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Year: 2018

Uber for X, Amazon for Y

It’s not that long ago when every startup in town had wanted to be the Uber for X, where X is a specific service such as Dog walking or Home cleaning. Here’s how Y Combinator described the phenomenon:

That idea – an app that lets consumers get a specific service when they need it, while giving service professionals (“pros”) immediate work – makes complete sense. It worked great for local transportation services, so why could it not be applicable across all types of industries?

Wired magazine had predicted that Uber, not any other startup, would be the Uber for X. So why then does Dara Khosrowshahi, Uber’s CEO, instead want Uber to be the Amazon for Y (urban mobility/transportation)? Here’s the relevant bits from his interview with Kara Swisher of Recode at the Code 2018 Conference [emphasis added].

Dara: A very, very important push for us is to innovate to lower costs, so the ride itself becomes much more efficient. And then, we are thinking about alternative forms of transport. If you look at Jump, the average length of a trip at Jump is 2.6 miles. That is, 30 to 40 percent of our trips in San Francisco are 2.6 miles or less. Jump is much, much cheaper than taking an UberX. To some extent it’s like, “Hey, let’s cannibalize ourselves.” Let’s create a cheaper form of transportation from A to B, and for you to come to Uber, and Uber not just being about cars, and Uber not being about what the best solution for us is, but really being about the best solution for here.

Kara: So bikes, scooters?

Dara: Bikes, perhaps scooters. I wanna get the bus network on. I wanna get the BART, or the Metro, etc., onto Uber. So, any way for you to get from point A to B.

Kara: Wait, you wanna start your own BART?

Dara: No, no, no. We’re not gonna go vertical. Just like Amazon sells third-party goods, we are going to also offer third-party transportation services. So, we wanna kinda be the Amazon for transportation, and we want to offer the BART as an alternative. There’s a company called Masabi that is connecting Metro, etc., into a payment system. So we want you to be able to say, “Should I take the BART? Should I take a bike? Should I take an Uber?” All of it to be real-time information, all of it to be optimized for you, and all of it to be done with the push of a button.

Kara: So, any transportation?

Dara: Any transportation, totally frictionless, real time.

Some, like Stratechery’s Ben Thompson, have claimed that this move is Uber’s bundle strategy.

This is very much a bundle, and like any bundle, what makes the economics work in the long run is earning a larger total spend from consumers even if they spend less on any particular item. To that end, as Khosrowshahi notes, the real enemy is the car in the garage; to the extent Uber can replace that the greater its opportunity is.

Bu whether Uber is a bundle or not depends on your definition of a bundle.

Here are some sample definitions: Investopedia defines it as “offering products or services together in order to sell them as a single combined unit. Bundling allows the convenient purchase of several products and/or services from one company.”, while researchers at Harvard Business School define a product or service bundle as “a set of goods or services at a lower price than the price charged if the customer bought all of them separately”.

If your definition is simply the convenience for customers to buy (à la carte) several products and/or services from one company (but not cheaper together than if those products or services are bought separately), then Uber is a bundle. But that’s like saying your neighborhood Walmart is a bundle because you can buy multiple types of products from a Walmart Store.

If your definition is that the combined product or service can be bought cheaper than if those products or services are bought separately (or if there’s no way to buy them separately at all)1, then Uber is not a bundle. At least, not quite yet.

Go back to that Chris Dixon blogpost referenced in Thompson’s Stratechery post.

What price should the cable companies charge to maximize revenues?

Note that optimal prices are always somewhere below the buyers’ willingness-to-pay. Otherwise the buyer wouldn’t benefit from the purchase. For simplicity, assume prices are set 10% lower than willingness-to-pay. If ESPN and the History Channel were sold individually, the revenue maximizing price would be $9 ($10 with a 10% discount). Sports lovers would buy ESPN and history lovers would buy the History Channel. The cable company would get $18 in revenue.

By bundling channels, the cable company can charge each customer $11.70 ($13 discounted 10%) for the bundle, yielding combined revenue of $23.40. The consumer surplus would be $2 in the non-bundle and $2.60 in the bundle. Thus both buyers and sellers benefit from bundling.

If the cable company offers ESPN and History Channel at $9 each à la carte, that offering is not a bundle. Offering both ESPN and History Channel at a discount ($11.70 instead of $18 it costs to buy individually) is the bundle that benefits both buyers and sellers.

