Alternatively, the rapid failure after IPO is a suggestion that their customers weren’t happy, but the plan was short term pump and dump.

It’s not always easy to say if something is stupid or fraudulent, but sometimes it really is just a scam.

We'd need a lot more information about this one particular case but I'd bet almost anything that a 90% drop in share price was caused by something a lot deeper than customers being unhappy at some unfinished features. I intentionally didn't address that half of his comment for that reason.

That’s reasonable, though I am not suggesting a pump and dump is only going to show up in the code base just that it would also look like they described.

I find it so weird to look at the stock market to approximate this kind of metric. Stock prices have more to do with things like the ebb and flow of the risk profile of institutional investors, than with customer satisfaction with specific features.

Institutional investors care a great deal about metrics like customer retention indirectly because it influences customer lifetime value which then impacts profitability all of which is dependent on customer satisfaction.

Groupon for example has decent consumer metrics, except they couldn’t keep the business partners happy which resulted in their ultimate collapse. Finding weaknesses in companies businesses models like this can both be extraordinarily profitable for institutional investors directly and create an aura of competence to attract more investors.

Theoretically. But then sometimes they're just switching from equities to bonds or whatever. My point is just that there is no simple function from customer satisfaction to stock price.

There's a joke on econ twitter whenever there is a big move in some individual stock, that the explanation is that the move clearly happened because the expected value of future cash flows changed. The joke is that under the efficient market hypothesis that's always the explanation, but in reality we all know a bunch of other stuff is going on all the time.

I agree with your point in general, a stock doubling or getting cut in half doesn’t necessarily have any obvious explanations. However, stocks aren’t a pure random walk, they are somewhat bound to the underlying business even if you don’t have enough information right now to understand what’s going on.

So, the kind of extreme stock shifts like dropping to 10% of a previous valuation are much more likely to have an understandable cause even if the trigger is random.

I'm in a cynical mood, but I'll begrudgingly accept "somewhat bound to the underlying business" :)

I guess at the root of my skepticism is that so many "growth" stocks never pay any dividends, so it's unclear to me what the connection between the stock and the company's cashflow is even supposed to be. If a company never returns any of its profits to its investors, isn't it just kind of a gentleman's agreement to pretend that the traditional way of valuing the company's equity still applies? It really does seem to me that the market for many stocks has detached from the company's business, and is instead driven almost entirely by competing memes.

Companies are ultimately controlled by their shareholders, no gentleman’s agreement needed. If that price falls far enough corporate raiders are happy to chop up the company for a quick buck.

Not really though. They're controlled by their executives, who can theoretically be removed by their boards, but often with significant difficulty. And the connection between the board and the shareholders at large is also more tenuous than I once thought.

It's true that a dropping stock price can lead to a takeover and new management, but again, that could happen to both a well managed financially strong business that has just lost the narrative game.

That's the only point I'm trying to make, that in theory stock prices are driven by financials, but in practice it's a mix between financials and narrative, and I think narrative dominates more than I was taught back in economics classes.

Narrative dominates day to day, but it’s also very easy to overstate its importance. In a bound random walk the bounds and the randomness doesn’t have consistent impact. In the middle of the range randomness completely dominates what comes next, and at the edge the bounds completely dominates the randomness. That’s IMO a better model of these things.

Take say money, second by second the value of USD is determined by people’s perception. However, people in aggregate are required by law to pay a fraction of US GDP in taxes based on the value of stuff besides money, like millions of cars and cans of soup etc. That relationship means without printing new money the value of all USD in circulation must be enough to pay taxes with or you get the monetary equivalent of a short squeeze. Which then represents a bound unlike say cryptocurrency which can actually fall to zero even as the economy continues normally.

I bring up the tax angle specifically because it’s normally irrelevant but changes the behavior at extremes. People tend to think of economies as fragile things because even minor changes have large implications, yet Ukraine’s economy continued even in the middle of an invasion and massive migration etc. Stocks seem divorced from reality up until the point where fundamentals matter.

Having the data isn't enough; someone also needs to look at the data and draw conclusions. If it's an environment focused on pumping up metrics for the newest thing then this may not be happening.

We don't know that, actually; you assume that the company has the data, and speculate that they interpret it correctly. But in practice, "customer loves using the app" and "customer puts up with the app but will replace it as soon as they find anything else" look the same right up until they don't, just as "high interaction" presents the same data as "app is disorganized and inefficient".

Usually the company is quite aware the feature is half finished, but the data shows that hardly anyone uses it, and hardly anyone is asking for it to be improved. So that time would be better spent improving things that there is lots of usage on.

There are multiple ways to interpret the same data. Your interpretation is a valid one. There are other valid ones all from the same data.

If your company sees the simple lack of feedback as a valid signal, then a feature with lots of usage but no feedback means you should not improve it but simply leave it be. It's used, so it seems popular and nobody complained about it, so it must be good enough. Build something else.

Of course your company may also view a simple lack of feedback as not enough signal. If a new feature that is half finished sees hardly any usage this can be taken as a signal that the feature needs to be improved. It's not useful enough in its half finished state to attract usage. Or your users may simply not be able to find the feature because in its half finished state it's too hidden and thus you have neither lots of usage nor feedback on it.

> the data shows that hardly anyone uses it

This is exactly why data can be a horrible guide for product development.

If you develop a half-baked feature, and the data says nobody uses, then maybe, just maybe, they're not using it because it's a half-baked feature.