Uber – Platform Business Firms

From glass boxes to black boxes

I wrote about how platforms are glass box organisations, given their network effects. But there are a lot of black boxes out there. In the digital platform space, there are a lot of firms that operate as black boxes. The glass box metaphor was heavily drawn on the internal culture of the firm, that is, what the employees of the firms do. This post is about what the employees don’t do.

Before I elaborate, let me highlight the work of one of my doctoral students, Sandeep Lakshmipathy. He elucidates four primary value creation opportunities of multi-sided platforms: Discovery, matching, transaction, and evaluation. In short, discovery platforms help reduce the search costs for the sides of the platform (think Craigslist), matching platforms use filters and algorithms to ensure that the preferences of both sides are catered to while delivering a match (think Tinder), transaction platforms reduce the frictions and transaction costs in interacting with the other sides of the platform (think MasterCard), and evaluation platform enable ratings/ reviews/ recommendations/ feedback of the service (think Yelp). Sure, some platforms provide multiple value, like Uber provides discovery, transaction, and evaluation; whereas Airbnb provides all four.

Our focus today is just matching platforms, and how they create more and more opaque black boxes.

Matching platforms

In one of our conversations about the difference between discovery and matching platforms, Sandeep quipped about the difference between choosing a tomato sauce on an e-commerce platform and choosing a partner on Tinder. While it is sufficient for me to like the tomato sauce, it is not important for the tomato sauce to like me! Unlike this, in a matching platform like Tinder, it is imperative for both sides to have liked each other. Here is where the algorithms kick in. Magic! Matching algorithms.

Matching algorithms are technically taught in graph theory. Graph theorists discuss two types of matching – common vertex matching and bipartite matching. Common vertex matching is used to match, for instance, people with similar interests, like students in a class interested in a specific project. On the other hand, bipartite matching is used to match two subsets with each other, like buyers and sellers in an e-commerce platform. There are various algorithms used by graph theorists, including Hungarian maximum matching algorithm, Edmond’s matching algorithm, and the Hopcroft-Karp algorithm. The specificity of the algorithms notwithstanding, each of these algorithms work on the basis of three things – the specific preferences as limiting criteria, the minimum matches to be returned, and the maximum matches possible.

Imagine when you search for books to read on a peer-to-peer book-reading platform, and based on your preferences, you get exactly one recommendation. Just not sufficient enough choice, right? On the other hand, irrespective of the filters you add, if the recommendations do not change (the same titles keep appearing) and you get a recommendation of 5632 books, you feel overwhelmed. Both sides of the matching algorithms, there is a problem – of underwhelming and overwhelming choice. It is exactly to solve these problems that matching algorithms collect enough data from the users.

Some algorithms start with providing random matches, and based on the expressed user preferences, gradually mature their matching. Some others start at one extreme – like the shopping assistant in a mom-n-pop retail store. Once she’s estimated your broad preferences and budgets, she is most likely to start with showing you options very close to your budget. Pretty much like the default sorting algorithm (lowest price first) in the case of travel/ hotel aggregation platforms. Again, the platforms “learn” based on your expressed preferences.

Expressed preferences to profiling

Now, how do these platforms learn, and what do they learn about your preferences is the black box. Based on a few expressed preferences, these matching algorithms may end up profiling the user, and start providing more and more matches based on that specific profile. Sometimes, these preferences may be specific to a context – like me searching for a business class air ticket (when the client has agreed to pay – I travel economy otherwise!). The algorithm has no way of separating out such preferences without access to a large number and variety of such searches. And such preference-based profiling is easy.

Have you wondered why you get “more relevant” advertisements on a Google search results page than on YouTube? Both owned by the same firm, and possibly can share the matching algorithms. However, the way one could profile search users based on their text inputs and matching them with appropriate websites and advertisements is far easier than in the context of video content. Video content may be devoid of explicit tags that are indexed, may contain sarcasm, the audio and video content may be inadequate, or just non-existent. So, there are days I have been left wondering why I was exhibited a particular advertisement in the beginning of a YouTube video. Sometimes, I conjure up my own hypothesis about what actions in my history, the cookies on my browser/ device (my kids do use my iPad), and what specific search terms triggered those. Black box!

