Author: Srinivasan R

Professor of Strategy at the Indian Institute of Management Bangalore. All views are personal. The views and opinions expressed here are of the author, and not those of the Indian Institute of Management Bangalore; and are not intended to endorse, harm, malign, or defame any individual, group, or organisation.

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.

100% FDI in e-commerce – will prices fall?

On the 29th March 2016, the Government of India allowed 100% FDI in Indian e-commerce firms. While there is reason to cheer about the fast-growing sector getting more access to much needed funds for fuelling growth, there are three interesting developments in the notification.

  1. The Government has explicitly defined what is a marketplace model, as different from an inventory model.
  2. The consequence of this definition means that marketplace ecommerce firms cannot have a single retailer selling more than 25% of the retailer’s sales.
  3. The definition also means that the retailer cannot provide discounts and promotional offers on their own, directly or indirectly.

The impact of these three definitional changes would in the short run, require marketplace e-commerce firms to discontinue price discounts they offer directly or indirectly. Amazon’s promotional funding to sellers, PayTM’s cash back offers, or Flipkart’s big billion sale have to end. Will this mean they would stop offering discounts? I do not believe they will. They will find other ingenious ways of providing the customer with discounts, given that they would have access to larger source of funding through the FDI investments. More on that below.

It’s the sellers that matter

This definition of the marketplace model would clearly lead to interesting dynamics on the seller side. For instance, an SMB seller who would otherwise be listing his goods across multiple e-commerce companies would now be wooed by more and more marketplaces, as they seek to expand their base of sellers. Do you realize that the firm that owns the site www.amazon.in is actually called Amazon Seller Services India Pvt. Ltd.? In order to expand and sustain their broad base of sellers, these marketplaces would now have to offer discounts and freebies to the seller side, rather than the buyer side as it was apparent in all these years of growth. These seller-side offers would eventually translate into lower prices for buyers in two ways.

One, in the traditional sense of the word, the seller bargaining power would go up; sellers’ multi-homing costs (costs of simultaneously offering their products and services across multiple marketplaces) would come down; and the volumes would go up. Larger sellers therefore, would invest in technology to manage their multi-homing costs, automate a lot of processes, outsource specialised functions like last-mile delivery to focused service providers, and would grow their own sourcing networks. Smaller sellers on these marketplaces would have no incentive to be remain small, and would either get gobbled up in a consolidation game or become second-tier sellers to the larger sellers operating on the marketplace e-commerce retail. This consolidation and growth of sellers on the marketplace would result in lower costs through economies of scale and scope, which the seller would eventually pass on to the buyers.

Two, the consolidation of the seller market would lead to fierce competition across sellers; and the basis of competition between the sellers is likely to be only price. Other differentiators like product variety/ features and brand are likely to be owned by manufacturers/ marketers (like Samsung), whereas service differentiators like distribution network, logistics and related customer service are likely to be managed by the marketplace. The only bases of competition for the sellers to compete would be (a) optimisation of inventory to reap appropriate economies of scale and scope, (b) managing distributed inventory through accurate prediction and forecasting of demand and supply, and (c) reducing costs through faster inventory turns as well as leveraging their bargaining power with manufacturers as well as retailers/ ecommerce firms.

Good times are here to stay (for the consumers)!

So, in effect these regulations do not necessarily mean that the prices in the ecommerce retail would rise and match the offline prices. There may be small adjustments; but in the long run, the discounting would shift from the retailer to the supplier. And the consumer would continue to enjoy lower prices (offered by the sellers) along with superior customer service (provided by the retailer, as this would be the only basis of competition across  marketplace e-commerce competitors).

About this blog

Hi: I have been wanting to write a blog for a long time. Thanks to my students and collaborators who have pushed me to write regularly, I start.

There are three things I would write about:

  1. Strategy, in general; Indian firms in specific
  2. Digital strategy, and what it means for the Indian firms, especially start-ups
  3. Platform business models, with a focus on Indian firms

Suggestions and comments welcome.

Reach me at srini108@gmail.com