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.

The “bundled” internet

The “bundled” internet

The perfect storm of expensive subscriptions, bad ads, and the need for growth is pushing Google and Apple to break the internet into bundles, changing it forever.

Back in the early days of the internet, people paid for newspapers to get delivered to their home, they bought music CDs, and Blockbuster was thing. There were set rules for how content creation, payment and distribution worked. Internet disrupted that. Yahoo, Google and then Facebook emerged and created an internet economy, supported by ads. Consumers started expecting most written content, if not music/videos, to be freely accessible. Newspaper publishers saw their fortunes dip.

Meanwhile, companies such as Netflix and Spotify emerged, creating large subscription businesses, and laying down a template for how to monetize video and music content. Believing that their journalism was worth paying for, newspaper publishers such as NYTimes committed to building a subscription business and saw reasonable success. Now other publishers such as Bloomberg, Business Insider, etc. have followed suit, putting a lot of their content behind paywalls. They are hoping to create a future where they will no longer be slaves of the ad dollars of Google and Facebook.

The net result is that we are in subscription hell 😈 and are only getting deeper into it. A lot of good content is now behind separate paywalls. The experience of navigating the internet in search of knowledge is no longer seamless, and the “good internet” has started to become very expensive 💰.

The “bundle” opportunity

The state of the internet as a subscription hell is not sustainable; consumers deserve better. It needs to be easier and more affordable to access good content.

Imagine most of the content available behind separate paywalls becoming accessible as part of subscription bundles sold by Google and Apple, bundles that serve all your content needs —written content (news, analyses), audio (music, podcasts) and video (TV series, movies, etc.).

Evolution of content monetization

When this happens, the market for separate paywalls will shrink (at least in number of paywalls, if not the $ value), and only the richest consumers with very specific needs till buy into subscriptions outside of these bundles. The product experience of accessing content through these bundles will be much superior to the fragmented experience of browsing through good content on the internet today, and Google and Apple will be able to price these bundles low because of their large user base. It will be irrational to pay for separate paywalls unless one really has to.

What about ads?

The question, especially in the case of Google, is why would it create subscription bundles when its entire strategy is predicated on making the content freely accessible and monetizing through ads. There are two main reasons why Google would do that.

Ads have peaked

Selling ads seemed like a good strategy for Yahoo and Google to monetize the large user base that they had built by organizing the information on the internet. However, ads are mostly a nuisance and, as a result, ad blockers have increased in popularity. Google recently launched its own ad blocker on Chrome in its attempt to discipline sites showing disruptive ads and to prevent consumers from installing more aggressive ad blockers that block all ads across all sites.

Google (and also Facebook) are also trying to make ads more relevant to consumers, so that they don’t seem disruptive and can also help these companies make more money per user because of improved targeting. It’s a precarious strategy because the better the targeting, the creepier the ads can seem. Consumers now have a greater awareness of the privacy (data) they might be giving up to access the so called free services (Google search and Facebook newsfeed). According to a recent survey, 30% of respondents distrust Facebook with their personal information, not a good sign for a company that is built on having access to that information. This is a big red flag.

Ads aren’t the best way to monetize the best customers

Eric Feng put across a great argument in this article that monetizing via ads is sub-optimal because you can’t make more money off your best customers (i.e. users who use your products the most). He attributed that to two fundamental aspects of ads monetization: frequency capping and ad load.

Then he went on to argue that what makes Amazon so powerful is its ability to monetize its best customers significantly more than its average customers, calling its strategy shared-value transaction. Its best customers enable Amazon to invest in building a compelling value proposition (cost of access and user experience) for the average customer, getting more and more of them onto the platform. Google and Facebook understand that ads might not be the best business model after all, and Google specifically has been actively trying to diversify its business.

Winners and losers

The large tech companies best positioned to capitalize on this bundling opportunity are Google and Apple, and the large tech company that is set to lose the most is Facebook. The other big tech company most likely to be effected negatively is Spotify.

Google and Apple have all the raw ingredients to offer great content bundles

  • Customer touchpoint: They have dedicated user bases for written content (Google News, Apple News), audio (Google Play Music, Apple Music, Apple Podcasts), and video (YouTube, Apple TV).
  • Supply of content: They have good relationships with content publishers and are increasingly getting aggressive about creating original content.
  • User experience: They have invested in defining great product experiences for the future, Google with its focus on web based experiences (AMP and PWA), and Apple with its focus on good native experience (e.g. Apple News allows customers to add their existing subscriptions to it).

Launching bundles is probably easier for Apple than Google because Apple doesn’t have a big ad supported business that it would need to cannibalize. There is news that Apple is already gearing up to do that.

The biggest losers in the “bundled” internet economy will be

  • Subscription businesses that only offer a subset of content today that is not differentiated e.g. Spotify. These companies will lose some of their pricing power to the bundle creators, Google and Apple.
  • Facebook (it deserves to called out separately)! It will lose because while it serves a lot of ad-supported free text and video content, it is not an app that customers of bundles will see as the entry point for paid good content. It will also lose because it has failed to build good relationships with publishers; Instant Articles was a dud.

What could further extend the lead of Google’s and Apple’s bundles will be the non-content offerings (specifically storage) they are capable of adding to their content bundles. That will hurt companies such as Dropbox. Google One is a potential start in that direction.

Google and Apple need bundles as much as consumers need them. These companies have become very large and there is a need for them to find additional sources of growth. Making more money from their best customers while solving an important customer need is a win-win.

iOS and Android are the nerve centers of the biggest ecosystems in tech. If Google and Apple are able to create attractive bundles, these ecosystems will become even more entrenched. Content will play the role that apps played in building the App Store (and consequently Apple), and the internet will never be the same again.

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.

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.