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.

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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

Learning from the Network meeting of the Peter Pribilla Foundation

I had the privilege of attending the 10th networking meeting of the Peter Pribilla Foundation on the 5-6 May, 2016 at two wonderful villas around Rome, Italy. Thanks Kathrin Möslein for inviting me again to participate in this wonderful network meetings in picturesque villas. This is not intended to be a minutes of the meetings, but my own notes and learning.

Manfred Broy’s keynote on Digital transformation

Digital transformation today is being driven by multiple forces: technology push, infrastructure maturity, market pulls, and startups that can leverage these business model opportunities. As markets, technology, and competence come together to create new business models, the economy is flooded with startups that could disrupt our lives in more ways than what we can imagine.

The talk brought to the fore three observations in my discussion.

  1. Software is eating our lives

As the digital transformation evolves driving on increased computing power, trnasmission power (bandwidth), and programming; Governments are struggling to regulate these business models. For instance, Skype as a software disrupted the international telecommunication industry that relied on massive investments in hardware at the backend and the consumer end. Blockchain has created an entire monetary system with no involvement/ interference of the State.

  1. From Internet-of-things to Internet-of-systems

More and more devices are being connected to the internet, and more and more data is being collected about every part of our lives. The evolution of the Internet has followed the linear path from (a) http or internet 1.0 that connected computers in a network, to (b) web 2.0 that allowed for interactive content in the form of search and social media, to (c) a semantic web 3.0 that allows for semantic search, including images, videos and other references, to (d) the mobile internet, that focuses on the App Economy – hyperlocal and mass-customized content, to (e) integration of IoT devices and servitization applications that lead the Interactive Industry or what is called Industry 4.0.

  1. Moral questions on how these data is used

As more and more data is being collected and collated by corporations, that are mostly monopolies in their markets, questions remain on the nature of consumer choice on what and how their personal data is being used, definition of trust and transparency of these data banks, and how these changes are affecting our professional, personal, and social lives.

Four sub-groups deliberated on actions, competencies, infrastructure, and promises around digital transformation.

Peter McKiernan and Anne Huff summarized the discussion and left us thinking on two axes.

  1. Has all this digital transformation driven us towards so much personalization and customisation that we excelled in marketing to a segment of one; while we have ended up destroying the social processes that form the basis of creating vibrant communities?
  2. With all these investments in digital transformation, what social problems are we solving in the developed and emerging economies? What are our contributions to sustainable management of our ecological environment, alleviate poverty, and manage active and forced migration of people across national and continental borders? What can we contribute to the improvement of human development, fostering inclusive growth, and evolve meaningful networks of social and economic competencies?

Albert Heuberger talked about the need to integrate research on hardware, software, and open problems. He talked about the various projects that Fraunhofer IIS was working in collaboration with the FAU Erlangen-Nuremberg and the Bavarian Government. His view of the future was to sustain research on

  1. Power consumption economics, including battery technology, to power smart devices that need to be ‘always on’.
  2. Devices, software, and problems that help improve mobility through increasing the digital range of smart devices.
  3. Integration of data from intrusive and non-intrusive biological data like glucose levels, fatigue)
  4. Consumer applications of hyperlocal environmental data, like pollution parameters (COx and NOx)
  5. Long range imaging, including gesture control
  6. 3D displays for mobile phones (VR apps for end consumers)

Helmut Schönenberger and Dominic Böhler from the UnternehmerTUM briefed us about the TechTalents program where they have batches of students and entrepreneurs being mentored by experienced mentors.

Peter McKiernan summarized the two talks about the need for engaged scholarship in the context of business research losing practical relevance. I could summarize the day’s discussion and thoughts as an interaction of two triads.

Summary

Our second day began with Mitchell Tseng talking about his rich experience of how the world has evolved in his talk on leveraging individual expertise in the context of global cooperation. As the world moves from optimizing supply chains to global value chains, we need to build three related capabilities

  1. Actively manage the shift from reducing waste, focus on core competence, and being responsive to customer needs to increasing the customer willingness-to-pay, focus on the value communication and delivery, and be responsive to changes in customer value perceptions over time.
  2. In a world dominated by network effects, value providers could realize value from even customer indifference. The old chinese proverb says, “the wool grows on dogs, and the pigs pay for it”.
  3. Rapid prototyping in a globalized world requires organizations to embed the product concept into the prototype and be able to test it across different parts of the value chain and in different cultures.

