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What Shopify and Amazon can learn from Mimetic Theory (Thoughts on Ecommerce Pt 2)

01 Intro

This is the second part of my essay on the state and future of ecommerce. In part one we discussed the current status quo of online shopping and looked at the different models and value chains behind Amazon and Shopify.

In this part, I’ll explain what I believe is currently missing in the online shopping experience and what Shopify and Amazon (or someone else) should build.

02 Cheap Options vs. Best Options

Let’s kick things off with a chart that Benedict Evans tweeted recently:

The easy way to read this chart is that consumers are becoming less interested in finding the cheapest options and are instead searching for the best options. I find that difficult to believe.

A perhaps more interesting interpretation is that Google’s “cheap” search queries are declining because users already know where to find the cheapest option: On Amazon (and other vertically specialized search engines like OTAs).

As we discussed in last week’s article, Amazon – like Google – is primarily a search engine. But since all its search results – unlike Google’s – are products, it’s easy to rank them by price. If you already know what you want, there’s no point in searching on Google first. Your shopping journey starts and ends on Amazon.

But what about the “best” option?

Amazon’s default search results are “Featured”, which factors in a variety of criteria (purchase frequency, availability, reviews, …) to show you the most relevant products. But that’s not the same as the best. (Side note: It actually turns out that the most relevant results also happen to be the most profitable for Amazon). You can also choose to rank results by customer reviews, but those scores don’t feel very trustworthy either.

As a result, non-price driven Amazon purchase journeys initially start on other sites which help users figure out what the best product for them is (through curation and reviews). This is similar to the Shopify model, which relies on discovery channels such as Instagram and Pinterest to drive users to its stores.

In contrast to Shopify though, there is not one – or even a few -dominating channels. Discovery is spread across many, many different websites, which Amazon rewards with its affiliate program. The fact that Amazon just drastically reduced its affiliate fees is perfect evidence of how little negotiating power these individual sites have in this value chain, despite their collective importance.

The high number of affiliate partners also explains why people are still using Google to search for “best” options. Not only do consumers need to figure out what the best product is – they first need to figure out what the best product review site is.

The second problem I see is that reviews only work for a handful of product categories. You can only rank and compare products if they have a strong utility. For example, you can determine what the best TV is by looking at screen resolution or HDR support. These features are easy to measure and compare.

But how would you decide what the best pair of sneakers is? Or the best handbag? You could look at build quality or materials, but those attributes are neither easy to quantify nor do they have an actual influence over what people perceive as the best.

So how do you determine what the best option is when utility isn’t the decisive factor in the purchase decision?

03 Mimetic Desire

A good framework to understand how consumers make purchase decisions is René Girard’s Mimetic Theory. René Girard was a French anthropologist and philosopher who has gained quite a following among people in tech in recent years, largely driven by the influence of his ideas on Peter Thiel.

The core idea behind mimetic theory is that human development is based on imitation. What sets humans apart from other species is our ability to learn by observing and copying others. According to Girard, this includes watching and imitating what other people desire.

This is not something most of us are aware of. We think we make autonomous purchase decisions based on objective facts (“These shoes are waterproof”) or personal preferences (“I like the way these sneakers look”).

In reality though, Girard argues, there is never a direct relationship between subject (the consumer) and object (the product). Instead, the relationship is always triangular between the subject, the object and a so-called mediator – someone the subject is drawn to and wants to imitate.

In other words: We don’t actually want the object itself. What we really want is to be like the person we admire. The object is just a means to an end.

The person we are trying to imitate might be a celebrity, but it could also be one of “the cool kids at school” or someone you discovered on Twitter or Instagram.

As a consequence, there isn’t a “best sneaker”. What you perceive as “the best” isn’t based on objective attributes, it depends on who you are trying to imitate.

As Alex Danco brilliantly summarizes in his essay on Girard, many advertisers already leverage mimetic desire in their campaigns:

Advertisers understand this principle really well: you’re not trying to convince somebody that they want Bud Light or a Ford F150; you’re telling them they ought to desire membership to a particular peer set, and the way to become a part of that group is to drink Bud Light and drive an F150. It’s why Abercrombie can advertise their clothes with models that aren’t actually wearing any of those clothes; the clothes aren’t the point.

This is also why influencer marketing works so well and why Instagram has become the perfect discovery channel for Shopify.

