One Simple Thing That Will Change The Way You Look At Shopper Insight Forever



I’ve billed this blog with a big title – and to any of you in the research community perhaps I’m over-promising. To some of you this will be obvious, and something that you knew ages ago. I’ve known it for ages too. But here’s the thing, and here’s why it’s worth all you shopper research and shopper insight guys reading on. Most people don’t get this. 

So here it is.

Shopper research data does not measure the number of shoppers who do something. It measures the number of shopping trips.

See? Simple right? But most researchers or marketers don’t get it. Most quantified data is presented with a headline that would read something like “45% of shoppers visited the category”. Pretty straightforward. That means that 55% did not, and that’s an opportunity.

But, that isn’t the whole story. Actually what was measured was not shoppers, but shopping trips. People do not shop the same way every day. So the ones that visited the category today, might not visit the category on their next shopping trip. When research companies produce their segmentation model for example, “24% of shoppers are frugal hunters” that is misleading. What it should say is that on 24% of shopping trips shoppers behave like frugal hunters. This is intuitive – we know from our own behavior that we can behave completely differently on a different day – but the consequences of this are not.

Let’s take an example. I go to a supermarket twice a week. About half the time I do a big shop, buy lots of stuff, stock up a little. And around half the time I pick up a smaller number of items. Now imagine you work for a juice company. You’d survey shoppers and you’d see 50% of shoppers buying juice, and 50% of shoppers not buying juice. Your report from your agency might read “50% of shoppers don’t buy juice”. But that is simply not true. 100% of shoppers buy juice: they just only buy it on 50% of shopping trips. 

The shoppers in a survey are merely a sample. They represent a larger group of people. The 55% of shoppers in the sample who did not visit the category do not necessarily represent a different group of shoppers. They represent a different type of shopping trip.

Whilst in our sample clearly they are different shoppers, that does not mean that the population they represent is necessarily different. It could be the same shoppers, just shopping in a different way.

So why is this important? Am I just playing with semantics here? Absolutely not. If genuinely only 50% of shoppers buy juice ever then we need to understand why they don’t buy juice at all. Is there a consumption barrier? Is there a reason they really don’t like juice? Or is juice far too expensive?  But if 100% of shoppers buy juice, but only 50% of the time, then the reasons for not buying might be very different. It might be that I already have stock at home; or perhaps on this trip I am picking up a few items on the way home from work, I’m on foot, and I simply can’t carry bit cartons of juice? Different barriers would imply that different solutions are required, and that leads to more effective shopper marketing activities.

For the shopper marketers out there who hadn’t thought like this, I urge you to go back and review your data and your conclusions: rethink the data as shopping trips rather than shoppers. What if these trips were actually the same people? Use the demographic data to see if there is a good match in terms of the profile of the shoppers. Consider using some qualitative research to understand how shopping patterns change over time, to see if people will be in different modes on different trips.

For those shopper research professionals who know this, one simple thought: Never assume your audience does!

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