Using Machine Learning and Data Analysis to Improve Customer Acquisition and Marketing in Residential Solar

High customer acquisition costs remain a persistent challenge in the U.S. residential solar industry. Effective customer acquisition in the residential solar market is increasingly achieved with the help of data analysis and machine learning, whether that means more targeted advertising, understanding customer motivations, or responding to competitors. New research by the National Renewable Energy Laboratory, Sandia National Laboratories, Vanderbilt University, University of Pennsylvania, and the California Center for Sustainable Energy and funded through the U.S. Department of Energy's Solar Energy Evolution and Diffusion (SEEDS) program demonstrates novel computational methods that can help drive down costs in the residential solar industry.