Reframing Audience Expansion through the Lens of Probability Density Estimation

Audience expansion has become an important element of prospective marketing, helping marketers create target audiences based on a mere representative sample of their current customer base. Within the realm of machine learning, a favored algorithm for scaling this sample into a broader audience hinges on a binary classification task, with class probability estimates playing a crucial role. In this paper, we review this technique and introduce a key change in how we choose training examples to ensure the quality of the generated audience. We present a simulation study based on the widely used MNIST dataset, where consistent high precision and recall values demonstrate our approach's ability to identify the most relevant users for an expanded audience. Our results are easily reproducible and a Python implementation is openly available on GitHub: urlhttps://github.com/carvalhaes-ai/audience-expansion