04 What Shopify Should Build

As we discussed in last week’s essay, Instagram is both a blessing and a curse for Shopify. On the one hand, it is the perfect discovery channel for the type of products that are typically sold by Shopify merchants: visually appealing objects you didn’t even know you wanted (fashion, homeware, furniture, etc). On the other hand, too much reliance on Instagram can become dangerous. A demand aggregator always has the upper hand over a supply aggregator as evidenced by the high tax Shopify D2C brands have to pay to Instagram in the form of ads.

Nevertheless, further integrating with Instagram is probably a good idea for Shopify. Instagram’s user behavior is a prime example of mimetic desire. Users can scroll through the life of the person they want to imitate to get an idea of what they should desire.

Shopify already announced a deeper integration with Instagram and Facebook last week, now shops can sell directly on Instagram. The ideal feature, however, would allow users to buy objects straight from the feed of their favorite influencers.

While brands will still be important (for signaling, among other things), I suspect that a lot of stores will become commoditized over time. Ecommerce will become more modularized as transactions shift from both retailers and D2C brands to individual influencers.

It’s not hard to imagine a future with a separate Instagram profile tab that lists all the products a user recommends. The user becomes the window display – the actual store is just an API in the background.

Similarly, should Shopify decide to make its Shop app an actual discovery platform, it should build its recommendation feed around influencers – not shops.

Rather than an algorithmic feed with random products, the app should feature collections of products that certain people use or recommend. Apps like Svpply and Kit have tried to build similar product recommendation services, but none of them have ever gained mainstream adoption. Yet I’m still convinced that there is a market for a stand-alone app that does curated product discovery.

05 How Amazon Could Leverage Mimetic Theory

Amazon is not a product discovery platform, it’s a search engine. It works best when you already know what you want to buy. When you search for “Sapiens”, Amazon will give you a variety of options to buy Yuval Harari’s bestseller (audiobook, Kindle, hardcopy, etc). Perfect.

If you don’t have a specific book in mind yet, however, and just want to discover a history book, Amazon becomes useless. It will show you a list of every SKU available that fits the history book description, but no real guidance on which book you should pick.

But what if you could filter and rank search results by mimetic desire?

Instead of a seemingly random list of books, Amazon should now only show me reading recommendations from people I admire. Who these people are could easily be derived from Twitter data, for example (users I follow + whose tweets I engage most with).

Search results are now ranked by my personal memetic score. I can also see at first glance why each particular book in the list is relevant for me. Not only would this feature improve Amazon’s search results, it would also turn the site into more of a discovery platform.

06 Closing Thoughts

Given their respective value chains, Amazon and Shopify both have an interest in becoming better at discovery. Technology companies have a tendency to (try to) solve discovery with automated recommendation engines, but that’s not how we make purchase decisions.

Algorithms are not the reason why we buy things, no matter how good they are. Mimetic desire is.

This is why curation is underrated – not because it is actually better than algorithmic suggestions, but because it is perceived as being better.

If this essay has inspired you to imitate me and my desires, feel free to follow me on Twitter. It would a be great honor to become your mediator.

Thanks to Gonz Sanchez, Kent de Bruin and Max Cutler for reading drafts of this post.

May 28, 2020  ×  Berlin, DE

Thoughts on Ecommerce (Pt 1): The Status Quo

01 Intro

With much fanfare and many hot takes on Twitter, Shopify launched one of their “most significant products ever” a few days ago: a consumer-facing shopping app, simply called Shop.

Many have been interpreting this as a massive shift in Shopify’s strategy to compete more directly with Amazon.

I’m not so sure that’s the case.

Inspired by the launch of Shop, I decided to write a two-part essay on ecommerce. The first part – the article you’re reading right now – looks at the current state of online shopping. It explains the business model behind Shopify (and Amazon) and how Shop fits into that strategy.

The second part – which I’ll release next week – is an outlook on the future of ecommerce. It describes what I believe is currently missing in the shopping experience and what Shopify should build next.

Let’s dive into it.

02 How Shopify and Amazon operate

To start things off, let’s first take a look at how Shopify and Amazon operate – because while both of them are ecommerce companies, their strategies are actually fundamentally different.

(Disclaimer: This section is essentially a brash copy summary of Ben Thompson’s excellent analysis on the same topic)

Shopify is first and foremost an infrastructure company. It provides a platform on which merchants can build their own stores to have a direct relationship with their end customers. This is why the type of business Shopify enables is often referred to as “direct to consumer” (D2C).