Customization-personalization or privacy: A trade-off

This is no simple trade-off: the one between customization and personalization and privacy. By exposing my preference for a particular sport (like cricket) to YouTube, I get to see a lot of interesting cricket video suggestions, right up front on the home page. By subscribing to specific channels, liking certain content, and commenting on some others, I help YouTube learn more about me. These actions provide enough inputs for the platform to customize the match and personalize suggestions. However, there are limits to when such expressed preferences breach privacy. Like when my phone’s AI assistant suggests a wake-up time for me based on my first appointment of the day, or when my wearable device chides for not walking enough during the day (how many steps can you walk in days like today, when the entire country is under lockdown?).

This is not new; and it could be creepy. Remember the 2012 story about the US retailer Target sending out mailers about baby care to a teenage girl, and the parent discovering it? Neither the local Target store had a clue, nor did the parent. The Target central database was able to predict something as personal as teenage pregnancy! Target realized that they were spooking people, and would randomize the offers, like putting ads for lawn mowers next to diaper coupons. But still, they knew!

Dealing with black boxes

I wish I had a set of recommendations for you! One day, possibly. But today, this is just a personal trade-off. As for me, I wouldn’t mind sharing my location data with my phone as long as it provides me good navigation services. And my food delivery app to know where I am, so that I get choices from hyper-local restaurants. I would go one step further and allow my photos app to have my location access so that I could organize my photos by date and location. But to allow location access to online newspapers, no.

Happy matching.

Stay home, stay safe, stay healthy.

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 glassdoor.com? Sure, glassdoor.com 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!

App-in-app?

I recently got an email from my airline app that I could book my car ride within the same app. It was a way of providing end-to-end services. Much like the home pickup and drop service provided for business class customers by the Emirates. What are the implications of these for the customer, the airline, and the cab-hailing firm? Let’s explore.

It is an app-redirect

First, read the terms of how it works in the case of Jet Airways and Uber here. The substantive part of the T&C is hidden in the paragraphs quoted below:

“PLEASE NOTE, YOU ARE MAKING THE PAYMENT TO UBER DIRECTLY. JET AIRWAYS IS NOT RESPONSIBLE / INVOLVED IN THIS FULFILMENT PROCESS. JET AIRWAYS WILL NOT BE LIABLE AND/OR RESPONSIBLE FOR REFUNDS, DELAYS, REJECTIONS, PAYMENT AND FULFILLMENT OR OTHERWISE OF THE SERVICES OR IN RESPECT OF ANY DISPUTES IN RELATION THERETO, IN ANY MANNER WHATSOEVER.” (emphasis original)

Then, what is the value of this app-in-app integration?

Customer perspective

For the customer, it has the potential to work as a seamless end-to-end service. I imagine a future, where you would find a partner using Tinder or TrulyMadly, plan your evening to a game/ movie using BookMyShow, find a restaurant & book your table using Zomato, and take Uber whenever you are ready to move on, or better still, have an Ola Rentals car waiting for you through the evening. All in one app. Wouldn’t you love it, if all of it were integrated in one App? Just imagine the convenience if your restaurant-finder knew that you are in a particular concert at a specific place and you are likely to head out for dinner at a particular time. This specific knowledge could immensely help your restaurant-finder app to customize the experience for you – for instance, it could not only provide you those restaurant options that are open late in the evening after the concert was over, in a location that is close to the venue; it could possibly alert the restaurant that you were arriving in 15 minutes, based on your Uber location. And through the evening, post your pictures on Instagram and SnapChat, check-in to all those locations in Facebook, and Tweet the experience live.

Yes, you would leave a perfect trail for the entire evening in a single place, and if you were to be involved in an investigation, it would be so easy for the officer to trace you! No need for Sherlock Holmes and Watson here – the integrator app would take care of all the snooping for you!

Convenience or scary? What are the safeguards related to such data sharing across different entities? How will the data be regulated?

