# Using Numbers to Build Confidence and Momentum in Good Ideas

## Using Numbers to Build Confidence and Momentum in Good Ideas

As organisations start their recovery from the most challenging period in living memory, innovation will play a key role in responding to the inevitable challenges and grabbing the fast-moving opportunities that the new way of working will bring.

Innovation is, of course, business activity that clearly carries associate risk. Innovation is about the future and doing things that haven’t been done before. This is the stuff of risk and uncertainty. So, what role should “number-crunching” play in the innovation process? Should innovation teams be encouraged to rely only on the entrepreneurial instincts, or can some numerical analysis actually help them on their way?

In this article we will present the case that sensible use of analysis and numerical modelling can, in fact, significantly speed up the innovation process as well as reduce some of the risk inherent in this kind of activity.

**What Colour are Numbers?**

William Thomson, 1^{st} Baron Kelvin, said; “When you can measure what you are speaking about, and express it in numbers, you know something about it. When you cannot express it in numbers your knowledge is of a meagre and unsatisfactory kind. It may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science.”

Good use of numbers is like an artist having a few more colours in the paint pallet. Some kind of “numbers model” adds significantly to the definition of the idea. An idea isn’t properly defined until there is a numbers model, related to the purpose of the idea, included alongside the written definition. New ideas should be about doing something better or, least, “less bad”. Perhaps the idea is to speed up a machine, or reduce cost, or maybe it’s for a product that is expected to contribute to profits. In every case, it is reasonable to ask the question “how much?”.

In the case of the machine, “significantly faster” clearly still leaves a high level of vagueness, whereas “50% faster” adds more to the understanding of the idea. This quantification would let a customer know how excited they should get. It also helps the technical people assess the proposed solution more objectively and get to grips with the logic of the idea more easily.

Perhaps most importantly, quantifying an idea enables much better identification of the assumptions that lie behind the idea. These assumptions usually contain uncertainties and these uncertainties can become the focus of a disciplined exploration using rapid application of “plan, do, study, act” cycles. Ultimately, using a numbers model helps reduce some of the risk involved in the new venture.

## Numerical Psychology – how to Measure Anything

Many idea inventors and innovation teams are reluctant to commit to a numbers model from the start. At least some of this reluctance relates to what they see as the purpose of the numbers model. It seems clear that even when ideas are at their infancy – when the least is known about them – many people actually fear committing numbers to paper in the belief that they may be held accountable for these numbers. They anticipate a conversation that sounds like “…but you said we would sell 1,000 of these in the first year…!”

It’s important to emphasise that the numbers model at the early stage is an estimate and not a commitment. Its purpose is to add colour and clarity to the idea and allow critical assumptions to be exposed so that they might be put to the test. Nothing is surer than that aspects of the numbers model will change as the innovation team learns more as it conducts the activities required for exploration.

In his book, “How to Measure Anything”, Douglas Hubbard notes that there is a limiting belief in many organisations that some things just cannot be measured. As a result, he says, “…resources are misallocated, good ideas are rejected, and bad ideas are accepted.” Hubbard notes that many people are prone to hide behind the term “intangible” to avoid the effort of having to think about how something can be quantified. Hubbard is adamant that anything can be measured. As he says “…if it can be observed at all then it can be measured. No matter how fuzzy the measurement is, it’s still a measurement if it told you more than you knew before.”

**Fermi is your Friend**

It should be noted that at this stage a level of precision in quantification **is not suggested** where it could not be justified either by the investment required to make the measurement or by the degree of accuracy that can be achieved through the measurement process itself.

Much of the quantification at the early stages of an idea’s life-cycle can be drawn from the work of Enrico Fermi, a Nobel Prize-winning physicist who was famous for his intuitive, some would even say “casual-sounding” measurements.

Fermi was well-known for teaching his students skills in approximation of potentially baffling and strange quantities. He would ask, for example, how many drops of water are contained in the Atlantic Ocean, or how many piano tuners would live in Chicago (Fermi taught at the University of Chicago after the Second World War). His foundation principles included the beliefs that we all know more than we might think we know about a given quantity, and that we can use logic to break down a seemingly unknowable quantity into contributory elements that we __can__ know something about and hence make __usable__ estimates. As statistician George Box is claimed to have said “…all estimates are wrong, but some are useful.” And this is key. Estimates don’t need to be __correct__ at this stage – they just need to be useful!

For Fermi’s piano tuners example, one way of reaching a useful answer might be to break the quantity down into elements like:

- The population of Chicago
- The average number of people living in a household and therefore the number of households
- The proportion of households likely to have a piano, therefore the number of pianos in Chicago that piano tuners could work on
- The average frequency with which a piano gets tuned
- How long it takes on average to tune a piano, therefore the number of hours work available in Chicago for piano tuners
- The average working hours available per piano tuner and therefore the number of piano tuners required to fulfil the likely demand

Whilst this model needs the estimator to make assumptions and rely on sketchy estimates, the numbers that emerge are more useful than the “how could I possibly know that?” exclamation that would most often be made by someone when first asked the question. This is the foundation of using Fermi estimates to bring some much-needed quantification to the innovation process. This process holds whether we are estimating speed increase, costs saved, products that will be sold, new employees that will be needed, lives that will be saved, or pain that will be reduced. These estimates can provide better clarification of the idea and the assumptions we need to investigate.

## Fuzzy Front Ends - Introducing Confidence Intervals

People talk of the fuzzy front-end of innovation with good reason. The term relates to the high levels of risk, uncertainty, and unknowns that percolate through the whole innovation project, and particularly at the outset. A concept that helps deal with all this uncertainty then is the idea of confidence intervals applied to estimates. Using a confidence interval (usually a 90% confidence interval for most purposes) helps persuade reluctant estimators to commit to a range of possibilities. The confidence interval allows the group to give a range for their estimate rather than confine themselves to a single number.

In the example of the new product, people involved would be invited to state something like they would be “90% confident that sales would be between 300 and 1500 units in the first year”, this estimate usually based on applying some Fermi analysis to break the quantity down into more knowable elements. In this case the knowable elements might be things like how many potential customers there are for the product, what the assumed business model will be, how much of this type of product customers typically buy in a given period, etc.

This type of estimate with confidence intervals gives an idea of how confident (or not) a group is in its work. If what appears to be a very wide range is suggested, then this might indicate that the group are really not confident at all. As mentioned earlier, the other things this kind of estimate with confidence intervals provides is access to the thinking and assumptions behind the group’s work – and these can then form the basis for much of the initial exploration that needs to be done to progress the idea.

## You do the maths

Having even a rudimentary numbers model in place from the outset greatly increases the speed and confidence of the team tasked with progressing a new idea. A tangible burden is normally lifted when the team understands the true purpose of the numbers model – not a commitment, but rather an essential part of the idea definition and the foundation for much of the early exploration work that must be completed.

In the heightened volatile world that business now find themselves operating in, greater confidence in the outcome of new ideas will be critical to enable senior leaders to make decisions and move forward at speed. Confidence in an idea is greatly enhanced by useful quantification – decision makers will find it much easier to picture a new idea when they have some notion of the dimensions. As confidence in the method grows, innovation teams become very adept at using Fermi techniques to help them think through an idea in the early stages.