Tag: OLA

App-in-app?

I recently got an email from my airline app that I could book my car ride within the same app. It was a way of providing end-to-end services. Much like the home pickup and drop service provided for business class customers by the Emirates. What are the implications of these for the customer, the airline, and the cab-hailing firm? Let’s explore.

It is an app-redirect

First, read the terms of how it works in the case of Jet Airways and Uber here. The substantive part of the T&C is hidden in the paragraphs quoted below:

“PLEASE NOTE, YOU ARE MAKING THE PAYMENT TO UBER DIRECTLY. JET AIRWAYS IS NOT RESPONSIBLE / INVOLVED IN THIS FULFILMENT PROCESS. JET AIRWAYS WILL NOT BE LIABLE AND/OR RESPONSIBLE FOR REFUNDS, DELAYS, REJECTIONS, PAYMENT AND FULFILLMENT OR OTHERWISE OF THE SERVICES OR IN RESPECT OF ANY DISPUTES IN RELATION THERETO, IN ANY MANNER WHATSOEVER.” (emphasis original)

Then, what is the value of this app-in-app integration?

Customer perspective

For the customer, it has the potential to work as a seamless end-to-end service. I imagine a future, where you would find a partner using Tinder or TrulyMadly, plan your evening to a game/ movie using BookMyShow, find a restaurant & book your table using Zomato, and take Uber whenever you are ready to move on, or better still, have an Ola Rentals car waiting for you through the evening. All in one app. Wouldn’t you love it, if all of it were integrated in one App? Just imagine the convenience if your restaurant-finder knew that you are in a particular concert at a specific place and you are likely to head out for dinner at a particular time. This specific knowledge could immensely help your restaurant-finder app to customize the experience for you – for instance, it could not only provide you those restaurant options that are open late in the evening after the concert was over, in a location that is close to the venue; it could possibly alert the restaurant that you were arriving in 15 minutes, based on your Uber location. And through the evening, post your pictures on Instagram and SnapChat, check-in to all those locations in Facebook, and Tweet the experience live.

Yes, you would leave a perfect trail for the entire evening in a single place, and if you were to be involved in an investigation, it would be so easy for the officer to trace you! No need for Sherlock Holmes and Watson here – the integrator app would take care of all the snooping for you!

Convenience or scary? What are the safeguards related to such data sharing across different entities? How will the data be regulated?

The Integrator perspective

Why would a Jet Airways provide an Uber link inside its App? Surely cab-hailing and air travel are complementary services. Plus, Jet Airways believes that its customers would find it convenient to book an Uber ride from within the Jet Airways app, as they trust the app to provide Uber with all the relevant details – like the estimated landing/ boarding time of the flight, drop/ pickup addresses, etc. Jet Airways also needs to believe that its customers would rather choose an Uber cab, rather than its competitor OLA Cabs, or any other airport taxi service. The brands should have compatible positioning. Given that Jet Airways is a full service carrier, and differentiates based on its service quality, Uber might be a good fit. But the same might not hold good for a low-cost/ regional carrier like TruJet connecting cities like Tirupati, where Uber does not operate.

Does integrating complementary services affect customer satisfaction, brand loyalty, customer switching costs, and/or multi-homing costs? In contexts where these services and brands are compatible, and there is a convenience involved in sharing of data between these services, there is likely to be some value added. Like airlines and hotels (hotels would like to know your travel schedule); currency exchanges and international travel (the currency exchange would love to know which countries you are visiting); or international mobile services. If there was no data to be shared between the complementary services, the user would rather have them unbundled. Think travel and stock brokerage.

That said, platforms find innovative complementarities. For instance, airlines (primarily the full-service carriers) have launched co-branded credit cards. In a recent visit to Chennai, there were more American Express staff at the Jet Airways lounge than the airline or lounge staff! And they were obviously signing up customers. What are the complementarities between credit cards and air travel, apart from paying from that card? A lot of business travellers have their business travel desks do the payments; consultants have their clients booking the tickets; and even for individuals and entrepreneurs, the credit card market is so fragmented that everyone holds multiple cards. And the payment gateways accept all possible payment options, including “paying cash at the airport counter”. They why co-brand credit cards – sharing of reward points/ airline miles. Either customers do not earn sufficient airline miles and using these co-branded credit cards help them earn more miles and retain/ upgrade their airline status (remember the 2009 movie, Up in the Air?); or they do not earn enough reward points in using their credit cards that they can redeem their airline miles as credit card reward points. Either ways, each one is covering up for the other.

In this covering up, or more diplomatically consolidation of rewards, the partners increase customer switching and multi-homing costs. Surely, redeemable airline miles might be more valuable to a frequent traveller than credit card reward points that have limited redemption/ cash back opportunities. But for loyalty to increase, it is imperative that both brands stand on their own – providing compatible services.

Mother of all apps

All this looks futuristic to you? A lot of you have been using an ubiquitous desktop app known as the browser for a long time, which has been doing exactly this! In a subtle form, though. However, there are firms that own multiple such apps, and they use a single sign-on – like all of Google services. Plus, even third-party sites like Quora allow for using your Google credentials to sign-in. The trade-offs are not always explicitly specified – it is always the case of caveat emptor – consumer beware.

Quora homepage

So, the next time you experience some cross-marketing across platforms/ apps, think what data might be shared across both the apps; and if you would really value the integration.

Cheers!

(c) 2017. R Srinivasan

 

Regulating Platforms

Over the past few months, there have been a lot of disputes between platform businesses, governments, and a lot of these have gone to courts as well. Last Friday (26 August 2016) issue of the Mint newspaper carried an opinion piece titled “the tricky business of regulating disruptors” (read it here). The editorial while labeling almost all platform businesses as disruptors, just stopped short of calling all of them disruptors. In this blog post, I dig deep into the issue of if and how platform businesses need to be regulated with respect to consumer protection, without impeding innovation and thence providing fair business opportunities to businesses (and returns to investors).

Defining the industry boundaries

One of the key determinants of “competitive” behavior is the definition of the relevant industry. What is competitive and what is anti-competitive can depend on how narrow or broad you cast your net while defining the industry. For instance, the Mint editorial explains in detail how in a 1953 verdict on DuPont’s monopoly on the cellophane as a result of “result, business skill, and competitive activity”, despite having over 75% market share in the cellophane market, because the courts defined the “relevant” market as flexible packaging material, and not cellophane, the product. However, in most cases against platform businesses like Uber, the competition commissions and other regulators have defined the market as app-based taxi services, and therefore looked at the market being usurped by monopolies (Didi-Uber combine in China) or a duopoly comprising of Uber and a local operator (like Grab in SE Asia, OLA in India, Lyft in the USA).

Is Uber a competitor or substitute to Taxi?

In a detailed response to Prof. Aswath Damodaran’s 2014 article on Uber’s valuation (read it here), Bill Gurley (a series A investor and board member of Uber) defined three things (read Bill Gurley’s blog post here).