Theoretically, Uber could become a mixed bundle if you could pay a flat fee (say $99 per month), and ride a limited number (say 100) of Uber+Jump+Bus+Metro rides (that may also limited in distance or dollar value, say sub $5 rides, so that you don’t uber from Seattle to Miami haha!). Individually these would have cost much more (let’s say $200). That’s definitely a bundle strategy, but Uber isn’t there yet. Simply offering a ridesharing subscription is not a bundle, and is unlikely to work because, like some other subscription businesses, it is simply a money-losing incentive for a subset of customers.

So if Uber isn’t a bundle, how does Uber’s Amazon for Y strategy work? It may be easier to analyze using a version of Amazon’s famous flywheel applied to Uber.

In essence, 1P providers are Uber’s own services such as Uber X, Uber Pool, Jump Bikes, Jump Scooters, Uber Eats etc. that are owned and controlled by Uber. Uber controls the pricing and a large part of the customer experience of the end customer. In addition to these 1P services, Uber would, for a percentage of revenue, allow any third party transportation provider to plug into Uber’s platform and offer their transport services. The 3P providers such as public buses, metros, or car rental companies would set their own prices but Uber would take a cut for the customer referral. And unlike a listing of links of the provider, I would imagine Uber would allow the customer to complete the entire transaction without leaving the Uber app. Perhaps even a curb-to-curb package delivery service that doesn’t involve bike couriers and simply uses existing Uber driver network?

The benefits of such a platform isn’t difficult to grasp. The more transport options (1P & 3P) that Uber can provide, the less downtime there is for drivers, the better the end customer experience and the more customers (and customer segments) Uber can attract. The more customers Uber attracts, the better it can distribute its fixed costs, leading to lower cost structure, lower prices, faster transport options, and eventually improved profitability.

Is there really a path to improved profitability when Uber has lost more money than any American tech firm of its age in history? As The Economist notes:

The intensely competitive nature of the ride-hailing business will continue to hit profitability. Though Mr Kalanick had hoped that Uber would quickly dominate ride-hailing around the world and enjoy fat profit margins close to those of Google, Mr Khosrowshahi is less sanguine. “Physical transport comes with lower margins,” he says, predicting that Uber will never claim fat “software margins”. So far, Uber has lost more money than any American tech firm of its age in history. In the second quarter of this year it reported $2.8bn of revenue, but lost $891m.

The truth is a bit more nuanced than “Uber is unprofitable”. To understand the nuances, let’s get a quick overview of Uber’s business model based on their unaudited income statement (here’s an Excel version to play with).

For most rides, Uber keeps ~25% (some say effective rake is 40%) of the fare to themselves. The fare is their Gross Booking, and the fee from the rake is their Gross Revenue. According to Uber, their cost of revenue (COGS) is approximately 10% of Gross Bookings, the cost primarily related to insurance. Since Uber charges around $1.5/mile, and the mean insurance and maintenance costs are about $0.15/mile, 10% seems right. At 25% rake, this is about 40% of revenue, implying gross margins of about 60%. From this gross margin, Uber incurs variable costs such as payment processing/credit card fees, payment fraud costs, refunds, promotions and incentives, customer service, dispute resolution, any driver service costs and local regulatory fees.2

There are five factors to consider in Uber’s path to profitability:

(1) Think of the first decade of Amazon – it was characterized by high spending (and several unprofitable years) in order: (1) to buy physical assets to build the physical infrastructure of its fulfilment network (this spend is capitalized and depreciated over time, so doesn’t show up as a one-time massive loss in the P&L statement), (2) to develop software to build the digital infrastructure (this spend is expensed in the year it was incurred, so shows up as massive losses in the P&L statement), and (3) to attract customers by price promotions, free shipping etc.

Well guess what, like Amazon, Uber will continue to spend heavily on both #2 and #3 in its early years. But just like Amazon, Uber’s software scales infinitely for all customers using the app. The marginal cost of serving one additional customer order is limited to their variable cost and software KTLO (and a small portion of fixed costs – primarily in Sales & Marketing – to fire up each new geographical location). To be profitable, Uber’s 25% rake on an annual basis simply needs to cover their variable costs and an estimated annual allocation of their fixed costs (as if they were depreciated over time instead of expensing them). Uber can do this by decreasing driver and rider incentives (perhaps even increasing its prices even if it means lower number of riders), better third-party transport provider utilization of their software (see #3 below), and better control over G&A expenses (currently growing 75% YoY). In other words, being GAAP unprofitable in the short-term doesn’t mean they’re losing money.