Amazon, on the other hand, is more of an aggregator. Alongside its own supply, Amazon lets any merchant sell their products on the Amazon.com website. But it is always Amazon which owns the customer relationship – never the merchant. The supply side becomes commoditized.

It’s not just the two companies’ strategies that are different – they also serve two completely different types of shopping behavior.

03 Pull vs Push Commerce

Any internet service can broadly be categorized based on two types of user actions: Pull and push.

Google is the perfect example of a Pull service. Users are actively looking for a particular piece of information or an answer to a specific question (e.g. “how to make pancakes?” or “are koalas bears?“). Google’s search engine lets you pull that information.

Facebook, on the other hand, is a typical Push service. The user behavior is a lot more passive since you don’t have to actively ask for information. Instead, Facebook automatically pushes the most relevant content into your newsfeed.

Amazon is essentially the Google of ecommerce. It’s primarily a search engine and works best when you already have an idea of what you want to buy. Amazon is not great at discovery though. It doesn’t show you things you didn’t even know you were interested in.

So who, then, is the Facebook of ecommerce?

That question is a little more difficult because there’s not one clear answer. Instead of one dedicated platform for product discovery, we have seen social networks like Instagram and Pinterest slowly morph into product discovery channels. And the fact that they are not pure ecommerce apps, but insert products between organic content, is probably exactly why they perform so well.

The types of products that work particularly well on Instagram are things that are visually appealing. You don’t see ads for HDMI cables or windshield phone mounts on Instagram – those are typical Amazon products.

Instead, it’s products like jewelry, cosmetics, fashion or home accessories that do well on Instagram – and those are classic Shopify D2C brands.

Shopify is powering the infrastructure of the “Facebook for ecommerce”, but it doesn’t own or control the entire channel. And that’s a risk.

04 Demand and Supply Aggregation

Shopify’s model actually looks a bit more like this:

While some of that Instagram traffic might be organic, the most significant chunk of customers is coming from auction-based ads (due to Instagram’s no-link-policy). This means that as competition increases (and it always does once one D2C brand in a particular segment sees some success, because there aren’t any real barriers to entry), ad prices go up.

As a result, many of Shopify’s merchants aren’t really direct-to-consumer brands, they are more like direct-to-consumer-but-with-Instagram-in-the-middle-eating-all-of-their-margin brands.

Instagram capturing most of the value is a perfect example of why demand aggregation is always more powerful than supply aggregation. Too much reliance on a powerful gatekeeper like Instagram is a risk for Shopify and its merchants.

Luckily for Shopify though, there are several ways to mitigate that risk.

05 Shopify’s Options

The first thing to note is that Instagram isn’t the only product discovery channel driving traffic to Shopify stores. As mentioned earlier, there is not one leading contestant for the role of “Facebook for Ecommerce”. Pinterest, YouTube and Twitter are also product discovery engines (among many other services trying to do the same).

One of Shopify’s options is therefore to diversify the portfolio of user acquisition channels its merchants can use. This is why I find Shopify’s Pinterest and Google Shopping partnership announcements from the last few weeks way more interesting than the launch of Shop.

Shop has been touted as Shopify’s own product discovery channel – which would be another way to tackle its current dependence on other platforms. Instead of relying on Instagram traffic, Shopify could simply start aggregating the demand side itself and become more of a marketplace like Amazon.

But if you install and open Shop, you’ll notice that that’s not really what the app does. Instead of a feed with product recommendations, Shop connects you with merchants you have purchased from in the past.

Here is why:

Another option to defend Shopify merchants against ever increasing customer acquisition costs (read Instagram ads) is to simultaneously increase customer lifetime value. This is exactly what Shop is supposed to do.

By connecting you with merchants you have bought from before, Shop will recommend you additional products that you might be interested in from the same sellers. The result is higher post-purchase loyalty and thus higher LTV which makes it easier to justify high initial acquisition costs via Instagram.

That being said, it’s not difficult to imagine a future where Shopify also starts recommending products from other merchants. What exactly this should look like is the topic of part two of this essay, which you can read here.

Do you have feedback or thoughts on this post?
If so, I’d love to hear them!

Thanks to Jan König for reading drafts of this post.

