Entrepreneurship and startups – Platform Business Firms

Remora Strategies

It is interesting how much management as a discipline borrows from other disciplines. Much like the English language. That is for another day. Today, working from home, I got reading a lot of marine biology. Yes, you heard it right, marine biology. It is about Remora fish, and its relationship with sharks and other larger marine animals. Students in my IIMB MBA class of 2020 have heard of it in one of my sessions in the platform business course and a couple of groups also used this concept in their live projects.

What is a Remora and what is its relationship with Sharks?

The Remora is a fish. It is possibly the world’s best hitchhiker. It has an organ that allows it to attach itself to a larger animal, like a shark or a whale. The sucker-like organ is a flat surface on its back, that allows itself to attach to the belly of a shark. That is a reason why it is also popularly known as a sharksucker or whalesucker. Remoras have also been known to attach themselves to divers and snorkelers as well. The sucker organ looks like venetian blinds that increase or decrease its suction on the larger fish’s body as it slides backward or forward. They could therefore be removed by sliding them forward.

Remoras can swim on their own. They are perfectly capable. But they prefer to attach themselves to the larger fish to hitch a ride to deeper parts of the ocean, saving precious energy. Their relationship with the Shark is unique – they do not draw blood or nutrients from the Shark like a Leech. They feed on the food scraps of the larger fish by keeping their mouths open. While the Remora benefits from its attaching to the Shark, it does not significantly benefit or harm the Shark. Some scientists argue that Sharks like the fact that Remoras feed on the parasites that are attaching themselves to the skin of the Shark and thereby keeping them healthy. Some others are concerned about the drag experienced by Sharks as they swim deeper in the oceans, which can be significant when there are dozens of Remoras attached to the Shark. Both of these are not that significant enough for the Sharks to either welcome Remoras to attach themselves to their bellies, nor have they exhibited any behaviour to repel these Remoras away (like they do with other parasites). Read more about Remoras’ relationship with Sharks here.

This relationship between the Remora and the shark can be termed as commensalism, rather than symbiotic. If the Sharks indeed value the fact that Remoras can help them get rid of the parasites from their teeth or skin, then we could term this relationship as mutualistic.

Remoras and platform start-ups

What is a platform researcher studying Remoras? A platform start-up could solve its Penguin problem using a Remora strategy. It could piggy-back on a larger platform to access its initial set of users, with no costs to the larger platform. Let’s consider an example. A dating start-up struggles to get its first set of users. While it needs rapid growth of numbers, it should ensure that the profiles on the platform are of good quality (bots, anyone?). It has two options: developing its own validation algorithm or integrating with larger platforms like Twitter or Facebook for profile validation. It could create its own algorithms if it needs to validate specific criteria, though. It could use a Remora strategy, by attaching itself to a larger Shark in the form of Twitter or Facebook. This has no costs to Twitter or Facebook, and if at all, contributes to marginal addition of traffic to Facebook/ Twitter. However, for the start-up, this saves significant costs of swimming down the depths of the ocean (developing and testing its own user validation algorithms).

Remora’s choice

Don Dodge first wrote about the Remora Business Model, where he wondered how both the Remoras and the Sharks made money, if at all. Building on this, Joni Salminen elaborated on Remora’s curse. Joni’s dissertation elaborates two dilemmas multi-sided platforms face – cold-start and lonely-user.

The cold-start dilemma occurs when a platform dependent on user-generated content does not get sufficient enough content in the early days to attract more users (to consume and/ or generate content). There are two issues to be resolved in this case – to attract more users to sustain the platform, and in the process balancing the numbers of content generators and content consumers.

The lonely-user dilemma occurs when a platform dependent on cross- and same-side network effects tries to attract the first users. A subset of the penguin problem, on this platform nobody joins unless everybody joins. There is no intrinsic value being provided by the platform, except that being generated by interactions between and among user groups.

The cold-start dilemma can be typically resolved using intelligent pricing mechanisms, like subsidies for early adopters. For example, a blogging platform can attract influencers to start blogging on their site, by providing them with premium services. As they resolve the cold-start dilemma, and they attract enough users to blog and read (generate and consume), they could get to a freemium model (monetize reading more than a specified number of posts), while continuing to subsidising writers. The key is to identify after what number of posts, does one start charging readers, as too low a number would reduce the number of readers and high-quality writers would leave the platform; but on the other hand, too big a number of freely available posts to read, the platform may not make any money at all to sustain.

The lonely-user dilemma can be typically resolved by following a Remora strategy. By leveraging the users on a larger established platform, the first set of users could be sourced easily en masse. However, just having users is not sufficient – there is an issue of coordination: getting not just sign-ups but driving engagement. It is important that registered users begin engaging with the platform. Some platforms need more than just engagement, they are stuck with a real-time problem: like in a multi-player gaming or a food-delivery platform, we need gamers to be engaged with each other real-time. Some other platforms need users in specific segments, or the transferability problem: that users are looking for others within a specific segment, like in a hyperlocal delivery platform, a matrimony platform or a doctor-finding platform. Such platforms need to have sufficient users in each of these micro-segments.

A Remora strategy could potentially help a platform start-up overcome these two major dilemmas – cold-start and lonely-user. By porting users from the larger platform, one could solve the lonely-user problem, and through tight integration with the content/ algorithms of the Shark platform, the Remora (start-up) could manage the cold-start problem.

Remora’s curse

The decision to adopt a Remora strategy is not just simple for a platform start-up. There may be significant costs in the form of trade-offs. I could think of five significant costs that need to be considered along with the benefits of following a Remora strategy. These costs include (a) holdup risk; (b) ceding monetization control; (c) access to user data; (d) risk of brand commoditization; and (e) exit costs.