(2) Unlike Amazon though, Uber’s flywheel is limited by geographic limitations. In Amazon’s case, a seller acquired in one part of the country could sell to customers in other parts of the country, and continue the virtuous cycle of the flywheel. However, in Uber’s case, a driver or a transportation provider acquired in one city will not be able to service customers outside that city. Thus, while Amazon could quickly (ahem!) utilize their physical assets and digital infrastructure to service customers acquired in any part of the country to turn profitable, Uber will need to turn profitable city by city. And as they turn profitable in one city, they will need to continue to spend on customer and rider acquisition in each new city expansion, eroding overall short-term profitability, but marching towards long-term market leadership and long-term positive free cash flow.3

(3) Demand for the variety of third-party transport services such as bus transit that Uber envisions customers of its app will be interested in. Given how much each city spends on transit upkeep (as an example, San Francisco just approved a one-time $194M, and a 10-year $266M operating cost overhaul of its smart-card technology), one could argue whether they are better off partnering with someone like Uber rather than EACH city developing their own technology and spending several hundred millions EACH on their upkeep. If (and that’s a big if) Uber can develop a plug & play model for cities around the world, they could get a better utilization of their fixed costs in developing this tech, and offer it to cities at a much lower cost that it would take for each city to develop it themselves.

There are two other factors – (4) regulatory scrutiny, and (5) building its own autonomous-car capabilities – both fleetingly mentioned in The Economist article referenced above, that will ultimately decide how profitable Uber can eventually become.

Dara is a diplomatic deal maker who knows how to push the right buttons to get partners on board, and this bodes well for Uber and its Amazon for Transport strategy. They need all the partners they can get on this platform in order to build the Uber Transport Platform (pun intended).


  1. aka Pure bundling, which refers to the practice of selling two or more discrete products only as part of a bundle. Mixed bundling refers to the practice of selling a bundle of the products as well as the individual products themselves.

  2. I’m not sure why insurance costs are not included below the line (in operating expenses), or why promotions and incentives are above revenue (below Gross Bookings). In my view, all of these are variable costs that should be below the line.

  3. Uber expanding to new markets is akin to how physical retailers start new stores, where there are short-term costs in getting the store up and running

Bezos’s ? Email Mechanism

If you want to know what a man’s like, take a good look at how he treats his inferiors, not his equals.

– Sirius Black, Harry Potter and the Goblet of Fire.

I vividly remember my first (of several) ? Jeff B. escalation emails. Sebastian Gunningham was still a “Section 16 officer”, and Amazon had only one CEO. I literally had no idea what I was supposed to do – surprisingly there was (and still is) no training manual for this kind of thing. After asking around, I figured out that I had 24 hours (the time to respond has since then been extended to 48 hours) to come up with a response that indicated a description of the issue, the root cause, any immediate fixes, and long-term actions that address the issue. The first Director in your chain of command emails Jeff your response and marks you and Amazon’s senior leadership team in the email. It can be unnerving but is quickly forgotten if the issue is addressed, or if there is a plan to fix.

Yes, this is a absolutely a REAL thing. I’m not kidding. Jeff literally forwards customer emails with a “?” to his senior leaders. Here are two examples; these are samples, based on (but not) actual emails:

In a recent interview with Ken Hersh during the Forum on Leadership at the Bush Center, Jeff described this mechanism. While the entire interview (below) is a worthwhile watch, he describes the ? email mechanism from 22m:35s.

Surprisingly, the response to his disclosure about sending ? emails has been mixed. Several are in awe like this one:

Yet, a few like the one below have rightly pointed out that although glorifying executives who invoke fear in underlings is part and parcel of Corporate America folklore, it should be questioned, like this one:


So what is it then? Is this a mechanism that works, or is this a questionable mechanism that deserves its own ? email?

Well, like most things in life, the truth is somewhere in the middle. I am in favor of using anecdotes to augment data in order to ascertain if systems are running as designed or can be improved further. Allow me to demonstrate using an example.

This example is an automatic email sent out by Amazon’s vendor systems to encourage vendors to spend on enhanced marketing content to improve their product sales (and Amazon’s bottomline). And, it’s darn obvious that this particular email is worthless to the vendor – they would’ve spent $0.36 in just reading the email.

