1/ It's a misconception that superior restaurant supply creates an impenetrable network effect amongst food delivery platforms. Yes - the below flywheel from $DASH's prospectus is real. Restaurant supply increases demand which in turn increases restaurant supply, and so forth.
2/ But the key is to understand that resto supply shows a diminishing return to network effects (see chart).

Restos fall into three categories; chains, differentiated SMBs ("local favourites") and undifferentiated SMBs. Local favs are the unique must haves, the top 10%.
3/ Local favs are so beloved and desired by consumers, they'll download a platform's app just to order from it. And they'll switch apps if the resto leaves. These are must haves, and platforms often provide lucrative incentives to sign them up exclusively.
4/ But undifferentiated SMBs make up the bulk of most restos. It's that typical shawarma or burger joint down the street. They have no unique brand or loyal customer base. They mostly get their sales from foot traffic and convenience. Most importantly, they're *substitutional*.
5/ It's this substitution that drives the diminishing returns. How many shawarma restos do you really need to see when you open the app? 10? 25? How much more likely are you to order if we go from 100 to 101 shawarma resto?
6/ There's a high chance that if I just show you 5, one of them will be "good enough". And this concept of "good enough" extends to all cuisine types, price points, etc. If you aggregate all the curves you'll see user conversion rates tend to plateau at about 150-200 restaurants.
7/ Now here's the interesting part. In large urban centres (ie NYC, Chicago, etc.), there are so many restos you don't need to add them all to show ~150 restaurants to a user (ie in Toronto I can see >500 in downtown). And this is where the network effect starts to break.
8/ Instead of trying to fight an uphill battle by adding or taking supply from an incumbent, I can go after the greenfield or new restos who aren't on any platform and still get to that magic 150.
9/ Which brings me to my final point. This is one of the reasons $GRUB is in such a precarious position. Their core markets are dense urban centres. $UBER and $DASH have been rapidly adding resto supply because theres so many of them. Ie $GRUB lost >1/2 it's mkt share in Chicago
10/ And this is why $DASH is in a strong position. In most suburban and rural markets, there literally isn't 150 restos for you to add on a platform. By adding all of the available supply and generating superior demand; there's no easy greenfield supply to use as a beachhead
11/ PS. this concept extends beyond just pure marketplaces. You can see it in a lot of businesses where the good or service is not 100% commoditized. ie $CVNA and $OPEN show similar demand and supply relationships.

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More from @ProtagorasTO

25 Dec 20
1/ Been following $BABA comments on FinTwit, not an expert but some observations and thoughts:

(1) Lots of ppl selling seemed to have bought the stock speculatively; assuming it'll go up and to the right
(2) China experts I've spoken to think the 13% drop is a major overreaction
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25 Dec 20
1/ So I use to run a competitive intel team and spent a god awful amount of time sourcing data.

If you aren't familiar with Second Measure they effectively aggregate online sales data (ie credit cards) which they then sell to co's as market share data

bloomberg.com/company/press/…
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Read 8 tweets
5 Dec 20
1/ $DASH about to IPO for around $30B. On top of the profitability question, there's another one about the durability of that profitability (moats). Having launched 2 and 3 sided mktplces for Eats, I can tell you the latter was exponentially harder to build *and* manage.
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3/ 2 sided food delivery different from ride hailing where it's viewed as an incremental earnings. If restos don't get any orders, that's OK because they're running their dine-in biz. This takes pressure off the aggregator, especially as they launch new markets and demand lags.
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1/ Finally bought some Sea ($SE) after months of due diligence. Current price is $177, here is my expected value:

Bear - $49 * 15%
Base - $140 * 60%
Bull - $377 * 25%

Price Target = $185

For a great summary checkout @juliey4’s substack. Instead, below is a thread on risks 👇
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