"It is said that if you know your enemies and know yourself, you will not be imperiled in a hundred battles; if you do not know your enemies but do know yourself, you will win one and lose one; if you do not know your enemies nor yourself, you will be imperiled in every single battle." - Sun TzuMy first job at Amazon was as the first analyst in strategic planning, the forward-looking counterpart to accounting, which records what already happened. We maintained several time horizons for our forward forecasts, from granular monthly forecasts to quarterly and annual forecasts to even five and ten year forecasts for the purposes of fund-raising and, well, strategic planning.
One of the most difficult things to forecast was our adoption rate. We were a public company, though, and while Jeff would say, publicly, that "in the short run, the stock market is a voting machine, in the long run, it's a scale," that doesn't provide any air cover for strategic planning. It's your job to know what's going to happen in the future as best as possible, and every CFO of a public company will tell you that they take the forward guidance portion of their job seriously. Because of information asymmetry, analysts who cover your company depend quite a bit on guidance on quarterly earnings calls to shape their forecasts and coverage for their clients. It's not just that giving the wrong guidance might lead to a correction in your stock price but that it might indicate that you really have no idea where your business is headed, a far more damaging long-run reveal.It didn't take long for me to see that our visibility out a few months, quarters, and even a year was really accurate (and precise!). What was more of a puzzle, though, was the long-term outlook. Every successful business goes through the famous S-curve, and most companies, and their investors, spend a lot of time looking for that inflection point towards hockey-stick growth. But just as important, and perhaps less well studied, is that unhappy point later in the S-curve, when you hit a shoulder and experience a flattening of growth.
One of the huge advantages for us at Amazon was that we always had a fairly good proxy for our total addressable market (TAM). It was easy to pull the statistics for the size of the global book market. Just as a rule of thumb, one could say that if we took 10% of the global book market it would mean our annual revenues would be X. One could be really optimistic and say that we might even expand the TAM, but finance tends to be the conservative group in the company by nature (only the paranoid survive and all that).
When I joined Amazon I was thrown almost immediately into working with a bunch of MBA's on business plans for music, video, packaged software, magazines, and international. I came to think of our long-term TAM as a straightforward layer cake of different retail markets.
Still, the gradient of adoption was somewhat of a mystery. I could, in my model, understand that one side of it was just exposure. That is, we could not obtain customers until they'd heard of us, and I could segment all of those paths of exposure into fairly reliable buckets: referrals from affiliate sites (we called them Associates), referrals from portals (AOL, Excite, Yahoo, etc.), and word-of-mouth (this was pre-social networking but post-email so the velocity of word-of-mouth was slower than it is today). Awareness is also readily trackable through any number of well-tested market research methodologies.
Still, for every customer who heard of Amazon, how could I forecast whether they'd make a purchase or not? Why would some people use the service while others decided to pass?
For so many startups and even larger tech incumbents, the point at which they hit the shoulder in the S-curve is a mystery, and I suspect the failure to see it occurs much earlier. The good thing is that identifying the enemy sooner allows you to address it. We focus so much on product-market fit, but once companies have achieved some semblance of it, most should spend much more time on the problem of product-market unfit.
For me, in strategic planning, the question in building my forecast was to flush out what I call the invisible asymptote: a ceiling that our growth curve would bump its head against if we continued down our current path. It's an important concept to understand for many people in a company, whether a CEO, a product person, or, as I was back then, a planner in finance.
Amazon's invisible asymptote
Fortunately for Amazon, and perhaps critical to much of its growth over the years, perhaps the single most important asymptote was one we identified very early on. Where our growth would flatten if we did not change our path was, in large part, due to this single factor.
We had two ways we were able to flush out this enemy. For people who did shop with us, we had, for some time, a pop-up survey that would appear right after you'd placed your order, at the end of the shopping cart process. It was a single question, asking why you didn't purchase more often from Amazon. For people who'd never shopped with Amazon, we had a third party firm conduct a market research survey where we'd ask those people why they did not shop from Amazon.
