The phrase began to lose meaning when people started applying it to any kind of data or information. Information does not equal insight. For example, the following items are all piece of information:

• Our Facebook page got 5,000 new fans last month.
• Our last tweet was shared 700 times.
• 100,000 people tweeted about the last episode of the Great British Bake Off.

That’s the kind of information that frequently finds its way into reports, even though it doesn’t really tell you anything useful. We’re all professional marketers, we’ve all played the game of including the most impressive looking numbers in our reports and hoping nobody asks any awkward questions.

Information is useless by itself, what we really need are insights. Insights explain and illuminate:

• Our Facebook page got 5000 new fans last month.

  • Far higher than usual, because our new print ad features the page address.

• Our last tweet was shared 700 times.

  • More than 10x higher than average. This seems to happen whenever we include a GIF of our product in the tweet.

• 100,000 people tweeted about GBBO.

  • But only about 10% of them were talking specifically about baking.

So now we know that something is happening and we know why it’s happening. Does that in itself make an insight ‘actionable’ – an understanding of cause and effect? To a certain extent, yes, but not always.

Knowing that including good quality images will make your tweets perform better is obvious, but useful. If you understand that your above the line advertising campaigns will increase your social follower counts, maybe that’s actionable if you’re good at connecting the dots, maybe it needs more context to be really useful. And knowing that only a small portion of GBBO tweets have anything to do with baking is interesting, but not exactly useful without further examination.

The difference between information and insight is clear, but what characteristics does an insight need to show in order to provide practical value?

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There’s a concept in software development known as ‘object oriented’ design, which essentially means that data and the code required to manipulate it are packaged up together in discrete bundles, called objects. I think we can borrow this principle. An ‘actionable insight’ should describe both the insight and the ideal action.

In other words, if the best course of action is not obvious from the insight, then you need to go back and keep adding additional layers of intelligence to it until it says something useful. For example:

• 100,000 people tweeted about GBBO.

  • But only about 10% of them were talking specifically about baking.
  • Within that 10% we have identified 5 highly influential food-bloggers whose tweets are heavily shared by the others.

If you’re in the business of selling baking products, it makes sense to add those bloggers to your influencer marketing efforts. And another example:

• Our Facebook page got 5000 new fans last month.

  • Far higher than usual, because our new print ad features the page address.
  • BUT many new fans leave the page within a few weeks of joining and rarely engage with any content.

This would suggest weak content on your Facebook page leading to a missed opportunity to build brand. The obvious course of action is to overhaul your social content strategy.

Instead of letting ‘actionable insight’ slide into the lexicon of meaningless marketing buzzwords, let’s take it back. Next time you’re putting a report together, don’t just throw the best looking numbers onto the page – keep adding those additional layers of information and insight until the best course of action reveals itself.

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