Content apps and points of failure

How the nature of content defines the long-term failure point in content-centric apps

The supposed great misery of our century is the lack of time — John Fowles

It’s no news that we are living in a world of content glut. There is more content than any of us can realistically consume in the time we have. Many a great businesses have been built around enabling humans to create and consume more and more video, audio and written content effectively. However, given that human creativity has no limits and consumer consumption preferences are fluid, it’ll continue to be hard to anticipate successes and failures of content-centric apps.

My goal here is to develop a framework for thinking about content-centric apps such that their outcomes become easier to reason. To start, let us first define content, specifically a “unit of content”. A unit of content is something that one can appreciate standalone i.e. without necessarily needing to consume any other content along with it. Some examples are an article on Medium, a video on YouTube, a song on Spotify, a season of a series on Netflix (an episode could be a unit only if the episodes in the series are largely independent or each other).

The content-centric app stack

Stack for content-centric apps

Any content-centric app works across one or more of the three layers — sourcing, curation and presentation.

Sourcing layer is the part of the app that enables the app to source more content by enabling new content creation or through purchase of existing content. For example, for Medium, its sourcing layer is its freely available blogging tool which I used to write this post.

Presentation layer is the part of the app that exposes the content to consumers in a suitable form. For example, for YouTube, its presentation layer is its website, mobile and TV apps with all their nuances such as the theater mode.

Curation layer is the part of the app that functions as the brain, taking the large volume of content gathered by the sourcing layer and deciding what content to push to the presentation layer at what moment for which consumer. For example, for Spotify, its curation layer is its ML models that power Discover Weekly and playlists recommendations with consumers playing their part in creating and sharing playlists.

Not all apps play effectively across all the layers of the stack at all times. For example, Anchor, in its current form, is primarily focused on the sourcing layer as a tool for podcasters to create podcasts easily. That said, apps can always expand to other layers of the stack.

While content-centric apps might want to be cutting edge across all the layers of the stack, it’s usually one layer of the stack that turns out to be the most challenging depending on the nature of the content and the business context.

Divergence in the nature of content

Apps mapped on agony vs. volume as a proxy of their unit of content they mostly have e.g. Newsletters for Substack.

It’s obvious, but the experience of consuming text is not the same as that of consuming a video, and the experience of consuming a short video is not the same as that of consuming a long video. What works for one type of content might not work for the other. One way to understand the nature of content is to look at it as a 2×2 of Agony vs. Volume.

Agony quantifies the extent of suffering one might experience on consuming a unit of content of average quality. Agony is lower for a unit of content that is entertainment focused and to consume which only a small amount of valuable time is required. Defined this way, most YouTube videos are low agony because they are short and one typically consumes them when there is nothing better to do. If you consume a few YouTube videos that weren’t truly great, you aren’t really upset. It also helps that YouTube is free.

By contrast, it is more agonizing to watch an average series on Netflix instead of spending that time watching one that all your friends are talking about. Though it is entertainment, it is supposed to be very high quality entertainment that is worth paying for, and out of which we want to extract maximum value. You also need to invest a reasonable amount of time in watching at least a few episodes to determine if a series would be worth continuing to watch or not.

Given this lens, long-form written content is the highest on the agony scale. Though it takes little time to consume, it takes a lot more focus than mindlessly watching YouTube videos. It isn’t entertainment and is instead something that we engage-in to learn. We typically need some discipline to set aside some valuable time for consuming such content, so it’s important that it better be worth it.

Volume quantifies how much content of a particular type is (or could be made) available on the web. Content type here is defined brutally in terms of length of the video or the article without making judgment about its quality. Volume of available content is determined mostly by how easy or hard it is to create a particular type of content of a passable quality.

Layers as points of failure

As mentioned earlier, it’s usually one layer of the stack that turns out to be the most challenging depending on the nature of the content and the business context. That layer is likely the point of failure in the long term. Eventually, a failure in any layer percolates through the stack i.e. sourcing layer will fail if curation or presentation layers aren’t set up to maximize the rewards for the content creators.

“Sourcing layer” failure

The risk of sourcing layer being the failure in the long-term is the highest for content that causes high agony but can only have limited volume, the most obvious example being a Netflix series. This puts a lot of pressure on the sourcing layer as we saw reflected in Netflix’s recent earnings. While this doesn’t mean that the curation layer is healthy, there is definitely an element of choice paralysis that is increasingly obvious whenever one turns on Netflix, it does mean that failure in the long term will likely come from the sourcing layer.

“Curation layer” failure

The risk of curation layer being the failure in the long-term is the most extreme for content that causes high agony and has high volume, the most obvious example being long-form written content. The bar for curation is just much higher. The failure of curation layer is usually a business model failure rather than an execution failure. Business model dictates what kind of mechanisms are put in place for curation to ensure that the best content creators get rewarded in a sustainable manner.