Hans Koller commented that even traditional businesses like aviation (free flights for passengers paid for by advertisements/ shopping), renewable energy (freebies for consumers who allow for installation of solar panels on their rooftops), and healthcare (providing free healthcare advise/ services in exchange for data collected from patients through embedded devices) are embracing two-sided markets. He also added that such rapid prototyping may leverage modularity (as propounded by Prof. Charles Baldwin) in product design and development. Building modularity across global products and value chains requires well-defined international standards for interfaces.

Peter McKiernan commented that research on value creation from the eyes of the consumers (perceived value) could learn a lot from the research on cognitive psychology literature. The definition of business value creation has over the years evolved from (a) the traditional industrial economics SCP paradigm to (b) Porter’s industry attractiveness frameworks to (c) mass customization and value creation to (d) the experience economy of the 21st century.

Members and fellows of the Peter Pribilla Stiftung (PPS) shared their wonderful work, research, and experiences. Unfortunately, the notes are not part of this document.

The afternoon was centered around two sub-groups working on (a) how the research group could work together in joint projects and (b) designing formats for digital transformation. It was discussed that the network should be largely expanded to include people from outside Germany, maybe leveraging each others’ personal networks. The need to collaborate with each other in applying for joint projects from organizations like the EU was emphasized. The group on designing formats elaborated on the need for an agency that could act as a platform that would evangalize, educate, and build strong networks of organisations that enable digital transformation with those that need their services like the Government, Universities, Schools, non-proifts, and corporations.

The networking meeting ended with summaries by Anne Huff, Frank Piller, and Ralf Reichwald.

We have come a long way from when we started in the last ten meetings. Too much of our discussion was centered around white, middle-class caucasian world. We need to expand our focus to the globalized world that includes a lot of problem. The second problem is that we have been largely academic-centric. We are the product of a system that pushes us to be more theoretical, abstract, and less practical and working with the firms. It is imperative that we move more towards pragmatic application of our energies to solve the big bad world’s problems.

Dynamic capabilities is about how organization’s change and evolve over time. We need to adopt the same approach and ask ourselves, look at our own unconscious biases, shift from the technology level of analysis to the more micro-social levels, include people from more varied disciplines like Psychology and Sociology to educate us.

We have learnt a lot about technology, digital transformation, and new business models. We are so proud that we heard from our PPS Fellows. We have over 50 fellows right now working, and it is heartening to see them do so well in their research and careers.

Thanks to Claudia Lehmann and her team for the wonderful organization.

Comments, observations, edits, and additions welcome.

 

Building a platform business is hard work, not for lazy people: A response to Prof. Ajay Shah’s column in the Business Standard

I read with interest what Professor Ajay Shah had to say about young men and women entrepreneurs of today wanting to become rich quick, with dreams of laziness in the Business Standard (see here). This note is a response to his observations/ allusions that businesses that run on network effects (a) are not-so-innovative, (b) operate in monopolies, and (c) are built around inferior products.

Building a platform is hard work, not for lazy people

Let me begin with the title – lazy businesses. The implication of laziness is that while there is opportunity and capability to do the hard work, these businesses (and by implication, its founders) are unwilling to work hard. I disagree to the notion that anything develops fast is not hard work. The implication that a business that grows slow is “steady” and the one that grows fast is cutting corners. True capitalism favours entrepreneurs who chase and capitalise on big opportunities, and that too pretty fast. Building network effects is not as easy as he alludes. He quotes the example of Google monopolising cloud-based email due to the network effects it has generated. Google was not the first entrant in the cloud-based email space, there were two large competitors operating when it entered – Hotmail and Yahoo Mail. Gmail entered with a disruption – it offered almost unlimited storage on the cloud, and two, it began with invitation only. It took a while for Gmail before it became open for signup, but given that innovative positioning of “never having to delete your email”, it was quick enough. Behind this innovation at the customer end was the hard work, the painstaking task of building server farms across the world with sufficient security and redundancy built into them. This is exactly the hard work Prof. Shah talks about, innovating around products – the Google innovation was riding on the falling storage costs and leveraging the power of global network connectivity to build a network of server farms, thus driving costs down. The fact that Gmail was able to unseat Hotmail and Yahoo Mail from their leadership positions is sufficient evidence that competition is working, and capitalism is safe too.

