For instance, insight is not typically generated by only looking at quantitative data. Quantitative data is information that is statistically significant. In other words, it is data that can be counted, measured or expressed using numbers. A piece of quantitative data, like how many motorists are on a highway during rush hour or how many men over 50 are on Facebook at 6am, these are simply one single data point. They might help lead you to a great insight, but they need proper context. Sometimes we find an outlying data point and we jump to conclusions. Resist the temptation. This can lead to the development of flawed insights which, in-turn, influence work that is less effective. When it comes to quantitative data, we should understand the kind of data we need and then look at it holistically, or in conjunction with other data. Remember, data like this can’t think for you. It’s simply a measurement of what’s happened. Consider Wayne Gretsky’s quote: “skate to where the puck is going, not where it’s been”. Quantitative data is where the puck has been. 

Observation is a type of qualitative data that’s often mistaken for insight as well. Qualitative data describes qualities or characteristics and can be derived from things like focus groups, one-on-one interviews and questionnaires. Things like the softness of a loaf of bread, how someone feels after exercise or the color of a sunset – these are pieces of qualitative data. When we see something happen, we need to understand and appreciate the perspective. Usually qualitative data begs the question “why?” Since insights answer the question ‘why’, we may think we’ve stumbled upon an insight through observation. The truth is, observation is only the ‘what’. And it’s only one single piece of information. Use it, but don’t jump to conclusions.

Finally, when we have a few pieces of data, we might be tempted to collectively call them an insight. Market intelligence is more than just one piece of data, properly defined as two or more of the following: who, what, where and when. Notice what’s missing? The why.

Here’s an example of quantitative, qualitative and market intelligence at work. A convenience store notices that there is an uptick in male traffic coming in, just after work, to buy peanuts and M&Ms on Friday evenings. Quantitative measurement can tell us how many more males than normal. Qualitative tells us that they are specifically buying peanuts and M&Ms. Market intelligence tells us that they are males 40-50, coming in on Friday night and buying snack food. So, why are they behaving this way?

Some initial digging reveals that John F. Kennedy High School plays football on Fridays. Some more digging tells us that the stadium convenience stands don’t sell peanuts or M&Ms. Even further analysis tells us that these males buy the type of snacks at the convenience store, because they aren’t messy. Apparently these football fans think the restrooms in the stadium are unsanitary. All of this digging leads to greater insight: A 63% increase in Friday traffic is due to fans of JFK High School football who prefer to buy easy-to-eat snack food for the game. It’s important to remember, every great insight addresses a dilemma. In this case, older male fans on JFK football didn’t like the snack food offered at the stadium concession stand because it was too messy. Without a dilemma, there is no change. And without change, there can be no insight explaining why a group is behaving a certain way. Finally, don’t miss this: the greater the insight, the greater the opportunity. This convenience store might decide to invest in becoming a destination for all JFK High School fans.

Data, observation and market intelligence are steps to overcoming a dilemma and building something meaningful and actionable. In other words: an insight. But, there’s one thing missing. All that and more explained in part three of the blog series. 

Read it here.