The failure of this layer is the most evident in the case of Medium whose excellence in sourcing stands in stark contrast to its suboptimal curation. Medium has failed in the curation layer because of two reasons: One, its compensation model ensures that that the only way for writers to make a living on Medium is to play the volume game i.e. publish a lot of articles so that cumulatively they can get eyeballs needed to translate the $5 per month subscription per member into something tangible for the writer. Two, it has failed to develop any higher level trust signals for the quality of content. Claps on articles are pretty much the only signal as opposed to say publications whose editors are incentivized to carefully curate content. Publications on Medium never really considered themselves to have an editorial responsibility beyond a certain basic level; they are all trying to just aggregate as much content as they could.

Medium now has the responsibility on itself to be the centralized curator which it possibly can’t do a great job at given the nature of the content (high agony and high volume). Compare this to Substack that considers the unit of content to be the newsletter as opposed to an article, and facilitates a direct connection between the newsletter creator and the consumer. The curation, therefore, happens at the human curator level which is likely to be very high quality, and there is all the incentive for it to be high quality because the creator of the newsletter is earning a stable subscription income for it.

“Presentation layer” failure

The risk of presentation layer being the failure in the long-term is very little. While presentation layers need to adapt to changing customer behaviors, successes or failures in this layer are mostly short-lived. The longer term advantage lies in the curation and/or sourcing layer depending on the nature of content. The apps most susceptible to these short-lived failures of presentation layer are either legacy businesses that haven’t yet caught up with large shifts in the new forms of presentations or businesses dealing with content forms that are in the state of flux.

An example of the former would be The Economist which, until recently, had a terrible mobile app that limited engagement inspite of excellent content. An example of the latter would be short-form videos which have been going through a shift to vertical video (and stories) with the increasing ubiquity of internet connectivity on mobile phones. Even long-form written content is going through some presentation layer flux with newsletters in email inbox gaining traction as the preferred method of consumption.


Creation and consumption of content is central to human creativity, and to us understanding and enjoying the world around us. Given this deep relation to human identity, content-centric apps are very powerful and also very hard to get right. I hope that looking at these businesses through the lens of the content stack and the nature of content is a helpful step in developing some intuition around these businesses. Feedback welcome.

Twitter @setparth

Physical assets & leverage windows

Physical assets & leverage windows

Is Tech’s new found love for physical assets justified? Yes, but only in certain scenarios.

Ownership of physical assets has historically been a dirty word in tech. The Valley has been spoilt by years of asset-light growth from companies such as Google, Facebook, and recently, Uber. The valuations of these companies have amazed even the most optimistic observers. Most of these business arguably have “network effects” that create winner-takes-all markets.

However, as customer usage patterns evolve and the opportunities to create large asset-light businesses diminish, there is a newfound interest around full-stack businesses. While full-stack business is a broader term that encompasses a range of operationally intensive businesses, my goal here is to talk about a subset of tech businesses that hold physical assets for the long term (a year or more) as a core part of their business + have a direct consumer touchpoint.

This classification includes businesses such as Amazon, WeWork, Sonder, b8ta, etc., and excludes businesses such as Opendoor which hold physical assets for the short term as part of a transaction. These businesses have built (or are building) demand on top of physical assets that sit at the core of their day to day. While a lot of businesses have physical assets, some of these businesses are special in that the physical assets are a significant point of leverage, resulting in high multiples.

When physical assets provide leverage

The best physical assets for tech companies are the ones that don’t exist but are really needed by consumers, and on the top of which it’s possible to build a demand driven network effects business.

I believe that as long as the 3 conditions below are met, holding physical assets for the long term provides a huge point of leverage.

Condition 1 — Demand for a new type of asset

There is a huge consumer demand for the kind of physical assets the business is creating, and the physical assets of that type either don’t exist or at least don’t exist at the desired scale

Condition 2 — Unlocks network effects

Significant network effects can be unlocked on the back of these physical assets that would provide the business defensibility

Condition 3 — Long expiry date

A shift in industry that could turn these assets into liabilities is far far away into the future (not at least in the next 5 years)

Deep-dive into some types of physical assets

Let’s look at a few examples to develop an understanding of the framework above. It’s important to note that physical assets that provided leverage yesterday might not be leverage points if investing in them today.

e-commerce warehouses

“Not a point of leverage any longer”

When they were set up, Amazon warehouses offered as high a leverage as one could imagine. With the promise of fast shipping, they triggered the demand that brought more suppliers to Amazon at cheaper prices. However, if a business was to set up warehouses today, Conditions 1 and 2 would both not be met. That is the reason Walmart is getting cold feet on future investments in its warehouse network. Today, the bar for creating network effects (Condition 2) on the back of warehouses is much higher; one has to promise one-day or faster shipping. What’s worse, a network of sophisticated third-party warehouses has already emerged, making warehouse aggregation a more realistic strategy (i.e. Condition 1 is not met).

Cloud kitchens

“A point of leverage today”

Food delivery is a hard business, with a constant need to provide consumers with the specific food item/cuisine they might want, faster. Cloud kitchens have emerged as a physical asset that meet all the 3 conditions. They trigger network effects (Condition 2) by generating demand within a food delivery ecosystem (say Uber Eats) that makes more such kitchens feasible, enabling the food delivery company to provide cheaper and tastier food, faster.

