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.

The brand “value chain”

The brand “value chain”

Consumers need for brands and their meaning is evolving. It will change where in the value chain most value accrues.

Established consumer brands have a good run for decades, servicing the majority share of increasing consumer demand. It’s no news to anyone that first the emergence of Amazon and then the emergence of DTC brands has completely disrupted the playbook of established brands.

The need for brands and the value they create is both changing. In a decade, the landscape of brands will look completely different, both in terms of the spread of brands that resonate with consumers and what they mean for them. Established brands in some categories will be replaced by no-name brands and in some other categories by DTC brands. This will change where the brand value accrues.

Evolving brand landscape and representative players in the value chain

Many no-name brands will be either Amazon’s private label brands or new Chinese brands selling on Amazon, and Amazon obviously will capture most of the value in these cases. Also, by being the virtual equivalent of a physical retailer’s shelf-space, it will capture most of the value for established brands as well, probably outside luxury brands.

In this post, I will NOT talk about Amazon’s value capture but instead focus on other entities that can expect to see significant value accruing to them as part of the changing brand landscape. These entities are:

  1. Platforms enabling DTC brands
  2. Secondary goods marketplaces
  3. “Social brand-network” focused on building communities around brands

1. Platforms enabling DTC brands

There is a lot that DTC brands have to get right when they are starting off. Mostly, there isn’t a lot of value in trying to recreate the technology (commerce) stack for selling to the customer. Platforms such as Shopify have made that commerce stack easily accessible. Below a excerpt from a recent article on Shopify.

What Shopify does is power all of that ability — from selling to payments to marketing. “We run the gamut of a retail operating system.” Like any platform, Shopify is building an ecosystem of developers, startups and ad agencies.

This evolution of Shopify from helping small businesses get online to helping venture funded DTC brands disrupt their markets is fascinating. It reminds me of how Nvidia found that its GPUs built for gaming are perfect for AI applications. As DTC brands increase in number and scale, Shopify (and other similar platforms) will accrue a lot of brand value. They are helping brands create their own virtual shelf space, not dependent on any retailer. As the needs of DTC brands grow, so will the tools that Shopify or other platforms offer to meet them, becoming the infrastructure layer for a part of the consumer brand economy.

2. Secondary goods marketplaces

Before the internet, brands were a proxy for trust. Buying from a known brand meant that you could trust what you were buying, short-circuiting the complexity of the buying process. So, it was enough for brands to just stand for trust. Today, Amazon has centralized trust, changing what it means to be a brand; this tweet captures it beautifully. Larger brands need to do more than just build trust. They need to stand for something to make consumers choose them over a no-name or a DTC brand.

This means adopting strategies that these brands would have scarcely used historically. Two come to mind, both centered around creating spikes of activity around the brand.

  • Taking a stance on social/political issue: An example of this is Nike’s ad campaign with Colin Kaepernick which led to significant increase in sales.
  • Engaging in product drops: Drops have emerged as a great way to create buzz. Streetwear brand Supreme pioneered it many years back and now more and more brand are adopting it. It creates scarcity for marquee products released in limited volume, giving the brand an opportunity to make itself aspirational and amplify what it stands for. Couple of examples of this are Adidas’ Alexander Wang drop and LV x Supreme drop.

In acknowledgement of these trends, Shopify has launched an app “Frenzy” to make it easier for consumers to know about upcoming drops and “buy at retail, not resale”. In my opinion, this only furthers the hype that these brands are trying to create, increasing the value of the products in the resale i.e. secondary market. eBay’s new ad campaign “It’s happening” speaks to this evolving strategy of brands. Below is an excerpt from the campaign.

Designed as more than just a brand campaign, we’re aiming to express to shoppers around the world what we’ve known all along: Everything that’s current, relevant and interesting is on eBay — and your audience can buy it now

The question then, as posed in this article around Supreme, is why would a brand let secondary goods marketplaces capture a significant part of the value it is creating. The answer is that secondary goods marketplaces help brands extend the buzz around them, increasing the brand value. They help more people feel part of the community that the brand is trying to create. They create a virtuous cycle in which both they and the brands benefit.

Secondary goods marketplaces have historically struggled at capturing the most value that brands create because of concerns around trust of the authenticity of the products. However, marketplace-provided authentication services (e.g. eBay Authenticate) are increasingly becoming a standard part of their commerce stack, resolving most of the trust issues.

With trust as a barrier mostly addressed, there is an opportunity for secondary goods marketplaces to more proactively participate in this trend. An example is eBay recently organizing it’s first-ever community sneaker drop, creating an artificial incentive for its sneaker-crazy buyers and sellers to trade on marquee sneakers, in the process increasing the brand value of the sneaker brands and accruing a lot of value to eBay.

3. “Social brand-network” focused on building communities around brands

As mentioned above, one of the biggest elements that makes brands valuable is the is sense that the customers of the brand get around belonging to the community. Historically one’s membership to the community could only come from one owning a product of the brand. That has its limitations; it can work well if you are a luxury brand but when millions of people own the brand, its hard to feel like you are a part of the community. There is a need for non-luxury brands to explore ways to build a network/community in a scalable way; this article articulates this very well.

As you would expect, internet has unique potential to help brands do that. Till date, social networks such as Instagram and Pinterest have been primarily helping DTC brands get off the ground by getting them in front of people. They haven’t built tools to continuously engage people around conversations with a brand. Glossier, a DTC beauty company that I highly admire, has been taking a community first approach to building its brand and its products, thanks to its origins from the blog Into The Gloss. It now plans to take the next step in its evolution by building a social network centered around beauty. Excerpt below from this article:

Weiss wants to build her own version of a social media and shopping mashup, something that will allow shoppers to get feedback from other users to find beauty products that are right for them. This is not a social network that sells ads for revenue: Instead, Glossier will sell its own beauty items on the platform.

