Based in Brooklyn, Packy is a startup strategy & Philly sports Enthusiast

Act 2: Why There Won't Be Any New Aggregators

Act 2: Why There Won't Be Any New Aggregators

Why Aggregators Won’t Be Disrupted By New Aggregators

It’s tempting to think that the Aggregators’ backward integration opens up the possibility for new, pure-play Aggregators to capture their market share. But I don’t think that’s going to happen.

Nor do I think that we are likely to see a massive Aggregator emerge in a new area that hasn’t already been tackled and reach the same kind of scale as the original aggregators.

Based on the Bureau of Labor Statistics’ 2017 data, US consumer spend breaks down as follows:

The largest categories, and the Aggregators that own them, are:

Housing (33%): Airbnb, Zillow

Transportation (16%): Uber, Lyft

Food (13%): Seamless GrubHub, DoorDash, UberEats

Personal Insurance & Pensions (12%): Government, Employers, etc…

Healthcare (8%): Its own animal due to regulation, to be addressed in a future post

Entertainment (5%): Netflix, Spotify

Apparel & Services / All Other Expenditures (11%): Amazon

Education (2%): this is the smallest category, but there is a lot of opportunity. More to come in future posts.

We will cover Personal Insurance, Healthcare and Education in future posts. For now, the important takeaway is that the largest non-regulated or lightly regulated consumer spending categories are controlled by Aggregators. Their scale makes it practically impossible for new platforms to catch using the same tactics that the Aggregators used.

Uber for 😵

Given that Aggregators have already staked claims in each of the largest consumer spend categories, we are unlikely to see the rise of new dominant Aggregators.

This is not just a theory. It has been borne out time and again by failed “Uber for X” companies, the belles of their respective balls just 2-3 years ago.

In March, The Atlantic studied the fates of 105 “Uber for X” companies, a group which had raised $7.4 billion combined.

Of this group, four—DoorDash, Grubhub, Instacart, and Postmates—are unicorns, start-ups valued at more than $1 billion. (Notably, all are in the delivery business.) Forty-seven are gone—28 simply closed down; 19 were acquired. But 53 are neither unicorn nor roadkill. They remain alive in the great morass of the economy, successful but lacking explosive growth; or stumbling along with scaled-back ambitions; or barely functioning, like zombie start-ups.

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These companies struggle for many of the same reasons that I wrote about in Startup Economics Lessons from Shen Yun’s Empire:

  • Low barriers to entry lead to increased competition.

  • Competition for customers forces platforms to charge customers less or pay more to acquire customers,

  • Competition for supply causes platforms pay workers higher cuts of transactions.

  • Platforms lose hundreds of millions, or billions, of dollars per year to get to scale and snuff out the competition, at which point they believe their economics will improve.

  • (And no, just because Amazon lost money for years and you’ve been losing money for years, does not mean you will be the next Amazon.)

The low margin unit economics of these business necessitate scale: at $3 margins per car wash, the Uber for Car Washes needs to facilitate 47,000 car washes to pay for 1 FT employee in San Francisco. Multiply that by hundreds of employees, plus office space, software, and marketing, and you begin to understand why it’s so damn hard to build a profitable platform business.

Ultimately, the vast majority Uber for X businesses are unlikely to reach escape velocity because of:

  • Competition: based on relative ease of launching a platform,

  • Low switching costs: “let me just check Handy to see if it’s running a better promotion than Homejoy,”

  • Relatively small market sizes: there’s a reason all four of the Uber for X unicorns are in food, a market whose TAM ARK Invest estimates could reach $3 trillion by 2030, and not in bottle service (sorry, BottlesTonight)


The Decreasing Marginal Advantage of Filling Voids

Uber is everyone’s private driver. Airbnb invites its customers to belong anywhere. When you are the first mover in a huge space impacted by a paradigm shift, you can go wide and shallow.

In Status-as-a-Service, Eugene Wei, a seasoned tech executive points out that it is much easier to build a large following if you are early on a new social network. Early Twitter users gained thousands of followers simply for being there first with a good enough brand for Twitter to recommend that people follow you. Those who joined later (other than celebrities with built-in fan bases) have had to build followings by producing content that appeals to certain niches.