  1. He argues that Uber has since transformed the industry so much that one’s market size estimates based on current taxi market sizes is flawed. In other words, Uber was providing customers with far more value and a very different set of value propositions than a traditional taxi service – quick discovery, easy payment, predictability of service, quality (dual rating of riders and drivers), and trust/ safety. He talked about how Uber’s customers are using it to transport young adults/ children or older parents in the “comfort and safety” of an Uber, rather than a taxi.
  2. He argued that given the economies of scale that arose due to the positive cross-side network effects, more and more drivers and riders adopted Uber, and Uber expanded to more and more geographies, and the prices fell. And the price elasticity contributed to more demand and therefore more network effects. The economics of Uber (and therefore other ride-hailing app-based services) are very different from the city Taxi services.
  3. Uber is not a taxi alternative – it is a car-ownership (or a car-rental) alternative. When the liquidity (availability + density) of Uber vehicles is so high in every geography you want to travel to, you would rather not rent/ buy a car, but use Uber. The convenience and reduced cost of Uber as an alternative to ownership is something that he substantiates with data and analysis.

In other words, Uber was indeed a disruptor, and therefore was entitled to be treated as a separate industry. It is not a competitor to the for-hire taxi, it is an alternative; much the same way Kodak was bankrupted by digital photography (and not by competitors like Fuji).

Creative destruction and Schumpeterian waves of technology innovation

The Mint editorial called for Honorable Judges to not set taxi fares, simply because these disruptors would transform the industry through their technology innovation, and any restraining regulation would hinder these Schumpeterian waves. It is therefore an indirect call for letting these disruptors alone, let the waves of Schumpeterian technology innovation hit the markets, before we arrive at a stability of sorts. Regulation can wait.

Can regulation wait, and allow for a disruptor, in the excuse that the market is a “winner-takes-all” market monopolize the market? The popular arguments against monopolies is that of consumer protection, and that when monopolies rule, consumers suffer – prices rise, service levels fall, and there may be no alternatives. This is exactly the case for another wave of creative destruction.

My primary thesis is that when such disruptions happen on the basis of network effects, leading to economies and scale, and the disruption is based on parameters like improved customer service, lower prices, and transparent/ fair transactions (trust/ safety and the like), monopolies are not necessarily bad. When such monopolies emerge and the customer experiences deteriorate, as dictated by traditional industrial economics theory, the market will be ripe for another wave of Schumpeterian technology innovation. The waves of market entry in the Indian airlines market is testimony to these (1990s – privatization and shake-up leaving two state-owned and two private competitors; 2000s – entry of low-cost carriers leading to the demise/ consolidation of all stuck-in-the middle competitors; 2010s – entry and strengthening of regional airlines, is it?) waves of creative destruction.

Yes, there is space for other competitors, but not so much for Uber replicas. The market is indeed a winner-takes-all market (as I have argued in the past), and therefore there is just enough room for small, losing replicators. Look around the markets for Uber competitors, you do not find any market fragmented. While differentiation and creating niches is the prescription for firms competing with Uber, I request the regulators to begin treating such platform businesses as an independent market and let the inefficient product-markets fail, if required. No one cried when the offline ticket counters of Indian Railways are declining sales, thanks to the volumes garnered by IRCTC (some claim that this is the world’s largest ecommerce platform, is that true?). No one complains about bookmyshow.com garnering huge market shares in the app-based movie seat booking market, claiming that the livelihoods of the ticket clerks are under threat. Why cry about Uber, or for that matter, OLA, Grab, or Lyft?

There is already sufficient discrimination against these disruptors. In a recent visit to San Francisco, I made an extra effort (okay, walked down a flight of escalators) to click a picture at the SFO airport that read, “app-based taxis to pick-up from departures level”. Honorable Judges, please leave them alone, enjoy your ride/ movies/ every other service, contribute to the economies of scale, and let the market be disrupted.

Cheers.

 

Breaking the Uber-Ola duopoly?

 

Okay, after a week’s break for personal reasons, the blog is back up. Writing from Berkeley, CA today.

The Karnataka Government (of whom Bangalore is the capital city) recently announced that it would like to have more private players in the ride-hailing app market, not just an Uber-Ola duopoly. Read the Transport Minister’s interview here. Which got me thinking, will this market sustain multiple competitors, if at all?

A classic winner-takes-all market is defined by three conditions – presence of strong network effects, high multi-homing costs, and the absence of any special needs. Let us first analyse if ride-hailing is a WTA market, and then talk about what kind of resources would another player require to compete in that market (remember Taxi-for-sure sold out some years back).

The ride-hailing app market enjoys strong cross-side network effects from both sides – more the drivers on the road, more the riders adopt; and vice versa. Simple. What are the multi-homing costs for the riders – just the real-estate on her phone for installing multiple-apps; and possibly any loyalty rewards, including maintaining her rider-rating. The multi-homing costs for the drivers are higher, though. He needs to affiliate with multiple firms; maintain multiple devices and payment/ banking information; and more importantly ensure sufficient rides taken on each of the platforms to sustain his incentives. Given the way Uber India and OlaCabs provide incentives (based on the number of rides per day), it would become increasingly difficult for him to multi-home. There are only two segments of customers in the ride-hailing app market: those who take them regularly (say 15-16 rides a week), and those who use them sporadically (say 2-3 rides a week). And both of these segments have the same preferences – low prices, high convenience, quick access to cars, and good customer service. So, this market seems like a WTA market, in the absence of a strong differentiation.

Differentiate

So, how does a new competitor differentiate? There are four options – long rides (say for instance, airport drops in a city like Bangalore); more variety of cars (larger vehicles for the big Indian family/ friends network); short/ weekend holiday trips; and rental cars (for self-driving by the riders).

Not that these needs are not being served – specialised competitors like Meru Cabs and Mega Cabs serve the airport market. In fact the Bangalore International Airport Limited (BIAL) has not authorised either OlaCabs or Uber to pick up passengers from the airport. Even in San Francisco, I saw a sign today morning, that said “all app-based cabs can only pick up from the departure level”! Some agreements need to be signed between the airports and the aggregators to ensure seamless experience for the riders. And this is true of a variety of airports across the world. Here is where, entrenched competitors like Meru can make a difference.

The large vehicle/ variety of vehicles was the forte of the neighbourhood taxi operator. The operator (or sometimes a local aggregator) would have on his list a variety of cabs ranging from the smallest hatchback to the large 15 seater van. You signed up on a hour-km base rate and a topup rate for exceeding either (time or distance, or both). Here is where a new ride-hailing app can begin differentiating. Take the example of Lithium cabs in Bangalore, which is appealing to the environmentally conscious consumer, by deploying only electric vehicles in the fleet (read here). Similarly, there could be specific apps for off-roading, mountainous trails (think the Manali-Leh highway – don’t forget to see the map in Earth mode), or for biking/ trekking/ hiking trips.

