July 12, 2021

Problems with Minimum Viable Product

Two Startups, Two Failures, One Common Problem.

Electroloom was a 3D-printer company that sought to simplify and revolutionize clothing manufacturing through a high-tech desktop device. Launched as an idea in 2013, co-founder Adam Rowley won a prestigious design competition and garnered a lot of early attention--and funding--from investors. Rowley told Engadget that this was the point when things began to snowball: he and his team began making unfortunate assumptions. He says they figured it would be easy to get a workable device up and running that would attract a community of users. Ultimately, they were mistaken: The machine was difficult to use, and the user experience was “terrible.”

Theoretically, this should have been an opportunity to learn, tweak, and improve the product, but Electroloom’s lack of pre-work was their undoing. As the aforementioned Engadget article relates, “Rowley didn't really know what Electroloom was for, or who would wind up using it.” After the early hype and a promising first funding round, Electroloom failed to scale and ran out of funding.

Another company, Standout Jobs, sought to revitalize the job recruiting industry via a customized platform. They were so sure of their product that they didn’t do much (really any) customer research. It resulted in their first release being a big flop.

What do these two very different failed businesses have in common? They both failed to do their due diligence before releasing their MVP. Instead of launching a minimum viable product based on data-driven decisions combined with evaluative user interviews with potential customers, along with a clear measurement of success, they went with their gut. For Electroloom, that meant addressing the wrong market segment. In the case of Standout Jobs, there was literally no understanding of the customer. In the end, they both lost the opportunity to provide value and learn about the viability of their products before it was too late. When things went wrong, they didn’t know why, so they couldn’t try to fix it. Ultimately, they failed.

Both of these products may have been successful—although we’ll never know—if the MVPs had been built and released only after the proper amount and type of research, along with a concrete, data-based hypothesis to test.

You’ve heard the cliche: If you fail to plan, you plan to fail. And that saying holds true, especially when it comes to your MVP.

Finding out that your product needs tweaks doesn’t mean your MVP failed. In fact, you only fail when you don’t set yourself up to learn. Most problems with an MVP come down to lack of due diligence before implementation—or improper understanding of what “due diligence” actually entails. In turn, this leads to decision making that isn’t driven by actual data. When things go wrong, no one knows why, or what to do about it.

This article will review the main problems people encounter when creating an MVP and how to avoid these pitfalls.

Measuring Success

Remember that the primary purpose of an MVP is to create value for users while assessing the viability of your product. Even more to the point, MVPs are put in place to create the MOST value with the LEAST amount of features—it’s called minimum for that reason.

Often, the biggest challenge people face is figuring out the right assumptions to test—aka coming up with the proper hypothesis. In scientific terms, a hypothesis is a data-based starting point for further investigation and measurement. In other words, if you can’t measure it, you can’t improve it. So if you don’t nail the hypothesis, along with your measure of success, how will you learn anything?

Hence, you must ask yourself some important questions:

  • What am I testing?
  • What do I think will happen?
  • What do I need to learn?
  • How will I measure success?

The answers to these questions must be informed by data gleaned from market analysis as well as user research and interviews. Sussing this out directly impacts how you’ll scope the features to include and the maturity of said features for the MVP. There is a fine line between lacking, good-enough, and overdoing it. Answering these questions before building your MVP is foundational to getting the MVP in the sweet spot between too much and not enough.

So how do you arrive at the proper hypothesis? Simply put, you need to begin in the right place: Seek to understand your market segment, and more specifically, your users and buyers. The Pragmatic Institute explains, “For an MVP, each feature must be tied to tangibly solving a top customer problem.” Then, it makes sense that you can’t find your MVP’s sweet spot unless you understand what those problems actually are.

To that end, the Pragmatic Institute coined the acronym NIHITO, which stands for “Nothing Important Happens in the Office.” Their point is, getting out into the real world and collecting data about real people is the proper way to understand their needs. In turn, this data informs how you can scope your MVP properly.