While there are already some third-party cloud kitchens (largest being Rebel Foods), there are very likely gaps in food types and density (location) that justifies companies such as Deliveroo holding cloud kitchens themselves. Over time, a more elaborate network of third-party cloud kitchens will undoubtedly emerge but there isn’t time to wait for that in the bloody food delivery wars; cloud kitchens would play their part in crowning the winner.

Co-working spaces

“Not a point of leverage today unless strategically differentiated”

The need for co-working spaces came about as it became easier to start companies and gig economy took off. Co-working spaces met all the 3 conditions at the time WeWork pioneered the concept. The network effects mentioned in Condition 2 were around the relationships and partnerships one could build by being in a WeWork as opposed to being a different space. Going forward, WeWork’s focus on membership benefits through the creation of a software stack would further extend the network effects beyond local network effects.

If one was to start a co-working business today, meeting Conditions 1 and 2 would require the co-working spaces to cater to a very different taste/community or use case. That is exactly what The Wing has done by promising deeper relationships and partnerships among women. Many other undifferentiated co-working spaces are unlikely to have any leverage, they will just be average businesses.

Service apartments

“A point of leverage today”

Airbnb exposed consumers to the wonderful world of alternate accommodation, and now consumers have an increasing preference for such accommodation over hotels. While there are many Airbnb accommodations, there is a dearth of reliable stays for professionals. That is where the new crop of startups (Sonder, Lyric, Domio), holding an inventory of service apartments, comes in. At this moment, they satisfy all the 3 conditions. Condition 2 is satisfied in the same way Condition 2 is satisfied for Hilton — more members means more certainty that one would find a Hilton on their next stay and the more the value of the loyalty membership.

Building new hospitality brands is the stated goal for all these startups. A few years later, building a business around holding these kind of assets would become much harder.

Retail stores

“Not a point of leverage today except in very few cases”

Most retail stores, though in-vogue, aren’t the point of leverage. They don’t trigger network effects (Condition 2). This includes stores for Warby Parkers and Caspers of the world. These stores are definitely good for business, but don’t drive network effects. However, the retail stores of the kind b8ta is building meet all the 3 conditions. They meet Condition 1 because online discovery for cutting-edge products is hard and there isn’t any way to try these products out without buying them. They drive network effects (Condition 2) similar to how Walmart stores drove them in their time — more stores meant better (and more) suppliers at better prices.


After a couple of decades of returns from asset-light businesses, it is time to appreciate how the physical and digital together can drive huge business outcomes. Physical assets held within leverage windows, and with a network effects based demand generation model on top, can be THE fuel for successful businesses of the future.

Service & Marketplace-model fit

Service & Marketplace-model fit

Services are ambiguous, and creating an online services marketplace requires finding a fit b/w the service and the marketplace model

The landscape of marketplaces for services is littered with failures. There are many a stories of promising startups that failed to create an enduring business. Services, by nature, are ambiguous and their lack of standardization makes it hard to create liquid online marketplaces. Plumbing, for example, isn’t a fixed priced SKU you can buy on Amazon and be 100% sure that it would solve the pipe leak in your house.

Services span a whole range complexity, from low complexity services such as food delivery to high complexity services such as home remodeling. There is no “one size fits all” marketplace model that works for all services. However, an understanding of the patterns of what marketplace models are best suited for what kind of services would make it easier to spot opportunities and to avoid failures.

My attempt here is to articulate these patterns by defining the 4 categories with a strong service & marketplace-model fit, and a couple that are misfits. Below is a chart that lays out services on complexity vs. frequency of service, and marks the 4 categories where successful marketplaces have been (and and can be) built.

Categories of marketplaces with a Service & Marketplace-model fit

Categories of Fits

Fit Category 1 — Lead generators for medium/high complexity (medium/low frequency) services

Given the ambiguity inherent in service marketplaces, lead generators have been the most obvious and the earliest way to bring services online. As the name suggests, lead generators are not involved in the transaction and make money simply on surfacing (or establishing connection) between the consumer and the lead.

The prime example of a marketplace in this category is Thumbtack, which is a lead generator for services across all complexities and across all frequencies. However, I suspect a big part of Thumbtack’s revenue comes from medium and high complexity services such as event photography compared to low complexity services such as home cleaning.

Lead generators could take a share of the market in any complexity level but typically, the value of the lead (and the trust the marketplace creates behind that lead) increases in proportion to the complexity of the service. There is no reason for one to use lead generators for procuring low complexity services when there might be specialized marketplaces, offering a much better consumer experience (Fit Category 2) for procuring those services.

A good example of a successful lead generation model in a specific domain (everything home related), but cutting across medium and high complexity services, is Houzz. It builds trust for the leads by showing detailed photos of their past projects, Houzz badges and awards, affiliations, reviews, etc. Arguably, Houzz has taken away some of Thumbtack’s market in home services but Thumbtack’s staying power comes from the trust and brand awareness it has engendered because of its breadth of services. Zola is another good example in the wedding domain.

