Glass box organizations: Platforms

Way back in September 2017, David Mattin of Trend-Watching wrote about Glass box brands. He argued that organizations are moving away from being black boxes (where customers could only see what was painted outside) to glass boxes, where everything that happens inside and outside of the organization is visible to everyone.

The primary arguments of the glass box world are: (a) in an era of social media and high organizational attrition, even the mundane activities like routines and rituals are visible to the outside world; and (b) trends like automation, inequality, and globalization have led to “meaningful consumerism”, bordering on activism. Therefore, consumers are making choices about their brand affiliation and loyalty based on the company culture and values, apart from other considerations.

If the internal culture is the window of the brand to the outside world, it is important for every organization to meaningfully nurture it, articulate it, and live it. I am not going to dwell on how to develop your internal culture and values, but the implications of the glass box metaphor in the context of platforms and digital organizations.

Multi-sided platforms as glass boxes

By definition, multi-sided platforms (MSPs) have many “sides” that drive network effects. For instance, a guest chooses to use Airbnb while travelling because she values the number and quality of hosts. When Airbnb doesn’t treat one side well, it directly impacts the quality of interaction with the other side and affects the strength of network effects. Which in turn, affects the willingness to join (WTJ) and willingness to pay (WTP) of the users on the other side. The quality of the platform deteriorates and can even degenerate into a “market for lemons”. Such dynamics of network effects ensure that platforms do not unduly favor one side over the other, especially when there are cross-side network effects. However, these do not include how the firm treats its employees – remember Travis Kalanick and Uber?!

Digitalization and glass boxes

The omnipresent social media and the constant need by employees and customers to document share their experiences online (most often with the general public, including strangers) has been one of the drivers of glass walling of organizations. Isn’t it why the digital platform that allows for employees to review their workplaces called Sure, monetizes its corporate side of the network through its recruitment services, but its primary differentiator is the large volume of anonymous employee reviews of the work culture and salary structures. We know that when the side that is being reviewed is monetized, it is in the interest of the firm to have good quality reviews on the platform, failing which it finds it difficult to attract enough quality candidates. There is enough incentive to witch hunt people who write bad reviews, as well fill the site with paid/ fake reviews to overshadow the “real” bad reviews. It is still a glass door after all, not a glass box!

Digitalization of employee experience holds a significant potential in managing the quality of the brand, as perceived outside the firm. A lot of firms focus on improving customer experience in their digitalization journeys, but employee experience is equally critical (read more about it in one of my earlier posts). Good employee experience ensure that the positive experiences have spill-over effects on efficiency, performance, internal culture, as well as customer experience. A variety of organizations including VMWare, SAP and IBM have laid explicit focus on improving employee experience in their digital transformation journeys.

Stay home, stay safe, stay healthy!

(c) 2020. Srinivasan R.

Predatory pricing in multi-sided platforms

Over the last few days, living and commuting in Nuremberg, I realised that I was not missing Uber. While just about a month ago, commuting for a week in suburban Paris, I was completely dependent on Uber for even the shortest of distances. The penetration of public transport and my familiarity with the city of Nuremberg aside, I began wondering how would Uber price its services in a city like Nuremberg (when it enters here, which I doubt very much would happen in the next few years), where public transport is omni-present, efficient, and affordable. It surely should adopt predatory pricing.

In this post, I will elaborate on the concept of predatory pricing in the context of multi-sided platforms.

Theory alert! If you are uncomfortable with theory, skip directly to the illustration and come back to read the theory.

What is predatory pricing?

Economists and policy makers concerned about market efficiencies and fair competition have been obsessed with the concept of predatory pricing for a long time. The most common definition of predatory pricing is through the application of the conventional Areeda-Turner test. Published way back in 1975, in spite of its limitations, most countries and courts have used it consistently, due to, in some ways, lack of any credible alternative.

The Areeda-Turner test is based on two basic premises. The recoupment premise states that the firm indulging in predatory pricing should be able to predict and be confident of its ability to recoup the losses through higher profits as competition exits the market. The assumption is that the firm could reasonably anticipate the (opportunity) costs of predatory pricing, as well as have an estimate of the future value of monopoly profits; and the net present value of such predatory pricing to push competition out of the market should be positive and attractive. In plain English, the firm should be able to project the effect of lower prices in terms of lower competition and higher profits in the future.

How low can this predatory price be? That is the subject of the second premise – the AVC premise. The firm’s prices (at business as usual volumes) should be below its average variable costs (AVC), or marginal costs in the short run. If the prices were indeed above the AVC, the firm would argue that they are indeed more efficient than competition, due to any of their resources, processes, or organisational arrangements. It is when the price falls below the AVC that the question of unfair competition arises – the firm might be subsidising its losses.