With that in mind, let’s look at the top-level issues that lead to an MVP that teaches you nothing.

The Main Problems With an MVP

Problem: There is no proper market research and validation

As Javier Trevino, Director of Technical Services at 3Pillar Global, says, “Companies and organizations may think they know what the end-user wants. If there is no real data, like that obtained from executing market research or from surveys/polls, then the MVP could be missing the mark.”

To avoid missing your target, start with market segment identification. Segments are discrete groups of users connected by qualities such as age, gender, profession, location, and other distinguishing demographics. The Chron.com offers this example: “If you own a local pizzeria with a sit-down restaurant and delivery, two key market segments might be families and college students. For a forensic consulting company, market segments include trial lawyers, in-house counsel for businesses, and law enforcement agencies.”

You identify these segments by conducting quantitative research. The research allows you to segment customers and prospects into high-level groups based on specific markers, such as demographics, buying habits, affiliations, etc.

Problem: There is no target user defined

This is a more granular and personal category than market segments. Segments can help narrow down the big-picture “who,” while user personas help you understand the underlying “why.” Keep in mind that users and buyers may not always be the same group. Be sure to differentiate users and buyers in your research.

This research involves identifying and understanding the problems you’re solving and the pain points people face. Because you want to make data-informed decisions, perform a combination of quantitative research (drawing on behavioral analytics) and qualitative research (including user interviews and surveys) to understand the pain points, underlying motivations, and problems of users and buyers.

Problem: You didn’t generate user journeys for your personas

Creating user journeys involves mapping out steps people take as they try to solve a problem. It can further reveal the emotions, pain points, and motivations involved, and shine a light on where your solution fits into their lives.

A user journey map can also help you clearly visualize the minimum number of steps (or features) needed that will also provide the maximum value in relation to your MVP.

Problem: There is no problem statement defined

Creating data-informed personas and mapping user journeys will naturally lead to the creation of your problem statement. Using the pizzeria example, a problem statement might be, “College students who are gluten intolerant need an affordable gluten-free menu option so that they can enjoy pizza with their friends.”

Avoiding all of the problems we’ve mentioned in this article, as well as doing the right preparation, will help you form your data-driven hypothesis to test, along with the measure of success. This will, in turn, inform your scope.

Problem: Features aren’t scoped/prioritized properly (as in there aren’t enough or there are too many)

It’s challenging to whittle down features. However, it is an essential step that must be done and done thoughtfully. As Michael Rabjohns, UX Practice Leader at 3Pillar, explains, “You really need to understand which features and functionality are most important—if there’s not enough ‘there there,’ users won’t adopt it. You need to be able to live with that decision; [It’s] harder for product owners when we know about all these other great features under consideration that users would love.”

However, if you’ve done the due diligence discussed above, then you can let go of your “want to haves” and focus on the “must-haves.” In other words, you’ll be empowered to make data-driven decisions about what realistically falls into that “just-right zone.” Why? Because you began by understanding your users. You now understand their problems, where and how they encounter these problems, and you have assumptions to test regarding how your product can solve the problem and deliver value.


Think back to the failed startups we mentioned at the beginning of this article, Electroloom and Standout Jobs. While it’s true that hindsight is 20/20, it’s easy to see how they could have given themselves much better odds of success if only they’d done their due diligence. Had they entered the market with a data-informed understanding of their potential target market and a clear hypothesis to test, they would have understood how to iterate and improve their product when things went wrong. Instead, they were unable to make any data-driven decisions. As a result, they failed.

Just about every MVP-related problem can be traced back to improper preparation. For your MVP to be the valuable learning tool it’s meant to be, set yourself up to learn BEFORE you ideate and build it. Do your due diligence in the form of quantitative and qualitative market- and user-research. This will lead to data-driven decisions about your hypothesis, measure of success, and feature scope.

To learn more about 3Pillar’s services and how we can help you create a minimum viable product to test and validate your assumptions with real customers, contact an expert today.