1/ Zomato S-1 just dropped.

FY20
--
Orders - 403M 🤯
GOV - $1.5B USD
Rev - $368M USD
Adj EBITDA - ($226M) USD

As you can see from the chart, COVID created a lot of volatility. Unlike in Europe and North America, a lot of restaurants were forced to close, esp in Q1 2020.
2/ You can also see they've been aggressively cutting costs. Adj EBITDA % losses as a % of revenue rapidly going down.
3/ Exciting growth ahead for India over the next few decades (anxiously waiting for more Indian IPOs).
4/ Cohorts spending more over time. I *think* this includes churned users which would be great.
5/ Supposedly they've dethroned Swiggy and have become the leader?

"According to RedSeer, we have consistently gained market share over the last four years to become the category leader in the food delivery space in India in terms of GOV from October 1, 2020 to March 31, 2021."
6/ $UBER owns about 9% of the business. Assuming they IPO at $6-$7B, this wouldn't be as good of a return as Grab or DiDi given the cumulative cash burn in India over the years 😞
7/ Personally, given all of the volatility in India, I'm going to wait and see how they perform in the medium term. The current private valuation ($5.4B) feels a little rich. I'm also interested to see Swiggy's numbers once they file later this year.
Direct link to the filing:

listing.bseindia.com/download/36315…

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More from @ProtagorasTO

27 Mar
1/ Something that keeps me excited about $UBER is it’s global operations. One of the secret sauces to scaling up Uber Eats so quickly was that we had small teams around the world ready to help launch a new product.
2/ Unlike a $GOOG or $FB, Uber is extremely decentralized. Partly because it’s a physical product and partly because execution and strategy require a lot of “boots on the ground” for localization (regulations, marketing, competition, etc).
3/ You have some centralized resources such as engineering, product, etc., largely based in San Fransisco. Then there’s Central Operations, they run scaled processes better done at a continental level (Latam, Europe, etc). And then you have Local Operations.
Read 8 tweets
15 Feb
1/ I spent a few years at Uber Eats watching us sometimes struggle to grow new businesses on top of rides. After a while I noticed a similar pattern at other big tech co's ($FB, $GOOG, $AMZN, $MSFT, etc). Below are my thoughts on why this happens and how it's related to marketing
2/ First, if you look at most big tech, they were all primarily started by engineers or "technophiles" and their core product grew virally with very little traditional marketing. Instead, there was a focus on "growth hacks" and improving the CX
3/ The consequence of this is that as the co grew, power, budget, and attention accrued to the engineering and product teams. Most importantly, the success of the original core biz created a dogmatic belief that "the best product wins" and "if you build it they will come"
Read 12 tweets
23 Jan
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.
Read 7 tweets
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

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