We believe it’s possible to overcome the barriers that prevent validating innovation strategies. By acknowledging the challenge, we’ll be a step closer to understanding what validation actually means.
We define validation as a way to confirm your assumptions regarding the desirability, viability or feasibility in order to de-risk the success of your innovation – in that order.
The innovation development process is made up of different stages. Validation and experimentation as a concept can be applied to any of them. At any stage, you can encounter a critical assumption that you have been making about your customer, user or market that, if proven wrong, is putting your innovation at risk. This is why we validate and experiment – to alleviate behavioral bias and validate (or invalidate) concepts.
Experiments are composed of many different methods and techniques, and which experiment to use is directly tied to what we want to validate. Regardless of which experiments are tested, there is a set of generic rules that apply to all – from digital experiments to empathy interviews.
Rule #1: it’s always the why, almost never the how
The importance of asking why when designing experiments for innovation
Let us tell you a story
A small company needed to offer a new on-the-go breakfast solution to their customers. They narrowed down their targeted customers to the ones who picked up their food every morning before going to work. They came up with a product that wasn’t only a tasty fast solution to the problem, but also had added nutritional value compared to traditional breakfast combos. When presented with the new offer, the customers didn’t nudge – they kept on buying the usual milkshakes.
While designing experiments, coming up with a solution that fixes the problem, and expecting it to work seems to be a universal misconception. This company’s new product wasn’t working. Not because of the what, but because of the how. Customers kept on consuming the product that was easier to carry with them, and that they could eat while on the way to work. If researchers would have asked the right questions – designed for the right why, they would have saved making an unwanted item.
The same is true about experiments. Understanding and designing for why is the key to achieving real behavioral impact.
Rule #2: getting to the reality
Dealing with the aspirational-self
Most times, consumers not only answer research questions from their aspirational-self perspective; but they’re sensitive to the way these are asked too. An example of this is confirmation bias. Think of the times you’ve been asked if you like something and you’ve answered “yes” without giving it a second thought.
Confirmation bias is a way of asking questions that hints the person to answer towards a certain answer. These types of questions are unable to gather authentic insights, and lead to researches that end up being empty in applicable content.
Getting the real-self to answer
Creating experiments is meant to offer consumers different options. When asking closed-ended questions, the assumption is that the person has given previous thought to the topic. This is rarely the case.
Think of how people lead their daily lives. An overload of information which they have to choose from, since the human brain can only handle so much. The same happens with experimentation. If you take an idea, concept, or phrase, and approach users with them, the answers will be limited to what they’re being shown. “Shop carting” questions is a method to recreate a real-life experience for them, so they have a wide range of options to choose from.
One of our past experiences within the healthcare industry helped us come up with an interesting example that reveals this behavior. While conducting interviews, we noticed users bringing up the type of content they followed – they chose governmental blogs and scientific journals over popular influencer posts.
In theory, it seemed we had the answers we were looking for. But, we decided not to settle and put it to the test. That’s why we developed an interactive Facebook feed with a series of different kinds of posts. We included everything available on the market, ranging from officially recognized institutions to the latest TikTok dance. Compared to the survey results, you would’ve thought they were, in theory, surprising…but were really not.
We collected information on users scrolling past the official publications they’d mentioned as their primary source of information. At the same time, we were able to see how most of them lingered on the popular-type of content. Our model proved the first questions were answered from the aspirational-self perspective, while the experiment showed that, in reality, they weren’t eating that healthy after all.
Rule #3: don’t fake it till you make it
Minimum viable products or low-fi prototypes are amazing ways to test products before developing them even further. This experimentation technique elevates the experiment by offering a real-world touch to the testing phase.
True experimentation is not about pretending something’s there, it’s actually developing a product and studying its performance with real customers. This stage of the process is a valuable and indispensable source of information on a product or service to be launched.
Designing experiments for innovation validation
When it comes to designing experiments, particularly social media ones, it’s common that businesses redirect their users to landing pages that might as well have been designed by toddlers with access to Sharpies. Not offering high quality interphases during testing stages leads to users dropping before engaging. This is acceptable from time to time, for example when the experimenting is based on clicks. However, secondary layers in testing phases expand on the reality of the experiment.
The best way to leverage this experimental scenario is by working on the way users interact with it. More real-like elements making up the experiment will lead to more detailed input on intent of buy, customer acquisition cost, rate drops, and survey responsiveness. All this by creating a believable shopping cart experiment.
Investing in these types of testing environments is especially important when trying to come up with disruptive technologies or products. The rate at which the market is expanding doesn’t always give us enough time to adapt to the newness. Realistic experiments help slow down the idea rampage while making the outcomes more efficient.
We’ve established that prototypes will be a crucial aspect that will elevate the quality of the information gathered through experimentation. And for that to be a reality, you should think about incorporating a designer as part of your team.
A good designer is an invaluable asset when creating an experiment. But it’s inevitable, sometimes we’re working solo. This shouldn’t stop you from experimenting, there are multiple free tools you can use to build a realistic high-end prototype. The time it takes to create these experiments is never wasted, but invested. Just as you can find free resources, you can get creative and realize there are users to test your experiments all around you. Friends, family, experimentation can be done well and on a budget.
Experiment design and more specifically thoughtful experimentation will help inform your research exponentially.