Improving Machine Learning Models by using Behavioral Data

Behavioral data, derived from interactions and actions of individuals or groups, is a potent resource for enhancing machine learning models, particularly in understanding and predicting customer behavior. Despite its potential, behavioral data is underutilized in machine learning due to complexities such as data scattering across multiple systems, challenges in ensuring accurate data lineage, and compliance issues related to privacy laws. An example from a Kaggle competition hosted by Airbnb illustrates how incorporating web sessions data can improve prediction accuracy by enriching the training data with behavioral features. The article highlights Snowplow, an enterprise Behavioral Data Platform, as a solution to simplify and automate the use of behavioral data in machine learning by providing a structured, comprehensive dataset while ensuring privacy compliance, thus making it viable for AI applications.