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Publications
  • January 2016
  • Teaching Note
  • HBS Case Collection

Groom Energy Solutions: Selling Efficiency

By: Michael W. Toffel, Kira Fabrizio and Stephanie van Sice
  • Format:Print
  • | Language:English
  • | Pages:10
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Abstract

This case examines a start-up service provider that helps clients improve the energy efficiency of their factories, warehouses, and commercial and office spaces by integrating and installing lighting, heating, and cooling technologies. The company seeks to double revenues within five years and needs to identify which service offerings to emphasize and which geographies and customer sectors to pursue. The case enables students to explore the “energy efficiency paradox,” which is that many energy-efficient technologies that offer both financial and environmental benefits are not being adopted. Students gain an appreciation of the potential causes of this paradox, including information asymmetry, the substantial investment required to gain expertise to evaluate technologies, measurement challenges associated with forecasting and monitoring the benefits of these technologies, and energy being underpriced compared to its social cost. Moreover, students discuss how Groom Energy can profit by helping companies overcome some of these barriers to adoption.

Keywords

Energy Efficiency; Energy Efficiency Paradox; Environment; Environmental Strategy; Growth; Energy; Energy Conservation; Entrepreneurship; Environmental Sustainability; Energy Industry

Citation

Toffel, Michael W., Kira Fabrizio, and Stephanie van Sice. "Groom Energy Solutions: Selling Efficiency." Harvard Business School Teaching Note 616-023, January 2016.
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About The Author

Michael W. Toffel

Technology and Operations Management
→More Publications

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    Manage the Suppliers That Could Harm Your Brand: Know When to Avoid, Engage, or Drop Them

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    T-tests: Theory and Practice

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More from the Authors
  • Manage the Suppliers That Could Harm Your Brand: Know When to Avoid, Engage, or Drop Them By: Jodi L Short and Michael W. Toffel
  • Assessing Prediction Accuracy of Machine Learning Models By: Michael Toffel and Natalie Epstein
  • T-tests: Theory and Practice By: Michael Parzen, Natalie Epstein, Chiara Farronato and Michael Toffel
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