Now, the metrics for this email automation might look just fine. It is unlikely for a handful of low value emails (like $0.36) to affect the open rate, click-through rate, action rate, and opt-out rate, and as a result the product manager may not even notice the need for filters to filter out emails that do not add a significant improvement to vendor’s sales. These vendors are unlikely to opt out because the other emails they receive might be valuable.

In such a case, it is clear that a ? email anecdote can be really powerful in making a small change to the email algorithm – if the product sales improvement is less than a threshold dollar value, or less than a percentage change, do not send that vendor an email. Of course, the team should have caught this and built these filters before launching the automated email product, but sometimes when you are moving fast and launching products at scale, you may miss out edge cases, and anecdotes are more powerful than data in catching those.

Sure, this is only an anecdote and I have not provided any empirical data that backs up why Bezos’s ? email works. But that’s the point isn’t it? Sometimes the anecdotes are powerful enough for you to make a decision. Make no mistake, a not-so-great leader can use the ? email mechanism to belittle employees for their mistakes, or simply to ask wtf? In fact, one of my peers once sent a ? email to his manager as a response to a request from the manager. Let’s just say, the manager sent more than just a request in the next email – he clearly wasn’t thrilled. Taunting employees, or making them feel ashamed of a mistake is not the intent of Jeff’s ? email mechanism at Amazon. The intent is to seek product improvement opportunities that data sometimes fail to catch.

In the end, though, whether you forward such customer emails with just a ? or take a few more seconds to write, “Please look into this.”, or ask “What do you think about this?”, that’s a choice you have to make. I know what I would do. Do you?


Note: This blog post does not contain confidential Amazon information; these are my personal views and does not represent the views of Amazon or its management.

Diplomat Dara and Facebook’s Hell Week

Over the last couple of weeks, two big tech companies in Silicon Valley – Uber and Facebook – have faced major crises. Here’s a sampling of headlines on major news organizations about these crises.

On Uber’s self-driving car accident,

The Washington Post:

Self-driving Uber vehicle strikes and kills pedestrian.

The New York Times:

Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam.

Fox News was less judge, jury, and executioner, instead attributing the statement to the police:

Self-driving Uber car kills Arizona pedestrian, police say.

Uber’s self-driving car | Image Source: Uber.com, ATG

And on Facebook’s Cambridge Analytica “data breach” controversy,

The Washington Post:

Everything you need to know about the Cambridge Analytica-Facebook debacle.

The New York Times:

Facebook’s Role in Data Misuse Sets Off Storms on Two Continents.

The Guardian:

Facebook’s week of shame: the Cambridge Analytica fallout.

The Economist:

The Facebook scandal could change politics as well as the internet.

The Economist’s Cover on Facebook | Image Source: (Ironic, but) The Economist’s Facebook Page

In both cases, if you don’t read past the fear mongering headlines (kills pedestrian, week of shame, data misuse, scandal), you would bet that both these corporations are pure evil. However, on closer examination, it will be clear that the crux of the Uber story is this: on a not-so-well-lit, multi-lane corridor where cars speed at 45 mph, a pedestrian crossing the road outside the crosswalk (walking her bicycle as she crossed) was fatally struck by an Uber self-driving vehicle with an in-vehicle operator (meant to take over the self-driving car during emergencies).

The Facebook story on the other hand is a little bit more complicated: In 2010, Facebook, pursing a platform strategy, provided access to their graph data to any developer willing to build software applications on top of Facebook (think Candy Crush!).1 Not only did they provide developers access to their graph data, they also announced this access publicly at their F8 conference and several news organizations (many of who are now gunning after Mark Zuckerberg and Facebook) reported exactly what data was being shared and why.  To access this data, a developer had to seek permission only from one user, and the developer would then get access to all the users’ friends’ info as well. This was originally intended to make your website experience better. For example, if a bookseller wanted to let you know that your Facebook friend likes a book, they had to know (1) who your Facebook friends are and, and (2) what books they like. And you have to give them access to do so (since your friends have already “told you” via their listing on their profiles what books they like. A question we should all ask is – who owns my data that friends of mine have access to (my phone number, my birthday, my likes, my dislikes), and can friends of mine share it with others for money or otherwise? Your answer to this question will determine whether you think there was an actual data scandal in this whole Facebook-Kogan-Cambridge Analytica episode. This post provides a well-reasoned, logical view of why this scandal is no scandal at all. Although Facebook prohibited developers from passing along or selling this information, there was and is no way to enforce it once the data leaves Facebook’s servers. I am unsure why Facebook users are alarmed now, in 2018, about the fact that developers had since 2010 (1) access to your information if your friends permitted them to get it, and (2) developers could easily share this information with others despite Facebook prohibiting them from doing so. Dick Costolo’s tweet perfectly sums this up.

