Flipkart Ads – Is there a shift in online advertising economics?

Yesterday, I read an interview with Sanjay Ramakrishnan, Senior Director & Head – Business development & Marketing, Flipkart Ads in Advertising Age India (read it here). It set me thinking, why is Flipkart into advertisements? Is it competing with Amazon or with Google, Facebook, and Apple as well?

Though I am tempted to label this development as the advertising market becoming a contestable market, I will refrain from doing so. Let me first explain what is a contestable market (in simple terms, of course, let me try; and in the context of platform business models), and then proceed to analyze if the success of Flipkart Ads is a source of worry for other platforms whose principal business model is based on advertising revenue.

The theory of contestable markets originated from the works of Baumol as early as 1982 in this seminal paper (available through JSTOR here). He (and his co-authors in subsequent papers) defined a contestable market as one with absolutely free entry and costless exit. Which implies that such a market would be vulnerable for a hit-and-run entry, i.e., by any competitor with no need for any specific assets, process capabilities, or differentiation.

A key characteristic of these markets is that the new entrant takes the prices prevailing in the market (of the incumbents) as given, and enters with the same price. In a perfect competition, any new entrant will increase the supply in the market, and should lead to a reduction in prices. Even when the market shares of incumbents and new entrants change, the industry price levels should ideally fall with increase in supply. In contrast, in a contestable market, the new entrant could enter the market with the same price as the incumbents. The justification for this assertion could be based on two arguments, that the new entrant enters the market at such a small scale compared to the incumbents that there is no visible change in the total market supply to warrant a price correction. The second argument is founded on the thesis that the incumbents cannot retaliate with sufficient speed to counter the threat posed by the new entrant, due to their systems and processes that bind them to a particular cost structure and a positioning in the market. In such a case, the new entrant could enter the market with a prior contract, preferably a long-term contract, at least as long-term as it takes for the incumbent to respond. In perfect competition or monopolistic competition, incumbent firms will adopt limit pricing strategies (if profitable for them) to keep new entrants at bay, i.e., as the incumbents sense the threat of new entry, they would reduce the prices to a level where it would be profitable for the incumbents and not for the new entrant. Take for example, when cola firms entered the bottled water market in India, the incumbent, Bisleri International embarked on a strategy of keeping market prices so low that it took a long time for Coca Cola Company, and Pepsico to break even.

The second aspect of contestable markets is the absence of any sunk costs whatsoever for both the incumbent and the new entrant. If any upfront fixed costs were to be incurred by a competitor either prior to entry (including in studying the feasibility of making money in that market) or at entry (like setting up manufacturing and distribution capabilities), the costs of entry will prevent this market from becoming contestable. Let me provide an example. In today’s world, setting up an online store entails no sunk costs for any retailer. The domain registration and hosting, website design, payment gateways, and fulfillment are all functions that are unbundled and offered as independent services (as SaaS) by different vendors, which makes all of them variable costs, rather than fixed costs. Such costs are neither fixed nor specific – one could use the payment gateway for any other online transaction, should this venture fail. Such markets with no sunk costs result in no barriers to entry and exit and therefore, are contestable. Contrast this with our previous example of Coca Cola Company and Pepsico entering the Indian bottled water market – this is a market that requires significant bottling and distribution capabilities. Though the cola firms enter this market with significant synergies from their core business, there were certain unique capabilities that the bottled water market required – sourcing of good quality water and plastic bottles, bottling lines that were specific to water, unique branding, and wider distribution networks.

The third characteristic of contestable markets is that the products are absolutely non-differentiable. That means, the new entrants can enter the market and imitate the products/ services offered by the incumbents at the same costs or even lower, and therefore maintain the same price levels. It is also possible that the new entrants enter with lower prices, and offer the same ‘standard’ products or provide additional features at the same or lower prices. Such standardization is highly visible in the context of platform services, like a C2C marketplace. In the absence of any product differentiation between competitors (any new feature is imitable quickly and is almost costless to do so), Quickr.com and OLX.in entered the market and took market share from incumbents like Sulekha.com or asklaila.com.

In summary, a market can be (or become) contestable when either of these conditions are met – no changes in prices (no limit pricing by incumbents), no fixed sunk costs, and no differentiation in products and services offered.

Is digital advertising becoming a contestable market?