While Glossier might be able to afford building a social media and shopping mashup to help it build a network of brand enthusiasts, most of the DTC brands won’t either have the resources or the category need to build a network of their own. That is where the opportunity lies for a NEW social network to think of shopping beyond lead generation and ads, and repurpose the concepts of forums, chat rooms, news feed, etc. to build a destination where consumers can truly connect with brands on an ongoing basis.

Instagram is the most likely candidate to build something like that with their new Shopping app but I am skeptical if they will be able to move beyond ads. Whoever ends up building such a destination, which I call a social brand-network, will accrue a lot of value that DTC brands are building. It won’t be a bad addition to Shopify’s commerce stack btw if they can pull it off.

 

As with any fundamental shift in any industry, there are winners and losers. Winners understand how the value chain is changing and how they are positioned to capture a large part of the value. The landscape of consumer brands is changing faster than expected. Amazon is without doubt the key driver of the change and capturing a lot of value. However, there is a lot of value to be captured elsewhere and I am excited about seeing how the different players rise up to the opportunities that exist.

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.

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.

Marketplaces and pricing

Marketplaces and pricing

An approach to building for growth through pricing

Marketplaces help demand and supply connect and transact, and in return, they extract some value from the transaction. One of the biggest levers that marketplaces have to acquire and retain demand is pricing. When the supply is still scaling and the low price — high demand flywheel hasn’t yet kicked in, pricing is typically hacked and has no correlation with reality. However, to build a sustainable business, marketplaces need to invest in understanding the pricing dynamics of their marketplace sooner rather than later and they need to be methodical about it. “Pricing products” can help them build this understanding and in the process, they can make pricing a differentiator.

I define “Pricing products” as products built in the service of using pricing as a lever to drive GMV growth

“Pricing products” manifest themselves in three levels:

The pyramid of “pricing products”
  • Level 1: These are products focused on removing lack of pricing transparency as a barrier to decision making for customers
  • Level 2: These are products designed to opportunistically deploy pricing tactics and influence customer decision
  • Level 3: These are products designed to offer guaranteed pricing consistently on certain goods/ services, thereby locking-in customers

Level 1: Removing lack of price transparency

Bringing price transparency is the first step for marketplaces to start adding value beyond the obvious “allowing demand and supply sides to connect”. By doing this, they abstract away the noise in pricing inherent in a fragmented supplier base, and present the supply-side goods/ services to customers in a more consumable form. The idea is to make sure that pricing noise doesn’t become a barrier in customers choosing to use the marketplace.

The earliest scalable implementation of this was Amazon Buy Box which allowed ‘n’ sellers, all selling the same product ‘a’ to offer low price in result of their listing showing up as the default buying option whenever product ‘a’ showed up in search results. It was simple but it took off the burden on part of the customers to find the best price for the product ‘a’ they wanted to buy. eBay, on the other hand, still requires customers to figure out the best deal, thereby, earning the reputation of a flea market.

A more nuanced example is Amazon removing lack of price transparency across thousands of SKUs of CPG goods by distilling all pricing down to one specific unit of comparison — ounces. This is powerful because it has not only allowed Amazon to minimize decision remorse among buyers, but has also enabled it to successfully demonstrate to buyers the value of opting for CPG subscriptions as opposed to one-time purchases. Subscriptions, as anyone can guess, is a great business, leading to a much more stable revenue stream.

Amazon showing “per ounce” price for all toothpaste SKUs

Level 2: Deploying pricing tactics opportunistically

Pricing tactics are a collection of opportunities where marketplaces consider that by inserting themselves into the supplier pricing, they can fundamentally influence the customer decision. These are opportunistic insertions meant to drive goals on customer acquisition and retention, while maximizing the value they can extract from customers.

Uber/ Lyft, with their approach to surge pricing, have long been the visible leaders in pricing tactics. Their switch to upfront pricing is an extension of that tactic, giving them even more leverage. Not only does it address the problem of price transparency referred to above, but it also acts as the foundation for loyalty programs such as algorithmic push of promo codes that can make a customer’s ride to destination ‘x’ on Uber cheaper than that on Lyft, resulting in him/ her choosing Uber for that ride. Underlying that pushed promo code is the understanding that converting the customer to choose Uber for that ride is net positive (higher LTV) for Uber.

Level 3: Acting as a price guarantor

Pricing guarantee is the act of marketplace offering fixed pricing for certain goods/ services. It differs from the pricing tactics above in that these price guarantees persist for days or months and are intended to lock-in customers for those goods/ services, and possibly beyond.

While Amazon Prime has been one of the earlier implementations of a price guarantee (in this case shipping price), Uber/ Lyft are more interesting examples. Uber launched a monthly pass while Lyft caps the price of rides in SF. These are initiatives designed to build/ retain the demand base with the hope that supply base will scale to meet the demand, in process reducing inefficiencies and creating an incentive for suppliers to fund the discounts inherent in guarantees themselves.

Lyft’s fare fencing in SF

The ability of a marketplace to execute on the three levels of pricing products mentioned above would typically increase as it matures and captures more data. However, if done the right way, these products should be built in a particular sequence. It is hard to become a price guarantor without having experimented with pricing tactics to understand customer response. Similarly, it is hard to experiment with pricing tactics without removing price ambiguity in decision making. The first step in removing price ambiguity is to understand what the marketplace is selling and how suppliers are pricing it. The earlier marketplaces get started on that, the better.