How to Avoid Building a Product No One Wants

We Just Built a Product No One Wants

How to Avoid Building a Product No One Wants
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I recently had a conversation with someone whose company spent a good portion of last year trying to build a “ground-breaking” mobile app. The company was behind the technology curve and needed to stay relevant, so a lot was riding on this project. Thousands of hours went into planning, building and marketing the app which, of course, meant a high cost. After a ridiculous amount of time, effort and money, the product launched with very little excitement or adoption.

Here was the problem: the company didn’t spend much time talking to the end-user. In fact, most of the time was spent interviewing a few product owners whose role was only represented by a small percentage of the user base.  As a result, many of the features the team spent time building didn’t solve the primary user’s pain point.

“Scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”

– Mathematician Ian Malcolm (played by Jeff Goldblum), Jurassic Park

Understanding Product/Market Fit

Before you spend a dime on building your product, you should consider conducting some experiments / tests to validate your idea and determine if you have product/market fit. Product/market fit is the degree to which a product satisfies a strong market demand. There are different approaches to understanding whether or not you have product/market fit, but it begins with answering a few questions:

  1. Do I understand the problem my customer/client/user faces?
  2. Is the problem prevalent or large enough for people to need a solution for it?
  3. Does the proposed product or service solve that problem?


“Your job isn’t to find more customers for your products, it’s to find more products for your customers.”

– Seth Godin

Answering “yes” to the above questions doesn’t imply you have a product/market fit. It just means you can begin testing your theory. Depending on your particular circumstances, you can approach product validation in several ways.

Customer interviews, surveys and focus groups all provide insightful ways to understand a customer’s problem better. One metric for product/market fit is if at least 40% of surveyed customers consider the product or service a “must have.” If you are evaluating an existing product, you should find out if at least 40% of surveyed users indicate they would be “very disappointed” if they no longer had access to a particular product or service.

If you are developing a brand new software solution, building a simulated or functioning prototype is one of the most cost-effective ways to get feedback, field test and observe how users respond to your product. There is a saying in the design world, “If a picture is worth a thousand words then a prototype is worth a thousand meetings.” Tools like InVision and Sketch make it very easy to communicate features and gain a better understanding of the user experience through visuals.

What about Internal Enterprise Applications?

Enterprises should not bypass seeking product/market fit just because their application is not public facing. If your app is for internal users, it is important that you ensure your product addresses the users’ needs.

In the case of a global product, it is important that you conduct separate interviews/research with users within the various regions. A recent Harvard Business Review article cited that “Not adapting the product offering” is a common mistake companies make when launching a new product globally.

“Companies achieve ‘product/market fit’ one country at a time. Yet all too often, companies try to launch identical products in different markets, ignoring the fact that they’re dealing with very different customers.”

The market for both enterprise and consumer technology products is more competitive than ever. Customers have numerous options to choose from and have become more discerning. The good news is that it has never been easier to validate your product idea, build it and get both qualitative and quantitative data to improve and enhance features.


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