The short weekend holiday trips are possibly the most underserved market in India. A lot of small families would drive out their own cars, leaving at least one member of the family super-tired and unable to enjoy the holiday as much as the others. Especially if the road is not very good, and the car is not in the best of the condition, it can be treacherous ride rather than a enjoyable holiday. Some may argue that the drive itself was the enjoyment, but that is a different discussion. Here is an opportunity for ride-hailing apps to easily extend their services. The daily office-going commuter is not on the roads during the weekends, and the cabs are being under-utilised. Here is a win-win for both the drivers and the riders. OlaCabs has just began the Ola Outstation service for serving just this need – it is early enough to get more drivers (and bigger cars) to get on the roads on weekends, but I am sure they will get there sooner.

The car rentals (driven by the riders, as in Hertz in the USA) has its share of competitors – Zoomcar is a good example. For someone on a day trip to a familiar city, such rentals would be a great service, providing flexibility, control, and convenience. However, these rentals have not attained sufficient scale for the network effects to kick-in as these are asset intensive (the cab aggregator has to own all the cars); caught in regulatory conundrums (is it a private vehicle or a taxi – white number plate or a yellow number plate, or black/ yellow); how is insurance managed; and the coordination costs are very high (see how the airport pickup from Bangalore airport works, including the limited number of drop-off locations – serious limitations on the last mile to home).

Address the special preferences

In summary, in order to fulfil the Karnataka Government’s wish to break the monopoly, we need competitors to differentiate. We need the airport taxis to become cheaper, more efficient, and provide better customer service; we need the taxi/ cab aggregators to not just include more and more variety in their cars – from electric vehicles to sedans to SUVs, but differentiate on the value proposition; expand the capacity utilisation of their cars during the weekend by serving the weekend holiday trips market; and car rentals to expand their network significantly (four drop-off locations in Bangalore when you take a car from the airport, seriously?).

Cheers and happy weekend.

Digital disruption – drivers, symptoms and scenarios

My students, colleagues, and leaders in firms who I mentor have been asking me to share my views on digital disruption of businesses. In this post, I try to define the contours of digital disruption and what it holds for the future of businesses, in my opinion.

What is digital disruption?

Disruption refers to a fundamental change in the value proposition of the business. When digital technologies form the basis of such a change, I call it a digital disruption.

Drivers of digital disruption

There are three primary drivers of digital disruption (adapted from this article). First, is the maturity of digital tools and technologies that uncover inefficiencies in traditional business models. Take for instance the sharing economy characterized by business models like Airbnb.com and Uber. These business models highlighted the underutilization of fixed assets in residences and cars, and shifted the consumer behavior from traditional business models of exclusive hotels and owned cars to shared residences and cars. A recent example of this sharing economy is www.flightcar.com, that allows for individual car owners to rent their cars parked idle in airports to other visitors to that city as self-driving cars!

The second driver of digital disruption is the increasing evaluability of performance parameters. In a traditional business like car hiring services, it was difficult to evaluate the quality of cars. In the sharing economy, ratings/ reviews/ recommendations from other users can help evaluate various parameters of the products and services. Uber allows for mutual rating of drivers and riders, alike. Such improvements in technology that increase the evaluability of parameters, hitherto not evaluable can significantly contribute to unique customer value addition.

The third driver of disruption is the increased dominance of mobile apps. What the transition from traditional PC-based software applications to mobile apps contributes is lower costs of customer adoption, richer data collection by the apps leading to better customization of experience, and mobility. Imagine using Uber through only a PC-based or a browser-based communication!

When do you know your business is being digitally disrupted?

The following table describes the characteristics and symptoms of digital disruption with some examples (adapted from this article).

Symptoms Examples
A proliferation of free or nearly free digital technologies in the value creation process Digital photography eliminating paper photography
Such technologies are provided by multi-sided platform firms Products like Gmail eliminating the need for organizations investing in their own email servers
Conscious shifting of value creating activities outside the firm, including open and user innovation processes Evolution of 3D printing enabling democratization of design and prototyping
Rapid prototyping and product development/ market entry made possible as a result of user/ open innovation Proliferation of platforms and forums like tech-shops that enable businesses and consumers to rapidly prototype and customize their products in low volume production contexts
Use of direct and indirect network effects to leverage economies of scale and scope Evolution of aggregators and marketplaces like Alibaba.com that leverage network effects for economies of scale and scope

Most digital disruptions are visible when the industry/ market is characterized by one of more of the above symptoms. If any of these symptoms are visible in your business context, organizations beware. Begin preparing to face/ counter these forces.

Planning for the digitally disrupted future

Prof. Mike Wade from IMD, Lausanne describes four scenarios of digital disruption (read the full report here).

  1. The global bazaar – industry and geographic boundaries blurring due to internet and mobile
  2. Cautious capitalism – data security concerns limit firms’ ability to monetize consumer data
  3. Territorial dominance – regional industry boundaries persist, with tight regulation
  4. Regional marketplaces – world divided into regional clusters with their own rules and governance, innovation fostered in regions with little or no international competition

The following figure summarizes the four scenarios with examples of firms that will dominate their respective markets. 13.1 Digital disruption scenarios

As you can see, these are just my preliminary thoughts, and I would strive to develop on them subsequently.

Comments, feedback, and experiences welcome.

Startups out there: What instant gratification do you offer to your customers?

 

Last week, Tim Romero of ContractBeast published an article on LinkedIn on why he turned down $500K, pissed off his investors, and shut down his startup (read here). Easily one of the best articles I have read in the recent past. A quick summary on the story – Tim and his co-founders had set up the enterprise, done beta testing and received good reviews from their customers. However, what was bothering Tim was that his customers were using their product only for a small proportion of their total requirements. Deeper analysis of early adopters of the product revealed that they did not get any value from the product that provided them with something of an “instant gratification”. In the absence of a short-term value add, it was difficult to turn these free users into paying users, once the trial was over. And they decided to pull the plug on the product and the enterprise.

Scaling your startup

A lot of entrepreneurs and founders keep discussing about how to ‘scale’ their business, either to achieve traditional economies of scale or to kick-in network effects. In their attempt at scaling, a recurring theme is the provision of subsidies, at least for one set of users. Some of them provide these subsidies for a limited time period; some offer differentiated products/ services under a ‘freemium’ model; and some others provide their services ‘cheaper than free’.

Providing subsidies is a time-tested model of scaling up a business. Traditionally such subsidies were provided as a trial period, during which the customer experienced the product as the product provided the customers with some functionalities, if not all of the full version. When the trial period ended, the product reminded the customer to pay and upgrade/ renew, but pretty much stopped there. Some smart products could have collected valuable data on how and what the customers used the product for; and therefore provided them with partially customized offers. Take the example of Dropbox. It began providing me with free storage space and allowed me with more and more storage as I invited friends; and began collaborating with others (sharing files and folders). It allowed me enough storage on the cloud so that I could store files that I needed to access from ‘anywhere’, allowing me to work seamlessly from home/ during my travels (on my MacBook). The upgrade reminder kept popping up whenever I came close to using up my storage space, but it was always easy to move out those files that belonged to finished projects off the cloud and free-up space for newer projects. Eventually, it took a long time to convince me to pay up for the upgrade (I paid up when I had to share large number of files with a variety of collaborators across the globe). What Dropbox provided me was the seamless integration of my desktop folder with cloud storage without the hassle of actively uploading a document using a browser. I saved it in ‘the folder’ on my office desktop, and it was available in ‘a folder’ on my home desktop/ MacBook.

