In a world where consumers are exposed to over 5,000 ads a day and only engage with about ten of them, it’s clear why better targeting is so essential. Luckily, over the past ten years, our ability to learn about and understand consumers has reached new heights.
Through micro-segmentation, we can leverage audience information to better target, understand and connect with our customers.
Let’s explore three approaches of audience segmentation and explain when each level of granularity is important (or “good enough”) to meet the goals of your campaign.
Targeting groups of people based on demographics such as age, gender or household income is a popular place to begin a segmentation strategy.
Of course, demographic targeting has its critics. In a world of ever-improving marketing personalization, demographics can be considered an over-simplified approach to targeting, because it doesn’t address exceptions, complexities and differences between the individuals within a broad bucket.
For example, Hitwise (a division of Connexity) found that 32 percent of people who visit top beauty and cosmetic sites are actually men. This means a beauty brand who markets exclusively to, “women age 18 – 45,” could be leaving out nearly a third of their potential audience.
That being said, the fact that demographic targeting is quite broad can also be an advantage; demographic segments often provide the greatest volume because they encompass such large groups of people.
For that reason, demographics can be especially useful for higher-level branding campaigns where you want to achieve greater reach and exposure — at a lower cost.
Targeting audiences on the basis of their “persona” can allow marketers to even more accurately understand (and even predict) their behavior.
A “persona” is not only defined by demographics, but can also be characterized by a combination of interests, purchases or patterns of behavior, which represent a “lifestyle.”
One way marketers identify “lifestyles” is by matching demographics to relevant online behaviors.
For example, a make-up brand advertising their latest lipstick line may want to target not just women 18 – 45, but specifically “beauty maven” personalities; women who have purchased make-up products or consumed content about beauty.
Targeting on the basis of lifestyles can improve the accuracy of your predictions, but they are not foolproof.
Consumers’ daily lives have become so saturated with media that they can usually spot campaigns that have essentialized them into a “bucketed” persona — and in some cases, they even resent this reductive approach.
Not all female “health buffs” will respond to a banner ad promoting, “flat abs now.” However, people’s attitudes, complexities and purchase intent can come to light when you start moving from tailoring to personalization.
Micro-segmentation involves layering hundreds or even thousands of data points to identify granular clusters of individuals.
Rather than looking at target “groups,” marketers can layer rich sets of first- and third-party data to identify hyper-relevant segments based on attributes like lifestyle, interests, attitudes, purchase behavior, search behavior, panel data, buyer stage and much more.
The result is a rich mosaic of tens, hundreds or thousands of micro-audiences, rather than just 10 or 20 segments.
Your first-party data—your own customer and visitor information—gives you an excellent jumping-off point for identifying your most valuable segments.
For example, in a Big Data report, McKinsey2 reveals how Tesco uses its own loyalty program and purchase data to analyze the buyer journey that leads up to a transaction: It uses this information to create micro-segments that inform their product mix, pricing and promotional strategy.
The more information a brand has on its customers (as in, the greater number of signals it collects), the more data it has to “pattern match” similar individuals and identify new micro-audiences outside of its own database. In order to identify and target new customers, marketers can leverage third-party data sources to identify consumers with similar attributes to their own best customers.
Stay up to date with all the latest in data-driven news by following @OracleDataCloud on Twitter and Facebook!
Read the rest of this article in The Data Source, Fall 2016 edition.