Platforms are innovative

One of the key tenets of capitalism is that factor endowments (like capital) flow freely from inefficient uses to the most efficient uses. The fact that the venture capital market is amply fragmented is the first signal that capitalism is working there. Let us turn to the platform business firms that seek these capital resources. As capitalism would have it, money should only flow to the most efficient uses of capital – ask any entrepreneur about raising money, and you would hear enough of how difficult it has been. Raising money has never been so difficult, as each of the business models have been unique. Yes, there have been replication business models that get funded, like I want to be build the Uber of Indian hospitals, but they are few and far between. Each of the business models that are flush with funds from the venture capital firms and angel investors are indeed innovative. May not be in the traditional sense of the product innovation like Google’s server farms, but a lot of them offer unparalleled service innovations. Take the example of Quickr, the C2C used goods marketplace. Though such used goods marketplaces had existed in the past, Quickr has managed to bridge the information asymetry between buyers and sellers in a variety of ways (photographs of products, contact details of sellers, premium services, and enabling within-platform communication through QuickrChat) as an insurance against the platform becoming a “market for lemons.” These innovations have not happened in one day, it has taken them years of competing with similar and local marketplaces and keenly listening to their customers on both sides – buyers and sellers.

Not all platform businesses operate in winner-takes-all markets

Prof. Shah alludes to the suggestion that most, if not all, platforms create and operate as monopolies, once they reach a threshold of network effects. Research in economics shows that there are three conditions for a platform market to become a winner-takes-all market – network effects are strong and positive, multi-homing costs are high for the users, and there are no special preferences for users. He has clearly defined the network effects in his article, and I would skip that part. Let me turn to multi-homing costs. Unlike switching costs which measure user costs of switching from one competing product/ brand to another; multi-homing costs measure the user costs of staying affiliated to multiple product/ brands. A good example of multi-homing costs is the number of emails accounts a user can efficiently own and operate. Even though most of cloud-based email is free to use, and a user can create any number of email ids for herself, what restricts her choices to a few is the costs of logging in to each email id, and making sure you do not miss out on important communication. This multi-homing costs ensure that the market has one social networking site, where people connect with friends, family, co-workers, as well as their business partners. However, not all markets have high multi-homing costs. Users (bargain hunters) do shop on multiple ecommerce sites and maintain their login/ passwords for each of these sites. The third condition for a market to demonstrate winner-takes-all economies is the absence of special preferences amongst the users. In the peer-to-peer networking space, where Facebook dominates the social networking market, professional networking (finding jobs and customers for one’s skills as a special need) has another player, LinkedIn. Passive job seekers would populate LinkedIn, while active job seekers would register with one of the many job sites like Naukri.com. The point I want to drive home here is that having network effects by itself does not guarantee a winner-takes-all economy. Firms expend time and effort in building multi-homing costs and enveloping any special needs to create a winner-takes-all market.

Successful platforms have a superior product/ service core

Though network effects make switching costs high, the history of platform business evolution is strewn with a lot of products/ services that have fallen by the wayside due to poor quality of its core product/ service. We did talk about Hotmail and Yahoo Mail, that did not innovate at the right time and lost out to Gmail. On the other hand, Friendster and MySpace failed due to Facebook’s superior quality and constant innovation. Google+ with the backing of the Internet giant, is an also ran in the peer-to-peer social networking space. Yes, switching costs exists and are non-zero, but given the right kind of strategy adopted by the challenger, that is apart from the superior product/ service, users can, and will shift.