For digital advertising market to become (and be) a contestable market, it has to allow for costless entry and exit, no sunk costs, and no differentiation. In the case of Flipkart Ads, I would argue that it would have cost Flipkart next to nothing to build the platform. The ecommerce store was in any case dealing with sellers, and all that they had to do was to extend the relationship to brands. And remember, in the Indian market, a lot of the brands had their own ecommerce retail operations and some of them were already on Flipkart as sellers. For instance, when I searched for the Prestige brand of pressure cookers on Flipkart, I found about 40-odd sellers including TTK Prestige, the brand owner.

And when Flipkart entered the digital advertising space, did Google and Facebook respond with limit pricing? I am not sure they did. A Feb 2015 LiveMint article that announced Flipkart and SnapDeal’s entry into online advertising space gave Google ad revenues as US$55bn compared to Amazon’s US$1bn (read it here). Given these sizes, it is unlikely Google and Facebook would have felt the need to respond to their entry by lowering prices.

Developing the advertising platform would possibly not involve any sunk costs for Flipkart. There is sufficient traction in terms of relationships with sellers and brands, the technology platform costs next to nothing to build, transaction costs are variable (including cloud storage and payment gateways), and even brand building is costless (as they are extending the same brand – Flipkart Ads).

It is the third condition of contestable markets that protects the online advertising market from becoming a contestable market, i.e., lack of differentiation. In the case of online advertising market, differentiation is created and sustained by superior targeting of advertisements to the right users. Measuring and monitoring engagement of the audience is the key in data collection; deep understanding of the consumer behavior and decision-making process is critical in analyzing this large volume of data; and close relationships with a wide variety of advertisers is imperative to ensure narrowcasting of advertisements to specific audience profiles. Here is where the product differentiation kicks in – the kind of browsing habit data that Google has access to is very different from the ‘buying intent’ that Flipkart can derive out of its customers’ behaviors. And especially in the context of mobile apps, the Flipkart app has access to other information like the person’s location, WiFi/ data connection information, and even his contacts; all of which could be useful to provide targeted narrowcasting (or even unicasting) of advertisements. Such shrinking of segments and the ability to serve what the marketers call ‘the segment of one’ customer can differentiate the new entrant, Flipkart’s services from the incumbent ‘Google’ and ‘Facebook’.

So, what are the implications for entrepreneurs?

First, evaluate if your market is indeed contestable, or is likely to become contestable. If there is a likelihood of your primary market being or becoming contestable, consider one of the following options:

  1. Change your business/ business model (pivoting is a fancy word these days in the startup ecosystem)
  2. Erect barriers to entry and exit – use regulation if you must (see how Airport Taxis in Bangalore are competing with OLA and Uber)
  3. Differentiate – even if it is not the most significant of your product offerings, focus on those value creation opportunities that involve sunk costs
  4. Wait for a new entrant and bleed him to death with limit pricing (you better have easier access to capital than the new entrant)!
  5. Wind up, sell out, and take your family (if you have one) on a holiday to Seychelles! And don’t forget to thank me!

 

 

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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.

StoreKing: Taking ecommerce to rural India

Each of my visits to Europe has taught me something new over the past few years. My recent visit in April-May, I had to travel through three countries – Switzerland, Germany, and Italy. What struck me this time was how much the local language was used in a lot of business and commerce, with English being the common language. While looking for similarities between India and the European continent, I was amazed at how much they value their local languages. For instance, my colleagues in Germany did my hotel bookings for Nuremberg and Rome through Booking.com and HRS.com, and the entire communication cycle was in German language. Not surprising. But it triggered the thought about “why don’t we have websites and mobile apps in India’s languages?” What would be the impact of an ecommerce site in a local language like Kannada on a rural consumer in say, the Dakshin Kannada district?

I began my exploration and in a recent road trip to Tiruchchirappalli (Trichy for the phonetically challenged) in Tamil Nadu, I experienced the power of StoreKing. StoreKing is not a traditional retailer or an ecommerce firm. It leverages the power of ecommerce and solves the three major problems faced in rural penetration of ecommerce – language barriers, non-specific addresses, and trust. A detailed description of the StoreKing business model is available in a write-up on YourStory.in (read here), but for the sake of explanation, let me briefly introduce the same.

StoreKing approaches rural retailers (brick and mortar) and convinces them to install the StoreKing kiosk/ buy a StoreKing tablet in their shops. These kiosks or tablets are powered in the local language, and has a large variety of SKUs, ranging from electronics, appliances to digital goods like mobile/ DTH recharges. Customers walk in to the store, and with the help of their trusted retailer, browse and shop on the StoreKing kiosk. Once they have placed an order, they pay the retailer in full, StoreKing communicates with the customer through their mobile phones in their local language. The problem of poor (ill-specified) addresses is taken care of by dispatching the goods to the local retail shop (from their central warehouse in Bangalore) within 48 hours. The retailers receive a 6-10% commission on the sale proceeds. Though I am not sure how StoreKing sources the goods, it uses the standard FMCG distribution network to ship the products to the retailers.

