Consumer Choice Modelling
Understanding consumer behaviour is critical if governments and companies are to be successful in driving the uptake of new low carbon technologies. While large-scale investors are well understood, domestic consumers often display seemingly irrational behaviour that confounds simple economic models.
Element Energy is a UK leader in the use of Discrete Choice Modelling to understand consumer behaviour. This quantitative approach uses survey work to ask respondents to ‘choose’ between hypothetical goods or services, each described by attributes such as price and running cost. Analysis of these choice data allows the generation of Logit models that can predict the market shares of new technologies. Critically, the technique is able to quantify a range of ‘non-price’ variables, such as the loss of space associated with solid wall insulation or the lack of infrastructure for electric vehicles.
We have applied this technique across the low carbon sector, including domestic microgeneration, energy efficiency for ‘hard to treat’ homes, and recently an electric vehicle project for the Energy Technologies Institute. Choice models are often at the heart of our approach to strategic studies, for example policy design work for DECC and the Energy Saving Trust.
We partner with leading market research companies such as Accent, GfK and TNS, which provide the survey sample and data collection expertise. Our experimental techniques have been reviewed by international experts from the University of California and the Oak Ridge National Laboratory. We offer the following services to our clients:
- Initial scoping of variables to be tested in the main consumer work, including focus groups and pilot studies.
- Generation of experimental designs. We use NGene software, widely used in the academic community, to generate state of the art ‘efficient’ designs.
- Estimation of choice models based on survey data. This includes interaction with demographic/attitudinal questions, and model calibration based on revealed preference data.
- Development of detailed uptake models, showing how deployment varies as a function of policy support, market barriers, changes in technology prices etc.