Hold-up risk: There is a significant risk of the established platform holding the start-up to a ransom, partly arising out of the start-up making significant asset-specific investments to integrate. For instance, the dating start-up would need to tightly integrate its user validation processes with that of Facebook or Twitter, as the need may be. It may have to live with the kind of data Facebook provides it through its APIs. It may be prone to opportunistic behaviour by Facebook, when it decides to change certain parameters. For example, Facebook may stop collecting marital status on its platform, which may be a key data point for the dating start-up. Another instance of hold-up risk could be when Google resets its search algorithm to only include local search, rather than global search, thereby affecting start-ups integrating with Google.

In order to manage hold-up risks, Remora start-ups will be better off not making asset-specific investments to integrate with the Shark platforms.

Monetization control: A significant risk faced by Remora start-ups is that of conceding the power to monetize to the Shark. For example, when a hyper-local restaurant discovery start-up follows a Remora strategy on Google, it is possible that Google gets all the high-value advertisements, leaving the discovery start-up with only low-value local advertisements. There is also a risk of the larger platform defining what could be monetised on the start-up platform as well. For example, given that users have gotten used to search for free, even specialised search like locations (on maps) or specialised services like emergency veterinary care during off-working hours, may not be easy to monetise. Such platforms may have to cede control on which side to monetise and subsidise, and how much to price to the larger platform.

To avoid conceding monetization control to larger platforms, Remora start-ups need to provide additional value over and above the larger platform. For instance, in the local search business, a platform start-up would possibly need to not just provide discovery value (which may not be monetizable) but include matching value as well.

Access to user data: This is, in my opinion, the biggest risk of following a Remora strategy. Given that user data is the primary lever around which digital businesses customize and personalize their services and products, it is imperative that the start-up has access to its user data. It is likely that the larger platform may restrict access to specific user data, which may be very valuable to the start-up. For instance, restaurant chains who could have run their own loyalty programmes for its clients, may adopt a Remora on top of food delivery platforms like Swiggy or Zomato. When they do that, the larger platform may run a loyalty programme to its clients, based on the data it has about the specific user, which is qualitatively superior to the one that local restaurants may have. In fact, in the context of India, these delivery platforms do not even pass on basic user profiles like demographics or addresses to the restaurants. The restaurants are left with their limited understanding of their walk-in customers and a set of nameless/ faceless customers in the form of a platform user, for whom they can generate no meaningful insights or even consumption patterns.

It is imperative that platform start-ups define what data they require to run their business model meaningfully, including user data or even operations. It could be in the form of specific contracts for accessing data and insights, and/ or co-creating analytical models.

Risk of brand commoditization: A direct corollary of the user data is that the Remora start-up could be commoditized, and their brand value might be subservient to the larger platform’s brand. It could end up being a sub-brand of the larger start-up. For user generation and network mobilization, the Remora start-up would possibly need to get all its potential users to affiliate with the larger platform, even if may not be most desirable one. On a delivery start-up, hungry patrons may be loyal to the aggregator and the specific cuisine, rather than to a restaurant. Given that patrons could split their orders across multiple restaurants, it could be the quality and speed of delivery that matters more than other parameters. Restaurants might then degenerate into mere “kitchens” that have excess capacity, and when there is no such excess capacity, these aggregators have known to set up “while label” or “cloud kitchens”.

It is important that Remora start-ups step up their branding efforts and ensure that the larger brand does not overshadow their brand. The standard arguments or relative brand strengths of complements in user affiliation decisions need to be taken into consideration while protecting the Remora’s brands.

Exit costs: The last of the Remora’s costs is that of exit costs. Pretty much similar to the exit costs from an industry, platform start-ups need to be clear if their Remora strategy is something temporary for building up their user base and mobilizing their networks in the early stages, or it would be relatively permanent. In some cases, the platform’s core processes might be integrated with the larger platform, like the API integration for user validation, and therefore may provide significant exit costs. In some other cases, the platform may have focused on their core aspects of their business during the initial years and would have relegated their non-core but critical activities to the larger platform. At a time when the start-up is ready to exit the larger platform, it may require large investments in non-core activities, which may lead to disruptions and costs. Add to this, the costs of repurposing/ rebuilding asset-specific investments made when joining the platform.

Remora start-ups, therefore, need to have a clear strategy on what is the tenure of these Remora strategies, and at what point of time they would exit the association with the larger platform, including being prepared for the costs of exit.

Scaling at speed

Remora strategies allow for platform start-ups an alternative to scale their businesses very fast. However, it is imperative to understand the benefits and costs of such strategies and make conscious choices. These choices are at three levels – timing of Remora, what processes to Remora, and building the flexibility to exit. Some platforms may need to attach themselves right at the beginning of their inception to larger platforms to even get started; but some others can afford to wait for the first users to start engaging with the platform before integrating. What processes to integrate with the larger platform is another critical choice – much like an outsourcing decision, core and critical processes need to be owned by the start-up, while non-core non-critical processes may surely be kept out of the platform. In all of these decisions, platform start-ups need to consciously decide the tenure and extent of integration with the larger platform, and therefore make appropriate asset-specific investments.

Maintain social distance, leverage technology, and stay healthy!
Quote of the times

Learning from failures

The recent suicide of Mr. V G Siddharth, the celebrated founder of Café Coffee Day prompted me to reflect on how individuals and organisations think about failure (read what he wrote in his note to the board, here). In our classes on innovation, we keep harping on why we should learn from failure, I have not had an opportunity to dwell on the “how” question, yet. Here are my thoughts on how firms can learn from failures.

One of my colleagues at IIMB, introduced me to the work of Prof. Amy Edmondson, especially on psychological safety. While reading about psychological safety, I came across her work on three types of failure, specifically in the popular HBR article titled, “strategies for learning from failure”.