StoreKing’s last-mile connectivity to its rural consumers addresses the three main problems faced by traditional ecommerce firms – lack of scale in rural markets to justify investments in delivery infrastructure, lack of sufficient data about rural consumer habits and preferences, and their (misplaced) perceptions about rural buying power. An older YourStory.in report talks about how StoreKing’s customers bought dishwashers (not one, but two for the same household) and iPhone 6s (read here). The lack of scale has been overcome by adopting a hub-and-spoke distribution system that piggy backs on the FMCG distribution network.

I am not sure this happens, but would it be possible for the customer to change the default language of communication? I appreciate that rural India would not have enough linguistic diversity to justify this, but if StoreKing were to penetrate into border towns like in Belagavi (nèe Belgaum) district, where multiple languages are used, it would definitely need customization.

StoreKing has partnered with Indian Oil petrol bunks (gas stations) as retailers (see here); as well as Amazon.in, presumably for expanding their breadth of products. The recent media reports talk about Amazon.in’s Udaan initiative to reach rural customers with limited internet connectivity, and the synergies Amazon.in will have through this partnership with StoreKing, but not the perspective of StoreKing. Amazon.in would leverage their deep local presence and established distribution network; and I would guess StoreKing would significantly benefit from Amazon.in’s breadth of products list.

StoreKing claims to be neither a discounter nor a premium seller of goods. The primary value proposition is the trust its customers have on the local retailer; and that has enabled them to even collect cash in advance, rather than cash-on-delivery that has become the dominant mode of ecommerce transactions in India. This trust placed by the retailers on StoreKing provides it with a significant first mover advantage. At over Rs.10,000 investment and significant local knowledge of the customers, the switching costs and multi-homing costs for the retailers are very high. Even when a competitor enters the market directly, it would be difficult to convince a retailer to shift out of the StoreKing kiosk/ tablet to another competing solution. It is here, that I believe StoreKing should follow the classic Wal-Mart strategy of “regional rural saturation”, and convince every retail shop/ kirana store in a particular geography to host a StoreKing kiosk.

Four questions pop up in my mind, for which I have no answers right now.

  1. Should StoreKing open its own exclusive stores, as they grow big? What are the costs of signing up with competing retail stores in the same village? Can these costs be overcome by stand-alone StoreKing kiosks?
  2. At the other extreme, should StoreKing allow for a tight integration of the brick-and-mortar retailers’ inventory and their inventory? For instance, if a customer ordered a Micromax mobile phone through the StoreKing kiosk, which was already available with the retail store in his physical inventory, should he fulfill it from his store (and forego the StoreKing sales commission) or block those items that he sells in his store?
  3. If these brick and mortar stores who are trusted by the local customers offer discounts and credit for their offline sales, how does that affect StoreKing operations and business model? Should StoreKing allow a retailer to extend the same credit terms he offers to his customers for ordering good through StoreKing?
  4. As StoreKing expands into more and more geographies (as of June 2016, they operate in the five South Indian States, plus Goa, Maharashtra, Gujarat, and Odisha), is this model scalable? What challenges would a market like Eastern Uttar Pradesh pose?

I am watching this firm and its growth trajectory from the outside. Any answers?

PS: I am nor in any way related to StoreKing or its investors/ founders.

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.

Network Mobilization in Platform Businesses

Network mobilization is a critical issue for building a platform business. In one of my earlier posts on how to build a platform business, I talked about firms having to solve the penguin problem. In this post, I would talk about the various ways of solving the penguin problem. Penguin problems manifest themselves when users on one side postpone adoption of the platform unless there are enough members on the other side of the platform. No one joins unless everyone else joins in. The metaphor arises from the behavior of penguins who wait at the edge of the ice file waiting to jump into the water to fish, but hesitate to do so for the fear of a lurking shark. Unless they are assured that there is no shark by a pioneering penguin who possibly was the hungriest and was willing to take the risk, no other penguin would jump in. Understanding of this behavior is key to network mobilization.

