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Excerpt from the article: I’ve been fascinated by the concept of Product/Market Fit for quite some time. The reason why it’s such an interesting and important concept is that getting to Product/Market Fit (PMF) marks a critical juncture in a company’s lifecycle. At least in theory, the life of a company can be divided into a “pre PMF” phase and a “post PMF” phase, with each of the two phases having very different objectives and requiring very different strategies. As Marc Andreessen famously said, “when you are before PMF, focus obsessively on getting to PMF”. Once you have PMF, you can start to focus on hiring, getting more customers, finding customer acquisition channels, optimizing pricing, and so on. In reality, there’s usually not a sharp line of demarcation that separates the “before” from the “after”. Rather, companies typically increase their level of PMF gradually. The problem with PMF is that it’s hard to precisely define and even harder to measure. So difficult, in fact, that I’ve heard several people resort to the “I know it when I see it” phrase (famously used by a Supreme Court justice to define pornopgraphy). Think about it. We have the concept of a demarcation line which calls for different strategies “before” and “after”, but we don’t seem to have a precise definition of that concept, nor the tools to measure whether a company is “before” or “after”! To make things worse, according to data from a Startup Genome Report “premature scaling” (i.e. spending significant amounts of money on growth before you find PMF) is the #1 reason why startups fail! Let’s look at what some of the smartest people in the industry have said and written about PMF.

  • SE

    Sean Ellis

    about 2 years ago #

    Good article. In my experience, getting to PM fit is the biggest growth challenge that companies face. And I agree that trying to grow without product/market fit is the fastest way to kill a startup.

  • JW

    Joy White

    about 2 years ago #

    This might be a stupid question, but how does the website know to put "Recommended for you" at exactly the articles that I want to read?

    • ND

      Nichole Elizabeth DeMeré

      about 2 years ago #

      Probably Machine Learning.

    • GF

      George Featherstone

      about 2 years ago #

      Not a stupid question at all. Machine learning for sure - but what exactly does that mean in this case?

      It may be the first time you have visited a site, so you wonder "how does it know what I want to see?".

      The simple answer for an extremely complex process is this: Other people read specific articles after reading this one, other people come to this article after reading specific other articles, people who visit the site from one source (say facebook) are more likely to read a certain category than are others from another source (say a referral from a specific site)... among other things.

      It's powerful tech, and less invasive than one might think, which provides a more relevant experience to users. It's also getting easier to implement. I recommend trying it out if you get the chance.

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