The venture studio model has gained significant traction in recent years, and for good reason. Research indicates that startups built within venture studios are 30% more likely to succeed compared to traditional startups, and return 2x the IRR for investors. This impressive statistic is made possible by the unique blend of advantages that venture studios bring to the table, including resources, expertise, and strategic support that can make all the difference in a startup's journey.
At the heart of the venture studio approach lies a crucial element: a systematic and objective validation process, generally absent from the passion-driven efforts of an entrepreneur. This process, which ideally begins before anything is built and continues throughout the startup lifecycle, is fundamental to the success of studio-built startups. Though too often skipped by entrepreneurs, validation is a simple concept, summed up by investor Tyler Hogge, for example, in only 3 words: “Sell, design, build.” Through validation, entrepreneurs seek to confirm their hypotheses to a few simple questions: that a pain point they’ve identified is real, significant, felt by others, solvable, etc. A validation process that confirms these hypotheses before anything is built ensures that the resultant startup is built on a solid foundation, ready to face the other challenges startups face throughout their journey.
As a venture studio that’s continuously evaluating startup ideas, we’ve vetted thousands of startup concepts and studied what comprises a good business idea. At the same time, we’ve extensively researched startup failures to better understand what went wrong so we can avoid the same pitfalls. While many are inclined to blame startup failure on a lack of capital and other resources, that’s more of a symptom of failure rather than a cause. In fact, startups usually fail because they don’t appropriately de-risk the business idea by validating market needs.
Harvard Business Professor Tom Eisenmann sums up this dilemma perfectly, “By neglecting to research customer needs before commencing their engineering efforts, entrepreneurs end up wasting valuable time and capital on MVPs that are likely to miss the mark. These are false starts. The entrepreneurs are like sprinters who jump the gun: They’re too eager to get a product out there. The rhetoric of the lean startup movement–for example–‘launch early and often’ and ‘fail fast’–actually encourages this ‘ready, fire, aim’ behavior.”
On the one hand, the “ready, fire, aim” model certainly works–it’s a surefire way to determine whether a business will succeed or fail, but failing after building is costly. In contrast, a reasonable validation process provides a framework for startups to significantly mitigate business and financial risk prior to launch.
Introducing a solution to a problem that customers don’t really care about is going to spell failure. This is even the case for new products whose huge enterprise umbrellas set them up for success in every other way possible—like Google Glass and Amazon Echo Loop.
Google Glass is a textbook case of market research failure. Without proper research, Google took a gamble that its customer base would buy the company’s novel smartphone-acting eyeglasses. However, it was clear early on that customers didn’t understand what would necessitate a product like this on a day-to-day basis. What could Google Glass do that you couldn’t accomplish as easily, and less awkwardly, on a smartphone? Moreover, the price tag of $1,000 to $1,500 limited the market to luxury buyers and was marketed as a fashion item. Unfortunately, Google Glass was far from fashionable, placing a significant social stigma on the few who test-drove it.
In essence, Google failed to do the market research to adequately understand market demand for such a product. Google spent hundreds of millions of dollars on the creation of a novel product that would spend less than a year on the market and earn Google significant negative press.
Similarly, the Amazon Echo Loop smart ring struggled to take flight due to a large lack of market research–not taking into consideration what customers desired from smart tech. While the market interest in wearable smart tech existed, the ring was too uncomfortable and impractical to wear all the time, which defeated the product’s purpose. Failure to tap into customer needs for modern smart tech and design around their preferences led to the product’s demise.
The bottom line is: without truly understanding customer needs and the intended market, a startup is nothing more than a gamble. But costly business mistakes like this can be minimized if every startup idea undergoes a validation process to de-risk it.
While all great studios have a codified validation process, they look very different from one studio to another. Some venture studios adhere to a rigid step-by-step process with strict stage gates and resource allocations; others focus more on validation principles. Philo’s process is a combination of both, using goals and KPIs to measure progress, but remaining principle-oriented as we explore, iterate, and look for signals confirming that we should be moving forward with a concept.