Network effects are hard to build

Prof. Shah’s piece asserts that network effects are easy to build and can be done quickly too. Building cross-side network effects are difficult. How would Prof. Shah like to be the first contributor of a new newspaper, not as established as the Business Standard? He writes a column for the BS because of the existence of network effects – he knows that his columns would be read by the “right audience.” Traditional businesses like newspapers have long known to subsidize one side of its user base, its readers, while making money from advertisers (and in some cases, even benefactors and sponsors). So is the case with the media industry. This is a classic “chicken-ane-egg” problem that network industries have to resolve. There are many ways to solve them, and subsidising one side is just one of them. For instance, Practo has invested heavily in building its practice management software, Practo Ray for its clinics side of the business, so that it could build cross-side network effects. Now that the clinics use Practo Ray, Practo can afford to subsidise patients discovering doctors/ clinics through Practo.com. Tough, hard work buidling and selling practice management solutions to clinics, before the subsidising began. Subsidising one side of users to build network effects is not in itself any bad, but such subsidising should not be at the cost of overall economic well-being. Founders/ VC investors (shareholders) and managers make money because the customers, at least one side of the platform, are willing to pay. And they are willing to pay in return for the value they receive.

In sum, building a platform business with network effects is not lazy work, it takes a lot of patience, investments, and creative solutions to succeed. Yes, they are unlike traditional “pipeline” businesses where value flows from one direction to another linearly. They are different, and in some kinds of ways, fun. They have multiple sides of customers to deal with, and are on the toes all the time to keep the fine balance intact. These are exciting times when traditional pipeline businesses compete with platform businesses.

Comments welcome.

Measuring E-commerce firms’ performance

The week began with the news of the online grocer PepperTap closing its grocery business to become a pureplay logistics firm (see here). And we just read that SnapDeal is recalliberating its performance metrics (read here). So, what exactly is the problem and what can we do about it?

The investor obsession with GMV

Throughout the world, venture capitalists and other investors have used the metric of Gross Merchandise Value (GMV) to measure the performance of E-commerce firms. Everyone manages what they are measured on. So, all E-commerce firms focused on increasing their GMV, that is increasing the gross value of their sales. What this obsession with gross sales does to firms is that there is significant incentive to pursue what I call as “profitless growth”, where only the topline matters, with no attention whatsoever on all other parameters. Especially so, when the entire industry thrived on deep discounting and low customer switching and multi-homing costs. Focus on just one parameter like the GMV might be valuable when the business just sets up to measure the initial traction amongst the target customer groups, but continued focus on the single parameter can lead to misplaced strategies.

Evolving other measures

After all, E-commerce is also a business that needs to provide sustained returns to its shareholders. As with all for-profit businesses, good measurement of performance should include a variety of metrics that reflect the organization’s priorities and strategies. For a consumer focused multi-category retail business, it would be prudent to measure performance on the following four parameters – (a) gross and net (of returns) revenues; (b) gross and net margins; (c) customer addition, loyalty, and attrition; and (d) distribution of sales across categories in line with the firm strategy/ priorities (merchandising mix).

The bane of COD

The boom of Indian Ecommerce industry and its reach to tier II and tier III towns in India could be attributed to the industry adopting “cash on delivery” as a means of payment. With the proliferation of mobile phones and 3G/ 4G coverage across the country, customers with smartphones, and with no access to any digital transaction platform (like a credit card/ debit card/ wallet) can easily buy goods online. And pay for them when they actually receive them through cash. The impact of this on Ecommerce companies is three-fold: adding more number of customers, providing time for customers to actually make up their mind – they could actually return the goods when they arrive with no liability at all (see the recent Flipkart ads), and larger investments in working capital for the industry (either the platform or its suppliers, or both). Therefore, it is imperative that the E-commerce firms measure not just their gross merchandise value, but include the GMV net of returns, or Net Merchandise Value (to account for returns).

E-commerce = discounts

The primary selling proposition of E-commerce firms in India have been around deep discounts. While the idea of a zero-inventory marketplace model (that Amazon pioneered over a decade and half ago) does provide sufficient economies of scale and cost advantages, competitive discounting in the Indian E-commerce industry has over the years shaped customer expectations to the extent of equating online buying to deep discounting. Therefore, measuring gross and net margins of the entire firm is imperative.

