1/ People often talk about $FB's network effects but for me what really flies under the radar is their SMB acquisition engine. This is part of a three step playbook that they've replicated for reach of their properties (Facebook "Blue App", Instagram, etc.)
2/ Step 1 - focus on user acquisition, engagement, and retention. Once they understand the primary behaviours and moments of delight that hook users and drive stickiness, do everything possible with the product to reinforce this.
3/ Step 2 - create organic opportunities and free tools for businesses to interact with users in the product. For ex., FB has >140M Business Pages. These pages not only provide utility to users and familiarize businesses with FB tools but they act as a funnel into step 3.
4/ Step 3 - provide paid self-serve tools for businesses to increase their interactions and exposure to potential customers (ie paid ads). Currently $FB has >12M active advertisers
5/ It's step 2 that's really the under appreciated part of $FB's strategy. Instead of building a massive S&M org, they've leveraged product to create a global acquisition engine. Not only has this saved $10B's but you couldn't even scale this fast if you relied on people
6/ This also help explains WhatsApp's current strategy. $FB wants to attract SMBs (ie currently at 15M in India) by providing free tools (ie Kustomer) to facilitate commerce and customer service. They'll then monetize through payments and increased ad prices by "closing the loop"
7/ PS. as you can see from the charts, COVID has led to a huge explosion in SMBs coming on to $FB's free platform. There's been a lag in conversion from free to paid advertising but expect that to jump as things normalize and SMBs are once again comfortable in spending

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More from @ProtagorasTO

3 Jan
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.
Read 11 tweets
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
(3) IMO if your primary source of info is the WSJ that's probably a red flag. Owning $BABA probably requires either first hand research and a network of experts. If not, then your going to be out arbitraged based on information flow alone (let alone the follow on analysis)
(4) In general, ppl have a hard time pricing political / regulatory risk. Ie. I'd approach it as a 20% chance of a 30% decrease in future cash flows = 6% discount to current price. Unable to calculate the risk, ppl bifurcate it as 0% or 100%. The latter sold their shares
Read 6 tweets
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/…
2/ In the old offline world, retailers such as Walmart, Target, etc. would sell their sales data to someone like Nielsen. Nielsen packages this up with consulting services and resells it back to manufacturers (ie P&G, Colgate, etc.) or other co's such as investment firms
3/ This structure has not replicated itself in the online world. Amazon, DoorDash, Netflix, etc. aren't sharing their data with anyone.
Read 8 tweets
5 Dec 20
1/ $DASH about to IPO for around $30B. On top of the profitability question, there's another one about the durability of that profitability (moats). Having launched 2 and 3 sided mktplces for Eats, I can tell you the latter was exponentially harder to build *and* manage.
2/ In food delivery, a 2 sided mktplace is a traditional aggregator. A sales team adds restos to the platform and marketing acquires users. Restos complete their own delivery, so the aggregator gets to skip all the messy parts and friction in the physical world.
3/ 2 sided food delivery different from ride hailing where it's viewed as an incremental earnings. If restos don't get any orders, that's OK because they're running their dine-in biz. This takes pressure off the aggregator, especially as they launch new markets and demand lags.
Read 10 tweets
27 Nov 20
1/ Finally bought some Sea ($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 👇
1/ Still Building its Moat - right now SEA’s entire strategic focus is on creating their flywheel which Free Fire (FF) is at the heart of. It generates the CF that is reinvested into Shopee and SeaMoney to drive growth and build their moats (free deliveries, R&D, etc.).
2/ Gaming Inconsistency - specific titles tend to ebb and flow as hits eventually lose their appeal to new games. Pre-covid, rev growth was expected to slow to <12%. Can Garena sustain FF’s growth, consistently create new hits, and renew it’s exclusive Tencent contract in 2023?
Read 10 tweets

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