The power of assumption in innovation - from risk to validated starting point

What would life be like if we were always right? Unfortunately, when people talk about things they don't know much about (and sometimes even when they do), they unknowingly take many things for granted that may not be true.

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Because, as humans, we continuously assess various situations. And to do this somewhat quickly, we supplement 'reality' with our own assumptions. 

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Example:

Adoption of contactless payment in stores is high. People find it safe. So people will also experience these forms of payment as safe in Public Transportation.

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Behavioral scientists call this, mildly derogatory, cognitive bias. Cognitive bias is a mental process in which people create their own subjective reality and then make choices based on it. For example: some young people in my village are getting into mischief. My (biased) conclusion: all young people in my village behave excessively.

From an innovation perspective, cognitive biases are a risk. After all, you get to deal with false positives: something seemingly validated, but whose validity is based on untruths or misinterpreted data. See this practical example of cognitive biases:

We know that older people prefer to stay at home longer, but they often find a lack of security to be a major hindrance. Our solution: IoT and smart sensors to reassure parents that they are taking their medication on time and that their home is safe from fire and well-protected against theft.

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But what is actually going on? The elderly test group is primarily afraid of falling. It's their children who think about all other matters. So, we are witnessing a wrong interpretation of needs applied to the wrong target audience, and thus not the correct solution.

So what you are actually doing is steering based on an assumption of reality. And that never guarantees success. 

At Makerlab, we take an assumption as the correct starting point to begin our work. Because within an assumption often lies a (potential) solution to a (possible) issue for a (yes, yes, potential) target audience. 

I'm going to show you how to separate sense from nonsense and quickly turn these kinds of situations into something you can work with concretely. Because with assumptions, you can also benefit, provided you assess the risks well.

An assumption, what exactly is that?

 An assumption is nothing more than "assuming that something is factually correct when you do not have enough evidence for it." Academics also refer to it as a hypothesis. You assume that reality is structured in a certain way. However, people often forget this nuance and confuse assumptions with reality.

An assumption is only useful if you can sufficiently demonstrate that it is true in all (or most) cases. In other words, a validated assumption. At MakerLab, we don't call these facts or "the truth," but a learning, or insight. Our goal is not to find out the truth, but to learn in which direction to take the next step in our innovation process.

The danger of assumptions in innovation

For product developers and innovators, there are a number of very dangerous assumptions that can cause your product to fail miserably. Consider, for example:

'We know what our customers need'

'We know exactly where our revenue comes from'

'The most popular idea has the greatest chance of success'

'Our current brand is suitable for this'

'The product only succeeds if we make something super sexy innovative'

However, these are the kind of assumptions that can lead us most swiftly to a path of success. Because: the riskier the assumption, the greater the likelihood of success if we can prove it to be true (valid). 

How do we find these assumptions? We start with the idea.

From idea to assumption

Customers often come to us with very good ideas. And whether we can bring these to life. But once we start building, it often turns out that we create something different from the original idea. 

Let's look at this example for a moment:

The idea: 

"A number of our customers say they find bol.com's app so convenient, so we now want an app for our store as well."

Our first question-"But why then...?"

After which, after some thought, comes the conclusion: "Because we want to get more sales."

The main assumption made here for the new product is: "By building an app for our store, we increase our sales."

Now if we dig a little deeper we can see what underlying assumptions lie beneath the main assumption:

'Our customers also shop at other stores via smartphone'

'Customers who don't need personal assistance prefer to buy online'

'Our (potential) customers are tech-savvy enough to pay via smartphone'

'Our (potential) customers use iDeal, Visa or Paypal'

'We are now missing out on customers who don't like physical stores'

'There is plenty of room in the online market for our range of products'

In other words, the original idea is unlikely to succeed until we learn whether or not these assumptions are valid. For each validated assumption, the probability of success of our idea increases. 

Some assumptions can sometimes be nicely (in)validated with some desk research. But often a more thorough examination of evidence is needed. And that can be done with focused experiments. 

How do you use assumptions to your advantage?

Okay, you have a list of assumptions, but what do you do with them next? And how do you keep it well organized? 

Now that we have translated the ideas into assumptions, the foundation for your research has been laid. The first step: prioritize! Which assumption is the most important at this moment? Because then you will know where to start. If this 'most critical assumption' is incorrect, there is a high likelihood that you no longer need to investigate the rest of your assumptions.

How best to prioritize your assumptions I will show you in Part 2. We will use a simple post-it exercise to identify the most critical assumptions so you know where to focus your attention. 

Finally, in Part 3, I show you how to test your assumptions and what you can do with the acquired data. Central here is the experiment card, which gets you from assumption to executable experiment.

Want to know more, or are you curious about the sequel? Follow me or MakerLab.