Take for instance, a start-up that is piloting an innovative technology. It may price its products/ services at a price below the AVC to gain valuable feedback from its lead users, but in the absence of a recoupment premise such pricing might not qualify as predatory pricing. On the other hand, imagine a new entrant with superior technology who can bring costs down to a level where the prices fall below the marginal costs of the competitors but stay well above the firm’s AVC, it is just disrupting the market.

Only when both the conditions are met, i.e., when the predator’s prices are below the AVC and the firm could project the extent of recoupment due to monopoly profits as competition exits the market, that we call it predatory pricing.

Predatory pricing in MSPs

There has been a lot of discussion about how ecommerce firms in India have been indulging in predatory pricing and how various platforms have been going under. I had written about subsidies and freebies from a consumer perspective a few months ago (Free… continue hoyenga). Let us discuss how and why it is difficult to assess if a lower-than-competition price is indeed predatory in the context of multi-sided platforms (MSPs).

  1. Multi-sided platforms have a unique problem to solve in their early days, that of network mobilisation. A situation that is like a chicken-egg problem, or a Penguin problem, where “nobody joins unless everyone joins” is prevalent in establishing a two-sided or multi-sided platform (for more details about the Penguin problem and network mobilisation strategies, read my earlier post here). In order to build a sufficient user base on one side, a common strategy is to subsidise, even provide the services free.
  2. Another common feature of MSPs is the existence of subsidy-sides and money-sides of users. The platform might subsidise one side of users and make money from the other side, while incurring costs of providing services to both sides, depending on the relative price elasticities and willingness to affiliate with the other side of the platform. And the prices for the subsidy side would surely below costs for that side. It is imperative that the overall costs and prices are considered while analysing these pricing strategies.
  3. These cross-side network effects will surely force the platforms to price their services most efficiently across both the sides. Even for the money side, the platform might not be able to charge extraordinary prices as such prices would themselves act against the sustenance of these cross-side network effects. It is likely that these extra-normal profits would evaporate through subsidies on the other side to keep the network effects active. Imagine a situation where a B2B marketplace charged the sellers higher than normal prices, only large (and desperate) sellers would affiliate with the marketplace, leading to buyers (the subsidy side) leaving the platform. In order to keep the buyers interested, the marketplace might either have to broaden the base of sellers by optimising the prices, or provide extraordinary subsidies to the buyers to keep them interested. So in order to maintain the equilibrium, the platform would have to price the sides efficiently.
  4. Finally, in a competitive situation, not all competitors might follow the same price structure. So, a reduction of prices by one competitor for one side of the market may not force all other competitors to reduce prices; they may just encourage multi-homing (allowing users to use competitive products simultaneously) or manipulate the price on the other side of users.

So, a direct application of the Areeda-Turner test might not be appropriate while studying predatory pricing in the context of MSPs.

An illustration

Let us imagine a market for home tutors supporting school students. The market is inherently geographically constrained; it is very unlikely that either the teacher or the student would travel across cities for this purpose. For the time being, let us assume that there is no technology (like video conferencing) being used.

This market is apt for the entry of a multi-sided platform, like LocalTutor. This firm provides a platform for the discovery and matching of freelance tutors with students. LocalTutor monetises the student side by charging a monthly fee (that includes a platform commission), and passes on the fees to the tutor. We need to make two assumptions before we proceed with competitive entry and predatory pricing: the market is fully penetrated (all the students who are looking for tutors and tutors looking for students are all in the market) and there are no new students and tutors entering the market; and there are no special preferences between student-tutor matches, i.e., the student-tutor pair does not form a bond like a sportsperson-coach, where they begin working like a team. In other words, the tutor is seamlessly (with no loss of efficiency) replaceable.

Now imagine a new competitor enters the market and engages in predatory pricing to kick-in network effects. The new entrant, let’s call it GlobalTutor (a fictitious name), drops the student-side prices to half. In order to attract the right number and quality of tutors, GlobalTutor has to sustain the same fees that LocalTutor provides its tutors, if not more. So, it starts dipping into its capital reserves and begins paying the tutors the market rates while reducing the student fees. Anticipating a larger surge in student numbers, more tutors sign up to GlobalTutor, and seeing the number and quality of tutors on GlobalTutor (at least if it is not inferior to LocalTutor), students first start multi-homing (use both services for their different needs, like LocalTutor for mathematics and GlobalTutor for music classes), and some of them begin switching.

