Simultaneously, privacy regulations are shifting the ground beneath traditional measurement methods, leaving marketers in need of new tools and strategies. In this pivotal moment, embracing AI and fueling it with data-driven approaches is no longer a futuristic aspiration, but a critical necessity for success. Can you tell us more about the session you are presenting at the conference?
“It’s called ‘Capturing Hearts and Minds in a Privacy-Centric World,’” says Rajdev. “We’re at a moment in time that’s pivotal for a lot of marketers. They need to think through a lot of different things, and it’s honestly an inflection point. It’s going to be as disruptive as many other big tent pole moments in time. And the two things that we touch on are catalysts of disruption. One is AI and its rapid acceleration. And two is the deprecation of third-party cookies. That is going to result in signal loss. Not every company has quite figured out where to go from here. We are trying to provide a point of view on what a marketer should be thinking about today.”
Let’s dive in there and first explore data leadership. What should marketers be thinking about?
“I tend to think about it in terms of data rich companies and data-poor companies,” relates Rajdev. “A data rich company would be like an online travel broker, like Expedia or Travelocity. I define them as data rich because they have access to a lot of data. When a consumer engages with them and they purchase with them, they capture a ton of information about them. As a result, they’re very data savvy. That’s not to say that companies on the other side of the equation are not data savvy, but data rich companies have less of a dependency on a third-party ecosystem to provide that level of data and granularity. It’s less of a necessity for them to survive versus a data-poor company, which I would articulate as perhaps a CPG brand that sells something at a grocery store.”
He continues, “I know a lot of CPG companies that are very data sophisticated and data savvy, but the reality is they have dependence on a third-party data ecosystem, and use data for targeting, measurement, attribution, all those different things. The way I talk about it is, we’re playing Jenga right now. And in Jenga, we have the tower. If you’re an advertiser and we’re playing Jenga, then you should be talking to your partners about what they’re doing, about cookie loss and signal loss and how they’re accounting for that. But when you’re playing Jenga, you might be playing, and the tower has toppled over and nobody’s telling you. And that’s kind of the reality. You’re still playing. That’s where I think there is a disconnect.”
Your point is fair but let’s say a significant enough portion of the community has already started this evolution. What are those questions that those marketers have asked and answered in the right way?
“It’s going from precision to prediction,” describes Rajdev. “Precision is centered around this concept of one-to-one matching. If you think about ten years ago, if you were a performance market or data rich company, your vision of what success looks like is stitching together a comprehensive view of everything that’s happening between all your marketing interactions, whether it’s online, offline, and all of your consumer interactions and connecting them, and you have one source of truth. And to that reality, I hear of so many performance marketers that have tried to build in-house multi-touch attribution now moving towards media mix models, which requires no user tracking. These companies are making shifts because they haven’t evolved their practices.”
That’s where you get into your observations about companies being left behind.
“The overarching issue is that media mix models were developed 20 or 30 years ago, maybe 40, and they have not evolved to the times of today,” he says. “If you are using your media mix model the same way it was outputted 20 years ago, you’re likely not getting the value if you updated it. Part of that is related to all impressions are created equally in an MMM. We talked about CNN versus MTV, you’re watching, you’re changing the channel, whatever; or you’re reading an article and you’re fully immersed on your tablet. You’re on the go. There are so many different consumer experiences. You must have your MMM reflected and recalibrated to reflect some of those things. Best in class marketers use multiple data points to make a decision. MMM is actually an extremely important tool, but it’s just one tool in the toolkit. It’s often complemented with experimentation and other things. They should triangulate to tell a story, but they should complement each other as well.”
Watch the complete video from the Media Insights & Engagement Conference as Seth Adler and Suraj Rajdev discuss experimentation, measurement, the use of AI, and a cookie-less future.