May 17, 2020  ×  Berlin, DE

AirPods as a Platform

01 Intro

One of the favorite activities of tech analysts, VCs and similar Twitter armchair experts is to predict what the next big technology platform might be.

The usual suspects that come up in these conversations are VR/AR, crypto, smart speakers and similar IoT devices. A new contestant that I’ve seen come up more frequently in these debates recently are Apple’s AirPods.

Calling AirPods “the next big platform” is interesting because at the moment, they are not even a small platform. They are no platform at all. They are just a piece of hardware.

But that doesn’t mean they can’t become platform.

02 What is a platform?

Let’s first take a look at what a platform actually is.

At its core, a platform is something that others can build on top of. A classic example would be an operating system like iOS: By providing a set of APIs, Apple created a playground for developers to build and run applications on. In fact, new input capabilities such as the touch interface, gyroscope sensor and camera allowed developers to create unique applications that weren’t possible before.

Platforms are subject to network effects: More applications attract more users to the platform, while more users in turn attract more developers who build more apps.

It’s a classic flywheel effect that creates powerful winner-takes-all dynamics. This explains why there are only two (meaningful) mobile operating systems – iOS and Android.

It also explains why everyone is so interested in upcoming platforms – and why Apple might be interested in making AirPods a platform.

03 Why AirPods aren’t a platform

In their current form, AirPods are not a platform. They don’t provide any unique input or output functionalities that developers could leverage. Active Noise Cancellation and Transparency Mode are neat but not new or Airpods-exclusive features – other headphones offer exactly the same. In either case, developers don’t have any control over them and thus can’t build applications that use these functionalities.

Some say that AirPods will give rise to more audio apps because they are “always in” which in turn will lead to more (and perhaps new forms of) audio content. That might be true – content providers are always looking for alternative routes to get consumers’ attention – but, again, it does not make AirPods a platform. You can use any other pair of headphones to use these audio apps as well.

If Apple wants to make AirPods a platform, it needs to open up some part of the AirPods experience to developers so that they can build new things on top of it.

04 On Siri & Voice Platforms

The most obvious choice here is Siri, which is already integrated into every pair of AirPods.

In contrast to other voice assistants like Alexa and Google Assistant, Apple has never really opened up Siri for 3rd-party developers. If they did, it would create a new platform that could have its own ecosystem of apps and developers.

But I’m not convinced that this is Apple’s best option.
Let me explain why.

Opening up Siri wouldn’t make AirPods a platform, it would make Siri a platform. This might sound like a technicality, but I think it’s an important difference. As Jan König brilliantly summarized in this article, voice isn’t an interface for one device – it’s an interface across devices. It’s more of a meta-layer that should tie different products together to enable multi-device experiences.

This means Apple has little interest in making Siri an AirPods-exclusive. Voice-based computing works best when it’s everywhere. It’s about reach, not exclusivity. This is part of the reason why Google and Amazon excel at it.

At the moment, Siri’s capabilities are considerably behind those of Google Assistant and Alexa. Again, this isn’t overly surprising: Google’s and Amazon’s main job is finding the right answers to users’ questions. The required ML capabilities for a smart assistant are among the core competencies of these two companies.

But even Amazon and Google haven’t really figured out the platform part yet, as indicated by the lack of breakout 3rd-party voice applications. It seems like the two platforms are still looking for their product-market-fit beyond being just cheap speakers that you can also control with your voice.

This is partly because the above-mentioned use case of voice as a cross-device layer isn’t something developers can build with the current set of APIs.

The other big reason I see is that people are mistaking voice as a replacement for other interfaces. Movies like Her paint a future where human-computer-interaction primarily occurs via voice-powered smart assistants, but in reality, voice isn’t great as a primary or stand-alone interface. It works best as an *additional* input/output channel that augments whatever else you are doing.

Let me give you an example: Saying “Hey Google, turn up the volume” takes 10x longer than simply pressing the volume-up button on your phone. It only makes sense when your hands are busy doing other things (kitchen work, for example).

The most convincing voice app I have seen to date was at a hackathon where a team used the StarCraft API to build voice-enabled game commands. Not to replace your mouse and keyboard but to give you an additional input mechanism. Actual multitasking.

05 What Apple Should Build

I’m not against Apple opening Siri for developers. On the contrary, given that AirPods are meant to be worn all the time, a voice interface for situations that require multitasking is actually a very good idea. But voice input should remain the exceptional case. And it shouldn’t be what makes AirPods a platform.

