Running Proof of Concept Tests to Validate Your Startup Hypotheses

Written by Monica Garza | May 2024

Throughout the startup journey, forming an optimally executed business is a common objective all entrepreneurs strive to reach. It is no secret that achieving optimization in startup development requires intentional, strategic efforts. By conducting tests, every individual component of a business model needs to be feasible and functional. It may certainly be perceived as a daunting task, however, it is reassuring to know that executing proof of concept tests before building a business is a wise path. Testing does not require a significant amount of resources and prevents potential business failure. Before bringing your business model to life, we’ll explore the process of conducting proof of concept tests; a critical step in the startup journey helping ensure that your business goes down an innovative and successful path.

Form & Prioritize Your Assumption Statements

Every business is built upon a number of assumptions made by the founder(s). For instance, a tech startup idea may be established upon the assumption that customers prefer its service features over that of other current market options. The first step in the testing process is to develop hypotheses based on the context of your startup concept. To develop your hypotheses, you would first need to form your key assumption statements.

Key assumptions are based upon the fundamental elements of startup concepts. In order for a business to function, its key assumptions would need to be undeniably valid. Most of the time, they encompass your startup’s value propositions. There are usually about 3–5 key assumptions in a business concept. Conversely, non-key assumptions are based on the additional components of a startup, such as customer preferences on supplemental product user features. Assumptions should be testable and specific. Be sure to brainstorm and list all of your assumption statements to best facilitate future test-stage organization.

By first identifying all of your startup concept’s key and non-key assumptions, you can then progress onto prioritizing them. Consider the level of significance that each assumption has on your startup’s functionality and list them from most to least foundational. With your key assumption statements at the top of the list, your most prioritized assumption will be the first one that you will work toward testing.

To provide a deeper insight into what assumption statements may look like, below are some general examples of three key assumptions and a non-key assumption statement:

Key Assumption Statements:

“My potential customers prefer my service over their other options.”

“My revenue model is financially sustainable and feasible.”

“Potential partners would be interested and willing to partner with my startup to fulfill the business’s functionality.”

Non-Key Assumption Statement:

“My potential customers value and prefer an online service over some other business competitors’ in-person service modality approach.”

Execute Your Proof of Concept Tests

There are many diverse mediums that can be utilized to execute your proof of concept tests. Identifying the best one for your assumption at hand is essential in ensuring effective results. You should opt for a testing modality that will help you obtain the most data in a reasonable timeframe, as well as quality data. It is important to consider which mediums offer convenience to your beneficiaries and are not too fiscally burdening. Some examples of testing mediums that can be used are surveys, interviews, focus groups, polls, cognitive walkthroughs, and simulations. Your data would be collected from people such as your stakeholders, beneficiaries, similar businesses, and experts in the industry associated with your startup. Establishing strong interpersonal relationships with them during the testing phase will help you have a future baseline of potential partners and customers with your trust and exposure.

It is crucial that you ensure that your collected data provides high-quality and reliable evidence. Face-to-face tests, such as interviews, although not as effective as surveys in obtaining dense data, offer an excellent opportunity to more deeply verify their stances. You will be able to pick up more from assessing their cues, such as body language and tone of voice in their responses. Another strategy would be to implement survey action items to reaffirm survey participants’ responses. An effective approach would be to offer survey participants the chance to join the waitlist for your future good or service. This can be an excellent opportunity to truly measure their level of investment in your product. As such, implementing strategic decisions during proof of concept tests will help you receive increasingly reliable and valid, high-quality test results.

Analyze Your Data with Threshold Targets

Once you have executed your tests and have collected your data, we can now progress on analyzing it to form optimal business decisions. To effectively assess your data findings use quantitative methods, such as statistical analysis . You can use visual aids in the form of line graphs, pie charts, histograms, or bar graphs. For example, you can state that 84% of your surveyed beneficiaries indicated that they prefer your startup concept’s service over other options. Your quantitative analysis can’t be executed without first establishing your reasonable threshold target for each of your hypotheses. The threshold target is the minimum value that your test’s principal quantity needs to reach for your assumption to be considered valid. Given the previously mentioned test quantity of 84%, if your established threshold value was 60%, you can be assured that your assumption is correct.

Moving forward with the development of your startup, you would now be implementing the business component associated with your validated assumption. On the other hand, if your data analysis indicated that your assumption was invalid, you would work toward making some enhancements and modifications to your business concept before retesting your new assumption. During testing, it is often common for result uncertainty to arise due to a lack of data collection or testing complications. In that case, you would repeat and improve your test with different strategies, such as opting for a different testing modality.

Proof of concept testing is fundamental to entrepreneurship. Although the possibility of assumption invalidation may seem daunting, embracing invalidations as an opportunity to improve your business strategies and model is critical for an optimally developed startup. It is certainly not a linear process, but the path itself undeniably leads to a better startup future. You’ll be guided to making excellent data-backed decisions for your startup that will take its trajectory to the next level.

This article is from the Innovation Capsule series, a commentary article series published by the Herb Kelleher Entrepreneurship Center (HKEC) that focuses on analyzing and creating dialogue around startup culture, best practices, innovation in the industry, and more. The HKEC offers a variety of dynamic resources to UT Austin students, including competitive funding opportunities, networking events, informative article series, mentorship and more.

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