I’m often asked how I predict future consumer behaviours.

Of course, no method for prediction can be fool-proof. But the one I’ve found most effective in my decade and a half in the industry is what I call Attitudinal Observation.

Most behavioural theorists agree that behaviour changes are typically triggered by some type of attitude change. They also agree that that attitudinal change is itself triggered by one or more specific events.

So to predict a new behaviour, you need to identify an attitude change – or, better yet, identify an event that’s likely to trigger an attitude change.

So how does the Attitudinal Observation model work?

People rarely change their behaviour for no reason, or just because they’re told to (except perhaps in a school or office). Typically an event triggers an attitude change, which in turn triggers a behaviour change. For instance, if a friend of yours who smokes gets cancer (event), you’re likely to become more worried about the negative health effects of cigarettes (attitude change) and you’re likely to endeavour to smoke less (behaviour change).

The stronger the action (e.g. the friend dies) or the more often it occurs (e.g. another friend or family member who smokes gets cancer), the more intense or long term the behaviour change will be (e.g. you decide to give up cigarettes altogether).

This is interesting to observe in individuals, but it becomes more important from a business perspective when applied to groups. When an event triggers a group attitudinal change, which in turn triggers a group behaviour change. For instance, if a celebrity who smokes gets cancer (event), many of the people who have heard of that celebrity will become more worried about the negative health effects of cigarettes (attitude change), encouraging many people to endeavour to smoke less (behaviour change).

If a company can predict that group behaviour change, or ‘trend’, before it happens – when they see the attitude changing or, better still, as the event occurs – it enables them to grasp a profitable opportunity or avoid a dangerous threat well ahead of their competitors.

Group behaviour change is further accelerated by the establishment of Subjective Norms. If the group whose behaviour changes is big enough, and the effect intense enough, it can develop into a Subjective Norm: in effect, ‘what people think’ about something: or what individuals think people think about something. This creates a snowball effect, changing yet more people’s attitudes.

For instance, once a large enough group became worried about the negative health effects of cigarettes, smoking began to be viewed as reckless. As a result, individuals who didn’t want to be considered reckless began avoiding cigarettes.

Attitudinal Observation has helped me spot several trend over the years, from Conscious Consumption to Digital Detoxing. It’s also a useful demand indicator for New Product Development. When streaming services like Spotify and subscription-based car clubs first started appearing, the lessening interest in ownership we’d observed among Millennials indicated there’d be strong potential demand for both. Meanwhile, the growing desire for community post-9/11 suggested that the growth of online social media in the early 2000s was going to be a lot more than a fad. AO can also help predict skills demands,

Whatever your futures needs – from strategising to sales prediction – the more time you spend time observing and analysing your customers’ changing attitudes, the more accurate your predictions are likely to be.