To understand how this concept applies to businesses, let’s take a minute to go back to the banana stand:


banana2.jpg

Now imagine that before you ever opened your banana stand, Rita had been running a fruit market for 10 years. Since Rita built the first fruit market, fruit sellers came from far and wide - some like Briana, who owned their own fruit farms, and some like Miles, who bought fruits from the Sallies of the world in order to sell it for a higher price at Rita’s fruit market. There were apples, bananas, pineapples, kiwis, cherries, and any other fruit you could think of. Some of the fruit was ripe and delicious, some was rotten and gross. But whether delicious or rotten, Rita got 15% of the price every time a fruit was sold.

Now you want to open your banana stand (because there’s always money in the banana stand), but you know that it’s not going to be as easy as hanging a shingle and telling people that you have the best bananas. You need to talk to the people at Rita’s market, learn what they like about the bananas there, learn what they don’t like, learn how bananas make them feel, learn about what their ideal banana experience would be. You need to learn where they hear about banana sales, and what state of mind they’re in when they’re thinking about buying a banana. So you do your user research, and you come up with a plan.

You learn that most of the people who aren’t satisfied with the bananas at Rita’s and would be willing to pay are 25-34 year old women, that they like slightly smaller bananas that are more ripe than the average banana at Rita’s, and that they would love to go to a banana stand the first time they try your banana, but would be happy to be able to text a banana concierge to re-order bananas after that.

So you go directly to Briana (Miles won’t be any help to you here), and you ask her to produce shorter, riper bananas and tell her that you’re willing to finance the run. At the same time, you build your banana stand and a new text-based concierge team, all suited to providing the best experience for 25-34 year old women. You get the word about your new banana stand out in all of the places online and offline that these women told you they hear about banana sales, and some new ones that you want to test out. You tailor the message to highlight the specific advantages of your bananas that would appeal to these 25-34 year old women.

When Briana’s bananas arrive and the banana stand launches, there’s a line of 25-34 year old women around the block, waiting to try your bananas. They are a hit! As customers re-order through your text-based banana concierge, you learn more about the types of bananas that they like and you continue to work with Briana to improve the bananas for your audience. While you are building up data on these 25-34 year old women, a funny thing happens. They start telling their 18-24 year old friends about the quality of your bananas, and those 18-24 year old friends start telling their 45-54 year old parents who want to buy what the cool kids are buying, so your customer base expands, and you learn more and more about what all kinds of different people like from their bananas.

You’ve built a data flywheel that you are able to use in your product development, de-risking your investments into Briana’s banana farm and improving the experience for your customers, all while taking home more of the profit from each transaction than Rita does at her fruit aggregating marketplace.

The lesson: when the early spoils of a paradigm shift have been snatched up, you need to find your niche and go narrow and deep. You can’t win by running the same playbook as those who came before you.

Airbnb, Amazon, Zillow, Uber, Netflix and Spotify are the startup equivalent of early Twitter users: they have a massive head start (because they were first-ish), and the mechanisms in place to ensure their leads only grow over time. The next generation of successful companies will need to build their own followings by creating products and experiences that resonate with a passionate niche and grow from there.


What have we learned so far?

  1. The internet shifted the balance of power from those who control supply to those who control demand.

  2. A wave of Aggregators were born in the first decade of the 21st century and took advantage of the new world order to aggregate segments of the economy with the largest consumer spend.

  3. These Aggregators are beginning to backward integrate into supply in order to capitalize on data advantages, superior customer experiences and better economics.

  4. There is a low probability that new Aggregators will be built and achieve the same scale as the Aggregators we have discussed.

By the end of 2019, all of the APLUSS (Airbnb, Pinterest, Lyft, Uber, Spotify, Slack) companies will likely be public. With that clearing of the deck, it’s time to take a look at what the next wave of successful consumer startups will look like in Act 3: The Rise of the Natively Integrated Company.

Act 1: From Linear Businesses to Aggregators and Back

Act 1: From Linear Businesses to Aggregators and Back

Act 3: The Rise of the Natively Integrated Company

Act 3: The Rise of the Natively Integrated Company