Instead of voice, I’d love to see other input mechanisms that allow developers to build new ways for users to interact with the audio content they consume.

Most headsets currently on the market offer the following actions with one (or multiple) clicks of a physical button:

These inputs were invented a long time ago and there has been almost zero innovation since. Why has no one thought about additional buttons or click mechanisms that allow users to interact with the actual content?

For example, when listening to podcasts I often find myself wanting to bookmark things that are being talked about. It would be amazing if I could simply tap a button on my headphones which would add a timestamp to a bookmarks section of my podcast app. (Or better even, a transcript of the ~15 seconds of content before I pressed the button, which are then also automatically added to my notes app via an Apple Shortcut.)

Yes, you could build the same with voice as the input mechanism, but as we discussed earlier, saying “Hey Siri, please bookmark this!” just doesn’t seem very convenient.

While podcast apps might use the additional button as a bookmarking feature, Spotify could make it a Like button to quickly add songs to your Favorites playlist. Other developers could build completely new applications: Think about interactive audiobooks or similar two-way audio experiences, for example.

This is the beauty of platforms: You just provide developers with a set of tools and they will come up with use cases you hadn’t even thought about. Crowdsourced value creation.

06 Closing Thoughts

(1) The input mechanism I describe doesn’t have to be a physical button. In fact, gesture-based inputs might be even more convenient. If AirPods had built-in accelerometers, users could interact with audio content by nodding or shaking their heads. Radar-based sensors like Google’s Motion Sense could also create an interesting new interaction language for audio content.

(2) You could also think about the Apple Watch as the main input device. In contrast to the AirPods, Apple opened the Watch for developers from the start, but it hasn’t really seen much success as a platform. Perhaps a combination of Watch and AirPods has a better chance of creating an ecosystem with its own unique applications?

(3) One thing to keep in mind is that Apple doesn’t really have an interest in making AirPods a standalone platform. The iPhone (or rather iOS) will always be the core platform that Apple cares about. Instead of separate iPhone, Watch and AirPods ecosystems, think about Apple’s strategy as more of a multi-platform bundle. Even as a platform, AirPods will remain more of an accessory that adds stickiness to the existing iPhone ecosystem.

Do you have thoughts or feedback on this post?
If so, I’d love to hear it!

Thanks to Jan König for reading drafts of this post.
Apr 19, 2020  ×  Berlin, DE

Signaling as a Service

01 Intro

One of the best books I have read in the last few years is The Elephant in the Brain by Robin Hanson and Kevin Simler.

The book makes two main arguments:

a) Most of our everyday actions can be traced back to some form of signaling or status seeking

b) Our brains deliberately hide this fact from us and others (self deception)

So we think and say that we do something for a specific reason, but in reality, there’s a hidden, selfish motive: to show off and increase our social status.

You may have heard about a similar concept before called conspicuous consumption. Conspicuous consumption describes the practice of purchasing luxury goods (or services) for the sake of signaling the buyer’s wealth in order to attain or maintain a certain social status.

A classic example of this would be a luxury watch: A Rolex isn’t better at telling the time than a cheap Casio – but a Rolex signals something about its owner’s economic power and thus their social standing.

This is not a new theory, but Simler and Hanson argue that a lot more human behavior can be explained by signaling. Here are a few examples from the book:

  • Consumption
    Signaling does not only explain luxury purchases but also consumption of all sorts of other goods: “Green products” are more about signaling a prosocial attitude than actually helping the environment. Other consumption signals include loyalty to a specific subculture (e.g. band t-shirts), athleticism & health consciousness (athleisure clothing) or intelligence (e.g. Rubik’s Cube).

  • Charity
    There are several indicators that suggest that giving to charity isn’t really about improving the well-being of others: The lack of effective altruism demonstrates that we don’t care much about the actual outcome of our donations and studies show that our charitable behavior is heavily driven by visibility (hardly any donations are anonymous), peer pressure (95% of donations are solicited) and mating motives (donations are higher and more likely when observed by a member of the opposite sex). Charity is about appearing generous rather than actually doing good.

  • Education
    You would think that going to school is about learning and acquiring skills, but then why do students pay tens of thousands of dollars for Ivy League schools when all of the learning material is effectively available online for free? Why do we use grading systems when we know that students learn worse when being graded? The answer, again, is signaling: Education helps with credentialing and signaling to potential employers.

