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A weekly digest of tech reviews, headlines, columns and your questions answered by WSJ’s Personal Tech gurus. Get Out’: Nicaraguan Unrest Shakes U. My 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. 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. It was easy to pull the statistics for the size of the global book market. 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. 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.

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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. 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. 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. 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. 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.

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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 rest, of course, is 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.

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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. And, as Jeff always noted, you can make micro-adjustments in the long run to tweak the profit leaks. For some really large, heavy items, you can tack on shipping surcharges or just remove them from qualifying for Prime. These days, some items in Amazon are marked as “Add-on items” and you can only order them in conjunction with enough other items such that they can be shipped with those items rather than in isolation. 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.

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. Many are not like Amazon where there are readily tracked metrics like the size of the global book market with which to peg their TAM. What’s the shoulder of the curve for something like Facebook?

Meh (Score:2)

Some of the limits to their growth are easier to spot than others. For messaging and some more general social networking apps, for example, in many cases network effects are geographical. Since these apps build on top of real-world social graphs, and many of those are geographically clustered, there are winner-take-all dynamics such that in many countries one messaging app dominates, like Kakao in Korea or Line in Taiwan. For others, though, it takes a bit more product insight, and some might say intuition, to see the ceiling before you bump into it. For both employees and investors, understanding product-market unfit follows very closely on identifying product-market fit as an existential challenge. Let’s start with Twitter, for many in tech the most frustrating product from the perspective of the gap between the actual and the potential. Its user growth has been flat for quite some time, and so it can be said to have already run full speed into an invisible asymptote.

In quarterly earnings calls, it’s apparent management often have no idea if or when or how that might shift because their guidance is often a collective shrug. One popular early school of thought on Twitter, a common pattern with most social networks, is that more users need to experience what the power users or early adopters are experiencing and they’ll turn into active users. Many a story of social networks who’ve continued to grow point to certain keystone metrics as pivotal to unlocking product-market fit. For example, once you’ve friended 30 people on Facebook, you’re hooked. Pattern-matching moves more quickly through Silicon Valley than almost any other place I’ve lived, so stories like that are passed around through employees and Board meetings and other places where the rich and famous tech elite hobnob, and so it’s not surprising that this theory is raised for every social network that hits the shoulder in their S-curve.

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In the case of Twitter, I think the theory is wrong. Given the current configuration of the product, I don’t think any more meaningful user growth is possible, and tweaking the product as it is now won’t unlock any more growth. The longer they don’t acknowledge this, the longer they’ll be stuck in a Red Queen loop of their own making. Sometimes, the product-market fit with early adopters is only that. The product won’t go mainstream because other people don’t want or need that product.

In these cases, the key to unlocking growth is usually customer segmentation, creating different products for different users. Mistaking one type of business for the other can be a deadly mistake because the strategies for addressing them are so different. A common symptom of this mistake is not seeing the shoulder in the S-curve coming at all, not understanding the nature of your product-market unfit. I believe the core experience of Twitter has reached most everyone in the world who likes it.

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It is heavily text-based, with 140 and now 280 character limit snippets of text from people you’ve followed presented in a vertical scrolling feed in some algorithmic order, which, for the purposes of this exercise, I’ll just consider roughly chronological. For fans, most of whom are infovores, the nature of product-market fit is, as with many of our tech products today, one of addiction. Because the chunks of text are short, if one tweet is of no interest, you can quickly scan and scroll to another with little effort. Discovering tweets of interest in what appears to be a largely random order rewards the user with dopamine hits on that time-tested Skinner box variable frequency. For infovores, text, in contrast to photos or videos or music, is the medium of choice from a velocity standpoint. Over time, this loop tightens and accelerates through the interaction of all the users on Twitter.

Likes and retweets and other forms of feedback guide people composing tweets to create more of the type that receive positive feedback. The character limit encourages this type of compression. This can be a contrarian idea or just a cliche encoded in a semi-novel way. Rewards some set of readers’ priors, injecting a pleasing dose of confirmation bias directly into the bloodstream. Blasts someone that some set of people dislike intensely. This is closely related to the previous point. Is composed by someone famous, preferably someone a lot of people like but don’t consider to be a full-time Tweeter, like Chrissy Teigen or Kanye West.

Is on a topic that most people think they understand or on which they have an opinion. Of course, the set of ideal qualities varies by subgroup on Twitter. Black Twitter differs from rationalist Twitter which differs from NBA Twitter. The meta point is that the flywheel spins more and more quickly over time within each group. The problem is that for those who don’t use Twitter, almost all of its ideal attributes among the early adopter cohort are those which other people find bewildering and unattractive. Many people find the text-heavy nature of Twitter to be a turn-off. The naturally random sort order of ideas that comes from the structure of Twitter, one which pings the pleasure centers of the current heavy user cohort when they find an interesting tweet, is utterly perplexing to those who don’t get the service.