Using Machine Learning and Data Analysis to Improve Customer Acquisition and Marketing in Residential Solar [electronic resource].
- Washington, D.C. : United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, 2017. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 515 KB : digital, PDF file
- Additional Creators:
- National Renewable Energy Laboratory (U.S.), United States. Office of the Assistant Secretary of Energy Efficiency and Renewable Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- 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.
- Published through SciTech Connect., 10/18/2017., "nrel/fs-6a20-70077", and Sigrin, Benjamin O [National Renewable Energy Laboratory (NREL), Golden, CO (United States)].
- Funding Information:
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