There are many more examples in the book (and I recommend reading the whole thing), the point the authors are trying to make is clear: Almost everything has a signaling component – we are just not aware of it. In fact, Hanson believes that “well over 90 percent” of human behavior can be explained by signaling.

Whether or not you agree with that exact number, I think it’s an interesting thought experiment to look at a specific behavior and think about what the hidden signaling subtext of that behavior might be.

Ever since finishing the book, the signaling behavior I’ve been thinking about the most is the adoption – and more importantly the monetization – of software products and services.

This is what this blog post is about.

02 Components of Signaling

Let’s take a closer look at signaling first.

The way I see it, signaling can be broken down into different components:

  • Signal Message
  • Signal Distribution
  • Signal Amplification

To better illustrate what I mean let’s take a pair of sneakers as an example.

The first component is what I call the signal message. This is whatever (hidden) subtext you are trying to convey. In the case of our sneakers this is probably something along the lines of “I can afford to spend $100 on a pair of shoes” and “I live an active, healthy lifestyle”.

In order to get your signal message across to other people you need some form of signal distribution. This is the second component of my signaling taxonomy.

So how are you going to distribute the signal message of your sneakers? You simply wear them where other people can see them. The obvious constraint here is that your signal distribution is limited to things you can display in public. This is why people are willing to spend hundreds of dollars on shoes but not on socks.

The third component is signal amplification: If everyone is wearing cool sneakers .. how do you make sure yours stand out? You could buy the pair with the most noticeable design or the one with the flashiest colors, for example. These signal amplifiers help you to better compete against status rivals.

Let’s recap: Signaling can be broken down into signal message, distribution and amplification. “Real world” products are great at visualising a signal message due to their physical nature. However, as a consequence there are also physical boundaries to distribution because there are only so many people you can signal to at once.

But what about software?

03 Software’s Signaling Limitations

Digital products have one crucial disadvantage over atom-based products and services: Intangibility. Apps live on your phone or computer. No one can see them except for you.

The signal message of a fitness app is the same as that of a gym membership or athletic wear (strength & fitness display), but the signal is much weaker because you can’t distribute it to anyone.

I believe that this is the main reason why consumer software companies have a harder time monetizing than their physical counterparts.

Here’s another example: eBooks have never caught up with paper books despite being more convenient. On the contrary, physical book sales have remained stable (and in some markets even increased) in recent years. Interestingly though, people spend less time reading them. Their value seems to stem from lying around the house to impress visitors (see also coffee table books) – a benefit digital books simply can’t offer.

Another point of evidence is the lack of luxury software products. People spend absurd amounts of money on jewellery, handbags and cars, but I can’t think of a piece of software with an even remotely similar price tag. Sure, people have tried to sell $999 apps but those never took off.

The app that comes closest to a luxury service that I can think of is Superhuman, which charges its users $30 a month for an email client (which you could also get for free by just using Gmail).

But there’s a difference to other software products: Superhuman has signal distribution built in. Every time you send an email via Superhuman, your recipient will notice a little “Sent via Superhuman” in your signature.

In a similar fashion, apps like Strava use their built in social networks as a signal distribution channel for their premium subscriptions. Users who have upgraded get a little premium badge and appear in exclusive premium leaderboards.

Another interesting way to solve the signal distribution problem is to add a physical product to a software’s premium offering, which allows signaling via casual contact (like fashion products).

Neobanks such as N26 or Revolut reward their premium users with a fancy metal card which doesn’t just look nice but is also noticeably heavier than normal credit cards. There aren’t a lot of other benefits that justify the hefty €17/month price tag these banks charge for their premium tiers – clear evidence that the primary monetization driver is in fact signaling.

04 Signal Distribution as a Service

While many digital products struggle to monetize as well as their real-word counterparts, the Internet has created a whole new kind of signaling utility: Distribution as a service.

Physical products are limited to the amount of people you see in public – but the Internet allows you to reach a virtually infinite number of people at once.

This is the primary value that social networks like Facebook, Snapchat and Instagram provide. These services don’t contain a hidden signal message. All they do is provide signal distribution at scale. Want to increase the number of people who can see your sneakers? Just take a photo and post it on Instagram.

A positive feedback loop of views, likes and comments helps you to quantify how successful your signal distribution has been.