Long story short, a researcher (Prof. Kogan) developed an app that asked users to share their info and who their friends are and what their interests are, and then passed this data along to Cambridge Analytica, who subsequently used this data to influence (some say manipulate) the US Presidential elections.2 One thing is very clear: every Facebook user (yours truly included) accepted the terms and conditions (who reads them anyway?) of using Facebook that clearly stated that Facebook stores info about my interests, my likes, my posts, who my friends are etc. and this info can be provided to 3rd parties.

Now, make no mistake, whether Facebook giving developers access to their data graph was a sound strategy is a completely different question. Posts from James Allworth and Ben Thompson are must reads about Facebook’s platform strategy. Facebook wanted to be a platform, and in order to attract developers to build on top of Facebook, they shared their graph data with them. It’s not a new strategy; in order to get developers on board, some companies throw money at them (hello there, Microsoft!) and some like Facebook, share(d) access to their data.

By now, it should be plainly obvious that I believe Uber and Facebook have gotten the short end of the stick from traditional media. In Uber’s case, a more truthful headline would have read – “Pedestrian in not-well-lit street hit by self-driving Uber” or “Pedestrian crossing outside crosswalk hit by self-driving Uber vehicle.”3 And in Facebook’s case, a more neutral headline would have been – “Facebook developer partner sells facebook graph data” or perhaps even “Facebook’s decision to open graph data to developers backfires”. One thing that’s clear from the contrast between my made-up headlines and the actual headlines is that people are warier about tech than ever before, so one of the easiest way to get your clicks and views is through click-bait headlines about big bad tech and polarizing political news. Clearly, my made-up headlines above are too bland to garner enough clicks and views, and would’ve have made the cut at any major news organization.

I digress. The point of this post isn’t to debate whether Facebook erred in sharing data with Prof. Kogan or Cambridge Analytica. This post is to highlight the difference between how Uber’s CEO Dara Khosrowshahi, and Facebook CEO, Mark Zuckerberg, managed their PR nightmares.

Uber could have very well argued that the pedestrian crossed outside of the crosswalk, or that the street wasn’t well lit, or that the operator wasn’t paying attention, or any of the numerous externalities that may have caused this incident. Yet their CEO, Dara Khosrowshahi and Uber’s communications team did not blame anyone else; they issued statements expressing their thoughts with the victim and preemptively grounded all their self-driving cars without waiting for a trial by the media and Twitterati, and well before any government regulation forces them to take these cars off the street. I was initially skeptical when Dara Khosrowshahi took over from a product-focused leader like Travis, but so far he’s shown that he’s a skilled diplomat: he’s shown that not only is he a master deal maker (more on that in another post), he also knows exactly when to be combative, and when to just lay low.

On the other hand, after the so-called Facebook-Cambridge Analytica scandal, Mark Zuckerberg did not show up for several days. And when he finally showed up, he went on a PR blitz through interviews with all major news organizations and newspaper ads (with an apology nevertheless). Yet, Facebook continued to play the combative blame game; for example, in their first post about this episode they blamed Prof. Kogan and Cambridge Analytica.

Aleksandr Kogan requested and gained access to information from users who chose to sign up to his app, and everyone involved gave their consent.

Although Kogan gained access to this information in a legitimate way and through the proper channels that governed all developers on Facebook at that time, he did not subsequently abide by our rules.

Cambridge Analytica, Kogan and Wylie all certified to us that they destroyed the data. Contrary to the certifications we were given, not all data was deleted.

They started the next post by playing victim right at the beginning:

What happened with Cambridge Analytica was a breach of Facebook’s trust.

After news reports of this episode indicated how people can check what data Facebook had about them, people started downloading their data and noticed their likes, their messages to people, and calls and texts (but not the actual content of these calls or texts) in Android devices, prompting Facebook to post one more update:

You may have seen some recent reports that Facebook has been logging people’s call and SMS (text) history without their permission. This is not the case.

In each update, they just made things worse by blaming others (even if that may be the truth). Exactly opposite of what Diplomat Dara did. He did not say Uber is to blame, but he also didn’t lay the blame on the pedestrian or anyone else.

M. G. Siegler puts it best when he calls Facebook, the “Foot-in-Mouthbook”.

To make matters worse, Facebook clearly has a bad case of foot-in-mouth disease. Whenever they try to respond to a situation, they just exacerbate the issue.

