Machine learning algorithms thrive on data—the more, the better. In digital marketing, AI-driven bid strategies rely on data volume for effective optimization. But how does segmentation impact this process?
The Balance Between Segmentation and Aggregation
Segmentation is essential for tailoring marketing efforts and allocating budgets efficiently. It helps predict behavior, optimize ad campaigns, and ensure spend effectiveness. However, excessive segmentation can fragment data, limiting AI’s ability to learn and optimize effectively.
For example, many businesses separate ad accounts by product teams, sales divisions, or franchise locations for budget control. While this approach prevents cross-contamination, it also isolates valuable data that could improve AI-driven performance.
Why Aggregation Drives Better Results
Over time, we’ve seen businesses benefit from data aggregation without eliminating segmentation altogether. The key is to segment based on audience behavior and value, rather than arbitrary (from the AI’s perspective) budget divisions. When businesses pool their data, AI has more meaningful signals to work with, leading to better-targeted campaigns and improved ROI.
Consider two neighboring breakfast restaurants—one selling waffles, the other pancakes. Each advertises only to customers searching specifically for their product. But many potential customers simply want breakfast, undecided between waffles or pancakes. By advertising broadly and letting AI guide customer decisions, both businesses could capture more demand and optimize spend, rather than both missing out on the potential business from undecided diners.
Real-World Success Stories (under NDA)
Nonprofit Organization: A charitable organization that wanted to equally service its affiliate organization across the US. They switched from individual geography-specific campaigns to nationwide campaigns organized by audience as part of a multi-year strategy to better utilize AI. Their post-marketing net proceeds more than tripled from the year prior to the aggregation to the year following.
A key component to growth from year 1 to year 3 was the audience consolidation that occurred part-way through year 2. Online Learning Provider: An online learning provider started out having individual budgets and campaigns for each course. After months of testing, they accepted that their audience was not great at self-segmenting at the point of ad targeting (they wanted breakfast, but didn’t specify waffles or pancakes). The switch to an aggregated budget (and structure) was a key component to 75% growth in year-over-year learners.
Aggregating budgets and data across course offerings led to massive growth for this online education provider. Construction Equipment Manufacturer: A construction equipment manufacturer continues operating with individual budgets by product, but has added more and more product groups into a shared ad account over the past several years. With more data to optimize from, even with budgets managed through separate campaigns, the business has experienced massive growth over all, as well as by product. This growth continues to accelerate with more and more data aggregation.
Aggregation of data leads to growth across the business, as well as across the individual product groups.
The Takeaway on AI-Driven Optimization
If your business can aggregate digital marketing efforts across units, do it. The more data your AI-driven campaigns have, the better the results. While not always easy, businesses that embrace this approach consistently see increased performance and ROI.