Moving the Needle: Measuring the Performance of an Energy Program Promotional Campaign
Bibliographic record
Abstract
A key component of any demand side management program is recruiting participants through marketing and outreach activities, and measuring the success of these activities is critical in ascertaining whether marketing resources are used effectively. We describe one effort to measure the success of a promotional campaign by estimating the incremental program participation or “lift”. We report on: 1) how to assess the performance of promotional campaigns using a quasi-experimental lift study; 2) key assumptions to consider when designing a lift study; and 3) challenges associated with rapidly assessing the effects of a promotion. In 2013 the Independent Electricity System Operator in Ontario launched a promotional campaign to increase participation in three consumer programs: an HVAC program, an appliance recycling program, and a demand response program. The campaign leveraged a popular Canadian consumer rewards program, the AIR MILES ® Reward Program, whereby consumers would register, participate in one or more programs, and then receive AIR MILES Reward Miles as an incentive. Following the campaign launch, we surveyed consumers exposed to the campaign and those who were not (a quasi-experimental design). Lower-than-expected program participation rates during the initial study limited our ability to estimate the promotional lift. We surveyed additional consumers throughout 2014 to refine our lift estimates. During this process, we learned several important lessons: 1) the need for a deeper understanding of program participation rates; 2) the importance of clearly defining target and control groups; and 3) considering the time necessary for consumers to become aware and participate in a program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".