Embedded Research & Evaluation - Likeliness to Convert

As part of this blog series, I am writing about our embedded research and evaluation (ER&E) process to explore how we can drive more energy savings projects through our ASHRAE Level II audits program. In this phase of our research, we are investigating audit participants’ motivation to pursue energy reduction projects as a result of participating in an energy savings audit. We hypothesize that this motivation assessment will translate into a “likeliness to convert” factor that can be used to determine where additional intervention might be required to further engage with the intent to persuade action.

What does our current data tell us?

An initial analysis of our historical energy audit participant data indicates that about 70% of customers who participated in an audit installed at least one program eligible measure post audit. This represents almost 25% of the total recommended measures’ estimated savings.

About 40% of customers who participate in an energy audit complete at least one project within 2 months. Lighting improvements have the highest conversion rates within the same year as when the audit was completed.

What does our current data NOT tell us?

All of this data and resulting analysis provides insight into what and when, but not into why or why not. Understanding what is driving customers (1) to participate in an energy audit, and (2) to enact audit recommendations, along with (3) barriers for moving projects forward, will help us to have more informed discussions with customers. We hypothesize that more informed discussions will help deliver higher energy savings. However, this is data and information that we do not currently have.

Future Data Collection and Analysis

Analyzing the participant data spurred more questions to investigate, some of which will require additional data collection. We will use each of our customer interaction opportunities to gather this information as projects progress. Questions include:

  • Are customers who have completed at least one project likely to complete more projects? If yes, in what timeframe is this expected to occur?
  • What are the reasons for completing projects by different measure type?
  • How can messaging and conversations with customers be structured to identify both interest in and barriers to moving projects forward?

In the Next Issue

In the next few posts, I will share more information about our “likeliness to convert” embedded research as it unfolds.
Back to Blog