Disruptive innovation in healthcare: Is COVID-19 the opportunity?

A colleague and I were organizing an online hackathon around the current pandemic, COVID-19. We were seeking innovative ideas both in the healthcare and policy spaces. We had a good number of responses, but I was struck by how little we are willing to “disrupt” the healthcare industry. For long, the Indian healthcare industry has not been disrupted, as much as many other industries have been.

Disruptive innovation: A primer

Before we proceed further, let us quickly understand what disruptive innovation means. The concept was introduced by the late HBS Professor Clayton Christensen. Disruptive innovation (DI) is a process by which a smaller company with fewer resources can successfully challenge established incumbents in an industry. As the incumbents focus on improving their products and services for their primary (most profitable) customers, they exceed the needs of some segments, and ignore the needs of some other segments. These overlooked segments are the targets for the new entrants, who deliver a specific functionality valued by these customers, typically at a lower price. Incumbents chasing their highly profitable customer segments, tend to not respond to these new entrants. The new entrants gain significant industry know-how, and move up-market, delivering those functionalities that the incumbents’ primary customers value, without losing the advantages that drove their early success with the hitherto overlooked segments. When customers in the most profitable segments (primary customers) begin switching from incumbents to new entrants, they begin providing new entrants with economies of scale. Coupled with learning and economies of scale, the new entrants “disrupt” the industry.

There are various examples of disruptive innovation. For instance, film distributors (cinema halls and multiplexes) were serving connoisseurs of movie consumers – those who valued the experience of the movie halls. The network of movie halls and the audio-video technology was the primary source of competitive advantage in that world. Netflix entered this industry targeting a segment of customers who wanted to watch movies but could not afford the travel to the movie hall and uninterrupted time/ attention. They would trade-off the experience against the quality of content, and therefore were willing to watch movies at home, using their own devices. Movie watching became a personal activity, rather than a community experience. Netflix, leveraged their library of movies as a source of competitive advantage, and captured this market with low prices in the form of an innovative pricing strategy – watch as much as you can for a monthly subscription fee (as against unit pricing of every movie that was the industry standard then). Armed with the learning of consumer preferences (now being digital, Netflix had micro-level data on consumer preferences than the multiplexes in shopping malls), it moved up-market. It leveraged on the convergence between entertainment and computing, as TVs became smart, and computer screens became bigger and bigger. The incumbents continued to ignore Netflix with the reasoning that it would take time for the connoisseurs of movies to shift. The allowed Netflix to compete for original content and piggy-back on the convergence in the home entertainment space.

Typically, disrupters have different business models, given that they target different consumer segments, provide differentiated value, and possibly have a different pricing scheme. A lot of these disruptive innovators adopt a platform business model intermediating between different user groups (like Airbnb or Redbus), servitization (like ERP on the cloud), or different pricing models (pre-paid pricing of mobile telecom services in emerging markets).

Innovation in the healthcare industry

I aver that Indian healthcare industry had not witnessed disruptive innovation for the following three reasons. One, even though primary healthcare is considered a public good, a lot of Indian consumers are willing to pay for high quality tertiary and quaternary healthcare (either they could afford it or have access to state/ private insurance). That marks low price sensitivity for the entire industry. Coupled with information asymmetry between the care givers and patients, the patients are risk averse as well. Two, given the high investments required in setting up tertiary and quaternary, the industry has become highly corporatized and consolidated. A few large corporations dominate the entire industry. The economics of the large corporate healthcare provider requires them to have a tight leash on metrics that matter for profitability (including, increased use of automation and robotics in high labour-intensive routines and use of manual labour in routines where unskilled and semi-skilled manpower is easily available, reducing the patient’s average length of stay, and optimizing the average revenue per occupied bed). Three, the organized healthcare providers have been quickly encapsulating all attempts at democratizing healthcare. For instance, when glucometers and pregnancy testing kits became consumer devices, the clinics and physicians began building an ecosystem around these devices to not let entire therapy become owned by the patient. When you went to your endocrinologist with your blood glucose charts, she would insist that home devices are error-prone and ensure that you test again at the clinic! Not so much for the additional cost of testing again (which could be minuscule), but the perception that healthcare is the domain of certified experts would be reinforced.

Data, ahoy!

As healthcare and data analytics come together, the industry is at the verge of disruption. Multiple wearable/ consumer devices capturing health data, medical equipment of the early 2020’s are more of computing devices generating gigabytes of data per observation, and increased adoption of remote sensors for public health (like thermal screening devices) would generate terabytes of data. Such a deluge of data is likely to overwhelm the legacy healthcare providers who hitherto relied on the extensive experience of the physician, for whom data was just a support.

There is an urgent need to allow for a variety of healthcare providers to operate in their own niches. For instance, given the developments in dental and ophthalmic surgeries, there should be no need to build infrastructure beyond ambulatory facilities. Increasingly, diagnosis and treatment should move to the home.

Disruptive innovation in times of COVID-19 pandemic

The unique nature of the COVID-19 pandemic means that healthcare must be provided at scale and speed. Given that the world is yet to discover a cure or vaccine, let alone a standardized treatment protocol, governments and healthcare providers need to move fast and scale up testing and care. Amongst the triad of quality, cost, and scale, the world would prefer low cost and high scale at speed, rather than wait for the best quality of care.

Axes of healthcare.jpg

That is a world ripe for disruptive innovation. The under-served segments are baying for an affordable care programme that is good enough. Will the governments of day make this trade-off to ensure that “acceptable quality” testing/ care is provided to large sections of the population that are infected/ likely to be infected, at affordable costs?

Stay healthy, stay hydrated, stay safe!


(c) 2020. Srinivasan R


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!


(c) 2019. R Srinivasan

Collecting small data in the world of big data

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

What is small data?

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

Small data for listening to customers

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

Small data for innovation

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

JOSEPHS® – the service manufactory

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

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

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

Please visit the website of JOSEPHS® at 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.

(C) 2016. R Srinivasan.

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