Spreading Commerce to the “hinterlands”

The first line of defense Ecommerce firms take umbrage to when someone accuses them of being focused on a single parameter is that “the industry is in its infancy, and we need to broaden our net”. True that the low penetration of E-commerce in India provides a big opportunity for growth, we need to define appropriate metrics to measure the firm’s performance on that front. It is therefore important that firms measure the total number of transactions (as a proxy for volume sales in the offline world), number of active customers (as a measure of customer concentration – or an ABC analysis of customers), number of new customers added (not registrations but at least on transaction), average GMV per customer (as a measure for identifying high-value customers), average contribution per customer (gross profitability), and the proportion of customers whose GMV increased over the past period. In addition to this, we need to also factor in the cost of acquiring customers (CAC), and derive the long term value (LTV) of customers to evaluate performance. As the firm matures, it should strive to bring the CAC lower than the LTV.

PepperTap’s source of worry (read the article on YourStory.com here) was its rapid expansion to new towns where the costs of servicing was far higher than the LTV of the customers in those geographies. As they cut down on the number of cities, their performance improved. Therefore, it is imperative that Ecommerce firms measure and report their CAC and LTV of their customers as a key performance metric.

Alignment with strategic priorities

For a multi-category retailer, the distribution of its sales, costs and margins across categories is a critical parameter to monitor. Firms may prioritize certain categories over others as per their market position and strategic priorities. Successful firms therefore need to measure and monitor their performance across categories, and benchmark against their intent and priorities.

Creating a holistic dashboard

In sum, E-commerce firms would do well to measure, monitor, and report their performance on the four categories of parameters including (a) the traditional GMV, and a GMV net of returns; (b) overall gross margins and net margins for the firm; (c) total number of transactions, number of active customers, new customers added, average GMV per customer, average contribution per customer, proportion of customers registering increase in contribution over the past period, and the cost of acquiring customers; and (d) distribution of GMV, GMV net of sales, gross margins, net margins, number of (net of returns) sales, and CAC & LTV numbers in each category.

Interesting times lie ahead for the industry, as the golden tap of venture capital finance dries up, leading to reduction in discounts and possibly consolidation of firms to leverage the traditional scale economies of a zero-inventory marketplace model.

Ratings, reviews, and recommendations in platforms

In my post last week, I talked about crowdsourcing ratings and reviews to create and sustain credibility of the platform. Almost every platform that operates in a multi-sided market has a mechanism for the users of one side to rate the other side. In this post, I will talk about how to design an appropriate system of measuring the quality of the entity/ product/ service.

Ratings

The dictionary definition of rating reads “classification or ranking of someone or something based on a comparative assessment of their quality, standard, or performance.”

At the end of every ride, OLA Cabs requests riders to rate the driver/ cab, and the driver ratings are available to the riders when they book the ride. Similarly, Uber has a two-way rating system, where riders rate the drivers and the drivers rate the riders. And the average ratings matter for the driver and the riders to continue using the platform.

The primary (definitional) issue with rating is that it is a comparative score. As a rider takes more and more rides in the OLA system, she is able to compare that particular ride with reference to the other rides in the same system. However, when a Uber loyalist (say for example, my colleagues from USA) takes an OLA ride while in India, he is rating his ride with reference to his Uber in San Francisco benchmark. And when someone who rarely takes an OLA (and otherwise relies on public transport like suburban trains/ buses) would rate his ride with reference to his bus ride. As the references change, the meaning of the same rating changes. Which brings us to the next concern with ratings. That it is always an overall score. The riders may penalize the driver with a lower rating for whatever reason: not able to find your destination, taking a longer route, not having the cab clean enough, or even for this things outside his control like a temporarily blocked road. And the same could be true of a superlative rating – depending on the rider’s benchmark, he could rate the driver a five-star rating in comparison to the crowded Chennai-Chengalpet suburban train, that he takes daily.

This is not to say that ratings are not useful. Over long periods, with sufficient data points, ratings do bring out the true quality and standard of performance. Underlined here is the “long periods of time” and “large number of data points”. Long periods of time provide sufficient opportunities for services with low ratings to improve their performance and sufficient data points provide for cushion against freak low (or high) ratings provided by irrational customers.

