With the rise of ‘lean’ methodologies and literature, product managers today understand that continuously generating user feedback throughout the product lifecycle can help mitigate the risk of wasting resources to build something no one wants.
In theory, user feedback can enable product managers to apply a data-driven approach to decision-making, so that only demanded features and products get built. But generating user feedback often turns out to be complicated for one frustrating reason: user feedback.
Folklore credits Henry Ford with once stating: “If I had asked people what they wanted, they would have said faster horses.” While he likely never said that, the sentiment is not lost on product managers.
It only takes a few misguided surveys to realize how inaccurate consumers are at estimating future behavior. For example, we’ll often find that in surveys, consumers report that user reviews are unimportant in making purchasing decisions. But in our experience, reviews are generally one of the first features that users navigate to when evaluating a product.
In his groundbreaking book, The Lean Startup, Eric Ries illustrates how entrepreneurs can build and launch minimal versions of their products optimized to generate reliable feedback before iterating on those products. His theory of the ‘Minimum Viable Product’ is based on the premise that by testing actual usage scenarios with customers, product teams can gain more reliable data than would otherwise be learned from merely estimating future behavior.
But the primary goal of utilizing MVPs is not and never was to simply build something – it was, and is, to alleviate risk by validating customer demand for a product concept earlier in the product lifecycle. For Ries, who was at the time working on a startup, this was done most efficiently by building a product and iterating based on the feedback received.
However, startups are very different from large companies. Product teams in large companies are not as nimble due to increased layers of communication and decision making, and increased brand and legal risk, among other factors. For enterprise product managers, validated learning comes far more rapidly utilizing other strategies.
Therefore, applying “lean” principles is quite different in the enterprise. Instead of a “Build-Measure-Learn” approach, enterprise product managers are taking a “Experiment-Measure-Learn” purview. Consider reading What Product Managers Need to Know About Minimum Viable Experiments for a more in-depth analysis of this shift.
The key to running experiments is mastering the capability to efficiently develop prototypes. Prototypes can communicate proposed value propositions to generate reliable feedback much more cost-effectively than engineered products.
In this regard, prototypes are the means by which experiments generate actionable data in line with that generated by minimum viable products. Not only do simulated prototypes marginalize user bias and inaccuracies, if utilized effectively they can also provide a framework by which product managers can evaluate and benchmark new product features iteratively. Perhaps Ford wouldn’t have learned much if he had asked users what they wanted before they knew cars existed. But had he relied on user feedback from prototypes of luxury options after the Model T became a world renowned product, he might not have lost so much market share to Chrysler and GM.
We wrote What Product Managers Need to Know About Rapid Prototyping as a guide to utilizing prototypes for the purpose of rapidly generating user insights. Rely on it as your handbook for frameworks and best practices, selecting the right tools, collaborating with stakeholders, and overcoming common hurdles. While the book does get quite technical, it is not a comprehensive UI style guide or a tutorial of various prototyping tools. Instead, we do our best throughout the book to refer you to necessary resources that can help you better understand the fundamentals of design and the technical aspects of specific tools.