Eugene Wei calls this Status as a Service:

By merging all updates from all the accounts you followed into a single continuous surface and having that serve as the default screen, Facebook News Feed simultaneously increased the efficiency of distribution of new posts and pitted all such posts against each other in what was effectively a single giant attention arena, complete with live updating scoreboards on each post. It was as if the panopticon inverted itself overnight, as if a giant spotlight turned on and suddenly all of us performing on Facebook for approval realized we were all in the same auditorium, on one large, connected infinite stage, singing karaoke to the same audience at the same time.

It’s difficult to overstate what a momentous sea change it was for hundreds of millions, and eventually billions, of humans who had grown up competing for status in small tribes, to suddenly be dropped into a talent show competing against EVERY PERSON THEY HAD EVER MET.

Social networks are subject to network effects: The more users join a network, the more valuable the network becomes. Your incentive to use Facebook increases with the number of people you can distribute your signal message to. This is why social networks are free to use – in order to maximize their signaling potential they need to acquire as many users as possible.

A social network like Path attempted to limit your social graph size to the Dunbar number, capping your social capital accumulation potential and capping the distribution of your posts. The exchange, they hoped, was some greater transparency, more genuine self-expression. The anti-Facebook. Unfortunately, as social capital theory might predict, Path did indeed succeed in becoming the anti-Facebook: a network without enough users. Some businesses work best at scale, and if you believe that people want to accumulate social capital as efficiently as possible, putting a bound on how much they can earn is a challenging business model, as dark as that may be.

While Path did indeed fail as a distribution provider, I would argue that keeping the network’s size small can still have benefits in line with my signaling theory: Deliberately limiting the number of people who can join a network (e.g. by charging a membership fee) creates scarcity which in turns makes the network more interesting. Network membership becomes the signal message. Users pay a membership fee to signal to other members that they are part of the tribe.

Some examples:

These social networks still rely on some critical mass and network effects, but need to set an artificial limit to the amount of people who can join. If membership isn’t scarce, the membership loses its signal message. The same applies to physical products: Apple will never offer a cheap iPhone to compete with low-end Android devices – it would destroy the company’s signal message that the iPhone is a luxury product.

In contrast to iPhones, there is another limitation that social networks with this strategy face: Like in the before mentioned software examples, signal distribution is limited to the in-group. Signaling however is strongest when you can signal tribe affiliation to both in-group members as well as outsiders. This is also the reason why luxury car manufacturers don’t limit their advertising campaigns to potential buyers but deliberately extend it to people who will never be able to afford the car.

But as we’ve discussed earlier, the intangibility of software makes signaling to the out-group difficult: You would instagram a photo from your Equinox gym, but would you share a screenshot of your MyFitnessPal Premium subscription?

Instead of monetizing network membership, the software products that monetize most successfully have chosen another strategy: Make memberships free and monetize signal amplification instead.

05 Monetizing Signal Amplification

Earlier, we defined signal amplification as product features that help users to increase the strength of the signals they want to convey to stand out of the crowd. In the example of our aforementioned sneakers, flashy colors help to amplify our signal message.

Similar amplifiers exist in the software world, but they often come in the form of a set of tools. Take the Instagram photo editor for example: Applying filters to your photos makes them look nicer and hopefully more noticeable in the app’s status arena – aka the newsfeed.

Eugene Wei calls these amplifiers Proof of Work:

Almost every social network of note had an early signature proof of work hurdle. For Facebook it was posting some witty text-based status update. For Instagram, it was posting an interesting square photo. For Vine, an entertaining 6-second video. For Twitter, it was writing an amusing bit of text of 140 characters or fewer. Pinterest? Pinning a compelling photo. You can likely derive the proof of work for other networks like Quora and Reddit and Twitch and so on. Successful social networks don’t pose trick questions at the start, it’s usually clear what they want from you.

While Instagram, Twitter and the other above-mentioned social networks are free to use, other companies have figured out a clever way to monetize their signal amplifiers. The two companies who have done this most successfully are Tinder and Fortnite.

Let’s start with Tinder.

06 How Tinder Monetizes Signal Amplification

Tinder is a social network for dating – or in other words, a signal distribution network to display your mating worthiness. Like other social networks, Tinder is subject to network effects: The value of the network increases with its size. The obvious strategy therefore is to make memberships free so that as many people as possible can join. (Technically, dating apps are two-sided networks. The value for female members increases with the number of male members and vice versa.)

