In defence of generational analysis

Does the King like Orangina? Seems like a silly question. Does it matter, except to the manufacturers of Orangina (and the King himself)? Probably not.

We ask this question in response to a very good Mark Ritson piece which has being doing the rounds over the past couple of weeks. In it, Ritson takes aim at generational analysis, illustrating his point with data from BBH Labs showing that in one dataset, whether or not individuals liked Orangina was a stronger determinant of correlation with other people then what generational grouping they fell into. Put another way, the data suggested that if you like Orangina, you’re more likely to have something in common with someone else that likes Orangina than you are with someone the same age as you.

Ozzy Osbourne and King Charles

It reminded me of the other old criticism of generational or demographic analysis: Prince King Charles and Ozzy Osborne are the same age (born just a few weeks apart in 1948) but you’d market to them very differently. Or, if they both like Orangina, maybe you wouldn’t.

In the piece, Ritson makes three criticisms of generational analysis, all of which I agree with.

1. People within generations are too different – a bit like the Orangina example, there are other, better ways of understanding audiences like attitudinal and behavioural characteristics
2. People are often as similar to people in other generations as they are to people in their own – increasingly so, as the meaning of age changes and we act less like stereotypical versions of particular ages.
3. They can be offensive – or at least used offensively, if they support generalisations about a group based purely on age and nothing else

All of these are completely valid. The idea behind generational cohorts is that because people were born around the same time they’re likely to have some things in common, and that they might be products of their time. This is true to some extent – Baby Boomers (born between the late 1940s and mid 1960s) didn’t use the internet or smartphones until they were adults, unlike Millennials (born between the early 1980s and mid 1990s). Going the other way, Millennials grew up in a world where food rationing was a distant memory and where the contraceptive pill was an established norm, unlike the Baby Boomers. Those things – what was the world like when we were born, and how did it change while we were growing into it – have an impact on the types of people we are.

Jimmy Carr and Jimmy Carter

But at the same time, someone born in 1982 won’t have had broadband internet until they were an adult, and would have been 25 when the iPhone was released. Is that really the same as someone born in 1994, who would probably have had broadband at home throughout their teens, and whose first phone was an iPhone? No, clearly not.

Generational analysis – like other demographic definitions – is clearly limited. You shouldn’t just think about your customers in terms of age and gender. But it is useful, either as a starting point or a component of your audience insight. If you’re an insurer looking to modify or launch an over-50s life insurance product, understanding the audience in terms of age is probably quite useful. Staying with the financial services theme, if you’re a mortgage provider it’s probably quite helpful to know a bit about the general experiences of Millennials over the past decade.

It’s also appropriate that as our demographics get more complex, our definitions adapt accordingly. It’s right that survey questions around gender and ethnicity allow people to express with much greater specificity who they are. It’s helpful for organisations to know this. When collecting age data, it’s helpful to ask people their age to the year, rather than just in bands (18-24, 25-34 etc). We should also forcibly retire the 55+ age band – within two years, this will include people born in both the 1970s and the 1920s. If you can see the limitations of Ozzy Osborne and King Charles being in the same group you can also recognise the futility of an age band that includes both Jimmy Carr and Jimmy Carter.

There’s another reason why demographics – especially age – aren’t going anywhere. That data is some of the most available data in the world. Demographic information on every country in the world is available through the UN, and the ONS (like most national statistics bodies) will tell you the age and gender breakdown of every local authority in the UK. That’s quite a handy starting point, and unless they start asking about attitudes to Orangina on census forms, is likely to remain one.

What does this mean?

• Let’s not get rid of generational analysis – and demographics – just yet. It’s still useful as a starting point or descriptive tool.
• The demographic questions we ask should adapt over time to reflect changes in the way we think about ourselves.
• We shouldn’t confuse differences between age groups for differences between cohorts. Currently, Gen Z (those born between the mid 1990s and about 2010) are the keenest to have adventure and take risks. Like previous cohorts, they’ll probably age out of it.
• Organisations should not confuse knowing who their audience is demographically with knowing who their audience is. Demographics should be a component of your segmentation, not the extent of it.