Fit Category 2 —On-demand transaction marketplace for low complexity (high frequency) services

This category encompasses specialized marketplaces focused on low complexity (high frequency) services such as food delivery, ride hailing and grocery delivery. Prominent examples are DoorDash, Lyft, Uber Eats, Instacart, etc. These marketplaces go beyond lead generation, and into transactions, and they owe their liquidity to the demand that is generated (and maintained) on the promise of a much better consumer experience for buying services compared to lead generation marketplaces. In that sense, they deepen the market and create more liquidity than there would have been otherwise.

However, given the low complexity (and the commoditized nature) of the services being provided by supply side, trust mechanisms in these marketplaces are less important, and marketplaces in this category have a hard time preventing supply-side multi-tenanting with direct and indirect competitors. Supply-side keeps on switching across marketplaces and jobs (same person could do grocery delivery today with Instacart and ride hailing tomorrow with Uber), and this leads to very tough unit economics. Andrew Chen has a good post about it. Such marketplaces will continue to exist, though there will likely be consolidation that combines multiple low complexity and high frequency services into one marketplace.

Fit Category 3 — Managed transaction marketplace for medium complexity (medium frequency) services

This category is relatively underdeveloped, with Puls and Setter being good examples in home services, and Soothe and Glamsquad being good examples in wellness. The services offered by these marketplaces sit in the sweet spot of being complex enough that supply-side is not constantly churning (and multi-tenanting) and not complex enough that the buying experience can’t be standardized into a few clicks. This enables these marketplaces to offer a consumer experience 10x of what the lead generators could provide, thereby capturing sustainable demand.

These transaction marketplaces have a high degree of “managed” component. Puls, for example, vets its technicians and provides a 90-day guarantee on its services. Its buying experience is also very much tailored to each service with as much clarity on pricing as reasonably possible. Setter, on the other hand, introduces a trusted home manager into the mix to instantly help find the right provider.

More specialized marketplaces, on the lines of Glamsquad and Soothe, might emerge in this category as more service buying continues to move online, and more segments of services (e.g. dog training — just a random guess) become large enough to justify specialized marketplaces.

Fit Category 4 — Managed transaction marketplace for high complexity (low frequency) services

This category is also underdeveloped, with Upwork being the best example of being successful in the desk-work space. Note that some of Upwork’s services might better fit Category 3 above. The marketplaces in this category are what James Currier called Market Networks. The focus for the marketplaces here is to bring as much of the offline negotiation involved in a complex transaction online, into an interface managed by the marketplace. The power of these marketplaces comes from a few aspects (not all might be present in one marketplace): a) vetting of suppliers b) SaaS tools to work with these suppliers (before and after one has hired them) and c) guarantee when things go wrong.

BuildZoom is an evolving example of such a marketplace for home projects in the consumer space. Managed by Q is an evolving example for office projects in the B2B space for some of its services. It should be noted that the consumer experience marketplaces in this category need to create is very different from the experience that marketplaces in the Category 3 above create. Therefore, it’s very hard for a single transaction marketplace to effectively span across the Categories 3 and 4.

Note: As consumer expectations are evolving towards expecting lower and lower friction, marketplaces in Category 3 and 4 would pose a threat to lead generators (Category 1). That, in my view, is the reason Thumbtack went through all the pain to change its matching approach to be more instant and effective.

The Misfits (or Loose fits)

While it’s interesting to understand the categories of fits, to appreciate the nuances of service marketplaces, it’s also important to recognize the misfits. Below are 2:

Misfit Category A— Lead generators for low complexity (medium/low frequency) services

Prominent example of a marketplace in this category is TaskRabbit. With its positioning as “get affordable help”, TaskRabbit cornered itself into mostly being an option only for low complexity (medium frequency) services that one wouldn’t be able to find elsewhere at a better price e.g. home cleaning, or low complexity (low frequency) services that one wouldn’t be able to find elsewhere at all e.g. standing in a queue for a ticket. To be clear, TaskRabbit is not a failure in absolute sense, but it turned out to be much more of a niche player than it seemed at one point of time because it did not do what was needed to build trust in buying medium complexity services.

Misfit Category B— On-Demand transaction marketplace for low complexity (medium frequency) services

Prominent example of a marketplace in this category is Handy. Its mainstay is home cleaning, which not only is medium frequency, but also suffers from the same supply-side churn we had talked about in Fit Category 2. The medium frequency and low complexity aspects of home cleaning makes it really hard to retain supply, and unfortunately for Handy, there also isn’t any silver bullet to providing consumers a 10x experience when buying cleaning from Handy as opposed to buying it from a cheaper lead generator such as TaskRabbit. This is unlike other low complexity services such as food delivery or ride hailing, where the high frequency and on-demand nature of services at least creates an opportunity for the transaction marketplaces to offer a 10x consumer experience compared to what a lead generator could provide.


