Reframing Audience Expansion through the Lens of Probability Density Estimation
This work addresses the challenge of creating effective target audiences for marketers, but it appears incremental as it modifies an existing technique rather than introducing a new paradigm.
The paper tackles the problem of audience expansion in marketing by improving the selection of training examples for binary classification, resulting in consistent high precision and recall values in a simulation study using the MNIST dataset.
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: \url{https://github.com/carvalhaes-ai/audience-expansion}