Types of failures

She elucidates on three types of failure – preventable failures, complex failures, and intellectual failures. Preventable failures occur when one had the ability and knowledge to prevent it from happening. Making silly mistakes (in a test) that you could have avoided, had you spent some time for review; deviance from a manufacturing/ service processes due to laxity or laziness; and just taking some things for granted, like jumping a traffic signal in the middle of a night, are examples of preventable failures. Such failures are clearly attributable to the individual, and therefore she/ he should be held accountable. One way of managing such preventable failures is to define and keep following checklists and processes; establish clear lines of supervision and approvals; and conduct a series of intermediate reviews at predefined critical junctures.

Complex failures happen in spite of having processes and routines. They happen due to failures at a variety of points, including internal and external factors, that individually might not cause failures, but when occurring together, may cause failures. Agricultural (crop) failures, business (startup) failures, or even industrial accidents like the Bhopal gas tragedy and Fukushima disaster are examples of complex failures. Such factors are difficult to predict as the combination of problems may not have occurred before. Fixing overarching accountabilities for such failures are futile, and these can be considered unavoidable failures. It is these kinds of failures that provide fertile sources of learning to firms. Firms need to be prepared to review such failures, dissect the individual factors, and establish robust governance processes so as to (a) sense such systemic problems when they occur at the individual factor levels; (b) erect early warning signals for alerting/ educating the organisation about escalations of these problems into failures; (c) and define options for counter-measures for managing each of these problems, in particular and the occurrence of failure at the systemic level.

Intellectual failures happen due to lack of knowledge about cause-effect relationships. Especially at the frontiers of science and behaviour, where such situations have not happened before. Such situations are ripe for experimentation and entrepreneurial explorations. Firms need to sustain their experimentation and entrepreneurially approach the problem-solution space. There could be situations where the solution is too early for the problem, or the ecosystem is such that the problem is not ready to be solved. Indian automobile industry’s (for that matter, all over the globe) experimentation with electric vehicles would fall under such experimentation. Processes such as open innovation and embedded innovation would greatly contribute to learning. One such experimental innovation boundary space is JOSEPHS, built in the city centre of Nuremberg, Germany as an open innovation laboratory.

Learning from failures

In order to learn from preventable failures, organisations need to strengthen their processes, embark on benchmarking exercises both within their organisation as well as others in their competitive/ collaborative ecosystems, and continuously evaluate the impact of their initiatives. The Indian telecommunications firm, Airtel, had a promise of providing consistent consumer experience to their customers across all the 23 telecom circles they operated in India. One of their initiatives was to constantly benchmark each circle’s performance on a wide range of non-financial parameters and enable other circles to either learn & replicate the process that led to the performance or justify why their circle had different processes that would achieve the same performance. Such justifications would be documented as new processes and would be candidates for replication by other circles. This enabled Airtel improve its performance to six sigma levels and provide consistent customer experience across all its circles.

Learning from complex failures require firms to undertake systematic and unbiased reviews of such failures, typically by engaging external agencies. Such reviews would be able to dissect failures at each factor level, interdependencies across all these factors, and the causes of failure at the systemic level as well. When unbiased reviews happen, they allow for organisations to strengthen their external (boundary-spanning) opportunity sensing and seizing processes; refine their interpretation schema to provide the organisation units/ senior management with early-warning signals; and create options for managing each of these problems well before they actually occur. For instance, in response to the Fukushima Daiichi Disaster, the Japanese Government decided to review its nuclear power policy and undertook a variety of counter-measures, including shutting down of old/ ageing power plants and introduced a slew of regulations/ restrictions on nuclear industries.

Learning from intellectual failures is possibly the easiest. The firm just needs to “persist”. One of the firms I was consulting to, referred to their experimental product-market venture as a Formula-1 track: failures are insulated there, whereas success can be easily transferred to mainstream product-markets. It is such kinds of mindset shifts that enable to continuous learning from intellectual failures. For instance, the failure of the E-commerce venture, FabMart in the early 2000s Indian market is an intellectual failure (this is well documented in the book, Failing to Succeed by its co-founder, K Vaitheeswaran). When we wrote the case on FabMart in the year 1999 (available in the book Electronic Commerce), we hailed it as a harbinger of change in the way India will adopt Internet and E-commerce. However, the business failed. The co-founders regrouped over the next decade and have created other E-commerce enterprises (Bigbasket and Again). The failure of the earlier venture provided them an opportunity to reflect on the specific reasons for failure, treat it as an experiment and learn from it. Intellectual failures, therefore, need to be celebrated and treated exclusively as an opportunity to learn from them.

In summary, mistakes lead to failures when we fail to learn from them and keep repeating them. Let us admonish repeated mistakes and celebrate failures!

Cheers!

Collaborative economy, sharing economy, gig economy … what are they?

If you have been reading any technology-business interface discussions recently, you must have surely heard of these (and more) words…. Almost all platform businesses have been described using one or more of these labels. In this post, I analyse their meanings and differences.

Collaborative economy

As the name suggests, collaborative economy is when different parties collaborate and create new, unique value that would not have been possible individually. For instance, economic activities like crowdfunding or meetup groups qualify as parts of the collaborative economy. Platforms like Kickstarter or Innocentive help people collaborate and create new value. However, platforms like Uber or Airbnb do not qualify – drivers (on Uber) and hosts (on Airbnb) do not collaborate amongst themselves or with their riders (on Uber) and guests (on Airbnb) – they do business with the other side.