Obviously, service marketplaces are hard, and I have been wrecking my brain to understand what leads to the varied outcomes. Hope the categorization of fits and misfits above resonates with you, and it provides a framework for evaluating opportunities and avoiding pitfalls. Please share any feedback.

Uber Mobility Cloud

Uber Mobility Cloud
 

Uber’s strategy has a new home — “mobility data infrastructure for YOUR city”. Blame it on competition and regulation.

There was this recent article by Eugene Wei around invisible asymptotes, defining them as the ceiling that a company’s growth curve would bump its head against if it continued down its current path. There has been a lot happening lately in the urban mobility market and it’s helpful to make a sense of the changes by thinking about the invisible asymptote of one of the key players: Uber.

Uber’s invisible asymptote has been the price of its service. Ubers are not a cheap mode of transport for vast number of use cases and a vast number of people, especially if you compare them with say the subway in New York. Uber has been aware of this invisible asymptote and has been continuously innovating to bring cheaper solutions such as Uber Pool and Uber Express Pool to market, and those have largely helped it grow at a healthy pace till date. Significant percent (though likely not majority) of Uber rides are now Pool.

While this is great, the mobility market has evolved and Uber’s invisible asymptote now seems to be more clearly visible. Uber doesn’t have many more tricks up its sleeve to continue to lower the price of its service. The result is a new strategy that is becoming the north star sooner that I had expected. Uber wants to be the mobility cloud for cities — the data infrastructure layer sitting between all the modes of transport and the apps/channels we use to access them.

Uber as the data infrastructure layer for mobility options in a city

Uber recently articulated this strategy by saying that it wants to take consumers from Point A to Point B even if it involves covering multiple modes of transport. There are multiple factors that have led Uber to this point and that make this strategy so compelling.

First, Uber has internalized the deeply geographical nature of the price competition. It found itself surrounded by deep pocketed competitors in international markets and figured that bleeding money through discounting wasn’t sustainable. It has ceded ground to Didi in China and Grab in Southeast Asia. This meant that some of the assumptions around the size of its user base supporting its valuation turned out to to be untrue, putting more pressure on it to penetrate its existing markets deeper to capture demand beyond taxis.

Second, the entry of bikes and scooters promised to substantially lower the price of short distance commute and shut Uber out. The users that scooters are going after are precisely the users that Uber is finding hard to win over because Uber is not cheap and, in this case, also not convenient. The only logical way to react to this was to acquire one of the companies and gain an entry into the market, which it did with its acquisition of Jump.

Third, it became clear that self-driving, which was Uber’s silver bullet to lowering the price of its service, won’t deliver for it. Self-driving won’t come soon enough (assuming a 2019 IPO for Uber) and Uber won’t be the one to get the self-driving technology right. In addition, there is an increasing threat that some players in the market might be able to cobble up a partnership to offer much cheaper rides to consumers w/o having to rely on Uber’s distribution. An example would be Waymo partnering with one of the big car manufacturers to have the capital muscle and expertise to deploy a fleet of self-driving cars in a city and enable consumers to hail them using Goole Maps which already has a large captive user base.

Lastly, Lyft, Uber’s primary competitor with a nicer brand image is becoming more aggressive with partnerships with cities (example Lyft’s partnership with the City of Phoenix), offering a path to lowering the price of service through subsidies. The entry of Uber/Lyft taught cities a lot about regulation and cities have realized that these new age transportation options are here to stay. They are finding that over-regulation (not allowing Uber/Lyft because of taxi unions) won’t work and so won’t under-regulation (letting city streets become scooter graveyards). Cities are increasingly trying to leverage their position in the mobility equation and are thinking of using Uber/Lyft as means to solving the urban mobility issues that the cities have always wanted to solve. If cities can subsidize Uber/Lyft rides to public transportation centers to incentivize higher usage of public transport, then its a win-win situation for Uber/Lyft and the cities, allowing them to offer a real alternate to car ownership.

This perfect storm of factors shaping the mobility market has resulted in Uber seeing itself more as the infrastructure layer for mobility, probably sooner than it might have imagined. This last point above around partnerships is the most important one because, going forward, cities will play an outsized role in defining the future of mobility. Bikes and scooters are not the last innovations in the space of urban mobility and Uber’s ability to capitalize on the future innovations will depend on whether it can be the data infrastructure connecting all the mobility options in a city which in turn will depend on whether or not cities allow it to do that.

If Uber can have all the mobility data and can offer cities a solution that optimizes the mobility options for price, convenience and utilization, then it surely will have a deeper moat than just offering a ride sharing solution. It is for this reason precisely that Uber is spending $500 Mn in ads to clean up its image and apologize for its past deeds. This campaign is not for consumers; it is to give city governments the cover to partner with Uber without the fear of a public backlash because governments don’t want to be seen partnering with evil entities.

Uber’s CEO Dara Khosrowshahi’s new positioning of Uber as a softer (less brash) is a pre-requisite for its push to be the mobility cloud. How open will this mobility cloud be is still a question but it’s now in Uber’s interest to push for a world where each city has at least one mobility cloud and it hopes that is can own the mobility clouds for most cities. In that sense, the change of guard at the top for Uber has been very timely and maybe one day, Uber will be able to tax all mobility the way AWS taxes all storage and computation.

