Extending the Value of Attitudinal Segmentation Through Data Fusion and Lookalike Modeling
Most discussions around data fusion and lookalike modeling start with improving predictive accuracy—better features, better models, better performance. At AT, we approach the problem differently. Our work begins with attitudinal segmentation. These segments capture how people think, what motivates them, and why they behave the way they do. In practice, this type of segmentation delivers the highest strategic value for marketing, brand, and customer strategy. However, attitudinal segments are often difficult to apply directly. Attitudes alone are hard to target in media, hard to deploy in customer databases, and difficult to score in real time. This is where data fusion and lookalike modeling play a critical—but secondary—role. Rather than using data fusion to redefine segments, AT uses it to extend the value of an existing attitudinal segmentation . Lookalike modeling becomes a mechanism for activation: linking rich, attitudinal insight to demographic, transactional, or behaviora...