Decoding the lead user innovation landscape
Without a doubt, the ability to innovate in an agile and sustainable manner has never been more essential for survival. But it has also never been harder. Today’s volatile business era is constantly disrupted by technological shifts, new business models, and rapid changes in consumer culture.
In the new “economy of unscale”, empowered by technology, AI algorithms and the consumer data explosion, small and agile challenger brands can effectively transform entire industries before the incumbent even see it coming.
In this environment, 90% of corporate innovations fail, according to Mark Payne’s analysis of innovations in the 21st century in his book “How to Kill a Unicorn”.
There are obviously no easy answers to this complex innovation dilemma, but what is evident is the need for a truly consumer-centric innovation practice that works from the bottom up and is focused on solving emerging real-world problems.
Innovation research has long shown that consumers themselves are the real pioneers behind breakthrough innovations. Those most engaged in a particular field; the ‘lead users’, have an inherent motivation to develop novel solutions and regularly create radically new products and services ahead of market demand. These front-running consumers adopt trends way ahead the rest, defining the future of the category.
Searching for Lead User Innovations is not a new concept, but its practical value has long suffered due to the time and cost it requires in practice.
So, we asked: what if we could use web as an innovation mine to detect lead users in a specific field of interest?
Our new method developed in partnership with the MIT Innovation Lab does just this, applying machine-learning techniques to online content to detect lead users in specific fields of interest.
This makes it possible to continuously learn from the changing unmet need landscape and discover novel solutions developed by these innovating users.
Using semantic algorithms, weak but highly relevant signals and patterns can be extracted, informing innovation strategies.
Beyond this, social and search data, the method can take the guess-work out of which products will be popular with trend and diffusion analysis of the emerging products or concepts.
A pilot study in the field of kitesurfing shows how this method can surface both radical innovations and product improvements, which can inform the development of new products to market. Read more about this in our paper: Decoding the Innovation Landscape.
