WalMart et al stock bingo cards. Teaching stores have them 10+ SKUs deep. That's one reason I thought BCC would sell - people were empirically happy to pay money for an inferior substitute.

From the article: "The worst of the harbingers? Repeat customers, those with a tendency to purchase a product like Diet Crystal Pepsi not just once, but over and over again. These customers really, really like the products that end up failing. Harbingers with a history of making four or more repeat purchases of a failed product are nearly twice as likely as other customers to buy another product that fails."

So no, it doesn't seem like it's as simple as early adopters or novelty-seekers.

That might explain that they're not really novelty seekers but it doesn't negate the point that they're just early adopters. The article goes to some lengths to make these customers look bad but do they also buy products that later succeed? The article doesn't clearly say.

Moreover, How do you distinguish an early adopter from a harbinger? I'd argue that there's very little in this article (as presented) that helps with that.

The stats (as they are quoted here) are just exactly what you would expect to see if you looked at early adopters.

New products typically fail. If you are a consistent early adopter, you will buy a lot more failures than someone who sticks to tried-and-tested products.

If anything, "twice as many" is lower than I would have thought. In my opinion, this suggests that early adopters are rare - that there isn't a novelty-seeker type of person who goes around trying new products for the sake of it.

What they should be measuring is slightly different than this. If these people buy a new product, does that make the product more likely to fail than it would otherwise? (Given that the product is likely to fail anyway)

As I've understood, this stricter statistic is not seen in the article. Even if we could identify people like that, I would put those people into the category of "temporarily unlucky early adopters" and would not expect them to have predictive power.

With that in mind, the conclusion of the article seems nonsensical.

The conclusion is that if you make a new product, you should ignore data from "typical early adopters" but look at "boring people with strong habits".

If people who ordinarily don't try new stuff are buying your product, then it will be a mainstream success.

If they don't, then noone cares if the consistent early adopters think that your product is great.

This is a customer group that would be really good for an online service.

For a physical retail store, items that are well liked by a 0.1% of population are a failure - since they carry much of the same fixed costs as a similar item liked by 40% of population, and extra variety costs them; and the best target audience of a physical retail store is "everyone who lives close enough" or "everyone of socal demographic X who lives close enough".

An online store can serve only that 0.1% audience based on their flavour preference. If there are a million people worldwide who really like Diet Crystal Pepsi? Great, there's a lot of money to be made on that product - even if it's unprofitable to stock it in Walmart and expect that a Diet Crystal Pepsi fan will wander in today.

Yeah, the economics for a SaaS application are in some ways very different than a supermarket. I imagine that many of the products that eventually get cut are still "profitable" in the narrow sense that each unit sold is sold at some margin above cost. But when you take into account the enormous opportunity cost of stocking that item and taking up shelf space for it that then cannot be used for something else that moves at greater volume, it becomes uneconomical.

A SaaS app doesn't have the same sort of physical opportunity cost -- every dollar earned is a good dollar. The only real opportunity cost involved is the creator/employees' time.

I want to take issue with "Every dollar earned is a good dollar" at essay length. It's widely believed to be true by devs starting SaaS companies and sometimes dooms good people/products by e.g. makin them scramble to find 50 $20 a month accounts when they'd sleepwalk to 3 $10k a month ones.

I normally like your comments, but I believe the OP addressed this. They said the opportunity cost is the creator's time, which I believe takes into account your objection.

(Of course, responding to the a standalone phrase, you're absolutely correct)

Providing support has an opportunity cost as does developing features requested by users. If your early users are "harbingers of doom" and you listen to them for feature requests, you're going to have a rough time.

Recently I signed up to test a new SaaS app that I thought would be useful. The marketing language on the site was a bit different from what I found inside the app once I got going. I asked the support team for help and instead of leading me on with promises of adding things in the future, they just told me the app was a bad fit and refunded my money. I appreciated that. I was not going to be a productive user for them. Focus is key.

I think this is one of the reasons apps like Stripe take so long to go international. The user needs are very different and there's a real cost to taking on the responsibility of grappling with them.

I think there is also a cohort of customers - people who are really, really attracted to 'new and weird' who are constantly on the lookout for products to fill their super-specific pain point. When something new comes along, they jump on it and get enthusiastically engaged, to the point of likely trying to alter the product direction/roadmap.

Going away from the supermarket example and onto software - it's easy to become engaged with these types of customers, particularly if they are well funded - and lose a connection with the general market.

That is where the real danger lies. Going back to the supermarket - if these customers could affect the Cola recipe, they'd be saying 'maybe add some salt' to tailor to the unique taste they have been looking for. As they increase their purchases when you add the salt, it's easy to think you're finding the market- when in fact you're moving further away from where everyone else is.

Yes, thank you. There are some insane conclusions drawn in this article that are the result of statistical/logical over-simplification and incorrect intuitive thinking.