As an engineer and tech worker, I’ve always wondered why some people think with their hearts and not with their head. Why can’t they see the logic that a car driven by the human would have also likely hit the pedestrian in the Uber accident, or that the number of miles driven without an accident is several times higher for automated cars? Why can’t they see that if nearly 300,000 clicked “Ok, grant permissions” to Prof. Kogan’s app and shared their information and their friends brief information to the app developer, it’s not Facebook’s fault. Yet, sometimes, even when you’re not in the wrong, even when you wonder why others can’t be logical and see someone else is at fault, it takes a diplomat to skillfully manage public opinion by not blaming others, by not combating public mood.

When faced with crises that you know may not be your fault, it takes both skill and tact to stop yourself from fighting the criticism with logical reasoning and simply empathize with those most affected by the incident. That’s exactly what Dara Khosrowshahi has managed, and for that Mr. Khosrowshahi, hats off to you!4


  1. This graph data is Facebook’s competitive advantage compared to Google’s or Amazon’s data. Google and Amazon know primarily only about you and your searches or your product purchases. Facebook, on the other hand,  not only knows about yourself but also your relationship with other users who also like or dislike something. For example, through their graph data, Facebook can determine that you don’t usually watch action movies, but when you do, you only watch it with your buddies from high school (Facebook thanks you for checking in with your 5 high school buddies to all those action movies last year). So what about this data? Well, if I am selling action movies, I can now not only target regular action movie goers, but also influence you through the exact 5 high school buddies who are most likely to make you watch something. Ever seen those ads on Facebook which say a particular friend likes a page?

  2. Although in my opinion their influence or manipulation is still up for debate given how miserably they failed to help their first client, Senator Ted Cruz.

  3. Death is tragic and this made-up headline is by no means trying to downplay the tragic nature of the incident.

  4. I’m not suggesting Uber is not at all fault here. I’m only suggesting that even though there are other factors Uber could blame, they have chosen not to do so.

    Also, the two crises are fundamentally different in nature, and so not ideal to compare. But I make the comparison only to suggest that Facebook and Mark Zuckerberg could learn a thing or two from Uber’s new CEO regarding diplomacy and laying low during an onslaught.

The Illusion of a Profitable Amazon Prime Now + Prime Fresh

It works like magic! That’s what Stephenie Landry, the Amazon VP in charge of Prime Now, intended this service to be. Nicknamed “Amazon Magic” by her team, it indeed does work like magic for customers. Order one of the over 25K products in any of the several cities where it is available, and your order will be delivered in 2-hours for free, and in 1-hour for a flat fee of $5.99. The only caveat – you have to subscribe to the $99/year Amazon Prime membership which offers among others “free” 2-day shipping on hundreds of millions of products, millions of video titles, and several other benefits.

It also works like magic for Amazon shareholders, by pulling a Houdini on them and never returning their invested capital. To learn how, let’s first study Prime Now’s business model.

Prime Now – The Business Model

Like most of Amazon’s other businesses, Prime Now was a bet on scale – if your variable cost per unit is below your revenue per unit (i.e. if you are contribution profit positive), at the right scale, your fixed costs can get allocated well enough for you to turn an overall profit (or more importantly positive free cash flow). And similar to Amazon’s core Ecommerce business (that I described in my Amazon Marketplace Profitability Analysis post earlier) – Prime Now has two business models:

  1. A Retail model (denoted by store “Amazon” on the Prime Now app) – where Amazon takes ownership of the product from the manufacturer, stocks the product in urban Prime Now fulfillment centers (FC), sets the price to the end consumer, pays for inbound (from manufacturers to Amazon FCs) and outbound (from FCs to customers) shipping, and is the seller of record. As of Jan-2018, customers who place orders worth less than $35 pay $4.99 delivery fee for 2-hour delivery (no 1-hour delivery option), and those who place orders worth $35 and above get free 2-hour delivery (or pay an extra $4.99 to get 1-hour delivery).
  2. A Marketplace model (denoted by other stores such as “PCC”, “Bartell Drugs”, “New Seasons”, or “Restaurants”) – where Amazon allows third-party stores and restaurants to sell to Amazon customers through the Prime Now App, taking a cut of each sale (aka referral fee/rake that is rumored to be about 20-30% of total sales). Unlike the MFN model of Amazon’s core Ecommerce business (and similar to the FBA model), Amazon pays for outbound (from restaurants’ or sellers’ locations to the customer). As of Jan-2018, customers who place orders worth less than $60 (for some stores $50) pay $5.99 delivery fee for 2-hour delivery (no 1-hour delivery option), and those who place orders worth $60 and above get free 2-hour delivery (or pay an extra $5.99 to get 1-hour delivery).

