Structural Anthropology simply put enables the study of culture (meanings, habits, rituals shared among a group of people). Techniques like human-centered design do a great job of getting to shared habits and rituals but struggle to understand underlying shared meanings that affect how human beings interpret issues and topics. It is the discovery of meaning that most concerns us at MotivBase because without it, it’s very easy to solve the wrong problem in the marketplace.
Culture is everywhere and everything in culture has meaning. Most often, these meanings are anything but logical or rational.
If you think about pretty much any topic that matters to your business, there are meanings that revolve around those topics. Which means anything that matters to your innovation efforts is a culture and comes with a set of shared meanings.
Consider this example –
If you are working on something in the area of plant proteins and you apply the anthropological lens on the issue, you’ll discover that there is much more to the topic than meets the eye. You’ll discover that the dominant meanings around plant proteins actually relate to a distrust and fear over the quality of animal protein (pesticide leakage, hormones, contamination etc.) Without this lens, you’ll only see the surface level and logical application of plant proteins – for healthier and more sustainable consumption outcomes – and either fail in your efforts or achieve lackluster results.
This is the value of the anthropological lens.
Don’t be mistaken by its softness. It offers a deeply scientific approach.
Science is nothing but the systematic exploration of a hypothesis – testing and validating until a conclusion is reached. Applying the anthropological lens to innovation is the same – a systematic investigation of the dominant meanings consumers inadvertently create around topics, issues, ideas that matter to innovation. Of course, when we marry the anthropological lens with big data and AI, it allows us MODEL and MEASURE the dominant meanings surrounding topics over time, enabling powerful predictions of outcomes, time frames, and even volatility.
It is this modeling and prediction of meaning that helps innovation figure out when and how much to invest, and with what platforms and technologies. Going back to the earlier example on plant-protein, a client of ours more than 18 months ago used these meanings to invest in improving the quality and traceability of animal proteins. Instead of blindly following industry narratives around plant-proteins, they made measured investments on both sides of the aisle based on an understanding of meanings (around plant-proteins) and their relative value to consumers. In the end, they saved millions in investments gone wrong and our client even got promoted.
If you ignore meaning, you’re basically choosing to go in blind and hope that what you build will land in the right place at the right time. Just think about all the plant-protein solutions that have launched in market focusing on sustainability and healthful benefits. Think about where they hoped they would all be versus where they are – stuck in a niche market with volatile sales results.
The meaning of something isn’t what it technically or logically means. It’s defined by how people use it, and in what context.
This realization isn’t new but it isn’t something that innovation has traditionally adopted or leveraged in its pipeline development process.
That’s what we bring to the world of front-end innovation.