Sharing economy

At best, Uber and Airbnb, in their purest forms qualify as sharing economy participants. In the sharing economy, participants share their surplus assets/ capacity with others (for a fee, of course); and there may be platforms facilitating this discovery and sharing (for a fee, surely). When this sharing is a capital asset like a house (in Airbnb) or a piece of high-value equipment (in Makerspaces), the economic argument is based on high fixed/ sunk costs and with low marginal costs, coupled with low capacity utilization/ spare capacity. This is when these economic transactions become sharing. For instance, when I am driving long distance alone, and want company for the distance (in the process, also sharing the cost), I could use Bla Bla Car, and that would be sharing economy, as the three conditions are met: (1) the asset shared (the car) is a capital asset; (2) the marginal cost of adding another passenger to the car is negligble; and (3) the car has space for the additional passenger. The value is created for both of us – I got company through the trip as well as reduced the cost; my co-passenger got to travel in a comfortable manner ‘with company’ at a much lower cost. I am not a professional driver, who is looking to make money by transporting passengers from city A to city B. That would make me a ‘gigzombie’.

The gig economy

Used almost in a derogatory manner, the gig economy refers to the idea of loosely connected people sharing their labor/ expertise for professional returns. These laborers are not employees, but are on real short-term jobs or ‘gigs’. These short-term gigs are provided by some platforms like Uber. What Uber has come to today is to create a marketplace for professional drivers. Uber does not employ them (with all the benefits and security of employment), but treats them so. Uber attracts professional driver-partners to serve their riders. The opportunity costs for these drivers are pretty high, unlike in the sharing economy. For instance, in the context of the Bla Bla Car, if I did not find a partner (or someone I liked), I would still drive that long distance, because I had work in that city. Co-passenger or not, I would still go. I am not dependent on Bla Bla Car for meeting my costs. What the a gig economy company like Uber does is to hire professional drivers (who would have otherwise been employed as drivers or in other roles) and give them business. This is fine as long as the ‘gig’ was a small proportion of your total work.

Take for instance a photographer, who has his own professional practice. He acquires his customers through direct sales, word-of-mouth, and search engine/ social media marketing. And when a gig economy platform like Urbanclap begins providing him business, it adds to his exisitng income. And he is willing to pay a commission to Urbanclap for getting him customers (which he would have otherwise found difficult to get). However, when Urbanclap provides him ‘all’ his business, the ‘gig’ economy kicks in, and the photographer is at the mercy of his one and only source of jobs. He is now a ‘gigzombie’, and the aggregator can steeply increase her commissions.

Point to note is that in the gig economy, there is no collaboration/ co-creation (no common value added), nor is there a shared-value creation (fixed assets, low marginal costs, and excess capacity).

Business models for collaborative, shared, and gig economies

Given that these three economies have different architectures of interaction amongst partners, we cannot have the same business models serving them. Collaborative economies require business models where the platform allows for parners to complement each others’ value creation efforts; sharing economies require business models to match excess capacity with demand; whereas gig economies require aggregation and improving efficiency of the overall system.

‘Bargaining’ power of gig economy platforms

Given that some of these gig economy platforms operate in winner-takes-all markets with high multi-homing costs, their barganing power (in the traditional sense of the term) increases significantly. Multi-homing costs refer to the bureaucratic (search and contracting) and transaction (including variable) costs incurred by a set of partners in maintaining affiliations with multiple platforms simultaneously (switching costs refer to the costs of abandoning one in favor of the other platform). For instance, given the high multi-homing costs for drivers on the Uber platform, it is nigh impossible for a driver-partner to fairly negotiate the terms of engagement with Uber. Combine this with the unorganized nature of the gigzombies, we have a potential for an organized exploitation.

Policy issues in gig economies

Gig economies typically attract ‘weak’ partners, at least on the one side. For instance, Uber drivers work like employees (full-time with Uber) with litle or no employment benefits. The imbalance is pretty stark when these platforms begin disrupting existing, established business models. Take the arguments against Airbnb around systematic reduction of supply of long-term housing in cities. When house-owners are faced with a choice of long-term rental contracts and short-term rentals with Airbnb, they may choose the more financially lucarative short-term Airbnb rentals; and when more and more home-owners do this in a city, the supply of housing reduces, driving up rental prices.

The impact of gig economy platforms on employment is further stark – especially when the labor is ‘online work’. In such cases, the worker in San Bernandino, California is competing with similarly trained workers in Berlin (Germany), Beijing (China), Bangalore (India), or Bangkok (Thailand). Obviously the labor costs are different, and lack of regulations favor significant displacement of work to cheaper options. Even in the case of physical labor like driving cars, developed countries have been dependent on countries with demographic dividend and lower costs of skilling (costs of education and vocational training) for immigration (some world leaders’ public stances notwithstanding).

Do not confuse the the three of them!

In summary, we are talking three different business models when we talk of collaborative, sharing and gig economies. And they need to be treated differently. Primarily, regulators and investors need to understand the differences and frame policies that are sensitive to these differences.

Cheers!

Far and near mindsets

Last month, I was at the Yale University and was listening to Prof. Nathan Novemsky on different mindsets. Of the various mindsets we discussed, psychological distance (and its impact on communication and marketing) caught my attention. In this blog post, I elaborate the concept of psychological distance, and why it is important in the context of entrepreneurship and multi-sided platform businesses.

Psychological distance: Basics

Prof. Novemsky’s (and his colleagues’) research indicates that as people get closer to the decision in terms of time, their mindset changes from a “far mindset” to a “near mindset”. When people engage with you on a far mindset, they are concerned about the “why” questions; whereas when they engage with you on a near mindset, they are concerned about the “how” questions.

Let me illustrate. When a customer downloads an Uber App for the first time, she is more concerned about how she is contributing to the environment by being part of the shared economy, and therefore is less concerned about issues like the minute features of the user interface/ user experience. On the other hand, a customer who is getting out of a day-long meeting with a demanding customer is worried more about the minute details of the ride, like the time taken for the car to arrive, type and cleanliness of the car, and driver’s credentials and behavior; as she is engaging on a near mindset.

