The Innovation Matrix revised for 2020 and beyond

AUTHORS: Bansi Nagji & Geoff Tuff, 2012

From 2012……for steady, above-average returns, firms need a balanced innovation portfolio and the ability to approach it as an integrated whole. Those that excel in this area invest at three levels of ambition, carefully managing the balance among them. 

That’s how it was described 8 years ago.

What I keep hearing in 2020:

Pick the right market –  you have a limited amount of time and a limited amount of resources to uncover the market.  Guess well, your career depends on it.

Pick the right use – how is the customer going to use the product? Guess well, your career depends on it.

What else do I have to go by?  What other data sets could there be?  Guess well, your career depends on it.

How to (re)Start

In 2020, any effort to find new markets or shore up existing ones needs better prediction about future customers.  The old tools are not working like they use to.  I keep seeing and hearing your stories that don’t point to a solution.  I have read that the pandemic did not change history but it accelerated it.  In 2001, we had an emerging search engine ecosystem that was reasonably priced.  In 2008, social was an emerging platform(s) via Linkedin and Facebook.  What do we have in 2020?

Before investing in sales, marketing, and product answers, start with “why people buy”, their personalities and desires – these are more predictive and accurate and when combined with what you are doing but will likely fix some unknowns and broad assumptions.

It turns out the personalities of your best, future customers look nothing like anybody else. Let’s not ignore that. Don’t blend, don’t average.

Old concepts die fast when no ball is played

The Moneyball concept pioneered in baseball for assessing the performance of a player or team creates a set of expected goals.  Not results…goals. These are based on the quantity and quality of chances. The idea is to come up with a better way to predict the outcome. 

What happens when you don’t even have a baseball team to analyze. That’s 2020.   What you knew about the past and present doesn’t really matter. People are the same but their priorities have changed. You’re in the way.

Nobody has ever swung at the ball.

You can use a case-based method but that sounds like consulting.  Real high-end stuff. Costly and time-consuming for too many.

Probabilistic mindset

Developing a probabilistic mindset allows you to better prepare for the unknown, especially when you don’t know the unmet needs of the customer. 

Even when there is an infinite number of factors, probabilistic thinking can help you identify the most likely outcomes and best decisions to make.  The best way to strike is to have a prediction of what the outcome is likely to be and not rely on historic data to create that prediction. You have to bring something else to the table.   What I invented is a set of data that predicts outcomes and works with your existing workflow and datasets.  It’s probabilistic thinking – a theory of what customers will do with your product in 2020 and beyond.

The innovation Matrix – in 2020

So what has changed?

How to win x Where to play: Start with ‘play’: whether you are using existing products, making incremental changes, or developing new products, you have to look beyond historic data as a guide.  You cant use traditional techniques alone to determine this data. 

Here is an example: a major retailer wants to sell more vitamins. My data set includes 22 million vitamin buyers.  When I looked at States with the least consumption vs high consumption per capita, the personality differences were vast.  So…..

In New Jersey, high vitamin usage appears to be a ‘battery’, extending the day. In Wyoming, vitamin usage is more biological in nature.  It’s required to survive the conditions. 

Averaging these two States is a disaster.  Stop.

What can predictive data sets do:

—> The personality traits of good, better, and best customers – they will be very different and helps define how to speak to your audience, who to speak to and what they desire from your product.  Avoid those who don’t care. Traits are why we buy.  They say all decisions are emotional – well, I quantify emotion using language psychology.  It helps define the creative process.

—> Based on that, I will get you look-alike CRM (customers that look just like the good, better, and best customers).  That helps with media planning.  I had a customer recently where the look-alike customers followed geographic patterns.

—> I will also help you define who not to market to. Predict the bad ratings of people.  Avoid them.

—> Last, how your marketing language matches to customers.  What verbs, language works best, and what should be avoided.  We can also look at ratings, etc.

My mission is to only promote ‘formed intent’ and never use it to engineer intent. Translation: don’t use it to sell but use it to enlighten those who need the product you are selling.  Life is too short for more products to fill the shelves of discount stores.

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