My ability to focus is dwindling. I notice it most at night, as I desperately try to calm my brain by counting sheep (which is more like “one sheep, two sheep, cute cat, cute dog, WHOO LET THE DOGS OUT?!”).
I sometimes wonder what my actions look like to marketers… “Ok, he clicked on the plaid button-down shirt again… checking the size chart… I think he’s finally going to… nope. He’s back to cute animal pics… and onwards to Reddit… Yup. We’ve lost him again.”
People are busy, complex, and flippant. We multitask, jump between devices, and get distracted easily. Our consumer “journeys” look less and less like Candyland game boards and more like a Jackson Pollock painting. And we all know our crazed paths-to-conversion will only get crazier as technology continues to progress.
How can marketers possibly make sense of such erratic behavioral data and do anything meaningful with it?
Let’s talk about audience characterization.
NO! ::ahem:: We mean… no… and let’s clarify that immediately. Audience segmentation is not the same as audience characterization.
Audience segmentation is the age-old process of grouping people based on past behaviors, interests, and actions to match a predetermined target audience (or “persona” if we want to get fancy about it). Segmentation typically relies heavily (if not wholly) on 3rd party data, most of which is not collected at the user-level. The resulting segments are groups of static cookie pools, void of intent signals, time and recency, contextual considerations (e.g. channel, publisher), etc.
Targeting audience segments is kind of like a “mass blast” approach to targeting. Group lots of people together, deem them “the same” or “similar,” and send shoot them the same ad at predetermined times. This approach does not help advertisers reach ideal prospects or customers along their non-linear consumer journeys.
Audience characterization is far more advanced and prescriptive than audience segmentation:
Think of it this way: based on the target description in the image below, audience segmentation would deem Marilyn and Tim as “similar.” They’d both be included in the same segment and served the same ad at the same time. Audience characterization would understand how Tim and Marilyn are similar or different, so they can be targeted individually if the ad/message and timing is relevant.
This is where data science and AI really struts its stuff. The process is pretty techy and complex, so here’s the “Cliffs Notes” version of Magnetic’s method:
Audience characterization results in Connected Consumer Profiles. Our AI uses them to precisely determine a specific person’s needs and desires at the moment, quantify his or her value to your marketing objectives, and bid accordingly for their impression (in real-time).
This completely changes how marketers identify people through their consumer journey. There’s no guess work, no more creating segments for various funnel stages. If you want to target people who are very likely to buy from you (or a competitor!) in the next 24hours, our AI platform will scour Connected Consumer Profiles, find them, message them, and bring them to your site to convert. Want to find people who are likely to be interested in your new brand or product? Same rules apply.
If you want to keep playing Candyland, that’s your call. But you’ll barely spin the wheel before AI leapfrogs your gamepiece and makes it to King Kandy. So don’t expect to find much waiting for you if and when you finally get there.
Want to learn more about all things AI? Stay tuned for more from our “Explaining AI” series, or check out our previous posts to dig into topics like Data Science, Predictive Analytics, and Machine Learning.
Magnetic is an artificial intelligence company that uses machine learning to deliver smarter, faster, and more effective advertising. Our powerful AI platform continuously analyzes the attributes of over 350 million live user profiles alongside real-time inventory supply and bid opportunities to deliver highly performant audiences and profitable campaigns for our clients.