Communication and marketing

The understanding of what mindset your customer is engaging with you is imperative to designing your communication. When you advertise a grocery home delivery service on television, you might want to appeal to the consumer’s far mindset … that talks about why he should choose your service rather than the neighborhood grocer/ vegetable market. For instance, the benefits of fresh produce straight from the farm (without middlemen) faster would make immense sense. However, when you communicate with your customer after he has decided to place an order, you might want to talk about specific discounts, receiving delivery at a convenient time, quantity changes, add-ons and freebies, and payment options.

What does this mean to start-ups/ entrepreneurs?

That’s simple, right. A founder communicating with a potential investor should talk to the “far mindset” rather than the “near mindset” if he has to raise money. However, a customer presentation has to appeal to the near mindset.

For instance, the home-health care start-up for pets (petzz.org) communicates convenience of all-day home-visits of veterinarians to its pet-owners; the specific plans available that pet-owners can choose from; and the significant increase in business for the veterinarian partners. However, when it runs camps to enroll pet-owners, it talks about “healthy pets are happy pets” communicating to the far-mindset.

However, the investor deck only appeals to the far mindset … how their business model leads to “healthy pets” and why this is a compelling value proposition for its pet-owners, veterinarians as well as other partners in its platform.

[Disclaimer: I advise petzz.org]

Implications for multi-sided platforms

Not so simple. I can envisage that there may different sides of a platform that may be operating at different mindsets and the MSPs may need to be continuously aware of. Take the example of the social-giving/ crowd-funding platform Milaap (milaap.org). The two sides of the platform are givers and fund-raisers.

Imagine a fund-raiser appeal … which one appeals to you most?

“help a school from rural Chattisgarh build toilets for girls”
“help support girls’ education”
“make sure girls like Shanti don’t drop out of school”

As you move down from option 1 to 2 to 3, you are increasingly operating from the far-mindset!

On the other hand, when Milaap attracts fund-raisers with the following messages

“you get the most socially-conscious givers at milaap”
“it’s easy to communicate with givers at milaap”
“it’s is easy to login, set up and free”

As you move down from option 1 to 2 to 3 here, you move towards a near-mindset!

It gets more complicated when the different sides of the platform are at different stages of decision-making. For instance, when a C2C used-goods marketplace platform like Quikr has a lot of buyers and lesser number of sellers; the messaging across the two sides has to be different! For the sellers who are yet contemplating joining the platform, the message has to be appealing to the far mindset (of decluttering their homes), whereas for the umpteen buyers who are looking for goods on the platform, the message has to appeal to the ease of transacting (near mindset).

Match the message to the mindset and the stage of the engagement

In summary, effective platforms have to communicate consistently across multiple sides of the platform, however keeping in mind the different mindsets of the respective sides. A cab hailing app has to communicate differently to its riders as well as drivers, while sustaining the same positioning. If the rider value offering was about speed of the cab reaching you, the driver communication has to be consistent – speed of reaching the rider. For the driver, it is near mindset (speed of reaching the rider is about efficiency), whereas for the rider, speed may be appealing to the far mindset (about not driving your own car and keep it waiting all day at an expensive parking place; or better still, reducing congestion in the city centers). And for sure, these messages also have to change over the various stages of consumer engagement, right!

Any examples of mismatched communication welcome!

Cheers from a rainy day in Nuremberg, Germany.

Beware the stupid!

During one of my random browsing through the internet on my mobile device, I came across an interesting set of laws – the basic laws of human stupidity. Yes, you read it right, stupidity. By Carlo M. Cipolla (read the original article here), an Italian-born former professor emeritus of economic history at University of California Berkeley. This is simply genius. This post is to help you find how these laws apply to the start-up ecosystem of today. Read on.

The five laws

Let us first understand the five laws. The first law states:

Always and inevitably everyone underestimates the number of stupid individuals in circulation.

They are everywhere and appear suddenly and unexpectedly. Any attempt at quantifying the numbers would be an underestimation.

The second law states:

The probability that a certain person be stupid is independent of any other characteristic of that person.

There is serious diversity at act here. No race, gender, educational attainment, physical characteristics, psychological traits, or even lineage can explain the incidence of stupidity in a person. He says a stupid man is born stupid by providence, and in this regards, nature has outdone herself.

The third law is also labelled a golden law, and presents itself into a neat 2X2 matrix. It states:

A stupid person is a person who causes losses to another person or to a group of persons while himself deriving no gain and even possibly incurring losses.

This law classifies people in this world into four categories – the helpless, intelligent, the bandit, and the stupid. Organized on the two axes of gains for self and others, the helpless is fooled by others who gain at his expense; the intelligent creates value for himself as well as others; the bandit gains at the expense of others; whereas the stupid loses himself in the process of destroying others’ value.

While the actions of others are justifiable, it is the actions of the stupid that are so difficult to defend – no one can explain why he behaved that way.

While it is possible that people may behave intelligent one day, bandit another day, and helpless in another place and context; stupid people are remarkably consistent – they are stupid, irrespective. No rationality at all – just pure consistent. And that makes stupidity extremely potent and dangerous. For the simple reason that you cannot erect a rational defence against a stupid attack, as it comes as a surprise, and more importantly, there is no rational cause for the attack in the first place.

Which leads us to the fourth law, which states:

Non-stupid people always underestimate the damaging power of stupid individuals. In particular non-stupid people constantly forget that at all times and places and under any circumstances to deal and/or associate with stupid people always turns out to be a costly mistake.

Even intelligent people and bandits (who are rational) underestimate the probability of occurrence of stupid people, are genuinely surprised by the stupid attacks, and are at a loss to defend themselves effectively against stupidity. Given the inherent unpredictability of stupidity, it is both difficult to understand in the first place, and any attempts at defending against it may itself provide the stupid people with more opportunity to exercise his gifts!

Which leads to the fifth law, which states:

A stupid person is the most dangerous type of person.

And by corollary,

A stupid person is more dangerous than a bandit.