Creating efficient markets

Creating efficient markets

Markets for goods & services are constantly evolving and are never efficient. Thinking of market efficiency as a milestone is delusional.

Efficient markets are defined as markets where buyers are 100% clear about what they are buying, how much of it is available for buying and how much they should be paying for it. Being clear about what one should be paying implies a complete understanding of the true value of the asset one is buying.

Though markets for goods & services might have seemed efficient at multiple points of time in the history, thinking of market efficiency as a one and done thing is delusional. Market efficiency is a moving target and the only way to create and sustain a great business is to keep chasing that target.

I have been fascinated by the small and big changes that companies make in their push for market efficiency, with the goal of being the “market makers” of their industries. Abstracting away, I have found three ways of thinking to be the most impactful in moving the needle on market efficiency.

  • Build a distribution channel that can drive fundamental change in supply practices
  • Leverage the distribution channel to bring in completely new supply into the market
  • Think about the “Job to be done” to find opportunities to compete

Build a distribution channel that can drive fundamental change in supply practices

Building a new distribution channel or rethinking an existing one can help create/shape demand, enabling one to push fundamental changes in supply practices, thereby setting the industry on the road of steadily improving market efficiency.

For example, let’s look at what Amazon has gradually done in the last couple of decades

  • When Amazon started selling books over the internet, it massively improved the access and convenience of buying books, primarily for titles that were not the hot selling ones. This activated the latent demand for these titles and gave Amazon a reason to procure these titles in bulk for lower price per book than one would pay for to buy the same title at a mom and pop store (if the title was to be found there!). Amazon was then able to pass some of the price benefit to its buyer base. These not-so-hot titles started to be priced closer to their true value, the sign of a more efficient market.
  • As online commerce grew, Amazon opened up into a marketplace, allowing sellers (most of them with warehouses) to be the conduits between the manufacturers and Amazon’s buyers. Not saddled with high real estate costs (unlike mom and pop stores), these sellers were able to sell the goods for cheaper and Amazon was able to push market efficiency in many more categories at once.
  • As the share of online commerce has increased, both Amazon and manufacturers (Nike for example)are finding it better to deal with each other directly , allowing both of them to capture higher margins while also offering better experience and lower prices to customers.
  • Now with online commerce becoming the preferred way of shopping for a large number of people, Amazon and manufacturers are finding that there is no longer a need to waste money on fancy packaging to draw attention of customers to products sitting on retail shelves. Fancy packaging had also made it hard to optimize shipping package sizes. With its Frustration-Free packaging, Amazon is truly demonstrating how a different distribution channel (here online commerce) can change supply practices and really drive prices down.

The net result of all of this is ever decreasing prices. Turns out what might have seemed like an efficient market with Walmart’s “Everyday Low Prices” promise was not efficient after all.

Leverage the distribution channel to bring in completely new supply

Differentiated distribution channels can also allow one to tap into a completely new source of supply that can change the market dynamics.

Take the case of Uber and Lyft. By making it so easy to start driving for money, they were able to tap into the private car market for drivers. These new drivers owned their cars anyways and didn’t have the huge loans/contracts (from medallions for example), allowing them to experiment with the new channel. The fact that the rides were subsidized, further helped in creating additional demand and supply. As a result, Uber and Lyft were able to reset the baseline on cab price in all the geographies, some of which might have seemed individually efficient prior to them entering those cities.

Think about the “Job to be done” to find opportunities to compete

Abstracting away from the product/service and thinking about the Job to be done can help one see new opportunities to bend the curve on market efficiency.

Take the case of Uber and Lyft again. The Job to be done is to get from Point A to Point B and there are multiple ways to do that, Uber and Lyft being one of them. While Uber and Lyft are great for medium/long rides, it is questionable if they are the best solution for rides shorter than 5 min (as long as you are not carrying grocery bags!). There should be a way to pay lesser for short rides. While Uber is trying to bring efficiency into this market with Express Pool, there is an opportunity to reimagine the solution for short rides. LimeBike and Bird are trying to do exactly that. By introducing a completely different product targeted towards a specific use case, they are driving further efficiency into the mobility market which might have seemed to already be very efficient with Uber and Lyft.

Another interesting example of finding opportunities based on the Job to be done is eBay’s new product page (iPhone example). Its an attempt to show buyers the spectrum of value (i.e. products in different conditions) for them to make a better decision on what to buy. While eBay can’t always compete with Amazon on offering lower prices for new items, by understanding that buyers have different Jobs to be done, they are still able to push the curve on pricing. eBay has been selling phones in different conditions for years but by simply showing its best pick for each item condition, it has simplified the buying decision like never before. While transparent pricing across conditions might not matter to some of us who are set on buying a new phone, it is definitely making the market more efficient for folks who want a good phone and are open to discovering what works the best for them.