Closed group invites others

The story of how Facebook began with building a network of Harvard alumni and then branching out to others is well known. The same method was used by LinkedIn to build its network. The founder Reid Hoffman was a serial entrepreneur who did not have to depend on others to invest in LinkedIn. When he started, the site began with 13 people associated with the company, who were provided with invites. They invited 112 people. This set of people were successful and had strong profiles that when they invited others to join in, there was a viral growth in the next two years. Until after two years of launch, LinkedIn hadn’t even thought of revenue streams! (Read the story here). This is a luxury most entrepreneurs starting today would give one hand a leg for, right?

Find a crowd puller

When eBay stated in 1995 as AuctionWeb in San Jose, it was intended as a marketplace for collectibles. (Read the story here). It began by inviting sellers to auction a wide range of collectibles to other retail customers. However, rapid growth began when it contracted with Electronic Travel Auction to use SmartMarket technology to sell plane tickets and other travel products. This third party licensing deal helped AuctionWeb in their rapid growth of eyeballs. From 200,000 auctions in the whole of 1996, the contract signed in November 1996 provided it with enough traffic to grow to hosting 2m auctions in January 1997. Though unrelated to the business of C2C auctions, this technology brought in the traffic to the core auction business.

Time it right

No other enterprise start-up story can match the timing of how Airbnb, the bed-and-breakfast renting firm started. (Read more). Struggling to pay their rent, the founders capitalized on a design conference that was happening in San Francisco to launch their venture. When they rented their own apartment and found that they could sell three beds for about $80 per night, they realized that this could be a great business idea fueled by shortage/ high prices of hotel rooms during festivals and conferences in the USA. They built a basic website that allowed local people to list their rooms and travelers to book them. They got their initial traffic through large conferences in big cities.

Build the money side through marquee users on the other side

Coursera built its money side (students) first by offering courses from reputed universities like Stanford, Princeton, and Michigan and U. Penn for free (read more). Once they built enough number of students taking these courses, they began offering Signature track courses for which students had to pay for receiving a verified certificate. What helped them was the fact the founders were Professors themselves at Stanford University. They began by partnering with a few reputed universities, built sufficient number of student traffic on the other side, which attracted more and more universities and professors to join the educator side, which in turn attracted much more students. And the cross-side network effects exploded.

Port users from another platform

The Indian local business listing website JustDial.com started as a tele-discovery platform. Yes, that is the reason, they are called Just-Dial (read more). The printed yellow-pages was clumsy, cumbersome, and people were finding it difficult to find what they wanted quickly, especially when they were traveling outside their own cities. JustDial invested in creating a repository of all businesses in a local market, and then providing it to search users on the telephone for free. Given that most businesses in a local market would be competing with each other directly, same-side network effects existed. Which meant, a business’ motivation to list on the JustDial platform was higher when every other competing business was listed. JustDial leveraged this network effect and created a subscription scheme. And used a simple to remember phone number (88888888 – or all eights) in every city/ town to reach JustDial. Coupled with extensive consumer promotion, JustDial was a market leader in local search. When internet arrived and local search shifted online, JustDial simply ported their database of vendors from the tele-directory to create an online directory, much before anyone else could even spell the word directory! Appreciate the fact that for most of these local businesses, presence on the JustDial platform was the only online presence – they did not need to build their own websites!

Vertically integrate

India’s ecommerce vendors like Flipkart.com had vertically integrated to build the network effects. Its subsidiary WS Retail was (till regulation hit them) Flipkart’s largest seller. It built its buyer base by listing products through WS Retail, and once the buyer traffic was there, it attracted more and more sellers. Same is the case with Cloudtail for Amazon.in. Read an earlier piece on how this will play out here.

Solve an existential problem for a class of users

PayTM started as a platform for mobile recharges/ payments and paying DTH and utility bills. The offline mode of recharge was pretty cumbersome for the principals, who had to contract with a wide network of distributors and last-mile retailers and collect cash from all of them. This problem was solved when PayTM offered mobile/ DTH/ utility service providers with an option of having the customer recharge/ pay through their own mobile phones. Coupled with a wallet, transactions could be tracked immediately and were absolutely cashless. In order to grow the network, PayTM did not even need to advertise, the utility service firms themselves advertised to their customers to use PayTM! Once you solved a critical problem for one side of the users, it is in their interest to grow the number of users on the other side.

Just subsidize!

OLA Cabs began its operations with huge subsidies for both its drivers and its riders. And a lot of people believe that OLA continues to subsidize! Once the network effects are set in, and the switching costs for the drivers have risen significantly, it would be easy for OLA to begin its monetization. Till such time, keep the subsidy flowing.

More ideas welcome. Cheers.

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