The danger of stupidity cannot be sufficiently understated than this law. Given the irrationality of stupidity, and the costs associated with stupid behaviour, a stupid person is far more dangerous than any other type of person. An intelligent person adds value to society, a helpless fool may transfer value from himself to others, a bandit may transfer value from others to himself; but the stupid erodes value to the society by executing a lose-lose strategy. There could be bandits who might border the stupid (someone who can kill a person for stealing $50 – the value they gain is lower than the value you lose; but the $50 for them is as valuable as life for you). But given the power of stupidity, they can create far more harm than one can even imagine.

The five laws of start-up world stupidity

Stupid business models are aplenty – they rear their head everywhere, every-time. Irrespective of the context, they are omni-present. No exceptions at all. Do you remember business models like Iridium (by Motorola) and the FreePC experiment? It exists even today … Casper Tucker wonders why he should make his own IP redundant (read here).
The probability of a stupid business model arising from a developed country, a venture of a large organisation, from the famed Silicon Valley (or Bangalore, Berlin, or Shanghai for that matter) is the same (and high). The start-up graves are littered with corpses of stupidity-induced deaths of both the firms, their investors, customers, and every other stakeholder you can think of. You think sandpaper for shaving or hair-removal is a bad idea, check this out!
Do I need to tell you the costs of stupidity in the start-up world? I have come across founders who in the first few months of the business taking off, begin talking valuation rather than growth. In the process, they have destroyed value squarely and truly for everyone around them, including themselves. Nothing can match the stupidity of a founder who sacrificed his employment to start-up a firm, acquire customers and force them to make asset-specific investments, make wonderful investor presentations and get a few to invest as angels, PE, or VC; and then instead of worrying about making the business profitable, chase valuation. I surely have mentored a few, and do not want to name them for obvious reasons.
The fourth law is the trick – stupid people thrive by their ability to surprise you by their conviction. And there are enough people who irrationally believe in them; but even the rational actors are unsure how to respond – till it all dawns on them. How many products listed in this article do you remember?
And they are just plain dangerous – they can bring the entire ecosystem down. Remember how the Real Value Vaccummizer brought the entire innovative company down (do you know the firm was the first to introduce a portable fire extinguisher by the brand name Cease Fire, which by the way stays a generic name for portable fire extinguishers)?

So, customers and investors, start-up founders and entrepreneurs, students and researchers, and everyone else, beware the stupid.

Cheers!

Management theory – just mumbo jumbo?

I am writing here after a really long time. During one of my train journeys between Chennai and Bangalore last weekend, I happened to re-read this article that I had saved for later reading on my mobile device – this appeared on The Economist six months back (read it here). The Economist argues that management as a discipline is similar to the Medieval Catholic church that was transformed by Martin Luther about 500 years ago (business schools = cathedrals; consultants = clergy; the clergy speaking in Latin to give their words an air of authority = management theorists speaking in mumbo-jumbo that only their peers can understand; and having lost touch with the real world).

The Economist avers that all management theory is about four basic ideas – consolidation across industries rather than competitiveness, fewer entrepreneurial success than celebrated in popular discourse; maturing organisational bureaucracies that slow down firms rather than speed; and the seemingly inevitable globalisation being reversed by trends (including ‘Make America Great Again’ and ‘Brexit’). A redemption is therefore called for. Let me re-examine the four trends in the context of Indian business context.

1. Consolidation rather than competition

There is surely a trend of consolidation in Indian industry. The telecom service business is a case in point – the entry of Reliance Jio has led to severe performance pressures and possible consolidation of the service providers (read here); SBI merging its associate banks and other possible PSB mergers (read here); or even the retail industry (read here). Is competition really going down? I think a variety of management theorists would say no. From high velocity environments and hyper-competitive environments in the mid 1990s through mid 2000s, the discourse has evolved to disruptive innovation in the late 2000s, to Industry 4.0, Internet of Things, Artificial Intelligence, and Big Data in the mid 2010s. The fads have changed, but the discourse has always been dominated by some fad or other. We management theorists have and will always seek to sustain our legitimacy by maintaining how difficult it is do business ‘today’ and in the near future. Has there been a time when a management scholar has said that “we live in stable times”?

2. Entrepreneurial failures

Yes, we live in interesting times. There are more and more firms founded, especially in clusters like Bengaluru (erstwhile Bangalore) and Gurugram (erstwhile Gurgaon). But as The Economist argues, there are more and more entrepreneurial failures that the media reports. And these failures come in myriad forms – simple shutdowns like the ones described in these articles (25 failed startups in 2016), or sellouts to larger competitors (see this list of acquisitions by Quikr).

3. Organisational bureaucracies

With so much churn in the ecosystem, we management theorists propound that business is getting faster. Yes, network effects allow for some businesses to scale faster than traditional pipeline businesses. However, given the ubiquity of such businesses and easy imitation of business models, a lot of startup failures are attributed to these businesses not scaling sufficiently fast enough! Think Uber in China, Snap Deal in India, and other such startups. Scaling is easier said than done.

4. Rise of nationalistic tendencies

This is possibly true of most economies in the world today – rise of nationalism, and the ‘de-flattening’ of the world. Right wing protectionism is on a steady rise, and in some countries, it has reached jingoistic heights.

Time for redemption. What can management theorists do?

It is high time we redeem ourselves and the discipline. Given these trends, here is a callout for management theorists to make their discourse relevant to the business environment of today. We (as management theorists) need to get off the ivory towers and start communicating to the managers of today and tomorrow. While research output as measured by top-tier journal publications is important for an academic career, it is equally important for translating that research into insights relevant for the business managers. The Indian management researcher has very little options to publish high quality research in Indian journals. The quality of Indian management journals targeted at academics leaves much to be desired. Neither are there a variety of high quality Indian practitioner-oriented journals. And the circulation numbers of these journals amongst Indian CEOs? And where are the cases on Indian companies? The number and quality of cases published by Indian academics on Indian firms are too low to be even written about.