Let’s forget all the 1-hour and 2-hour fee differences and complications, and perform a quick gut-check to comprehend the unit economics of Prime Now’s business.

Start with the variable costs first. If you’ve ever worked in physical retail, you know that the average contribution profit in the kind of products that Prime Now sells in the Retail model is about 10% of topline without delivery costs. In Prime Now’s case, the physical store still exists (the urban fulfillment centers in case of the Retail model above, and the actual physical store such as Bartell’s in case of the Marketplace model above); yet they incur additional variable costs – from fulfillment to delivery – that eat into this contribution profit.  I performed a very scientific study of asking every Prime Now delivery associate I saw how many orders they had delivered in the last hour. Across 25 associates, the average number of orders was only 3.  To recover and breakeven on fixed costs (such as software development, or cost of building Prime Now urban FCs), Amazon needs to make substantially higher number of minimum value orders per driver per hour. Simply scaling number of orders don’t help profitability; the orders have to be from the same apartment or neighborhood in order for the drivers to increase the number of deliveries per hour.

To be fair, Prime Now can be sustainable if the only sold profitable items (e.g. if they only sold $1000 smartphones) or if they increase the prices of products sold (e.g. if an apple from Prime Now is suddenly priced at $35), then sure, the contribution profit will not be 10%. Prime Now can also be profitable if they increase the minimum order value from $35 to let’s say $70. That’s exactly what Amazon has done with Prime Now’s Marketplace model. As of Jan-2018, they have increased the minimum order from $35 to $60 for marketplace orders (from Bartell or others).1 Customers who place orders worth less than $60 (for some stores $50) pay $5.99 delivery fee for 2-hour delivery (no 1-hour delivery option), and those who place orders worth $60 and above get free 2-hour delivery (or pay an extra $5.99 to get 1-hour delivery). Arguably some orders are much higher than the minimum orders, bringing down the number of orders needed to breakeven; however, remember, we haven’t even got to fixed costs component yet. Even if they are slightly contribution profit positive, Amazon is looking at a significant time to recover fixed costs.

While using the contractor-model of Amazon Flex delivery drivers has helped offload the fixed salary component of hired drivers to a variable component of on-demand drivers, Amazon still needs to be contribution profit positive for Prime Now to be sustainable. All this would be okay if you could bring down variable costs through economies of scale in the long-term. Yet, for that to happen, you need to make more number of orders per hour per driver – only possible in extremely dense urban locations such as New York or London.

The other important argument (that any Amazonian will definitely put forth) is the impact on the Prime Flywheel, i.e. downstream impact from new Prime customer acquisition and existing Prime customer retention. This is similar to the argument that one should view Prime Now through the CAC and LTV lens. Since Prime member data isn’t publicly available, it is unclear how much impact Prime Now has on the Prime Flywheel; but unless every dollar that Prime Now loses is made up by an additional dollar of profit from spend on the overall Amazon platform by Prime customers, Prime Now will continue to be unsustainable.

+ Prime Fresh

So, what about Amazon Fresh, the $180 per year grocery delivery service, then? Well, tack on the variable costs of specialized delivery bags, Amazon-owned fresh trucks, Amazon-employed delivery drivers, higher produce shrinkage and the additional fixed costs of refrigerated fresh fulfillment centers, and you get the picture. Even with the additional ~$15 per month from each customer and a minimum order of $40, Prime Fresh is unlikely to cover the additional costs incurred (compared to Prime Now). The bottom-line, if Prime Now is unsustainable, Prime Fresh is UNSUSTAINABLE in all caps.

+ Whole Foods

Amazon now has Whole Foods, that should change things for Prime Now right? Amazon has already announced Prime Now deliveries from Whole Foods, and in some ways yes, the fixed cost of Urban FCs and the fulfillment portion of the “nominal fulfillment and delivery” costs will now allocated to the physical store business’s P&L (which in Whole Food’s case is profitable). But that said, the variable costs of Prime Now’s delivery will still make it unsustainable at current prices. Again, unless every dollar that Prime Now loses is made up by an additional dollar of profit from spend by Prime customers on the overall Amazon platform – whether at Whole Foods or elsewhere on the platform, Prime Now will continue to be unsustainable.