Tinder’s primary proof-of-work mechanism is to optimize one’s profile picture for a maximum number of swipe rights. But with millions of rivals on the same platform, competing for status with just a few pixels of profile picture real estate becomes a really hard task.

Luckily, Tinder offers a variety of additional signal amplifiers that help you to stand out. The sole purpose of features like Tinder Boost and Super Likes is to outcompete status rivals by giving you preferential signaling treatment. And guess what – they come with a price tag.

Tinder’s entire business model is built on the assumption that people are willing to spend money on signaling. That assumption seems to be correct: Tinder made a staggering $1.2 billion in revenue last year making it one of the most successful apps world wide.

07 Fortnite – The Ultimate Status Battleground

Fortnite has seen even greater levels of financial success: In the last two years combined, the game has brought in more than $4 billion in revenue – and like Tinder, it too monetizes primarily with signal amplification.

For the longest time, the monetization model of games used to be – and for many still is – one-time upfront payments which then allowed you to play the game for as long as you wanted to.

That business model changed with the emergence of mobile games on iOS and Android. Instead of charging players upfront for access, mobile games are typically free to play. However, in order to progress faster and win the game, users will eventually have to pay for small upgrades with in-app purchases.

Similar to these traditional mobile games, Fortnite is also free to play. As a multiplayer game that many play with their real-life friends, this strategy makes a lot of sense – the network becomes more valuable the more people join.

In contrast to mobile games however, Fortnite is also free to win. None of the in-app purchases available impact the core gameplay. You can’t buy more powerful weapons or stronger armor that give you an advantage over other players.

That’s because the core gameplay isn’t the core signaling layer – and thus also doesn’t offer the greatest monetization potential.

Fortnite is more than just a game. It’s more like a giant virtual theme park, or the closest thing we have to a metaverse even. People don’t just come for the battle royale game – they come to meet and hangout with friends.

But if The Elephant in the Brain has taught us anything it’s that you don’t just meet people for fun. You are engaging in a constant battle for attention and status. Signaling is the meta game that Fortnite provides – and monetizes.

Fortnite’s monetization model is based on cosmetics: The skin your character wears; the looks of your glider and the tools you use; the way your character dances (emotes) – all of these are signaling amplifiers with different signal messages to uniquely express yourself in the game. And you have to purchase them.

Fortnite has pulled off what so many other software products have been struggling with – monetizing a purely digital product whose value is not based on utility or entertainment but solely on the one thing we all secretly care so much about: Signaling.

08 Summary

While the physical nature of material goods and services is perfect to visualize hidden signaling messages, there are natural limitations to distribution and amplification.

Software perfectly complements physical goods by distributing their signal messages at scale. Maximizing scale, however, prevents it from monetizing said distribution. This is why social media services are free to use. The added signaling value is solely captured by the physical products that are being shared.

The intangibility and fungibility of software also makes it difficult to create and share (and monetize) software products that contain hidden signal messages of their own. This explains why there are no software equivalents of luxury products such as Rolex watches or Louis Vuitton handbags.

The financially most lucrative strategy for software companies is to provide distribution for free and instead monetize users who want to stand out of the crowd with paid signal amplification.

A closing thought: I tell myself that I write these blog posts “just for fun”, but let’s be honest … all I really want is to signal how smart I am. So if you could head over to Twitter and give me a Like or a Follow, that’d be great. Thanks!

Thanks to Gonz Sanchez, Jan König, Max Cutler and Robin Dechant for reading drafts of this post.
Mar 28, 2020  ×  Berlin, DE

Hello World.

So I’ve decided to start writing again.

Not on Medium, not on a Tumblr blog, not via a Revue newsletter but on a classic, old-school, self-hosted WordPress-based weblog.

I don’t want to be dependent on some 3rd-party platform and, more importantly, I want to have full control over the design (the look and feel) of my content.

As the name suggests, this is site is supposed to be a digital image or map of my life. It’s a space for the pictures I take, the thoughts that I have and the the various life logging projects I’m doing. I want as much of this content to be actual output of mine rather than reposts of stuff that I found online.

This is an experiment. We’ll see how the content, style and frequency of my posts evolve. For now I’ll try to commit to at least one post per week to make writing a habit again.

Wish me luck.

Apr 25, 2017  ×  London, GB
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