Free … continue hoyenga?

Well, writing here after a really long time. Finished teaching three courses, two mentoring assignments, and two cycles of a customised executive education programme for a client in the interim. A bad throat induced rest (well-deserved, that’s what I would like to believe for myself) gets me to write this short post.

Let them eat FREE

The title is inspired by the advertising tagline of India’s latest entry into the already crowded (dozens of competitors) in a highly penetrated market, Relinace Jio (see here).

Today morning’s story from Rohin Dharmakumar at The Ken is titled Let them eat free! His basic argument is that as regulators and governments are discouraging service and convenience fees, consumers are getting used to free services; which will eventually kill the free markets by taking away the pricing power of enterprises (especially in a highly competitive market).

Over the past few years, I have been studying platform business firms where one of the first concepts one learns in multi-sided platforms is that at least one of the sides of the platform (either the demand side or the supply side) needs to be subsidised to leverage network effects (or mobilise the network from scratch). Is subsidy therefore any different from free? I would say no. There are free lunches, for at least some people in a business. The business may therefore decided to charge someone else for giving these free lunches.

Think free food in temples and gurdwaras… prasads or langars. In fact the mess food at most military establishments in India is called a langar. In a society where there are a lot of people struggling to earn two meals a day, a free lunch provider was a celebrity. The village elder, the temple management, the birthday girl, or just about a casual visitor. Oh, this is religion and philanthropy, you argue. Business is different. Business is for-profit.

Subsidies

Businesses subsidise one side and make money from another side (think Internet search, where search is free, listing is free, and SEO/ Ad is paid); subsidise one product and make money from another product (think Gillette’s razors and blades, HP’s printers and cartridges); subsidise today and charge you tomorrow (think airline dynamic pricing); and/ or subsidise one segment of customers to charge from another segment (Aravind eye hospitals, Robin Hood). Remember, Skype is free (well almost); this WordPress blog is hosted on a free plan; so does your email (well almost all of it).

Is subsidy bad? No, as long as the “customer” who receives the subsidy knows where it comes from. If the business model is clear, and the subsidy receiver knows that she is receiving the “free lunch” because someone values giving it to her for free, it should not be a problem.

Subsidy is bad when someone receives a subsidy in return for particularly nothing. It is inefficient for the entire market when the customers do not know where the free is coming from, what the firm is going to do with all those intangibles (information about me, my behaviour, my preferences, and my network) I provided with them when I signed up.

Government subsidies

What happens when the government gives you something for free? Like the social security? Do you know why it is free? And how is it financed? In countries like India, the annual presentation of the government book of accounts is a celebrated ritual. See the official website here, and the Bloomberg “live” reporting here.

For long, successive governments in the Indian state of Tamil Nadu have been providing freebies to the citizens both as an electoral gift as well as a welfare measure. Most of these have been funded by the state monopoly liquor retail shops, the TASMAC (read here). But when these shops close/ scale down, the state government has to find new sources of funds and/ or scale down welfare spends.

Enjoy it till it lasts

A lot of my friends enjoy these subsidies (for instance a discount from ecommerce companies) knowing very well that the provider is giving it to them from their investors’ wallet. Like the Reliance Jio offer, like the cheap OLA ride to the Bangalore airport, or just the discounted products on the Flipkart’s sale day! They say, enjoy it till it lasts! The assumption is that they would attrition out when the prices rise, or the firms begin charging for whatever was hitherto free. Don’t the firms know this … they are trying to build and leverage multi-homing costs for your products/ services.

Be aware

I would therefore say, be aware; enjoy it till it lasts; use it as a trial; choose whether you want to multi-home and retain the flexibility to signout, and have fun.

Cheers.

FirstCry.com: Leveraging the power of offline

In my blog post last week, I wrote about how a hybrid online and offline strategy is useful for collecting small data. As a couple of my readers pointed out, what marketers and strategists call small data, ethnographers and sociologists call as thick data. Honestly, I had not heard of thick data. @fernandogaldino introduced thick data to me. I dug through the online references on thick data, and realized we are talking the same thing. Exactly the same thing. Thank you Fernando! So, I am going to continue using the term small data (that is what I have read academic articles about) with the caveat that small data is also thick data. Last week, I promised to delve deep into FirstCry.com and its online-offline strategy. Here it goes.

FirstCry.com

The firm was founded by Supam Maheshwari and Amitava Saha in 2010 as a pure online venture. By 2011, FirstCry.com opened its first offline stores. In an interview to TechCircle, co-founder Supam Maheshwari elucidated how a vertically focused ecommerce firm could survive and make money in a market dominated by horizontally spread competitors (you can read the interview here). He talked about replicating Quidsi’s business model in the Indian market, by owning a set of vertical markets like diapers.com and soap.com. In replication, firstcry.com has a sister website goodlife.com. The key difference, he said, between the Indian and the US market for baby care products was that, more than 95% of the products were imported. In fact, that was the seed for the enterprise – his own difficulty in finding good quality products for his child in India, whereas he could buy a lot of them during his international travels. That effectively makes this business inherently inventory-heavy. One needs to leverage economies of scale and scope in sourcing, hold inventory and invest in logistics to be able to service customers across the length and breadth of the country (read about firstcry.com inventory model here).

Omni-channel strategy

Here is where the omni-channel strategy helps. Instead of keeping inventory in dark warehouses, ready to be shipped, it was possible for firstcry.com to open retail outlets in tier II and tier III cities (where real estate was also likely to be cheaper), where ecommerce penetration was not as much as the tier I cities and the metros like Mumbai, Delhi, or Bangalore. The inventory holding was thus distributed across the various franchised retail outlets. The outlets also provided customers with the look and feel of the products before they bought them – you need to appreciate that baby apparel and shoes dominate the market, only to be followed by toys and diapers. Clothes and shoes … when was the last time you bought your own shoe purely online? Inventory provided increased footfalls to the store, created brand awareness, and inventory off-take. The decision to have the same prices between online and offline stores, coupled with large touch screen interfaces to shop online from within the offline store could have provided exponential growth in traffic and sales.