The Refresh (Prime Now + Prime Fresh + Whole Foods)

Given that there are rumors of a consolidation between Prime Fresh and Prime now already underway, I wouldn’t be surprised if Amazon announces a complete shutdown of the Amazon Fresh business within the next one year. As for Prime Now, it will live to fight another day. That other day, though, will not look like today. In the current “delivered to the home” model, Prime Now + Whole Foods will need to make two major changes to their pricing – (1) increase the minimum order to $80 to increase the per order contribution profit, and (2) institute delivery fees of $10 (to cover for admin, sales & marketing, and fulfillment costs) for orders below $80. Increasing prices to these levels, however, will quell customer demand, and while I don’t have any data on price elasticity for Prime now customers, my intuition says that customer demand at these prices will be insufficient to sustain the Prime Now business.

For all these reasons above, in the long-term (perhaps 5 years or so from now), Prime Now will eventually go the way of several other experiments at the World’s Best Place to Fail.


Note: This blog post does not contain confidential Amazon information; these are my personal views and does not represent the views of Amazon or its management.

PS: Unexpectedly, this post briefly trended on the front page of Hacker news and I received some fair criticism and feedback about my analysis. The fair ones: (1) In an earlier version of this post, I used estimates that were are not backed by data. I can access this internal Amazon data on fulfillment costs and delivery costs if I want to but did not, and will not disclose it even if I did since it is confidential Amazon data. (2) I had incorrectly assumed SG&A as a variable cost; it is actually fixed costs, so per order SG&A should decrease with scale. (3) Prime subscription revenue isn’t baked in to this analysis. I only talked briefly about downstream impact from higher prime member spend because I believe that Prime subscription revenue is offset by higher Prime shipping costs. (4) Future tech and scale. If automated cars, drones and delivery robots become a thing (and it will eventually, just a question of when), yes, things will look very different for Prime Now. I also agree that scale (more number of orders per hour by the same driver) will improve profitability.

The point of this post wasn’t to get into the details of Prime Now’s financials; rather it was to suggest that Prime Now, like Shyp and other unprofitable on-demand businesses, will struggle to be sustainable in the long run due to their negative unit economics. Thanks for the feedback.


  1. When Prime Now first launched $20 was the minimum order.

Amazon Org Chart

A few years ago, Manu Cornet, a Google programmer published a now famous set of diagrams depicting the organizational charts of big tech companies that later made its way to the pages of New York Times and recently Microsoft CEO Satya Nadella’s new book Hit Refresh. When I came across the diagrams then, I found it to be spot on in its representation of all the major tech companies, particularly Amazon with its neatly laid out org structure.

Fast forward a few years, and having now worked in multiple Amazon teams, I’ve come to realize that there’s not one Amazon structure. Each org works differently and each team within those orgs works a little differently. In fact, “depends on the team” is the oft-repeated statement to describe anything and everything Amazon, both inside and outside the company. What’s funny is that each team thinks they are Amazon: AWS thinks Amazon = AWS (especially since they supposedly bring all the profit); Amazon Video thinks they embody Amazon’s leadership like none else; if you ask Retail, of course, they think they are Amazon, after all, that’s where it all began. The truth is that none of them truly represent Amazon. Here’s my best attempt to adapt the original Manu Cornet version to depict the org structures of different Amazon teams.

All right, all right, all right, I’m just kidding; it’s not exactly like that.

But joking aside, Amazon, in my view, is structured into several self-contained business units. While officially, Amazon has “only” three CEOs, I believe that each of the other businesses, such as Devices, can grow to have their own CEOs eventually. And businesses within can grow to have their own SVPs (for instance, SVP for Alexa, SVP for Physical Stores and so on). Theoretically the central Finance, HR and other functions may report directly to Jeff B. and support all business units; but for all practical purposes there are separate teams within these central functions to support the disparate business units. Here’s a view of what that looks like.

Goes without saying that there are large businesses within each of these, for e.g. EC2 in AWS, or FBA & Marketplace in Ecommerce. Similarly, each of these follow a single threaded leadership model with self-contained product, software, and s&m teams that allow them to, for the most part, move fast, experiment and build their businesses, eventually charting their own course and creating their own destiny.


Note: This blog post does not contain confidential Amazon information; these are my personal views and does not represent the views of Amazon or its management.

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