Transcript
A (0:00)
Nerd Alert. Learning is important, right?
B (0:02)
Yes, exactly. What a bunch of nerds.
A (0:04)
Nerd alert.
B (0:06)
Marketing Architects. Hello and welcome to the Marketing Architects, a research first podcast dedicated to answering your toughest marketing questions. I'm Elena Jasper on the marketing team here at Marketing Architects, and I'm joined by my co host, Rob demars, the chief product architect of misfits and machines. Hello. Hello. We're back with your weekly Nerd Alert. Every week, I'll take a deep dive into academic marketing research and translate its complex ideas into simple, understandable language for Rob, and of course, for all of you. Are you ready to nerd out, Rob?
A (0:35)
I'm feeling so nerdy. I just set my password to supercalifragilisticexpialidocious with an exclamation point, which is great because I can never access my account again.
B (0:46)
I was going to say I would not be able to spell that. All right. This week I read Reach Measurement Optimization and Frequency Capping and Targeted Online Advertising under K Anonymity by Juan Gao and Mu Kwai from LinkedIn. The study looks at what happens to reach frequency capping and ad performance when platforms can no longer track individual users and instead have to rely on privacy safe user groups instead. But before we get too far, Rob, when you hear the phrase privacy first advertising, do you think, all right, this is a future, this is where we're going, or do you worry that it's going to break a lot of what currently works in advertising?
A (1:19)
I don't worry about it at all. It makes, it makes me think less tracking, more thinking. I think it forces us to stop, you know, pretending precision equals persuasion. I think it just continues to elevate. Creativity matters. We're shifting away from a strategy of surveillance and focusing on what really matters.
B (1:41)
And you work at a television agency that doesn't. I do on this type of. So you're not that worried about. It doesn't keep you up at night, it keeps a lot of marketers up at night because they're dealing with it, with their digital channel. So that tension is what this paper tries to resolve. What happens to our advertising effectiveness when individual tracking is gone and everything has to work at the group level instead? So the study, it centers on a privacy framework called K anonymity. Instead of identifying individuals, platforms place users into groups of at least K people based on shared traits like geography or interests. So instead of saying Rob specifically has already seen this ad twice, the system only knows someone in group A has seen this ad. So that's a big shift in how Digital advertising works once individual tracking disappears. If it does, frequency capping becomes probabilistic instead of precise. So a platform won't be able to say this exact person has seen this ad three times, stop showing them the ad. It can only estimate the likelihood that the next impression will reach someone who hasn't hit the cap yet. So frequency capping doesn't go away, but it gets more difficult. The authors call this probabilistic frequency capping, and it sounds a little odd, but the goal is still the same. Avoid overexposing people while maximizing reach. The difference is that instead of certainty, you're working with more of a probability. So, Rob, does that sound like any other traditional media channel we know exactly right. So that's how traditional media like TV has always worked. You never know who saw the ad, just the odds that someone in a household or an audience segment did. So digital could be being pulled back towards that same model. But let's talk about our favorite topic, reach under K anonymity. Reach is no longer a clean count of unique individuals. Instead, it becomes an expected value. So platforms, they can still report reach, but it's an estimate, not a headcount. The more privacy you add, the larger the groups and the more uncertainty there is in that estimate. The study also looked at optimization, specifically how platforms should bid when they don't know who the individual user is. So traditionally, frequency capping was binary. Either the user was eligible for another impression or they won't or they weren't. Under privacy, that system discounts bids based on probability. So if there's a high chance the impression will hit someone already over the cap, the platform bids less aggressively. If they think they could reach someone new, it's going to be more aggressive. This creates this decline in bidding intensity instead of a hard stop. So one other interesting insight is that a small number of highly active users can distort everything. So. So if a few people are generating the most impressions in a group, the system's going to become overly cautious, even though most people in the group haven't seen the ad yet. Which again, feels familiar if you've ever planned around heavy TV viewers. So we're kind of getting back to a little bit of traditional planning. The researchers also ran simulations to understand the trade off between privacy and performance. So, Rob, what would you expect to happen to add performance as privacy increases? Do you think there's going to be some total collapse, A great gradual trade off? What do you think would happen?