The Integrator perspective

Why would a Jet Airways provide an Uber link inside its App? Surely cab-hailing and air travel are complementary services. Plus, Jet Airways believes that its customers would find it convenient to book an Uber ride from within the Jet Airways app, as they trust the app to provide Uber with all the relevant details – like the estimated landing/ boarding time of the flight, drop/ pickup addresses, etc. Jet Airways also needs to believe that its customers would rather choose an Uber cab, rather than its competitor OLA Cabs, or any other airport taxi service. The brands should have compatible positioning. Given that Jet Airways is a full service carrier, and differentiates based on its service quality, Uber might be a good fit. But the same might not hold good for a low-cost/ regional carrier like TruJet connecting cities like Tirupati, where Uber does not operate.

Does integrating complementary services affect customer satisfaction, brand loyalty, customer switching costs, and/or multi-homing costs? In contexts where these services and brands are compatible, and there is a convenience involved in sharing of data between these services, there is likely to be some value added. Like airlines and hotels (hotels would like to know your travel schedule); currency exchanges and international travel (the currency exchange would love to know which countries you are visiting); or international mobile services. If there was no data to be shared between the complementary services, the user would rather have them unbundled. Think travel and stock brokerage.

That said, platforms find innovative complementarities. For instance, airlines (primarily the full-service carriers) have launched co-branded credit cards. In a recent visit to Chennai, there were more American Express staff at the Jet Airways lounge than the airline or lounge staff! And they were obviously signing up customers. What are the complementarities between credit cards and air travel, apart from paying from that card? A lot of business travellers have their business travel desks do the payments; consultants have their clients booking the tickets; and even for individuals and entrepreneurs, the credit card market is so fragmented that everyone holds multiple cards. And the payment gateways accept all possible payment options, including “paying cash at the airport counter”. They why co-brand credit cards – sharing of reward points/ airline miles. Either customers do not earn sufficient airline miles and using these co-branded credit cards help them earn more miles and retain/ upgrade their airline status (remember the 2009 movie, Up in the Air?); or they do not earn enough reward points in using their credit cards that they can redeem their airline miles as credit card reward points. Either ways, each one is covering up for the other.

In this covering up, or more diplomatically consolidation of rewards, the partners increase customer switching and multi-homing costs. Surely, redeemable airline miles might be more valuable to a frequent traveller than credit card reward points that have limited redemption/ cash back opportunities. But for loyalty to increase, it is imperative that both brands stand on their own – providing compatible services.

Mother of all apps

All this looks futuristic to you? A lot of you have been using an ubiquitous desktop app known as the browser for a long time, which has been doing exactly this! In a subtle form, though. However, there are firms that own multiple such apps, and they use a single sign-on – like all of Google services. Plus, even third-party sites like Quora allow for using your Google credentials to sign-in. The trade-offs are not always explicitly specified – it is always the case of caveat emptor – consumer beware.

So, the next time you experience some cross-marketing across platforms/ apps, think what data might be shared across both the apps; and if you would really value the integration.

Cheers!

Surge pricing for food delivery: when not to use surge pricing?

This post comes to you from Friedrich Alexander Universitat Erlangen-Nuremberg, where I am visiting for the past one week. I have been teaching a course on Platform Strategies here for the past four years. While in Nuremberg, the question has always been about food, how does a vegetarian, teetotaler survive in Franconia, Bavaria, Germany? To be fair, I have had great vegetarian food here in Nuremberg over the past so many years, and this year has been exceptional – we (my teaching assistant and I) have found great Indian restaurants, that I have had an Indian vegetarian meal for dinner every day of my stay here (except one night of Italian food). Thank you, Nuremberg.

Coming back to food, I was intrigued when I read in the Uber company blog (read it here) that Uber Eats (Uber’s food delivery service) would begin charging customers surge pricing. Much like the way they charge for their ride-hailing services. I began looking for when and how surge pricing can work. I believe it is a function of customer willingness to pay in part, but most importantly, the platform’s ability to scale up and down service levels at will on the other part.