Promotion: The FirstCry Box

Firstcry.com began promoting using traditional mass media – television and online ads. They invested in Bollywood’s longest serving (possibly) celebrity, Mr. Amitabh Bachchan as their brand ambassador and launched a few television advertisements (see some of their ads on YouTube here). However, they soon realized that mass media advertising was highly expensive and yielded low returns for a niche range like baby care products. That is when the idea of the FirstCry Box was born. The FirstCry Box is a bundle of some essential products that the mother would need during the first few days of the baby and mother reaching home from the hospital. Firstcry.com has agreements with over 6000 hospitals, through which these FirstCry Boxes are gifted to the new mothers, congratulating them on the birth. These boxes also contained gift and discount coupons from major brands of baby products, that the parents could redeem at either online at firstcry.com or any of their retail outlets. This ‘welcome kit’ to parenting provided firstcry.com a significant opportunity to build brand equity and recall amongst the over 70000 mothers receiving these kits every month. Some marketers call this permission marketing (read about it here), or direct-to-parents strategy. For me, it is a wonderful platform, a two-sided platform mediated by firstcry.com. Parents, especially first-time mothers, are initiated into parenting with the help of these grooming products (basic diapers and lotions) and the gift coupons for free. The new mothers as a subsidy side is being financed by the brands that provide the products and coupons to be included in the box and act as the money side in the platform. For the brands, this is highly targeted sampling of their products, and most mothers would stay loyal to quality brands/ retail stores in baby products. In the entire transaction between the mothers and the brands, firstcry.com benefits significantly in three ways: (a) store loyalty resulting in increased sales, (b) small data about how these mothers use these products, the basket, frequency of purchase, and willingness-to-pay for quality; and (c) good quality prediction of demand in specific geographies, leading to efficiencies in inventory and supply chain management practices.

By the way, such welcome kits are not entirely new – a lot of employers have been on-boarding their employees with such welcome kits. I first heard/ saw such welcome kits when I was part of a team that delivered a customized training programme for the ITES service provider ADP India, a few years back. It was fascinating to see how the entire family was on-boarded into the firm! Not just ADP, a variety of other new age firms, I see have adopted this practice (read this article on how some Indian organizations welcome their employees). I wish I was welcomed like this by my employers!

Are hybrid models here to stay?

I would say, yes. We saw how Amazon was opening stores in our blog last week. We also discussed how Amazon.in was using firms like StoreKing to reach the Indian retail hinterland. I read last week that their Indian competitor, Flipkart.com was also opening offline store to reach users in small cities (Flipkart to open offline stores as well). And in vertical markets like baby products, it has become all the more important to target your promotion very narrowly, and focus on the backend (inventory, supply chain, and logistics) efficiencies, while at the same time achieve scale.

Is vertical ecommerce a winner-takes-all market?

Three industry conditions define a platform market as a winner-takes-all market: presence of strong cross-side network effects, high multi-homing costs for the users, and the absence of special requirements. The baby products retail market is dominated by imported brands, is a highly fragmented industry, and the brand owners are dependent on their retail partners to promote their brands. The demand for these products are relative price inelastic, and consumers would be willing to pay premiums for sustained quality and reliability. An aggregator platform like firstcry.com would significantly aid in establishing and reinforcing the cross-side network effects between the brands and consumers. Second condition – the quality and reliability concerns of the parents would ensure significant store loyalty and brand loyalty. As long as there are no serious concerns, consumers would be loath to switch; and when the fill rates are high (there are no stock-outs of items that they want to buy) in their preferred stores, would not multi-home. In other words, consumer switching costs for brands are high, and as long as these brands are available with their favorite retailer, they would not shop from multiple outlets. And most infants have the same needs – diapers, creams, lotions, oils, and basic toys. Special preferences begin showing up only when they ‘grow up’. Some of them don’t ever grow up, but that is a different matter!

Firstcry.com and BabyOye merger and further consolidation

Given the industry conditions of geographically distributed year-round demand, operational efficiency and leveraging economies of scale and scope become key success factors. Consolidation is inevitable to achieve both backend (sourcing, inventory, and supply chain/ logistics) efficiencies as well as frontend scale (online and offline stores distributed across the country). That is why we would see waves of consolidation in such strong vertical markets. Like how firstcry.com and BabyOye merged their operations, I agree with Supam that this market will see more and more such mergers (read his interview here).

Lessons for enterprises focused on vertical markets

Based on what we have discussed over the past few weeks, I would urge enterprises focused on vertical markets (like firstcry.com) to (a) seriously consider your business model to include online and offline consumer touchpoints … for instance, online furniture store, Urban ladder is ‘pivoting’ to offline stores (read the news here) and are positioning their offline stores as customer experience centers; (b) invest in collecting and analyzing small (or thick) data through these omni-channel (or hybrid) business models; and (c) critically evaluate if the market conditions favour winner-takes-all dynamics.

Hope my readers from India and the diaspora had a great deepavali festival! Greetings from Bangalore.

Disclaimer: I am in no way related to FirstCry.com, Goodlife.com, its investors, or its founders.

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 http://www.josephs-service-manufaktur.de/en/. For more information on how the concept works, you could watch the YouTube video at https://youtu.be/eoW3zJkYqzw. [If you would rather watch it in German, please visit https://youtu.be/MIwKdYa3_9A and https://youtu.be/0ndvx-LrBBI]. 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 https://cb.hbsp.harvard.edu/cbmp/product/IMB567-PDF-ENG.

[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 FirstCry.com 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.

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Types of failures

Learning from failures

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