Some products provide customers with so much subsidies that it could become ‘cheaper than free’. For instance, Indian taxi aggregation market has become so competitive between Uber and OlaCabs that they are raising large sums of capital, and pumping them into the market as lower fares for riders and subsidies for drivers. These drivers get their incentives once they complete a certain number of rides per day, get to keep pretty much what they earn, and have the flexibility to sign up with other operators (or in platform-business terminology, multi-home with other operators). The story is wonderful and sustainable until the incentives last and keeps the drivers motivated. However, a caveat in the Indian market is that driver is not ‘the entrepreneur’ as what Uber and OlaCabs would like to believe. The company refers to them as driver-partners, and treats them as if they were independent. The truth in many cases is that, most of these drivers are paid employees of car-owners and their incentives are not the same as that of the car-owners. So when we introduce a third party in the transaction, a lot of traditional incentive schemes fail – does ride incentives benefit the car-owner or the driver? That depends on the terms of employment of the driver with the car-owner. Some owners lease the car for a fixed fee per day, some others pay monthly compensations to the drivers, and some others a combination of a fee and revenue/ profit shares. In this context, it would be difficult for Uber and OlaCabs to design an incentive system to shift these driver-partners from enjoying these freebies to a more (economically) sustainable model of revenue/ profit sharing. However, Uber’s ability to lock-in the driver by secularly increasing the number of rides required to earn incentives has increased the switching costs of these partners (car-driver-owner combine).

Instant gratification

In order to scale (either linearly or through network effects), firms would need to provide some form of instant gratification to its customers/ partners. However, it is imperative that the value provided should lead to increasing the switching and multi-homing costs for the customers. Take the case of Romero’s product, ContractBeast. What Tim observed during the trial period was that the customers were indeed multi-homing with other competing products and services to manage their contracts, and were not using ContractBeast for managing a majority (if not all) of their contracts. Had ContractBeast provided a value that did not allow for its SMB customers to multi-home, the story could have been different.

Increasing multi-homing costs

I perceive three levers for increasing the multi-homing costs of customers in a platform business model – asset specificity, not absorbing sunk costs, and integration with other systems and processes. Asset specificity refers to the requirements of the customers to invest in certain specific assets to adopt your product/ service. For instance, the B2B supplier platform IndiaMART requires SMB sellers to invest time and energy in uploading their product details, photographs, technical specifications, contact information and all details about their firm as part of the registration process. Such an intensive registration process ensures that the seller will focus all his energies on a single platform rather than multi-home. Quick reference, see the registration process in the dating platform eHarmony (the relationship questionnaire)! If you have filled that long a questionnaire once, you do not want to do that again and again in multiple platforms, right?

The second and the easiest lever for increasing multi-homing costs is the absorption of partner sunk costs. For instance, OlaCabs subsidizes/ absorbs the cost of the phone that is used by the drivers. This subsidy ensures that the drivers are free to multi-home with other taxi aggregators, as they have incurred no or little sunk costs. On the other hand, firms like Tally require you to invest in the license (albeit very inexpensive) to be able to use the full functionalities of the product/ service offerings.

The third lever for increasing multi-homing costs is to integrate your product/ service with other systems and processes of the customers. Take the example of Practo. Practo has ensured that clinics need to invest in Practo Ray, the practice management software that manages a lot of processes in the clinics, including managing electronic medical records and integration with pathological laboratories. Such tight integration with the processes ensures that their customers – the clinics – do not multi-home, and increasingly use Practo.com (the doctor-patient discovery platform) exclusively for all their appointments.

Startups out there: Can you tell me how you do it?

That thing Tim Romero missed with his product! High multi-homing costs. So my dear entrepreneur friends, define (a) what is that instant gratification you offer for your customers? (b) does that value-add require temporary or permanent subsidizing, and (c) what is your strategy for increasing your customers’ multi-homing costs – increasing asset specificity? Not subsidizing their sunk costs? Or tight integration with their processes? Or a combination of these?

Would love to hear from my startup friends and followers.

Surge Pricing: The importance of focusing on the supply side

The Delhi Government, Karnataka Government, and even the Union Transport Ministry in India has been sieged with this issue of surge pricing by taxi aggregators. While there has been a lot written about surge pricing (see my earlier post, more than a month back), a lot of what I read is incomplete, misleading, and sometimes even biased. Here is adding to the debate, by clarifying what surge pricing and how it differs from other models of price setting. And I draw policy implications for dealing with the phenomenon by focusing on the supply side, rather than focus on just the price.

What is surge pricing?

Surge pricing is an economic incentive provided to the suppliers of goods and services to enhance the supply of products/ services available in times of higher demand in the market by (a) incentivising those suppliers who provide them, (b) ensuring that these suppliers do not go off the market in such times, and (c) rationalise demand through fulfilling only price inelastic demand. As a driver in a taxi aggregator system, it makes economic sense for the driver not to take his breaks during the peak demand times, and ensuring that only those riders who desperately need the service, and are price inelastic avail the service. A price sensitive customer should ideally move off the aggregator to a road-side hailing service (if available, as in Mumbai) or simply take public transport.

Who is a typical surge pricing customer?

A recent study talked about riders being more willing to accept surge pricing when their phone batteries are about to die, and they need to conserve the same (read here) before they reach home. A city with good public transportation infrastructure that is designed for peak hour loads should ideally witness the least surge pricing (please don’t ask me about Bangalore, or should I say Bengaluru?). In most Indian cities, the typical cab aggregator rider is someone who is a regular user of cabs and autorikshaws (three wheel vehicles) to commute short and medium distances. Typically either the origin or destination of the ride is in the city centre or a high-traffic area (like a train station or airport). It is when the public transportation infrastructure fails that these riders are forced to use cabs for their regular (predictable) transport needs.

Let us take an example of an entrepreneur (call her Lakshmi, named after the Hindu Goddess of Wealth) whose work place is in the city centre and she commutes about 15km every day. She should ideally use public transport, or if her route is not well connected she should have her own SUV or a sedan (remember her name!). She would possibly have a driver if her work involves driving around the city to meet customers/ partners, or her daily work start and close time are not predictable. The only time she would use a cab aggregator is when she is riding to places with poor parking infrastructure, for leisure, or say a place of worship. She is price inelastic.

Take another example of a front office executive at a hotel. Let us call him Shravan. His work times are predictable, he works on a fixed remuneration, and is most likely struggling to make ends meet. He is most often taking public transport to work, or self-driving his own budget car/ 2-wheeler. He would only take a cab aggregator for his leisure trips with his young family during the weekends; and when the entire weekend out with family is an experience in itself, he is unlikely to be price sensitive to a limit. However, when surge pricing kicks in beyond a limit, he would baulk out of the market, and take public transport or other options.

