For decades, we've been taught that reaching the right audience
with the right message at the right time is the holy grail of
marketing. But is it? And even if it is, do we have the tools we
need to achieve such a lofty goal?
After all, many brands and agencies struggle to identify just
the first of these three "rights," and, truth be told, traditional
media processes and practices aren't helping. Consider this: a
client tasks a media agency with targeting people looking for a new
car. Or maybe it's consumers interested in on-demand entertainment,
or shoppers seeking an alternative to high-calorie snacks. The
target audience is then translated into a demographic: 18-34, male
or female, urban, $50k+, ABC1.
While these kinds of blunt instruments may have been suitable in
an offline world (where selecting among a limited number of
properties maximized relevant reach), their true effectiveness in
the digital world is limited at best.
Putting audiences first
Why? Because the digital world thinks audience first, not media
first. Grouping online audiences by demo produces very narrow
targets that do not necessarily represent a brand's most productive
audience. We call potential consumers who do not fit a rigid
demographic profile "valuable wastage."
A recent analysis for a blue-chip client revealed that 35% of
its sales came from valuable wastage, or consumers outside its
broad 18-34 target, and a whopping 50% of sales came from outside
its 18-24 bullseye.
If this client had relied on demographic targeting alone, it
would have severely limited its brand awareness and sales
potential. Not to mention the fact that campaign results would have
been poor, causing the agency and brand to question anything from
messaging, to pricing, to creative. This is not a unique example.
In 2016, many brands realized that over-targeting brand loyalists
(or people who look like them) was restricting their ability to
reach new growth audiences.
So how can the industry overcome this problem? Dropping
targeting altogether is equally likely to produce waste. Instead,
the answer lies in a productive middle ground between mass reach
and segmentation targeting, where more productive audience models
can be built based on Behaviors, Emotions and Moments. In the BEM
approach, demographic data should be used only to remove
Powered by consumer data, the BEM approach helps us
Behaviors: Have consumers demonstrated (or exhibited proxy
behavior) that indicates interest in a specific or related product
area? Have they actively sought out or mentioned a particular
product or service?
Emotions: Has a particular product or service suddenly become
more relevant? Are consumers posting emotional responses that
suggest they would be receptive to certain brand messages?
Moments: Has a trigger like weather, transportation snarls, or
other live events caused a product or service to become suddenly
relevant? Has the consumer entered a specific location where
helpful products are easily available?
The BEM model allows planning and buying teams to improve
targeting and tailored messaging by combining programmatic buying
with new data sources and triggers including conversation scrapes,
content emotion analysis and real-world factors.
Layered on top of brand-building activity that may deliberately
have a broader reach, BEM targeting can identify consumers moving
into a consideration phase and speak to them directly with relevant
messages. The results can be dramatic. Brand preference and
purchase intent lift can nearly double when compared to traditional
BEM in action
One brand that has benefited from using the BEM model is Air
Asia. Instead of using demographics alone, the airline leveraged
its own CRM database to define and value audiences based on travel
frequency and brand advocacy on social media. Programmatic buying
was used to serve individual consumers with custom messaging. Its
new approach led to a 58x return on ad spend.
Meanwhile, New Balance used emotional targeting to increase
brand awareness levels in Japan by targeting video to consumers
exhibiting pre-set emotional receptivity signals. The result was a
135% increase in awareness vs. control.
These are just two examples of brands using BEM to turn data
from filter to active facilitator, while still enabling the scale
needed for brand growth.
Not trashing demo data
Netflix created some buzz last year when VP of Product Todd
Yellin publicly described demographic data as nearly irrelevant.
"Geography, age and gender? We put that on the garbage heap," he
said, before describing the company's practice of targeting
audiences based exclusively on their content choices. If a
21-year-old man in New York and a 52-year-old-women in New Delhi
stream similar content over time, so be it.
Because clients and brands come in all sizes and shapes -- and
may have differing objectives over time -- I'm not tossing
demographics out with the trash. But I am saying that targeting can
no longer rely upon rigid ideas, such as where a person lives or
how much money she makes. We have subtler signals that have greater
meaning today, and brands would do well to leverage them.