{"id":16650,"date":"2026-06-02T10:00:13","date_gmt":"2026-06-02T10:00:13","guid":{"rendered":"https:\/\/userpilot.com\/blog\/product-failure\/"},"modified":"2026-06-15T14:05:31","modified_gmt":"2026-06-15T14:05:31","slug":"product-failure","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/product-failure\/","title":{"rendered":"Product Failure Isn’t Random and How You Can Avoid It"},"content":{"rendered":"

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Product failure in SaaS is usually less dramatic than people imagine. I’ve seen features launch with strong internal support, only for usage to flatten a few weeks later. The instinct is to blame onboarding, messaging, or marketing. More often, the team built something customers didn’t need enough in the first place.<\/p>\n

You’ll often hear that 90% of products fail, but that number comes from research on consumer packaged goods and has been loosely applied to software for years. More recent studies put digital product failure rates closer to 40-50%. That’s still high, but it suggests product failure is less random than the statistic implies.<\/p>\n

What’s different today is that AI has made it easier to build software, easier to copy features, and easier for customers to switch. If you’re building something people don’t actually need, the market tends to tell you much faster than it did a few years ago, which makes customer retention<\/a> the real scoreboard.<\/p>\n

In this article, I’ll work through why that happens and what catching it early looks like in practice.
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Is the 90% product failure rate true?<\/h2>\n

Short answer, no. I’ve seen it quoted in blog posts, conference talks, investor decks, and sometimes it’s even stretched to 95% when people are talking specifically about startups or tech products.<\/p>\n

The thing is, when you start digging into where that number comes from, it gets a lot less convincing. It largely traces back to research on consumer packaged goods from decades ago. Those studies were looking at products competing for shelf space in supermarkets, which is a very different environment from SaaS, where success depends on retention, recurring revenue, and continued usage over time.<\/p>\n

When researchers look at digital products directly, the numbers are much lower. Analysis drawing on McKinsey data estimates a product mortality rate of 35% and a broader failure rate of 48%, while UserVoice’s research puts digital product failure<\/a> closer to 40%. Whether the real number is 40%, 48%, or somewhere in between isn’t really the point. What matters is that we’re talking about something closer to one in two than nine in ten.<\/p>\n

Such a distinction changes how you think about failure. If you believe 90% of products fail, it’s easy to conclude that success mostly comes down to timing, luck, or being in the right market at the right moment. But if failure rates are closer to 40-50%, then it becomes much harder to dismiss failures as random. You have to start asking what those teams got wrong.<\/p>\n

Not every failure is a product failure. Sometimes it’s an execution failure.<\/p>\n

McKinsey and the University of Oxford’s study of 5,400 large IT projects<\/a> found that they ran 45% over budget, 7% over schedule, and delivered 56% less value than expected. That’s very different from building something nobody wants. Samsung’s Galaxy Note7 is another good example. There was plenty of demand for the product, but battery defects forced Samsung to discontinue 2.5 million units. So that’s an execution failure.<\/p>\n

Once you separate those two things, the conversation becomes much more useful because you’re no longer treating every unsuccessful outcome as evidence that the product itself was flawed.<\/p>\n

\ud83d\udca1 Read related blog posts:<\/strong> Product-market fit framework: How to find and maintain PMF<\/a><\/div>\n

Why do most products fail?<\/h2>\n

Most products fail because product teams lose touch with what customers need.<\/p>\n

This is a point Tony Ulwick, founder of Strategyn and one of the originators of the Jobs-to-be-Done framework, has been making for decades<\/a>: product innovation consistently fails because cross-functional teams lack a shared definition of what a customer actually needs.<\/p>\n

Sales hears feature requests and treats those as customer needs. Marketing thinks about needs in terms of positioning, messaging, and perceived value. Engineering translates needs into technical requirements. None of those perspectives is inherently wrong, but they’re not the same thing.<\/p>\n

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Three functions, three definitions of “customer need.”<\/figcaption><\/figure>\n

Ulwick’s solution is to anchor customer needs around the job the user is trying to get done. What is the customer trying to accomplish? How do they measure success? Where are existing solutions falling short? Teams that get this right usually have a strong customer feedback<\/a> loop that pulls language from users, not a Slack channel of internal interpretations of what users probably want.<\/p>\n

That’s why I think “no market need” is often a misleading post-mortem conclusion. CB Insights found that<\/a> 43% of failed startups cited it as a primary reason for failure, but in many cases, the market need wasn’t absent. It’s just that the team never aligned on what the need was.<\/p>\n

Once you look at failures through that lens, many of the commonly cited causes start to look like downstream effects of the same issue:<\/p>\n