As a policy maker, the demand side (riders’) welfare should be higher on priority than that of the supply side (drivers and aggregators). The demand side is large in numbers, is fragmented, and has very few options (especially in times of high demand). Price ceilings are justified when riders who are desperate to reach are price elastic. In other words, those who need the safety, security and comfort of the taxi services cannot afford it. Like the sick desperate to reach a hospital or children reaching school/ back home on time. These are segments best served by other modes of transport, rather than taxi aggregators – the Governments of the day should invest in and/ or ensure availability of good quality healthcare transport services (ambulances) and public/ private school related transport infrastructure.

Surge pricing is dynamic pricing

Dynamic pricing is not new to the Indian economy. Almost the entire informal economy or the unorganized sector works with dynamic pricing. What the rate per hour of plumbing work in your city? Depending on the criticality of the issue, the ability of the customer to pay (as defined by the location/ quality of construction and fixtures), and the availability of plumbers, the price varies. So is the case with domestic helps, and every other service provided by the informal sector. Why even professional service firms like lawyers and accountants use dynamic pricing based on ability to pay and criticality of the issue.

What surge pricing by taxi aggregators do is to take the entire control of dynamic pricing out of the suppliers’ hands and places it with the platform. The drivers may be beneficiaries of the surge price, but they do not determine the time as well as the multiple. Plus, given that the surge price is announced at the time of cab booking, the riders have a choice to wait, change the class of service (micro, sedans, or luxury cars in the system), choose an alternative aggregator, or choose another mode of transport. A fallout of the transparency and choice argument is that the “bargaining” for price is done before the service provision, and not after the ride. This transparency and choice empowers the riders, and as long as the multiple is “reasonable”, we could trust the riders with rational economic decisions. What is reasonable may vary across riders and the criticality of the context. While Lakshmi may be willing to pay a 4x multiple on her way back from work at 9pm in Hyderabad, Shravan may only a 4x multiple at 9pm when he has to reach the hospital on time to visit his ailing mother.

Data is king

The amount of data collected by the cab companies about individual behaviour and choices can enable the aggregator design appropriate pricing structures, customised to each customer, a segment of one. For instance, Uber can run micro-experiments with surge pricing and tease Shravan with different multiples at different points of time/ origin-destination combinations, and learn about Shravan’s willingness to pay, far more than what he can articulate it himself. Powered with the data, Uber should be able to define something like ‘Shravan will accept a surge price of at most 2.2x, as he is trying to return home from his workplace at 10.30pm on a Friday evening.’ Over long periods of time and large number of transactions, this prediction should mature and get close to accurate.

Given that the aggregator platform would be armed with this data, it is for the policy maker to ensure that such data is not abused to further its own gains. How does policy ensure this? By capping the multiple through a policy decree, no! Rather ensuring a market mechanism that caps the surge pricing multiple would generate significant welfare to all the parties. In order to ensure a market mechanism that makes profiteering out of surge pricing unviable, the Governments must focus on developing robust public transportation infrastructure. As attributed to a variety of leaders on the Internet/ social media, ‘a rich economy is where the rich use public transport’. These investments would provide significant alternatives to attack supply shortages in the market, and make them more efficient. This supply side intervention would do the market a lot of sustainable good, by ensuring that the Shravans of the city need not use the taxi aggregators more frequently, and thereby increasing the price inelasticity.

Policy recommendation

In conclusion, the entire analysis of the demand-supply situation leads me to recommend one simple thing to the policy makers – focus on the supply side. Get more and more public transport (greener the better) on the road; provide better and efficient alternatives to all segments; and in the short run, just ensure that there are enough ‘vehicles available for hire’ on the road.

Comments welcome.

Durability of network effects – importance of multi-homing costs

In their recent HBR article, David Evans and Richard Schmalensee argue that winner takes all thinking does not apply to the platform economy. In the article, they cite instances of how popular multi-sided platforms like Facebook, Google, and Twitter haven’t won every market. In fact, in spite of being near monopolies social networking, internet search, and micro-blogging, they compete very hard for the advertisement revenue. They also posit that network effects are not durable enough in the case of digital goods, as compared to physical networks like railroads and telephones. In this blog post, I am going to discuss these two assertions.

In the meantime, I ordered their book, Matchmakers, and my favorite ecommerce bookseller just delivered it to my desk, as I begin writing this blog. Will read the book in the coming week, and possibly update the note; but for now this blog post is based on their HBR piece. Now, if you have not read their HBR post, please read it.

Winner-takes-all markets

In their very popular HBR article Eisenmann, Parker, and Van Alstyne elucidate three conditions for a market to exhibit winner-takes-all (WTA) conditions. One, the network effects should be strong and positive; two, multi-homing costs should be high; and three, there should not exist any special needs by the users.

Network effects

In the case of the three multi-sided platforms that Evans and Schmalensee quote, the network effects are very strong. You signed up to Facebook because all your friends, family, and acquaintances were on Facebook (same side network effects); you use Google search because Google has learnt enough about you and only pushes “relevant” advertisements to you (cross-side network effects); and you micro-blog using Twitter because everyone who you want to reach are already looking for you at Twitter, as well as everyone who you want to follow are micro-blogging using Twitter (a combination of same and cross-side network effects).

Multi-homing costs

Multi-homing costs imply the costs of affiliating/ maintaining presence on multiple platforms at the same time. My most popular example is the case of internet-based email services. Even though it is literally free for anyone with an internet to have an unlimited number of email accounts, most of us cannot really maintain more than three email accounts. The monetary costs of creating and operating multiple email accounts may be zero, but the effort required to remember passwords, periodic logins to each of the accounts, and ensuring that you are communicating using the right email account is too much for most people. These are multi-homing costs.

Multi-homing costs exist in all the three markets we are discussing – social networking, internet search, and micro-blogging. In the case of social networking, it is difficult to maintain multi-home as the updates that we are likely to share in multiple networks are likely to be the same. And, the strong network effects (all my friends are on Facebook) make sure that there is virtually no-one else who is active in any other competing social networking site who is reading my updates. Multi-homing costs in internet search manifest in the form of the search engine’s ability to customise its advertisements and offers to my preferences and behaviour, which is based on my behaviour over time – with my past preferences, I have actually trained the search engine to customise. Search on the same key words across different internet search engines are unlikely to provide different results, but it is the overall experience including advertisements and personalisation that matters in the case of Google. This is somewhat similar to being loyal to a particular airline and gaining miles in that frequent flyer program; as splitting one’s travel across multiple airlines’ loyalty programs would ensure that one does not remain a frequent flyer anywhere! Similarly, having invested sufficiently in training Google on my personal preferences, I would rather stick with Google search. Similar is the argument for Twitter – the network of micro-bloggers and followers exist on Twitter; and I have carefully curated the list of which micro-bloggers I want to follow. Multi-homing costs include creating multiple lists of people I want to follow, and getting others to follow me.

