Researchers from Georgetown University have conducted numerous studies illustrating this point. In one of their experiments, users were presented with the concepts of two different alarm clocks: one simple clock with the alarm being its only feature, the other – more complex with features like reading the weather and traffic patterns to the user, among a dozen other features. Users invariably picked the product with the most features. However, when those products were actually produced and sold, users invariably picked the simpler product. From a distance users prefer desirability, but up close prefer feasibility.
MVPs vs experiments
Many organizations have turned to Minimum Viable Products as the solution to psychological distance. While MVPs do indeed solve the problem, they’re not an optimal solution for large organizations.
The goal of an MVP launch is not to launch the first version of a product. It’s simply to start gathering feedback and observing users actually interacting with something. This distinction is critical.
Startups, by and for whom the MVP concept was designed, are typically focused exclusively on a product and users, and can therefore launch and iterate rapidly, sacrificing all other concerns. Larger organizations are more complex, so singularly focusing on iterating on live products is very difficult.
The larger the organization, the more people have a say in what must be a part of the feature set, which results in a complicated, all-too-familiar mess. But by arming product owners with the structure of an experiment, they can push back against feature-creep and design by committee. Actual user data trumps Aeron-chair quarterbacking every time.
When colleagues warn that it’s not even worth launching a product in such bad shape, remind them that it’s not a launch. The goal is to get actionable data, not hyper growth right out of the gate. Continually and rapidly launching, learning, and iterating new product ‘experiments’ delivers more user insight than any 400-page specification.
Prototyping drives experiments
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.