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In advertising, there’s a common belief that we can directly influence consumer behavior by targeting the “right” people. Marketers often categorize potential customers into distinct groups and tailor campaigns to appeal to each. But what if this assumption about buyer behavior is flawed?

Enter regression to the mean, a fundamental concept that challenges how we view advertising targeting.

What is Regression to the Mean?

Regression to the mean is a statistical phenomenon that occurs naturally in human systems. It suggests that consumer behaviors, i.e., states of being as buyers…heavy buyers, light buyers or non-buyers—tend to move toward the average over time. In the context of advertising, this means that the target actually becomes the monthly, weekly, or daily purchase as opposed to the actual person being the target.

Regression to the mean works such that the same consumer can be a heavy, light or non-buyer in any given week depending where they are in the buying cycle.

Heavy buyers: People who frequently purchase a product tend to, over time, buy less often than they did during the time period that initially labeled them as “heavy buyers.” Their behavior naturally regresses toward the mean over time.

Light buyers: People who buy less frequently tend to purchase more often as time passes, moving closer to the average frequency of the category.

Non-buyers: Those who haven’t been purchasing are likely to become buyers at some point in the future.

This natural shift means that the segments we use to define audiences—heavy, light, and non-buyers—are fixed categories but are made up of different people each week. They are fluid and constantly changing. The “heavy buyer” from last week could very well be a “light buyer” this week, and vice versa.

Targeting is a Dynamic and fluid process

The idea that certain people are strictly “heavy” or “light” buyers is an oversimplification. Instead, buyer behavior is dynamic and can change depending on a variety of factors, including time, place, and even external influences like advertising. People don’t fall into rigid categories, and that’s where regression to the mean comes into play.

For example, let’s say you’re trying to reach people who regularly eat out. The pool of potential customers is constantly shifting. Each week, some of your heavy buyers will fall out of the purchasing pool, as they may have reached a point of satiation, some of your light buyers will increase their interest in the purchase, as they may have been away for awhile, and some non-buyers will start making purchases, perhaps out of interest due to proximity or reception of an ad.

Therefore, the group you target each week isn’t static; it’s different every time. The advertising reach you achieve and the mental awareness that remains with consumers—residual from past campaigns—will dictate who from the pool of potential buyers you’ll be working with that week. This means that even non-buyers or light buyers can be drawn in during any given week with the right exposure and timing.

Programming and Media: The Same Principle of Regression to the Mean Applies

It’s not just buyer behavior that follows the principle of regression to the mean; media consumption behaves the same way. The audience of any given TV show, radio program, or digital content is not fixed. The same viewer who tunes in regularly for a time, may drift away from the content and become a light user while others are light users who may only view occasionally or to specific content and then could shift into becoming a heavy viewer due to a programming change for example. Some might even drop off entirely after a few showings, and new viewers are tuning in all the time.

This is important when considering your media buys. Even though you may target a specific program or medium, the audience will naturally fluctuate from week to week. Some heavy viewers will tune in less often, while others will start watching more frequently. So, buying the same program over time can still be effective, especially if that program is highly rated and affordable.

Why This Matters for Targeting Strategies

Understanding regression to the mean should shift the way we think about targeting in advertising. Instead of obsessing over narrowly defined categories or ideal targets, we should focus on consistently reaching a broad pool of potential customers week in and week out, because people are constantly shifting in their purchase and viewing habits.

Here’s how you can apply this understanding to your advertising strategy:

Focus on reach: Rather than limiting yourself to heavy buyers, think about consistently reaching a wide audience. As behavior fluctuates, you’ll have a better chance of capturing those who might shift from light to heavy buying or from non-buying to buying.

Advertise consistently: Since buyer behavior is dynamic, and every week there are consumers in the marketplace, it’s important to maintain an ongoing presence. This allows you to influence the group that is in-market at any given time.

Use broader media targeting: Media consumption follows the same pattern of variation. To ensure you’re capturing new audiences, make media buys that give you steady exposure over time, even through buying the same programming over time.

Conclusion: Let Go of Fixed Categories and Embrace Fluidity

The concept of regression to the mean reveals a powerful truth: buyers who make up audience categories are not fixed. Instead, individuals shift between buying categories naturally, and it’s crucial to design advertising strategies that account for this fluidity.

By understanding that buyer behavior changes over time, we can move away from overly rigid targeting practices of individual human beings/groups. Instead, we should focus on consistent, broad-reaching advertising that allows us to take advantage of anyone who happens to be in the market at the given time.