Special preferences

The third condition for a market to exhibit winner-takes-all characteristics is the absence of any special preferences. Let us take the case of social networking – when professional networking and sharing of professional thoughts is a special need, different from social networking, LinkedIn thrives. Most people with a need to separate out their personal networks from the professional networks will maintain a Facebook account, as well as LinkedIn account. And, when a LinkedIn user turns into an active job seeker (from being a passive expert), she would open an account with a focused careers site like Monster.com. Similarly, someone’s work/ passion may require sharing large sized file attachments over email, and therefore push her to open multiple accounts for different kinds of uses.

In sum, winner-takes-all markets are characterised by the presence of strong network effects, high multi-homing costs, and the absence of any special needs. What Evans and Schmalensee ignore in their HBR post is the presence of high multi-homing costs. Yes, these firms do contest in the market for advertising revenues, but in one side of their respective markets, their strategies have been to continuously raise multi-homing costs. Take Facebook’s acquisition of WhatsApp for example. When more and more people took to social networking using a mobile phone than the ubiquitous desktop, and were increasingly constraining the breadth of audience for their posts, it was important for Facebook to be present on the users’ mobile phones, not just enabling broadcast social networking (with its Facebook mobile App), but also including narrowcasting or unicasting social networking using WhatsApp. Same is with Google – over the years, Google has come to dominate the internet search in more ways than one – YouTube and Maps to name a few.

Durability of network effects

The second thesis of Evans and Schmalensee is that network effects in multi-sided platforms are not durable. They cite how easy for a new entrant to challenge these leaders with little or no physical investments. Digital goods like software have high fixed costs and almost zero marginal costs for every additional unit produced. Economics has taught us that in markets with near-zero marginal costs, prices will fall continuously to eventually make the product free. There are a variety of other goods where such cost structures prevail. Take for instance, news media. The cost of replicating (or is it plagiarising) a news article across multiple outlets is close to zero, and therefore news producers are under tremendous pressure from consumers to respond to the threat of potential new entrants to provide news at prices cheaper than free. Yes, cheaper than free, which means that you may actually be paid to consume news! Like what Google did to the handset makers to use its mobile OS (for more details, read here). In the initial days of building the platform, firms are under severe pressure to kick-in network effects, and adopt pricing strategies that are cheaper than free. For instance, the Indian cab aggregator OLA Cabs, incentivises drivers handsomely (as the markets mature, the incentive rates are falling) to undertake a certain number of rides per day. This is apart from the amounts they earn from the passengers. In the entire bargain, drivers get paid by both the riders and the aggregator, and OLA keeps the rider fare low to encourage more usage, leading to faster growth of network effects.

Evans and Schmalensee argue that faster the network effects grow, faster they will disappear. I contend that this may not be true in markets with higher multi-homing costs. Take the OLA Cabs business model for instance. At the rider’s side, there are no significant multi-homing costs; at best it is limited the real estate available for multiple apps on the rider’s smartphone. It is the drivers’ multi-homing costs that are of interest here. OLA Cabs and its primary competitor Uber, have been working hard on increasing the driver’s multi-homing costs by limiting the incentive payouts only when the driver completes a certain number of rides per day. And as the market grew, this number of rides required to earn incentives has risen sharply. That means, a multi-homing driver has to ensure that he completes at least the minimum number of rides on one of the aggregator platforms before accepting rides on another. And soon, drivers who cannot meet the minimum required for earning incentives on both platforms would choose one of the two, and those drivers who cannot even meet the requirements of one aggregator would leave the market. Even though the cost structure of cab aggregation is similar to digital goods (high fixed sunk costs incurred upfront) and close to zero marginal cost of adding a new driver/ cab to the fleet, these firms have sustained the winner-takes-all characteristics by increasing the multi-homing costs of the drivers.

To sum up, network effects are durable when the platforms invest in increasing multi-homing costs of at least one side of the platform. Better so, the money side (not the subsidy side) that has the highest switching costs. These multi-homing costs arise out of asset-specific investments that the participants make in affiliation with the platform. In the case of OLA Cabs, multi-homing costs do not arise out of having to carry multiple devices, but in ensuring minimum number of rides per day on a particular platform to earn incentives. And these incentives are significant proportion of the drivers’ earnings, as the aggregators keep the rider prices low.

The importance of multi-homing costs

Evans and Schmalensee write:

With low entry costs, trivial sunk capital, easy switching by consumers, and disruptive innovation showing no signs of tapering off, every internet-based business faces risk, even if it has temporarily achieved winner-takes-all status. The ones most at risk in our view are the ones that depend on advertising, because even if they dominate some method of delivering ads, they are competing with everyone who has or can develop a different method.

In this post, I argue that creation and maintenance of high multi-homing costs is an effective insurance against low entry costs, trivial sunk capital and easy switching by consumers. Fighting disruptive innovation requires platform firms to understand the economics of envelopment, which we will discuss next week.

Cheers

How to build a platform business?

In his recent convocation address at our institute (Indian Institute of Management Bangalore), Mr. Nandan Nilekani stressed on how platform firms have come to dominate the Indian (and global) markets, and the need for our graduating students to understand them well (see http://www.iimb.ernet.in/convocation-2016). In this post, I would focus on categorizing different types of platforms, and some key issues in building a platform business.

Platforms are firms that operate in multi-sided markets. Unlike firms where products and services flow in one direction (remember, Porter’s value chain?) in a pipeline fashion, and money flows in the opposite direction, platform business models connect multiple sets of users. In the traditional sense of the word, a railway platform helps passengers find their trains and vice versa. The train station manager sets the rules, provides the infrastructure, and enables a smooth discovery and transaction between the different sides (trains and passengers). Imagine trying to find and board trains like you would board a taxi in the streets of Mumbai or New York! Generalizing this, the firm that provides the infrastructure is the “platform provider” and the one that sets the rules and norms is called the “platform sponsor”. In some cases, the platform provider and sponsor could be the same firm (like in the case of a railway platform); and in some other contexts, the platform provider could be different than the sponsor (like in the case of Uber or OYO rooms, where the cabs/ rooms are owned by independent entrepreneurs and the rules of the exchange/ transaction is set by the aggregator).

Platforms match different sides of users. In their role as matchmakers, they provide different value propositions – discovery, quality assessment, norms for interaction, setting expectations, and provide feedback – for each of the sides. Let me discuss how to build each of these value propositions (when you are setting up a platform business model) in detail, with examples from established platforms.