In summary, the idea here is to drive home the point that markets are never efficient. Its wrong for established players to rest on their laurels and think that they can milk money since they are the “market makers” of their industries. Similarly, its wrong for startups to give up on markets thinking that they are already efficient and there isn’t an opportunity. Thinking deeply about the nature of the distribution channel and Job to be done is a good way to find an opening to move the needle on market efficiency.

Why are so many upstart brands in love with subscriptions

Why are so many upstart brands in love with subscriptions
Digitally native vertical brands are betting that subscription led bundling can counter Amazon led unbundling

New York Subway is a strange world, there are many things that one might be amazed by and ads are probably last on the list. But one can’t escape noticing them and recently I have seen so many DNVB (Digitally Native Vertical Brand — sorry, this is a doozy, but here is a good article on them) ads that I had to take time to make sense of them.

The one ad that really got me thinking was quip; they sell toothbrush on subscription, adopting the razor blade model, with the toothbrush head replacing the razor blade. My first instinct was why would anyone need this and why are so many upstarts foolishly trying to replicate Dollar Shave Club’s success. No doubt that CPG brands have been selling us overpriced products with very limited innovation for decades and they need to get disrupted, but why would I want to subscribe to a toothbrush!

Though I picked on quip, quick research will tell you that subscription model is all rage with DNVBs (MeUndies, Harry’s, Ritual, etc.) The interesting thing is that these brands don’t sell on Amazon! Contrast this to my recent post where I posited that Amazon marketplace is the AWS of brands which will (and has) led to an explosion of new brands, born on Amazon and selling to Amazon customers. However, these DNVBs are going for something else. They have an alternate vision of retail, one where they can co-exist with Amazon, owning categories and customers. They are betting on a brand led future of retail.

This vision is audacious and true success can only be guaranteed if these new brands can build (retain) pricing power over a large enough base of customers over a long period of time.

Building a brand led future of retail with subscriptions

The most important thing to appreciate around pricing power is that a brand can have it only if customers are coming to it directly and it has built defensibility against the hundreds of competitors that can challenge its portfolio of products, one by one. Selling on Amazon is inherently unbundled, it is search based and customers go about filling their cart one product type (detergent, deodorant, etc.) at a time. This exposes every individual product to head-on competition with tens of similar products, leading to an unsustainable race.

Alternate models of online retail: Bundling driven by DNVBs owning categories and unbundling driven by Amazon

DNVBs will build pricing power only if they manage to create a future where the bundled model on the left (above) can exist i.e. customers visit A.com (instead of Amazon.com) for buying all the products that they need for that category. DNVBs are hoping that subscription is the silver bullet help them get to that world. Let’s see how.

Subscription is a good test of customer need

Buying a subscription by definition means that a customer is agreeing to have a continued relationship with the brand; it is a privilege that the customer gives the brand. This gives subscription brands a better customer touchpoint than brands which model themselves around a one-time purchase. For sure, it’s harder to get a customer to buy into a subscription but simple trial plans (e.g. Harry’s) can help with that. Not all categories are suited for subscription but it is a model worth exploring for categories where product use is more frequent or a behavior around frequency can be shaped but no fundamental R&D is required to create great products.

Thinking “subscription first” pushes brands to design products and marketing that fits into the subscription model and see if customers are willing to give them that privilege to have the continued touchpoint. It’s a leading signal of a category’s readiness for disruption.

Subscription buys time

Customers get bored easily, novelty of products fades quickly, and there is always something that comes along that promises to change their lives. This means that customers are constantly prone to churning. So, one anchor product is good but brands need to keep it exciting for customers by making tweaks to the anchor product and introducing more ancillary products that make it worth it for the customer to continue the relationship with the brand. Doing this is not easy. With subscription, brands buy time to get to the point when they have more to offer to customers without having to reacquire them. This time window might be a few months, but it is still better than not having any.

Once DNVBs have solved enough jobs to be done for customers in a given category, they have likely given customers a reason to come to them directly, thereby proving success in the bundled model of retail.

Subscription enables building a product portfolio in a cost effective way

It’s well known that CAC has been rising steadily, requiring new brands to need more and more VC money to build a sustainable business. Given that CPG is not a winner takes all market generally (unlike tech), it is important for brands to figure out how to grow with less cash for the endeavor to make sense both for the founder and the VCs.

The secret sauce for CPGs historically has been that they have been able to cross-promote new products to their large customer base to ensure that CAC for any new product is under check. These products then sell on Amazon in mass (though at decreasing margins). Owing to an established touchpoint with the customer, subscription gives DNVBs a similar cross-promotion channel, allowing them to build a portfolio of products without spending exorbitant amounts of money and then selling them to a smaller set of customers but at better margins.

Subscription makes distribution more cash efficient

The need for high levels of working capital is one of the biggest issues faced by startups selling physical products. Inventory management costs and buyer/ supplier payment terms are a big part of that. By allowing better demand prediction and shorter payment terms, subscription allows them to be more cash efficient. Any cash conserved can then be put into product innovation or branding.


