Say you're a luxury carmaker that wants to target advertising specifically at young men over 18 who listen to rap. How do you go about reaching that key demographic?
In the traditional world of media, you ran ads next to articles on rap music and hoped for the best: that at least some of the readers who saw them fit the demographic profile you were looking for, had a driver’s license and met the salary threshold you were after.
As far as strategies go, it wasn’t exactly laser focused.
Today, though, more personalized approaches exist. And the effect is win-win: advertisers get better ROI while publishers preserve inventory among readers who love rap, but may not be able to drive.
How do they create that kind of relevancy? It’s all about segmentation.
Why is relevance important?
According to emarketer, digital ad channels will overtake traditional advertising globally by 2021. But not all digital advertising is growing. There’s a clear demand for higher quality and more relevance from both advertisers and consumers. In fact, according to a study from market research company Epsilon, 80 percent of consumers are more likely to make a purchase from brands that offer personalized experiences.
It’s why publishers are prioritizing their audience segmentation capabilities, with many consolidating audiences from different properties into one unified platform so that their total audience can grow and segmentation opportunities can flourish. This lets them reach the right consumers rather than getting lost in a one-size-fits-all approach.
The power of segmentation
Once your data is in one place, out-of-the-box segmentation software can enrich your profiles with online and offline third-party data, then use artificial intelligence (AI) to connect the dots between readers, allowing for granular demographic, behavioral and socio-demographic profiles.
Typically, different audience segments group consumers according to the following profiles:
- Demographic segmentation: Age, education, income, gender and occupation
- Behavioral segmentation: Purchase habits, consumption and lifestyle patterns
- Psychographic segmentation: The psychological aspects of consumer behavior, including lifestyle, personality traits, values and interests
- Geographic segmentation: Geographical boundaries and location
How AI helps
By using AI to expertly perform a specific task that human beings are ill-suited for, segmentation algorithms find relationships between millions of online and offline data points, then group users based on those relationships.
And one of the most powerful ways to generate data for audience segmentation is by using an AI-powered DMP to connect different online and offline data sources. This allows a publisher to find and group all users relevant to an advertiser for a given campaign. By running machine-learning algorithms on the available data, the DMP automatically finds additional users that look and act like the most valuable ones, thereby significantly increasing the size of the segment and the effectiveness of the campaign. Which in turn means higher revenues for publishers.
Having a solid tech foundation in place — along with a data-literate team to helm the segmenting initiative — is key. But publishers who use a DMP to combine historic offline data with online data will achieve a solid understanding of what makes the consumers they’re looking to target tick.
Which is exactly what it takes these days to be a digital advertising superstar.