Discovery

This is in fact the first thing to focus on when you set up the platform. Setting up the infrastructure to facilitate interactions is the easiest thing to do. The most difficult part of process is the populating the sides with users. Here is where new platforms encounter the classic “penguin” problem, where users on one side postpone adoption till such time there are enough users on the other side. How would you like to be the “first” person to be listed on a dating site, seeking to find a date? You would affiliate with a dating site only when you are sure that there are already enough members on the other side. Platforms need to overcome this inertia by incentivizing one side to affiliate, in anticipation of affiliation by a large number of right kind of members on the other side. Various platforms have solved the penguin problem differently. For instance, Facebook solved the penguin problem by starting small and being focused on Harvard University students and alumni. Practo  solved it by building and selling their practice management software (Practo Ray) to clinics before opening the patient interaction platform.

Having solved the penguin problem, i.e., having built enough members on both sides, platforms have to ensure that the discovery engine is powered to ensure quality, current, and relevant results. For an interesting take on how Indian ecommerce firms stack up on search results, see this post by Aditya Malik.

Quality

In a platform where products/ services/ information are provided by independent parties on one side, it is imperative that the platform ensures quality. It would require verifying the genuineness of the information provider as well as the veracity of the information. For instance, Quickr.com (an Indian C2C marketplace for used goods) positively discriminates posts with pictures of the items being offered for sale than those posts without pictures while sorting the search results. IndiaMART (the B2B marketplace for industrial goods) certifies the sellers with a TrustSEAL, by verifying the antecedents of the seller’s businesses, including their legal compliance, manufacturing facilities, and product range. Verification of quality comes with a cost, and provides the platform with high credibility and enables loyalty of users.

Some platforms use user-ratings and reviews as indicators of quality (like zomato.com, the restaurant discovery platform). Crowd-sourcing of ratings and reviews might provide higher credibility to the platform, but has to be used cautiously. These could be gamed by users. More on this later.

Norms and rules

As a platform sponsor, it is important to set the norms for communication and interaction among users across the different sides. These norms should set the boundary conditions for interactions, like the terms of sale (delivery charges, delivery times, returns policy) in ecommerce marketplaces.

In pure discovery platforms like JustDIal.com (an online yellow pages) or Quickr.com (marketplace for used goods), indiscrimately providing mobile numbers could be abused. Quickr.com has in the past few months introduced a secure chat service whereby sellers and buyers could chat with each other within the platform without having to provide each other’s mobile number. Even when the agreement is reached and the transaction has to be completed, Quickr allows for an anonymous delivery service (Quickr doorstep), where the users need not know each others’ personal contact details.

BharatMatrimony (the online matrimony match maker) allows only paid members to initiate communication with others. What this does is to ensure that brides and grooms who are actively seeking matrimony to become paid members, as the other side is unlikely to respect someone who is “not even willing” to pay for discovering his potential partner!

Setting expectations

The platform should allow for users on both sides to clearly set expectations apriori, to ensure that there are no surprises during the transaction. More the information sought and shared during the discovery phase ensures smooth transition to the transaction and fulfilment phases in the platform. Here is where the platform should ensure that there is a mimimum amount of high quality information available about the entity/ prodcut/ service being matched. Imagine trying to book a hotel room on a travel website without information on the hotel location, types of rooms available on that particular night(s), and the rates! It is therefore an important consideration that platform designers need to keep in mind when designing the infrastructure and rules. For instance, dating sites like trulymadly.com ensure that users provide their facebook pages during the registration process. While actual verification of each users claims on the dating sites might be difficult, linking their facebook account to the trulymadly account ensures that they do not lie (too much!) with respect to critical information about themselves (like marital status). Now you know why the job search portal you just signed in wanted you fill in pages of information, and links to your LinkedIN profile.

Providing feedback

Even after ensuring quality, defining the norms, and setting expectations, there could be some errors. The platform architecture should therefore provide for immediate feedback on all four parameters – quality of the entity/ product/ service/ information; relevance/ adequacy of information; currency of information; and the quality of the discovery, transaction, and fulfilment processes. Cab aggregators like OLACabs request for feedback on the quality of the cab and driver as soon as the ride is completed. However, there is no provision (not that I could find) to provide feedback on all the discovery stage of the platform – the time it took for the app to find me a cab/ auto, or the accuracy of the location services within the app.

 

Surge pricing: Implications for India

Last weekend, I was in Chennai and I keep using taxi-hailing apps a lot when I am outside Bangalore. About half the time, I was offered rides with surge pricing, ranging from 1.4x through 2.5x of the base fare. And in some cases, for rides in the middle of a hot afternoon, I was offered an upgrade from a “mini” to “sedan”. Riding through the upgraded sedan, I was flipping through the news article, and found an article on Uber’s surge pricing lawsuit (see http://www.bloomberg.com/news/articles/2016-03-31/uber-antitrust-lawsuit-over-pricing-green-lighted-by-judge).

On Monday, 4th April 2016, the Economic Times, Bangalore Edition carried this piece about the Karnataka Government regulation of ride-hailing service (see http://economictimes.indiatimes.com/small-biz/startups/karnataka-nixes-surge-pricing-by-taxi-hailing-apps-like-ola-uber/articleshow/51678792.cms). In a nutshell, this regulation caps surge prices, encourages drivers to operate for multiple services (multi-home), and relaxes norms for drivers to affiliate with a ride-sharing platform.

I was left wondering, what would be the implications for the lawsuit in a duopoly market like India, where OLA and Uber compete. Would the economics be any different? In this post, we will understand surge pricing and its economics, in conditions of a duopoly, and the implications of the lawsuit in India.

What is surge pricing? How does Uber justify it?

The Uber website explains surge pricing thus: (https://help.uber.com/h/6c8065cf-5535-4a8b-9940-d292ffdce119).

“Uber rates increase to ensure reliability when demand cannot be met by the number of drivers on the road.

Our goal is to be as reliable as possible in connecting you with a driver whenever you need one. At times of high demand, the number of drivers we can connect you with becomes limited. As a result, prices increase to encourage more drivers to become available.

We take notifying you of the current pricing seriously. To that end, you’ll see a notification screen in your app whenever there is surge pricing. You’ll have to accept those higher rates before we connect you to a driver.”

The core argument is that surge pricing incentivises more drivers to be available during times of high demand. At the core of this argument is that Uber cannot “mandate” drivers to be available when the demand is likely to be higher, and therefore has to “incentivise” them. Just like tipping the driver to be available. The difference is that the amount of the tip is pre-defined. The effects of surge pricing are well documented in the case study by Chicago Booth School faculty here.

There are two challenges: who pays for the incentive – is the charge on the riders justified? Do drivers like this?

Riders’ perspective on surge pricing

A lot of riders (at least in India) do not like the haggling and negotiating with taxis and autorikshaws for a ride both on price and whether they do want to go to your destination. Most of us using public transport in India are familiar with the famous rant “I have to come back from there empty”. Uber and OLA implicitly promised to eliminate it with its fixed and transparent pricing. Add to that, the ease of hailing a cab to your doorstep/ boarding point, thanks to the mobile app and navigation tools available for both the driver and the rider. In that sense, cab-hailing apps should have their loyalist converts. However, when surge pricing is applied, a typical rider would think twice before confirming the same – she is confronted with the same behaviour the autorikshaw driver on the street would have told her – “this is my price, take it or leave it.” In a sense, it brings out the haggler in the rider, just that now the firm is haggling, and the rider is not even sure the driver benefits out of it (more on the drivers’ perspective later).