In a fully penetrated market, the only way for LocalTutor to compete is to respond with its price structure. It has two options – reduce the student-side prices to restrain switching and multi-homing behaviour; and tweak the tutor-side prices and incentives. The first option is straightforward; it is cost-enhancing and profit-reducing. The second option (which is not available for pipeline businesses) is interesting in the context of platform businesses.

There are various ways of responding to this threat. The intent is to arrest switching and multi-homing behaviour of tutors and students from LocalTutor to GlobalTutor.

  1. Increasing multi-homing costs of tutors by providing them with incentives based on exclusivity/ volume: Like what Uber/ Ola provides its drivers – the incentives kick-in at what the company believes is the most a driver can do when they do not multi-home. In other words, if you multi-homed, drove your car with both Ola and Uber, you would never reach those volumes required to earn your incentives in either of your platforms.
  2. Contractual exclusion: This might not be tenable in most courts of law, if these freelance tutors were not your ‘employees’. Given the tone of most courts on Uber’s relations with its driver-partners (drivers’ lack of control in most of the transaction decisions including choice of destination, pricing, and passenger choice), any such contracting would imply that the tutors would be employees, and that would significantly increase the platform’s costs (paying for employee benefits are always more expensive than outsourcing to independent service providers).
  3. Increase contract tenure: LocalTutor may increase multi-homing and switching costs by increasing the tenure of the contracting from monthly to annual. Annual contracting will reduce the flexibility that students and tutors have, and might result in reduction in volume.
  4. The next options for LocalTutor are to work at the two restraining assumptions we made at the beginning – penetration and perpetual matching. LocalTutor might want to add in more and more students and tutors and expand the market, providing unique and differentiated services like art & craft classes, preparation for science Olympiads, or other competitive tests. LocalTutor might also communicate the value of teaming of student-tutor pairs in its success stories, in a bid to dis-incentivise switching and multi-homing.

To predate or not to predate is not the question

Given the differences between pipeline and platform businesses new entrants seeking to mobilize network effects have very little option but to resort to predatory pricing. The choice is not if, but how. And as an incumbent, should you be prepared for a new entrant who would resort to predatory pricing? Surely, yes! And how? By being ready to expand the market and increasing switching and multi-homing costs. Unlike in the tutoring business that is inherently geographically constrained, a lot of businesses could span across markets. Even tutoring could leverage technology to reach a global audience.

Just one comforting thought, predatory pricing as a strategy to eliminate competition is inefficient in the long run. The new entrant might adopt predatory pricing to eliminate competition in the short run, but the act of predatory pricing breaks down most barriers to entry, and sends signals to others that there is a market that is easy to enter. It might attract a more highly capitalised competitor to enter the market with the same strategies … making the market a ‘contestable market’. And no one wants to make a fortune in a contestable market, right? More on competing in contestable markets, subsequently.


© 2017. R Srinivasan


Free … continue hoyenga?


Let them eat FREE









Government subsidies



Enjoy it till it lasts


Be aware



(C) 2017. R Srinivasan.

Collecting small data in the world of big data

It is a chilly morning in late October in Bangalore, India. As I return back home after a short walk to the bus stop to drop my daughter off to her school, my colleague walking with me begins collecting bird feathers on our way back, of all hues and sizes. We start debating which birds have what kind of feathers, and when she is done collecting four different kinds of feathers, she stops. Another colleague urges her to collect more, but she says “four is good for today”. And she sets me thinking on what is the power of small data. While the world is raving about leveraging big data and the power of mass customization, I argue in this post about why successful firms must also invest in small data.

What is small data?

The best definition of small data comes from none other than Martin Lindstrom, who wrote a book titled “Small Data: The tiny clues that uncover huge trends”. He distinguishes big data from small data thus: “Where big data is all about drawing correlations, small data is about identifying causation” (read more here). Big data is typically collected through a variety of sources, from your credit card spends, loyalty card behavior, search algorithms, and mining of transaction data. What big data analytics can do is pretty visible and known to all of us – patterns that can aid prediction. In his book and other writings, Lindstrom write about the need to uncover the causation behind these patterns. One of the examples he often cites is how a US bank found customer churn using big data, and with the help of small data, discovered that they were moving their assets and mortgages around, and possibly leaving the bank not because of poor customer service, but they were going through divorce!

Small data for listening to customers

A couple of days back, I read an interesting article on why Amazon is opening physical stores by IMD Professor Howard Yu (read it here). In that article, Yu labels Amazon’s book stores as not so much distribution channels, but “research laboratories”. Laboratories where customer journeys are observed, what they like and how they spend their time browsing; simple things like which aisles do they reach first, do they pick up the books first or read the reviews pasted below, do customers get influenced by recommendations, and the like. Small samples, but rich inputs on causation. Retail stores have long been using small data – have you not read about why bread and staples are placed at the end of the alleys and chocolates at the check-out counters? Small data like this helps identify why certain shoppers behave the way they do, whereas big data will be good to classify shoppers into dashers, economists, the pros, and the candy store kids. [Dashers know what they want and dash in and out of the store, picking up her favorite brands/ products/ pack sizes and rushes out. Economists, on the other hand, rummages through deals and offers, and typically shops at warehouse clubs and wholesale shops. The pros are those who do considerable research on the deals and offers, analyze value for money, wait for the right time to buy (like festive seasons), and typically get the best deals. The candy-store-kid is the retailer’s delight; she behaves as the name suggests – impulsive, compulsive, and extensive shopper. Read more about it here.] On the other hand, small data will help analyze when does a typical dasher behave like a candy-store-kid. I was in Barcelona recently, and typical to my urban foreign travels, I was shopping in supermarkets. I noticed that a lot of these stores had “male zones”, where typical electronics, electrical goods, FC Barcelona memorabilia, and beer are stocked. Small data, could suggest that men would hang around the ‘zone’ till the women shop for all the essentials, and just as they reach the counter, these items are added to the cart and billed. Given the festival season, maybe even the textile showrooms of the famed Chennai’s T. Nagar might have implemented this!

Small data for innovation

There is no better use of small data, unless you listen to customers. And better still, if you could listen to your customers at the prototyping stage, well before product design and introduction. User innovation spaces provide opportunities for firms and innovators to collect valuable small data well before the product design. In fact, such small data could help innovators listen not just to the prosumers (innovative proactive consumers, who engage with the firm and are typically early adopters), but a wide variety of consumers as well. One such experiment on early-stage user innovation platform is a physical store-like service manufactory at the Nuremberg city center – JOSEPHS®.

JOSEPHS® – the service manufactory

JOSEPHS® is a unique concept, where user and open innovators could come together with real consumers, consumers who could walk-in to the store as if they shop for goods and services in the city center. The ambience and feel is designed to look like a retail store with spots housing different innovators and a coffee shop at the entrance.

Set up by the Fraunhofer IIS in collaboration with the Freidrich Alexender University at Erlangen-Nuremberg in the city center of Nuremberg city, Germany; JOSEPHS® is envisaged to be a platform for bringing University researchers, Fraunhofer scientists, innovative entrepreneurs, and retail consumers to co-create services. Much like the prototyping TechShops, MakerSpaces, HackerSpaces, or FabLabs for designing products, JOSEPHS® aims at integrating users (randomly walking in) with innovators; a micro-factory for services.

In order to attract walk-in customers, JOSEPHS® has a coffee shop at the entrance. In order to sustain the innovation and create spaces for co-creation, there is denkfabrik, a workshop space, and meeting areas.

Please visit the website of JOSEPHS® at For more information on how the concept works, you could watch the YouTube video at [If you would rather watch it in German, please visit and]. If you are an academic and want to learn more about JOSEPHS® and teach about it in your class, you can download a copy of my case on JOSEPHS® from the Harvard Business Publishing for educators at

[Disclaimer: I am a visiting professor at FAU, Nuremberg and have been involved in the conceptualization of JOSEPHS®, as well as the author of the case mentioned above. Read about my journey to FAU here. And about my course at FAU here.]

Summing up

So, why does Amazon open retail stores? How does manage its online and offline ventures? Think small data. Time to integrate small data with big data to get real deep insights. In the next post, I will delve deep into the business model of FirstCry and elucidate the synergies between online and offline stores.

(C) 2016. R Srinivasan.

Regulating Platforms

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

Defining the industry boundaries

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

Is Uber a competitor or substitute to Taxi?

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

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

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

Creative destruction and Schumpeterian waves of technology innovation

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

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

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

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



Breaking the Uber-Ola duopoly?


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

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

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

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


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

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

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

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

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

Address the special preferences

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

Cheers and happy weekend.




Scaling your startup




Instant gratification


Increasing multi-homing costs






Would love to hear from my startup friends and followers.

Surge Pricing: The importance of focusing on the supply side


What is surge pricing?


Who is a typical surge pricing customer?





Surge pricing is dynamic pricing



Data is king



Policy recommendation


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 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 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 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.