Economics of the surge

A market is made up of demand, supply, pricing and the norms around exchange. For a market to function, the norms of exchange should be fair and acceptable to the transacting parties. Some markets are defined by the actions of intermediaries who set the norms of exchange, like a stock exchange, a municipal council, or a platform like Uber. In most cases, these intermediaries are third parties in the true sense of the word, “third”, meaning independent of the transacting parties. And in a ‘efficient market’, the intermediary sets the boundaries of behaviours of the transacting parties, and let them transact with little or no involvement. However, in platforms like Uber, the intermediary takes a much larger role, say in pricing. It not only decides the prices of the rides (for both riders and drivers), it also uses pricing as a tool to modify demand and supply conditions. Surge pricing is used as a mechanism to increase supply of cars (by motivating more driver partners to join the system at that point of time), and decrease the demand for cars (by getting riders to either postpone their rides to off-peak times or move away from Uber to other modes of transport, like bus or train). There is enough that has been written about surge pricing, including in this very blog, previously.

Surge pricing in food delivery

Alison Griswold wrote in the Quartz online magazine about why surge pricing for food delivery by Uber Eats is a bad idea (read his article here). She definitely writes wonderful stories about the sharing economy. She argues that once Uber Eats introduces surge pricing, customers would shift away from Uber, and move on to other services, may be even Amazon (with its Prime services). Given that food delivery services do not have high multi-homing costs (customers can simultaneously affiliate with multiple service providers at the same time), and some services may cater to special preferences like a specific cuisine, customers might surely switch in terms of choosing their delivery partner, their restaurant choice, or both. But that can be overcome by just simple speed and other aspects of service quality.

However, her main argument is that the economics of surge pricing might work for increasing more delivery partners to join the system in times of peak demand, but might not get the restaurants to produce more food. She avers that increasing the supply of food available for delivery is not the same as increasing the supply of delivery partners. Fair point. But, don’t restaurants anyway plan for increase in food supply during lunch and dinner times? Don’t they build in some buffer of raw material, ingredients, and/ or semi-processed food before they toss them on the stove? Aren’t there some limits to which they can extend?

Where does surge pricing not work?

Surge pricing works in markets where the intermediaries can, at least at the margin, increase the supply of goods and services and/ or decrease the demand for goods and services. In the case of ride-hailing services, surge pricing can shift people away from ride-hailing to use buses/ trains or just walk. Surge pricing works best when there is idle capacity not available to the users – when the driver partners are present but are themselves taking a break (not logged in) and are not available to take rides. Surge pricing motivates these ‘idle’ capacity to join the market, and restores the balance. In summary, surge pricing works when the demand side has ‘substitutes’ and the supply side has ‘excess capacity’.

If either of these conditions are not met, surge pricing might not work. Take an instance when a cricket/ football game or a concert ends in the middle of the night, and there are no public transportation options. Any amount of surge pricing is unlikely to reduce the demand for cars. Or try surge pricing of rail tickets in Indian trains. Any amount of surge pricing is not going to motivate the rail authorities to increase capacity to balance the market (I am not even convinced it should be called surge pricing – it is just differential pricing of different tickets, depending on whether I am the first person booking the seat or the last). In both of these conditions, differential pricing might be grudgingly accepted by the transacting parties, without any impact on the demand-supply mismatches. Take for example, Kayani Bakery in Pune, India, where by noon they are sold out! Surely, no amount of surge pricing is motivating these businessmen to increase supply. In fact, the scarcity increases the demand for these biscuits.

What are the welfare effects of surge pricing?

Scarcity principle tells us that when supply is far less than demand, prices will rise to ensure that supply matches demand. In an ideal world, both supply will increase and demand will fall. However, in contexts where supply is limited or inelastic, it will be demand that has to come down. In the case of essential goods and services (inelastic demand), prices continue to rise to point where only the wealthy could afford it. This is precisely the reason why governments indulge in market intervention mechanisms. For those interested in how commodity prices can bring down governments, read this!

The lesson for platform business firms: engage in surge pricing only when you can work towards increasing supply, or your demand side has (at least imperfect) substitutes.

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).

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.
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.
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.

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.

Differentiate

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.

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).

The global bazaar – industry and geographic boundaries blurring due to internet and mobile
Cautious capitalism – data security concerns limit firms’ ability to monetize consumer data
Territorial dominance – regional industry boundaries persist, with tight regulation
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.

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.

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