The second thing that puts off the rider requesting the ride is the number of cabs available on the map before the surge pricing announcement is made. If surge pricing was indeed designed to get more drivers available, what is happening to all these cabs stationed around my pickup point? Here is where customers begin their multi-homing behaviour (having/ using multiple apps at the same time). She would immediately try the other app – OLA or Uber to see if there is surge pricing there.

It hurts when there is an emergency or a discomfort, like having small children/ elders waiting with you; a flight/ train to take; having to reach for a meeting on time; making another person wait on the other side; or a combination of the above. In effect she is made to negotiate, haggle, bargain for a ride. She does feel she is being taken for a ride, literally.

What does all this result in – a poor experience to begin with, resulting in lower driver ratings. Poor driver, he is being penalised because sufficient number of other drivers were not available. Yes, he may make more money, but at the end of the day, the overall economics may not make sense. Surge pricing acts as a “moment of truth” for the rider, and she resets her expectations. When I am paying 2.1x times the normal fare, I expect the driver to be extra courteous, cab to be clean, and even the traffic to be lighter. On the contrary – surge pricing happens mostly during peak hours when everyone is either getting to work or back home, and on the road; and traffic is likely to be very heavy. All this plays on the riders’ minds and they are lowering the drivers’ ratings.

Drivers’ perspective on surge pricing

A typical driver joined the Uber system (Uber or UberX) with the intention of leveraging her/ his car for monetary gains. Compared to a taxi license, which is highly regulated (check out how to get a cabbie license in London, or what the dominant political parties think of cabbies in Mumbai), or a company employment that can be highly restrictive in terms of business, driving your car for Uber or OLA provides a combination of independence and profitability. At the core of the decision is the value of economic choice the driver has – he can choose when to “switch on”, or become available for a ride, and therefore, how many number of rides and how many hours of the day he wants to ride. This economic choice is also guided by the incentives provided by the cab-hailing firms to the drivers on the number of rides they take per day. Given the very low market penetration in India, most drivers multi-home, i.e., they sign up for both Uber and OLA. And make consolidated economic choices, viz., distributing the amount of time/ number of rides for the two firms so that they can maximise the incentives.

Surge pricing for drivers mimics the pre-disruption world. When there is high demand, I charge more. In fact, OLA (in India) has mandated that drivers should be available through the peak period to be able to earn incentives. While the peak period varies across cities, it is still such a large window that drivers could balance the demand across both the firms (Uber and OLA). And drivers make the choice of going online to that system where surge pricing is high. Even though they do not know in real time when and how much surge pricing is applicable, once the rider has accepted the ride, they would know. And with experience (and a little experimentation), it is easy to estimate. So, if it is likely that OLA would have higher surge pricing rates than Uber, the drivers would shift to OLA, thereby decreasing the supply available for Uber, triggering surge pricing in Uber. While the increased number of cabs for OLA should eventually bring down the surge pricing, it is not quick enough, as multi-homing riders (with both their apps running) are choosing the ride with the lower surge price.

Elasticity of demand and supply in a duopoly

This brings us to the question of how truly “elastic” is the supply, give the duopoly in India? Unlike in markets where there is just Uber and competition is poor, India suffers from a duopoly where the drivers’ multi-homing artificially increases/ decreases supply in one system. And when the riders are also multi-homing, the system stabilises and behaves like a monopoly, albeit with some time lags (than a monopoly market).

Would a flat peak-time pricing work? It may, but given the technology and its ability to discover real-time demand and supply, surge pricing is any day superior algorithm to peak-time pricing. Fixed peak-times are things of the past, when people went to work at the same time in the morning and returned back home around the same time in the evening. Flexible working hours, working from home, working for overseas customers in different time-zones … peak-times are stretched throughout the day in urban metropolises like Bangalore and Mumbai.

The law-suit [Meyer v. Kalanick, 1:15-cv-09796, U.S. District Court, Southern District of New York (Manhattan)]

The law suit against Uber’s CEO, Mr. Kalanick (note, the suit is against the CEO, and not the firm Uber) claims three things:

  1. Uber is not just a technology company, selling apps, but a transportation company
  2. Drivers are employees and not independent contractors
  3. Price fixing by the CEO of Uber, while using fixed prices despite using non-competing independent contractors as drivers

The arguments against these charges by Uber and its CEO are that Uber is just an aggregator, and the drivers as independent service providers have wilfully entered into an economic arrangement to agree to the policies set by Uber to find good quality and quantity of riders. Implying that without a platform like Uber, drivers would find it difficult to discover good quantity and quality of riders. And vice versa for riders. The transportation contract is between the rider and the driver. If this were to be entirely true, then the rider should have absolute choice of drivers/ cabs, as well as drivers to have absolute choice of which rider to take. Given that Uber make the match, and provide you “one” driver-rider combination, this true contract is questionable.

Drivers as independent contractors may be defensible with the argument that drivers have the choice when to login to the service. They may choose to switch off when they want, after fulfilling some minimum conditions. If they were employees, the firm would mandate more than a set of minimum conditions and behaviours; and would not provide the driver with the absolute economic choice provided in the current arrangement. It is to Uber’s economic policies that they have signed up to, and that explains why drivers with different economic expectations can co-exist in a single platform.

The price fixing charge is defensible with the argument that since drivers are independent contractors, it is important to “incentivise” them rather than “mandate” them to be available in peak demand times. To argue that these drivers do not compete is flawed, as the extent of surge pricing is determined by the supply of drivers in relation to the demand. In order to be available on Uber, each driver must maintain certain service quality, and do a certain number of rides. There is definitely competition amongst the drivers – they would want to be available where demand is likely to be higher than supply; and be there before other drivers. For accusing Uber of price fixing under anti-trust laws, Meyer should establish that in spite of varying demand and supply, Uber maintains the same price, coordinating with independent contractors (drivers) who do not compete.

Here is where surge pricing comes to Uber’s rescue – it is their most effective defence against price fixing charge.

Implications in India

The Indian law is uncertain, to say the least, on the regulation of platforms. The (in)famous case of a Uber male driver raping a female rider in Delhi is a case in point. Uber was first banned by the Delhi Government, the ban revoked by the High Court, only for the Delhi Government to subsequently not approve its application. Uber and OLA then went back to court and the courts agreed to revoke the ban when they promised to replace diesel cars with CNG vehicles. What this means in terms of legality is that Uber and OLA are in fact, undertaking on behalf its independent contractors.

The courts and everyone else in India would be waiting for the judgment of the class action suit in the USA on how this market pans out. Given the size of the Indian market, classifying drivers as employees, and Uber and OLA as transportation companies would kill the platform business model. While I do not believe that it would not go that extreme, interesting times lie ahead on how the courts and regulators interpret the developments.

%d bloggers like this: