Hope is not a strategy
Do you ever feel behind the eight ball as the year draws to a close? Like Indiana Jones scrambling to escape the Temple of Doom, time is quickly running out, and the light at the end of the tunnel is getting dimmer with each footstep. Welcome to the life of an Energy Efficiency Program Manager in the knee of the end of year ‘hockey curve’: racing by the second to claim every watt of savings to meet expanded goals on limited budgets. How does your Program guarantee that targets get met? That your Trade Ally base is activated? That you are keeping your senior leadership abreast of progress? More likely than not, the honest answer to this question is lots of long nights…and hope. And I hate to break this to you, but hope is not a strategy. If you would rather have your Program running out the proverbal Program Year clock than throwing up Hail Marys, it’s time to modernize your operations by investing in data analytics.
Utilities have terabytes of useful data to mine for insights, such as customer energy usage data and historical program participation data. However, gleaning meaningful insights from these datasets require an analytics platform capable of overlaying propriety utility data with third party augmentations, such as NAICS industry codes. This approach helps measure the future potential of various customer segments from past performance, simplifying the Program Manager’s job of reaching kWh reduction targets or decarbonization goals through environmental electrification. Data insights can be operationalized to help a Program do more, with less. However, where does a Program Manager begin to find a bespoke solution, and what features must this platform offer to drive the most value? Data analytics should simplify the task at hand. For our implementations, the most secure and straightforward interfaces we design are build on the Power BI platform. I’m going to highlight the core features every analytics package should offer. These features should increase operational efficiency, drive better performance, and move a Program away from a strategy of hoping for results to one of measuring certainty.
Staying on top your game – Performance Forecasting
An excellent analytics package must visualize where a Program has been, where it’s currently at, and predict where it’s going. It should provide a top-level overview of expected performance at least eight weeks out to track performance against goal. The visualization needs to be simple to understand and identify year to date performance with remaining goals and weekly targets. It should be able to compare cumulative goals to paid or predicted performance and toggle between weekly and monthly calculations. Performance forecasting helps the Program Manager stay ahead of goals, and the visualizations improve upper management’s understanding of future demand.
Providing certainty into measure performance and incentive structures
Programs need to know how cost-effective measures are preforming and where those measures are getting installed. A data-analytics platform should be able to compare electric or gas savings by measure type to determine the Program’s top measures. It should be able to drill down into specifics of high preforming measures and provide insights into specific projects. To provide the most value, the measure level data should be overlaid on a map to identify total installs and incentive dollar spend by county. It should be able to quickly toggle between savings and incentives and filter data by program type, statues, or zip codes. These features better help a Program Manager identify underserved communities or to target certain measures to localities that need to free up additional distribution capacity.
Helping to better serve customers
Energy efficiency programs are an excellent asset for serving customers, deepening relationships, and increasing JD Power scores. Data analytics can help a Program Manger better service their customers by comparing current participants to the total population. It should be able to segment those customers by usage bins. Beyond segmentation, it should create simple visualizations that depict energy consumption by NAICS code to show where the largest consumers are at a glance. It should filter this information by energy customer usage and be capable of drilling down from industry codes to detailed segments, say from Retail Trades to Convenience Stores. This data needs to help the Program Manager identify individual customers who have not participated in the Program and help match measures that resulted in savings from other similar customer types to those potential participants. It should be able to identify differences across premises for individual accounts, like comparing consumption and savings ratios to identify potential opportunities and map this information to help the Program Manager point resources in the right direction.
Identifying and recognizing Rockstar Trade Allies
Trade Allies (TA) are the lifeblood for utility incentive programs typically represent 75% of a program’s kWh savings. Trade Allies are the key channel for delivering a program’s benefits deeper into the customer decision making process. A good analytics platform should quantify TA performance by contractor specialty. Knowing which TAs drive the most savings by measure type helps a Program Manager match potential participants to Rockstar TAs who are proven to drive results. Publishing TA results on a Trade Ally portal, a dedicated website for TAs, helps create friendly competition by allowing an individual TA to rank and benchmark themselves with their peers.
We live in a world of choice and limitless features. I hope this blog has helped demystify some of the complexity around analytics platforms and helped inform you of the core features needed to begin operationalizing the useful data your utility already has. If you would like to know more about Power BI and the bespoke interfaces that DNV GL can build for your Program, please feel free to reach out and request a live demo. I wish you the best as the Program Year comes to a close and hope that your future strategy is grounded in data science and analytics…not just long nights and hope.