How Real-Time Data Analytics Can Optimize Marketing ROI

Read more: See here

From my early days as an assistant brand manager at P⁘G through my second CMO role, I've always strangely looked forward to annual budget planning. I've seen all sorts of models used: build from the ground up, modify from what we did and learned last year and this year, work within the bounds set by the finance team and so on. Regardless of the paradigm, I look at the annual process as a puzzle—especially in today's rapidly changing environment.

CMOs have to solve two big problems. First, how do we most effectively connect with our audience in those critical moments that matter? Second, how do we do that most cost-efficiently?

CMOs are operating under more scrutiny than ever before. To succeed, we must demonstrate measurable business outcomes of our marketing activity and clearly understand the ROI. This poses a significant challenge with budgets at their lowest point since mid-2021 and the rapid pace of technological change, which disrupts consumer behavior, throwing chaos into the media mix and attribution modeling.

This tempo of change does, however, offer an opportunity to marketers savvy enough to use real-time data analytics to drive (and prove) marketing impact and efficiency.

Like a new car, the value of traditional, large-market data starts dropping once you pull it off the lot. Although this data can help with big-picture, strategic decisions—like in the annual budgeting process—I find it's less helpful for optimizing marketing plans throughout the year.

Instead, we can use real-time consumer search intent data to understand consumer behavior, wants and needs. This kind of data allows us to turn insights into action. It enables marketing teams to be agile and quickly adjust campaigns based on immediate performance feedback.

Instead of thinking tactically about the application of search data, marketers should look at their data strategically and use it to inform go-to-market strategies, product releases and consumer intent profiles. For example, think "search-intent segmentation" rather than just "demographic segmentation."