In our validation efforts, we’ve found experiment-driven validation, modeled after David Bland’s assumption mapping methodology, to be an incredible tool. After identifying a pain point we’re interested in solving and potential solutions to that pain, we set out to identify the hypotheses we need to test to feel confident a startup built around that particular pain and solution can thrive. Each hypothesis is something that needs to be true in order for a business to be successful. These assumptions are structured as positive statements such as, “We believe that customers would pay $800 for a solution to this problem.” Most of our assumptions revolve around three areas of product focus: desirability, feasibility, and viability.
Desirability: There are customers who have a meaningful pain point, and desire a solution to that pain. The pain felt by customers is a top priority for them to address. Our proposed solution has a unique value proposition that makes it more desirable than other solutions currently available. Customers desire the solution enough that they’d be willing to pay a meaningful amount for it.
Assumptions regarding desirability are most effectively tested by talking to target customers and, ideally, getting some commitment from them to use a product if built. Are they willing to swipe their credit cards based on a description of a solution? Great, move on to the next step! Are the customers too busy to talk to you about your solution? Move on, and figure out what other problem they’re most concerned with addressing.
Feasibility: A solution that successfully solves the problem that can actually be built, and there aren’t any technical, regulatory, safety, or compliance issues that would limit or prohibit us from building the solution.
To test out our assumptions regarding feasibility, we rely on insights from engineering and legal experts and other industry professionals who understand the regulatory or technical landscape and can quickly provide go/no-go guidance around feasibility.
Viability: The economics of the proposed solution allow for a profitable business. The market is big enough to justify the up-front R&D investment.
When addressing our viability assumptions, we speak to product, business, and finance professionals who would understand the financial implications of a product. Following our conversations, we then work to build a pro forma business model.
After we’ve determined the desirability, feasibility, and viability assumptions of a proposed solution, we go through an assumptions mapping exercise. This is usually a fun group exercise where team members brainstorm assumptions and categorize all of our assumptions onto a matrix like the one below, based on our level of confidence in an assumption and the importance of that assumption.
As you can see, the vertical axis illustrates the importance of an assumption. This is where we ask ourselves, “How important is it to the success of our startup that this assumption be true?” If it’s extremely important for the assumption to be true, then you know it’s mission-critical for your business, and place it as a higher priority.
The horizontal axis reflects whether an assumption has evidence or no evidence of being true. Here, we look at both the quality and quantity of the data backing up our assumption. For instance, a customer who is already paying for a product is pretty strong evidence of an assumption around willingness to pay. In contrast, informal customer feedback or even survey answers may fall closer to the “no evidence” section of the matrix.
By mapping our assumptions on this matrix, we quickly identify the riskiest assumptions most critical to the success of the startup. Assumptions that are most critical are those that have little to no evidence and are most important to the success of the business–so those within the upper right quadrant of the matrix.
An assumption found within the upper-right quadrant–again, representing those that are highly important but have no evidence–is what is called a leap of faith assumption. Leap of faith assumptions are prioritized for validation through quick, low-cost experiments to help us gather more evidence and confidence behind that assumption.
Following research and testing, if one of our leap of faith assumptions turns out false, we take a step back to refine our hypotheses, or table the concept in favor of others that have fared better in our validation process. Only when we gain conviction in our assumptions do we start building a new company.
Assumptions mapping is only one of many frameworks for validation. The simple design, sell, build concept introduced previously is another great way to think about validation. If you think you’ve identified a pain point and have a solution to it, put together some quick designs around the solution and go sell the concept! If a handful of customers are willing to “buy” it, that’s great validation–start building! Regardless of the framework or process followed by a founder to validate their concept, the hard work is worth it, giving the founder confidence that they’ve mitigated the most significant risk to the success of their startup. At Philo, using a formalized validation process has enabled us to launch startups with minimal, calculated risk, and an already existing customer base–positioning our startups with a distinct, invaluable advantage at launch.
October 25, 2024