Tag: Platform business models

Regulating Platforms

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

Defining the industry boundaries

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

Is Uber a competitor or substitute to Taxi?

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

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

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

Creative destruction and Schumpeterian waves of technology innovation

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

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

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

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

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

Cheers.

 

Pelf: Is venture capital money for startups evil?

A common discourse these days in the print and social media is the persistent rant by “old school” economists and businessmen about how easy access to venture capital and private equity for startups have spoilt the ecosystem. And any failure is attributed to this easy access, any news in fact. In this post, I throw open three challenges for the startup ecosystem.

First things first, definition of pelf. The word has disappeared from a lot of English-speaking countries, but still survives in India, at least in my mind. It refers to money and wealth, which is ill-gotten, or through a dishonorable way. For the more lexicologically inclined, here is a link with more details.

As the startup economy generates any news, like Jabong being on sale; or ANI Technologies (the firm that runs OLA Cabs) reports losses, I scroll down to read the comments. And a large majority of them are rants about how these young twenty-something entrepreneurs have so much access to easy capital, that they do not care about business failures. I would imagine it is very fashionable to say – yes, failure is good and you should encourage failures, but fail with your own money (bootstrapping is okay; VC money is not). On the other hand, entrepreneurs would argue for the need for sufficient capital to invest in developing the ecosystem (low internet penetration, poor logistics and last-mile connectivity, inadequate payment infrastructure, and heightened competition from MNC subsidiaries like Amazon.in). Platform-businesses need capital to kick-in network effects, including subsidizing users (like Uber); so do a lot of infrastructure-dependent businesses that need patient capital before all pieces of the ecosystem fit with each other (PayTM).

The first challenge I pose to the entrepreneurs is to communicate the nature of your business model to your stakeholders very well. What is the source of your network effects? In just the last week, I heard at least four entrepreneurs pitching to investors, using the platform word in their first slides of the presentation, without ever talking about when and wherefrom network effects would kick-in. If you need capital to kick-in network effects, elucidate. Over the last few years, enough has been written about platform business models and network effects, a simple Internet search would educate you enough. Please put up at least a pictorial representation on your website (most of the websites have pages titled, how it works, which are craving for such content). For an example, please visit the homepage of Tarnea Technology Solutions (disclaimer: I advise them).

The second challenge is about pivoting. Entrepreneurs (ab)use this term a lot, that quite a lot of times, I am left wondering if the business had any specific plan in mind at all. When one thing does not work, it is natural to seek another business. When a large plan does not yield great results, it is important to seek business results from whatever succeeds in the overall scheme. But to use the word pivoting with a sense of pride is unnerving. I would urge entrepreneurs to take pride in whatever you do, fail, bounce back. But to pivot with pride, I am not sure. You may have used entrepreneurial bricolage (making do with whatever is at hand) to build your business, nothing wrong. The classic (okay, my favorite) academic paper on entrepreneurial bricolage is here. Bricolage explains how entrepreneurs recombine their limited resources at hand and create unique products/ provide unique services that challenge institutional norms. For examples of digital bricolage and what it entails for new age entrepreneurs, read this latest article at HBR.org.

The third challenge (may be a request) I pose to entrepreneurs is to provide credible estimates of performance. The old age firms, especially the ones listed in the stock market need to provide the (Wall or Dalal) street and analysts with performance expectations. Yes, forward looking statements. The stock market penalizes firms who do not provide reasonable estimates of performance, or fail to perform in line with estimates. Entrepreneurs, even though you do not have the retail investors and analysts chasing you with quarter-on-quarter performance expectations, it makes for good governance to keep stakeholders informed. Please provide us with credible forward looking statements. It is actually good time-pass these days to initiate conversations with OLA and Uber drivers about when and if at all these firms will ever become profitable. Neither the drivers, nor the riders have a clue to the way forward, and it is good fun listening to various viewpoints. I am very impressed with Snapdeal founder Kunal Behl’s collaboration with Ashvin Vellody of KPMG to publish this report on the Impact of e-commerce on SMEs in India. Not so much the report (it is definitely well written and contains insightful analysis), but the act of writing itself is commendable for me.

To summarize, entrepreneurs in the startup world, if you want to change the perception that all your startup capital was easily obtained, is pelf, and therefore not justified, I challenge you to

  1. Explicitly communicate the source of your network effects
  2. Don’t pivot; use entrepreneurial bricolage
  3. Provide credible estimates of performance

Cheers.

Digital disruption – drivers, symptoms and scenarios

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

What is digital disruption?

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

Drivers of digital disruption

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

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

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

When do you know your business is being digitally disrupted?

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

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

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

Planning for the digitally disrupted future

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

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

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

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

Comments, feedback, and experiences welcome.

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

 

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

Scaling your startup

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

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

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

Instant gratification

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

Increasing multi-homing costs

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

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

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

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

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

Would love to hear from my startup friends and followers.

Surge Pricing: The importance of focusing on the supply side

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

What is surge pricing?

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

Who is a typical surge pricing customer?

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

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

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

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

Surge pricing is dynamic pricing

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

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

Data is king

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

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

Policy recommendation

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

Comments welcome.

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

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.

How to build a platform business?

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

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

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

Discovery

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

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

Quality

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

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

Norms and rules

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

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

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

Setting expectations

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

Providing feedback

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

 

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