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.
4/ Large countries like France, Brazil, etc tend to have dedicated Local Operations on top of managing other smaller markets in the region. The “primitive” or basic make up of these teams is a GM, an Ops Manager, a Marketing Manager, and a Restaurant Manager (for Eats only).
5/ Together, with a modular and flexible tech infrastructure, these teams create an almost “Operations-as-a-Service”. You can build (or acquire) new products and use these teams as a platform to quickly iterate and scale new businesses.
6/ Which is one of the reasons why I’m excited by Uber’s recent acquisitions of Postmates (white label delivery), Cornershop (grocery), and Drizzly (alcohol). Integrating a business globally is hard but having teams around the world to use as a springboard is a big advantage.
7/ The last piece I’ll add is that Uber to a certain extent is borrowing a page from Masa’s “time machine”. Local teams find products, tactics, etc that work well (sometimes from competitors) in one country and export them globally.

H/t @lillianmli
8/ Ie Uber’s been implementing parts of DoorDash’s US playbook globally such as “over the top” and Dashpass.

So, I think there are some real headwinds to the biz such as regs and competition but I think the real optionality from global Ops tends to go under appreciated.

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More from @ProtagorasTO

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
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

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