Both converged, without any ambiguity, on one factor. You don't even need to rewind to that time to remember what that factor is because I suspect it's the same asymptote governing e-commerce and many other related businesses today.
Shipping fees.
People hate paying for shipping. They despise it. It may sound banal, even self-evident, but understanding that was, I'm convinced, so critical to much of how we unlocked growth at Amazon over the years.
People don't just hate paying for shipping, they hate it to literally an irrational degree. We know this because our first attempt to address this was to show, in the shopping cart and checkout process, that even after paying shipping, customers were saving money over driving to their local bookstore to buy a book because, at the time, most Amazon customers did not have to pay sales tax. That wasn't even factoring in the cost of getting to the store, the depreciation costs on the car, and the value of their time.
People didn't care about this rational math. People, in general, are terrible at valuing their time, perhaps because for most people monetary compensation for one's time is so detached from the event of spending one's time. Most time we spend isn't like deliberate practice, with immediate feedback.
Wealthy people tend to receive a much more direct and immediate payoff for their time which is why they tend to be better about valuing it. This is why the first thing that most ultra-wealthy people I know do upon becoming ultra-wealthy is to hire a driver and start to fly private. For most normal people, the opportunity cost of their time is far more difficult to ascertain moment to moment.
You can't imagine what a relief it is to have a single overarching obstacle to focus on as a product person. It's the same for anyone trying to solve a problem. Half the comfort of diets that promise huge weight loss in exchange for cutting out sugar or carbs or whatever is feeling like there's a really simple solution or answer to a hitherto intractable, multi-dimensional problem.
Solving people's distaste for paying shipping fees became a multi-year effort at Amazon. Our next crack at this was Super Saver Shipping: if you placed an order of $25 or more of qualified items, which included mostly products in stock at Amazon, you'd receive free standard shipping.
The problem with this program, of course, was that it caused customers to reduce their order frequency, waiting until their orders qualified for the free shipping. In select cases, forcing customers to minimize consumption of your product-service is the right long-term strategy, but this wasn't one of those.
That brings us to Amazon Prime. This is a good time to point out that shipping physical goods isn't free. Again, self-evident, but it meant that modeling Amazon Prime could lead to widely diverging financial outcomes depending on what you thought it would do to the demand curve and average order composition.
To his credit, Jeff decided to forego testing and just go for it. It's not so uncommon in technology to focus on growth to the exclusion of all other things and then solve for monetization in the long run, but it's easier to do so for a social network than a retail business with real unit economics. The more you sell, the more you lose is not and has never been a sustainable business model (people confuse this for Amazon's business model all the time, and still do, which ¯\_(ツ)_/¯).
The rest, of course, is history. Or at least near-term history. It turns out that you can have people pre-pay for shipping through a program like Prime and they're incredibly happy to make the trade. And yes, on some orders, and for some customers, the financial trade may be a lossy one for the business, but on net, the dramatic shift in the demand curve is stunning and game-changing. (...)
Prime is a type of scale moat for Amazon because it isn't easy for other retailers to match from a sheer economic and logistical standpoint. As noted before, shipping isn't actually free when you have to deliver physical goods. The really challenging unit economics of delivery businesses like Postmates, when paired with people's aversion for paying for shipping, makes for tough sledding, at least until the cost of delivering such goods can be lowered drastically, perhaps by self-driving cars or drones or some such technology shift.
Furthermore, very few customers shop enough with retailers other than Amazon to make a pre-pay program like Prime worthwhile to them. Even if they did, it's very likely Amazon's economies of scale in shipping and deep knowledge of how to distribute their inventory optimally means their unit economics on delivery are likely superior.
The net of it is that long before Amazon hit what would've been an invisible asymptote on its e-commerce growth it had already erased it.
Know thine enemy.
by Eugene Wei, Remains of the Day | Read more:
Image: Stratechery, uncredited
