “discrete” job. If you are an actor, you need to do well on the day of your play, and off-days during non-performing days can be easily forgiven. Similarly for a musician and so forth.

Now the advantage of a “discrete” job is that you can conserve your energies for the big occasion. You can afford the occasional slip-up during non-performing days but as long as you do a good job on the performing days you are fine. On the other hand, if you are in a continuous job, off-days cost so much more, and you will need to divide your energies across each day.

If you are of the types that builds up a frenzy and thulps for short period of time and then goes back to “sleep” (I think I fall under this category), doing a continuous job is extremely difficult. The only way that it can be managed is through aggregation – never giving close deadlines so that you can compensate for the off-day by having a high-work day somewhere close to it. But not every job allows you the luxury of aggregation, so problem are there.

Now, my challenge is to find a discrete job in the kind of stuff that I want to do (broadly quant analysis). And maybe give that a shot. So far I’ve been mostly involved in continuous jobs, and am clearly not doing very well in that. And given my track record in important examinaitons, it is likely I’ll do better in a discrete job. Now to find one of those..