In a fast-moving global economy, the ability to manage innovation better, faster, and more profitably than competitors, is indispensable. An important step in the process of innovation management is moreover making the fuzzy concrete, translating strategies into actual procedures.

In this article, we will discuss an analytics-based innovation model that has worked so well with client organizations that I now teach it to my students. It introduces a practical work agenda to enable managers and leaders of innovation to get on with what most often seems a compelling, but fuzzy subject.

On the basis of my thinking is the contention that – in order to innovate – analytics should precede creativity, and not the other way round.

Framing Innovation

The literature generally promotes the view that the ability to innovate is linked to having a well-developed right brain, and that additional schooling in creativity will do the rest. I believe this view could not be more wrong, for two fundamental reasons. First, innovation starts with the ability to recognize a reality “as it is”, i.e. at the left-hand side of the brain. Second, I am firmly convinced that creativity comes naturally to all of us, as long as we allow our inborn curiosity to get the better of us.

Our framework for innovation has three steps:

  1. Input – Business need and analytics.
  2. Output – Focus, Testing, and Buy-in.
  3. Outcome – Implementation and keeping score.

These three steps are a neat linear process. What matters of course is the work agenda to be applied for each of the steps. In the following, we will take you through such an agenda for the second part of step #1: the analytics of innovation.


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Structuring the use of Analytics for Innovation Management

We use analytics to generate a set of realistic ideas about what innovation might look and feel like. When we say “realistic”, this does not mean we stay inside a box, but that these ideas need to be connected to the real world of the product, service or organization we want to innovate.

These analytics require structure in two ways:

  1. An agenda of three activities, to be deployed to the object of innovation, which may be a product, service, process or anything else:

Activity #1: Take it apart. Look into its components. This stage is a “zooming in” movement.

Activity #2: Identify what the object is a part of. What are the larger frames that contain the object of our attention? This is a “zooming out” movement.

Activity #3: Explore the past. An understanding of the past is indispensable for an understanding of the present moment, as well as the multiple realistic potential futures that lie ahead.

  1. The use of logic trees. These trees are to be MECE (Mutually Exclusive, Collectively Exhaustive). Being MECE here means positioning the object of innovation into distinct, non-overlapping parts, and thus making sure that no aspect is overlooked.

Below we will look at the mechanics of this agenda and discuss an example. Each of the three agenda points is represented like a journey down a transportation system, a step on the journey to innovation management.


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Let’s work through the example of a running shoe.

Each of the five stages of the journey can be broken down. For example, features (product characteristics) may include:

Breathable, lightweight, reflective, waterproof (or water-resistant), sole (broken down into marking vs not-marking and sole materials), upper materials (broken down further in terms of nature, origin, colour, quality, thickness etc.), windproof characteristics, printing etc.

Let’s now break down its components:

Outsole, space trusstic, midsole, gel cushioning, lasting, sock liner, tongue, eyelet, heel collar, heel counter, upper etc.

Functionalities, benefits and experience can be broken down in the same manner.

During this first activity, we are trying to gain insight into the nature of the object we are working with. These insights are crucial for understanding the reality we have in hand, before trying to change it in a way that makes sense.

Innovation Management

This journey again has five stages. Our aim is to explore, which categories a running shoe belongs to. Let’s start with lifestyles:


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Linked to social position, styles of thought (clusters of attitudes, interests and opinions) and styles of action. When we break these down, we can identify extraverts, movers, traditionalists, defensives – as well as bikers, riders and so forth.


Bohemian, arty, chic, classic, exotic, flamboyant, glamorous, romantic, sexy, sophisticated, western, preppy, punk, tomboy, rocker etc. Also (in terms of distribution): Fast fashion, intermediaries and so forth.

During this second activity, our goal is to obtain insights into broad areas of repositioning for the product/service we have in hand. 

Innovation Management

 Looking into the past can produce some of the most powerful insights into what we can make the future look like. This is in particular the case when we develop new premium products and services:

Traditional crafts in the sport shoe business include Measurements, Making patterns, Stitching, Welting, Lasting – and technologies, including Goodyear, the McKay method, Blake-Rapid, Norwegian, Bologna, Adhesive – while traditional materials include leather, wood, canvas, vulcanized rubber, tree glues and natural-fibres.

All of these are useful pointers to identify areas for innovation.

So, what’s next in the process of Innovation Management?

Systematically structuring the analytical process helps us to discover numerous new insights about our product or service. This enables us in turn to make connections between any number of our recovered insights.

For example: where did the idea for skate shoes come from, if not from a connection between a shoe and a skateboard? When will we add “green” and have an electrically powered shoe?

Most often, a few good ideas will suggest themselves immediately without great effort on our part. All we need to do then is to do a Quick-And-Dirty viability test: what needs to be true for this idea to deliver value?

I will examine this question in my next article. I hope you enjoyed reading about managing innovation by use of an analytics-based model.


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