Start at the End

To begin the Intervention Design Process, you need to identify and validate a potential insight. 

The process of assessing how we can modify our potential consumers’ behavior is called the Intervention Design Process, or IDP. It begins with a special type of observation called a potential insight. When you have one of these insights, you’re perceiving a gap between the world as it is (the real world) and the world as you want it to be (the ideal world). 

To make this somewhat abstract definition more concrete, let’s dig into an example. Back in 2012, when the author was helping Microsoft develop the search engine Bing, he and his team had a potential insight: it seemed like children weren’t using search engines at school anywhere near as much as one might expect. 

At first glance, children, schools and search engines should have been a perfect combination. After all, children are brimming with questions, schools are supposed to foster their curiosity and search engines can help them answer nearly any question they might have. In an ideal world (at least from Bing’s perspective), they would be conducting numerous online searches per day (preferably on Bing, of course). But the team suspected a gap between the real world and the ideal world; something was driving a wedge between the two. 

Now, the keyword here is “suspected.” At this point, no one at Microsoft had any empirical evidence to support the notion in question; it was just a hunch. That’s why it was a potential insight; it hadn’t yet been confirmed as an actual insight. 

For all anyone at Microsoft knew at the time, their hunch could have been wrong, and the gap they were perceiving could have been a figment of their imagination. In that case, it would have been a waste of time, money and energy for the company to throw resources at solving a problem that didn’t exist. That’s true of any potential insight, so it’s crucial to test your insight. 

To do that, you need to seek out quantitative or qualitative validation of your insight. For the author and his team, that meant collecting data about children’s internet usage at school – an example of quantitative validation. It also meant going to schools and watching kids’ computer usage in person – an example of qualitative validation. 

It turned out their hunch was correct: on average, each student was conducting less than one search per day. The gap between the ideal world and the real world was proven to exist; their insight was validated.

If the same can be said of your insight, you can now proceed to the next step of the IDP.