One insurance against inclinations to rate a service at either of the extremes (no central tendencies work here) is to decompose the ratings into various service touch points. For instance, the Jet Airways’ service tracker seeks feedback on every aspect of the flying, making the entire responding to the online questionnaire a drudgery. Such long questionnaires would therefore only attract people who have a reason to provide you feedback – who really had a bad experience and want to express their distress, or those who had a superlatively (and unexpected) great experience that they take the effort to fill-in the forms. When the service is as expected (good or bad), one wouldn’t expect customers to fill in long forms (unless mandated). Isn’t this why most of us teachers’ feedback scores have high standard deviations?

Reviews

As a service aggregation platform, one would want to supplement rating scores with a descriptive assessment (justification) of the rating. For instance, the OLA cabs app would request you to provide the reasons for a low rating by choosing one of predefined set of options. One could not choose multiple options – for instance, it is possible that the driver was late, as well as had his car dirty. This is where open ended responses add value. Again, like long itemised rating forms, open ended questions attract respondents with extreme experiences.

Restaurant aggregators like zomato, ecommerce firms like Amazon.in, and travel sites like Booking.com have implemented reviews along with ratings. Zomato’s review forms require reviewers to provide details of their visit to the restaurant, and the food they ate. In the absence of such information, such reviews may not be relevant to the readers, who intend to use these as the basis for their decision making.

Reviews add value by highlighting specific peculiarities in the product/ service offerings that could not be captured by the ratings. For instance, a sensitive Uber driver who would play appropriate music that is appreciated by the rider would not be a standard data point that Uber wants to capture for all its drivers. However, such an information would be a great input to subsequent riders of that particular driver, who may choose to engage with him about the music. When this becomes a sufficient enough point of discussion in the reviews (enough people write about it about sufficient number of drivers, positively or negatively), Uber might take cogniscance of this to add this to the standard rating form. This is where detailed analytics of the reviews is required.

The dictionary definition of review is very insightful to our discussion: “a formal assessment of something with the intention of instituting change if necessary.” Good analysis of reviews should lead to change, if necessary.

Like the different benchmarks issue with ratings, reviews suffer from an assessment of credibility of the reviewer. It is important that the reviewer is an expert/ has demonstrated that he has used that particular product or service. Amazon.in certifies reviews with a tag “verfied purchase”; and provides the readers of the review an option of rating the review, if that was useful at all or not. Travel sites like booking.com ensure that reviewers have actually booked their stay on that particular hotel and provide the exact details of the reviewers’ credentials to provide the review. In the absence of such credibility, reviews could be abused and gamed in various ways.

Recommendations

Ratings and reviews are good apriori inputs to customers making product/ service selection choices. However, in the case of platforms like Practo, where one chooses physicians (doctors), I am not sure ratings and reviews are sufficient. When the client-service provider relationship is being evaluated (where the service provider is more knowledgeable than the service consumer; unlike a customer, where the customer is more knowledgeable than the service provider), ratings and reviews fall flat. Would you choose your dermatologist based on ratings by other patients, or by the recommendation of your trusted general physician?

The dictionary meaning of recommendation is revealing: “a suggestion or proposal as to the best course of action, especially one put forward by an authoritative body.” Notice the phrase – authoritative body. Credibility not just by consuming the product/ service, but other certifications would be required for a recommendation to be taken seriously. Most popular doctors might not be most efficient. And mind you, the ratings are reviews might just be about the quality of the infrastructure, waiting time to meet the doctor, friendliness of the staff and the doctor, as well as other clinical processes followed by the doctor and her staff. However, while seeking a recommendation for a serious illness, there could be clients who trade-off these against doctor’s effectiveness in curing the illness. Here is why platforms like Practo would require doctors to add their certifications and academic credentials, and mandate that they update them every six months, apart from the ratings and reviews by patients.

So, when you design you platform’s user experience and feedback system, choose carefully – is a rating sufficient, or would you also want a review and a recommendation?