Overall, subscription is a great selling (distribution) model to explore and comes with many inherent advantages. Expect to see more of it with upstart DNVBs in commoditized categories with frequent product use. Online commerce is especially suited for experimentation around this model and, given the right incentives, there is always scope to shape customer behavior.

Marketplaces and trust

Marketplaces and trust

Trust is one of the most important aspects of building a liquid marketplace, but trust is hard to quantify. Lately, I have been wondering if there is a playbook around building trust or does the answer depend on what type of marketplace one is building. I have landed on the conclusion that there is playbook as long as marketplaces can confidently answer the following question:

How likely is it that trust in a seller would influence the decision making of a buyer during a transaction?

This question is important because it helps bring clarity on how big a part of the trust equation should sellers be, resulting in a strategic decision on which of the two paths to take to build trust:

  • PATH 1: Deeply focus on building trust around individual sellers. Prominent examples are eBay, AirBnB, Thumbtack, etc.
  • PATH 2: Commoditize seller trust and build trust around the platform. Prominent examples are Amazon, Uber, etc.

To answer the question above, marketplaces need to have a good understanding of the uniqueness of the inventory on the marketplace as the buyers perceive it. PATH 1 is the natural choice if the perceived uniqueness of the inventory is high whereas PATH 2 is the natural choice if there are many sellers selling almost indistinguishable goods/ service. Below are some examples to illustrate the point.

eBay: Pioneer of PATH 1

eBay started as a P2P marketplace for selling unique items. Even if the item on sale was a Motorola Razr phone, there were many of them in many different conditions (on the spectrum of new and used). These conditions increased the perceived uniqueness of the inventory, effectively resulting in buyers treating them as different SKUs. In the absence of objective criteria for decision making, trust on the person who is selling (i.e. seller) became much more important. Buyers defaulted to sellers with higher positive feedback under the assumption that those sellers were honest about describing their products with details such as “the phone has a scratch on its back” as opposed to newer sellers who might be selling the phone for lower price but might be hiding something.

The figure below shows how eBay likely viewed its inventory. It considered that only a small minority of exact same items would be sold by multiple sellers. Given this view of its inventory, eBay defaulted to building a trust machinery centered around the seller and the platform receded in the background.

eBay: Only a small minority of exact same items would be sold by multiple sellers

Amazon: Pioneer of PATH 2

From early on, Amazon behaved like a retailer. It figured that by almost always selling new products, it can push seller trust to the sidelines and still kickstart a liquid marketplace. This made sense because if all the sellers on the platform were selling brand new Motorola Razr phone, the buyer decision making would simply be around price and possibly shipping. There would be no subjectivity around the SKU since it is brand new. That would put the platform in a position to commoditize seller trust as long as it was able to get enough sellers to create perfect competition among them.

The figure below shows how Amazon likely viewed its inventory. It considered that a large majority of the exact same items would be sold by multiple sellers. Given this view of its inventory, Amazon defaulted to building a trust machinery centered around the platform and the seller receded in the background. It built products such as Amazon Buy Box to truly commoditize seller trust.

Amazon: Large majority of the exact same items would be sold by multiple sellers

AirBnB: Best adopter of PATH 1

AirBnB almost replicated eBay’s model of building trust because the perceived uniqueness of inventory on AirBnB is as high as it gets. There is no SKU (room) that is listed by two or more sellers (hosts). Host has an information advantage and therefore, having trust in the host is important.

The figure below shows how AirBnB likely views its inventory. It considers that no exact same item (room) would be sold by multiple sellers. Given this view of its inventory, AirBnB adopted PATH 1, leading to it launching trust programs such as AirBnB Superhost.

AirBnB: No exact same item (room) would be sold by multiple sellers

The important question is that if AirBnB’s trust model is exactly like eBay’s trust model, then why do we trust AirBnB more in its category (travel accommodation) that we trust eBay in its category (goods)? The answer is that AirBnB is in a category that is hard to standardize; there isn’t an Amazon play possible at scale. And with features like Trips, AirBnB is trying its best to further deepen the uniqueness of inventory and make decision even more subjective. That is the reason I call AirBnB as the best adopter of PATH 1.


Now, how does this thinking about trust translate to services marketplaces like Thumbtack? Does it matter if the buyer is getting interior design from Service Provider A or Service Provider B? Yes, it does. How about plumbing? Maybe not, depending on how complicated a plumbing job it is. So, it is likely that some services might lend themselves to the Amazon model of commoditizing Service Provider trust while others won’t. Thumbtack has taken the approach of building trust via PATH 1. No surprise that Amazon Home & Business Services has defined the services (SKUs) very specifically and has adopted PATH 2, leveraging the trust it has already built as a platform.

For context, eBay has seen its focus shift towards selling new items and is experimenting with PATH 2 as evidenced by these fancy new product pages, applying the Amazon Buy Box concept.

eBay tweet.png

Overall, the decision of how to build trust comes down to the perceived uniqueness of the inventory of the marketplace. Being thoughtful about it, with an eye on how the category and the competition are evolving can make all the difference.