• Jason Partridge

Testing Concepts: Using AI Anthropology to Test Innovation Concepts



When people ask us where we fit in the innovation lifecycle, we often like to reply by saying this:


MotivBase sits at the front end of innovation.


But while early trend identification and culture mapping represents a lot of the powerful insight work we do, our team is increasingly being asked to help verify a hypothesis or potential execution throughout the concept creation process.

In fact, the clients that have celebrated the most success in the last year, haven’t just started their journey with us. They have returned to our AI Anthropological model to validate their thinking once they have built concepts.


Our job has been to assess if these concepts will be relevant when analyzed using a consumer-led lens.

The ideal situation is one where we are used at the very outset. We provide a foundational report on a culture that is critical to a business line or category where our clients know they need to make inroads to remain relevant.

This initial work is often high level but reveals the key consumers who are shaping the culture, identifies the microcultures that are most prevalent, and it gives us a prioritization based on what is growing in relevance in the market.

This work is often the jumping off point for our clients.

It provides a cultural perspective that's then married with third-party sales data (one of many integrations and partnerships we are currently working on behind the scenes) and points an innovation team in the right direction.

Naturally, many clients use this initial report to identify a key territory that not only excites consumers, but that aligns with their companies’ operational goals. With this added focus, we may “zoom in” on a culture and uncover additional nuance or detail that will drive better concept creation.

But here’s what might surprise you.

Once the concepts are created, many clients are running another sprint with us to validate the ideas. In fact, some of our clients, who have concepts generated before they started working with us, will have us validate ideas that are new, even though they were not built using our “anthropological” insight gathering.


Why? Because during the concept creation stage, teams may end up using language that either may not mean what they intend it to mean, or may create the wrong implicit perception about a product or concept when it's eventually put into market.


So here's how we help our clients through the concept development and testing process:

1. Concept creation phase


With an idea in tow, clients will provide us with the overview of the concept. Our job is to identify “consumer language” for the key elements of the idea. This process validates (or may challenge) that the concept aligns with critical consumer expectations when it comes to envisioning a solution.


For example. If you think ‘wellness’ and ‘wellbeing’ can be used interchangeably, you would be wrong. The consumer believes these to be two distinctly different things, and therefore, understanding which microculture your innovation serves is critical to its success.


But identifying this language also allows us to go on to step 2.


2. Concept Sizing and Testing


Once we have matched or married the consumer-led language with the concept, we can now explore its relevance today, and calculate its predictive relevance for tomorrow.


But we can also examine the key consumers (or tribes) that are most engaged and driving growth in these microcultures. With a deep understanding of the motivations, values, attitudes and fears, clients are able to develop a more empathetic understanding of the consumers that are most likely to be interested in a concept without ever needing to take the flawed song and dance approach of recruiting an audience and asking them questions.


Sometimes this validates that a concept is hitting the target the client wanted to attract.


Sometimes it reveals tweaks or changes that need to be made.


Sometimes it reveals untapped, profitable cultures that our clients never knew were reachable by a solution they could bring to market.


3. Concept Decoding


When we dive into the meaning that consumers are linking to a potential concept, we can validate that our clients understand the most dominant truths, needs and reasons to believe. Not only does this allow us to identify the "specifications" necessary to make the concept believable to the consumer. We may also uncover previously unknown benefits that better encapsulate the solution and that will do more heavy lifting when the product goes into in-market testing, or eventually, into full execution.

Conclusion:

The best innovation process is an agile and iterative process. While we wish we could take credit for this model, the truth is it was born out of requests made by our clients who saw the value in our tool, but asked if there was a new way we could leverage our data. The result is it's fast (5-10 days of research) and it is a predictive model that assures a concept aligns with where culture itself is headed.

But the real lesson from this MotivBase innovation is that AI Anthropology isn’t just relevant to the frontend of innovation.

It is a tool that can be used and applied at multiple points during the innovation lifecycle to assure that you never lose sight of the people who are most critical to a product's success.

If you are interested in learning more about how we are testing and validating innovation concepts, please reach out to me at jason@motivbase.com.

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