Congruence bias refers to the fact that as a species, we prefer to only research against specific, direct outcomes. For example, a subject is presented with two buttons and told that pressing one of those buttons, but not the other, will open a door. The subject assumes that the button on the left opens the door in question. A direct test would be pressing the button on the left; an indirect test would be pressing the button on the right.
The latter is still a valid test because once the result of the door's remaining closed is found, the left button is proven to be the desired button.
In retail, the majority of data brands and retailers use is sales based. That is to say the data analysed is mostly based on what was sold. In other words, historic data relating to sales activity. There is a massive opportunity to be had by looking at what didn’t sell, and understanding why
- Attention – How many shoppers that go into a store even see your brand?
- Appeal – Of those that see your marque, what percentage does it appeal to?
- Engage – Of those in store, how many of those that see you brand and find it appealing engage with it on shelf?
- Sales – And finally sales, how many of those that engage go on to buy. This is the piece of the puzzle that there is plenty of data on.
Once you look at sales data in context of attention, appeal and engagement, you not only realise where your biggest opportunities lie, but you are also able to optimise every step of the process. If you increase any of Attention, Appeal or Engagement, you are more than likely to improve sales as a direct result.