Right Person, Right Message - The value of AI for optimizing utility programs
- Oct 31, 2019 10:45 am GMTOct 29, 2019 9:55 pm GMT
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Smart thermostats, efficient appliances, distributed energy resources and other advanced technologies are rapidly transforming the global utility industry. Yet the approach many North American utilities take to get customers to sign-up for various demand side management (DSM) programs doesn’t take advantage of cutting-edge technology and data analytics.
“Right now, the approach is typically mass marketing,” said Matt Hale, a product manager at Bidgely, a Mountain View, California-based software company that works closely with utilities around the globe to better engage with their customers. “It’s billboards, radio ads and a fair amount of dabbling with social campaigns.”
Some utilities have also utilized home energy reports and online surveys as a lure to draw customers into DSM programs; the idea being that showing their customers how their energy consumption compares to their neighbors will motivate them to embrace programs that can help slice their bills. But even these techniques don’t have highly personalized data about a homeowner’s energy use and needs. That is only possible when a homeowner fills out an online survey, something that only around 10% to 15% of the people actually do. What about the vast majority of people who don’t fill out the surveys?
An even more fundamental challenge around the use of mass marketing approaches to get customers to sign up for DSM programs is that they often don’t accurately identify the customers who would benefit the most from increasing their energy efficiency. Of course, targeting the customers who will reap the biggest savings and benefits from the use of technology like a smart thermostat also helps utilities run more cost-effective programs, which opens up the possibility of expanding successful programs or innovating even more with new pilot projects and initiatives.
While it’s important to remember that targeting the right utility customers with the right offers can stretch marketing dollars a lot farther, that’s not really where you find significant savings. “The much more compelling argument is to stop wasting your rebate dollars on people who don’t save much energy,” said Hale. “Because marketing spend might be 10%, maybe 15% of program cost, but rebate spend is typically 70% to 80%.”
But highly-personalized and data-driven marketing simply hasn’t been the norm at most utilities. In a blog post for the consulting firm Accenture, Mark Sherwin argued that even though utilities were comparatively slow to embrace the benefits of data-driven marketing, doing so in the future was important to their success. “In fact, data-driven marketing holds the key to success across customer acquisition, retention and service,” wrote Sherwin, managing director of Accenture Digital. “This is because data-driven techniques enable utilities to profile the customer on an individual level, helping utilities better target their campaigns.”
Accenture research underscores the importance of being relevant to individual customers. According to the company, the typical customer devotes about 10 minutes of their attention per year to their utility. Taking advantage of that tiny window requires impactful communication.
But how can utilities more effectively target customers who can most benefit from DSM programs? One tool utilities now have at their disposal is artificial intelligence, or AI. Leveraging AI, utilities get a level of transparency and intelligence about the household energy usage of their customers – down to the individual appliance level – that has never been possible before. In other words, AI provides visibility into what appliances customers have, and how and when they use them. It’s highly individualized information that can be used to understand the savings a customer might reap in a utility program without them having to provide any information themselves.
Put another way, AI is the equivalent of sending out an army of energy auditors to do a deep dive into how all of a utility’s customers use energy.
These precise household and appliance-level insights allow utilities to more effectively target customers and encourage them to participate in DSM programs. It starts with where to focus their outreach and marketing efforts. Indeed, utilities know exactly the level of savings they want to achieve with each of their DSM programs. With that strategy in mind, tapping the power of AI to do a house-by-house and appliance-by-appliance analysis of which customers can achieve the highest potential savings by replacing an inefficient air conditioning unit or installing a smart thermostat provides a blueprint for marketing efforts.
Let’s say, for example, that a traditional utility program sets a goal of enrolling 1,000 homes in order to reach its savings goal. But with better targeting - and marketing messages tailored to individual homeowners - a utility would only need to sign up 700 homes. That’s because each of the targeted homes would reliably reap higher savings. This means that the cost of the program is smaller even though the overall savings are about the same.
There are plenty of reasons to believe that the targeted approach to DSM participation that is enabled by AI is set to increase. In part, it’s simply due to the fact that more utilities are looking to non-wires alternatives – including energy efficiency and customer-sited DER – as a tool to meet peak demand that doesn’t involve substantial investments in new substations and other infrastructure.
There is also an increasing body of evidence pointing towards the need of utilities to embrace a pay-for-performance (P4P) approach, particularly to their energy efficiency programs. A research paper released by the Energy Policy Institute at the University of Chicago examined what it termed “the conventional wisdom” about energy efficiency policies leading to investments that both pay for themselves financially and reduce greenhouse gas emissions. “However, this belief is primarily based on projections from engineering models,” wrote the paper’s authors, Meredith Fowlie, Michael Greenstone and Catherine Wolfram. In other words, the designers and implementers of energy efficiency programs could reach their goals based on assumed savings rather than actual meter data.
By examining around 30,000 Michigan homes enrolled in the Department of Energy’s (DOE) Weatherization Assistance Program, the researchers came to a very different conclusion. “The findings suggest that the upfront investment costs are about twice the actual energy savings,” wrote the authors. “Further, the model-projected savings are more than three times the actual savings.”
Findings such as these have elevated the interest among policymakers in adopting P4P metrics to evaluate energy efficiency program success. A report released by the Natural Resources Defense Council (NRDC) detailed the rationale for policymakers to pilot P4P energy efficiency programs. “The need to further ramp up EE to avoid greenhouse gas emissions from energy production, along with an interest in better use of digital energy meter data and analytics to encourage efficiency, has led policymakers in states like California and New York to consider expanding the use of pay-for-performance,” said the study’s authors. “P4P programs reward energy savings on an ongoing basis as energy savings occur, often by examining data from a building’s energy meters, rather than providing up-front payments to fund energy-saving measures.”
As utility program implementers adapt to the P4P approach, it will become critical to target their outreach to the customers who will achieve the most significant savings. That will increase the need to better understand individual customers. "Pay-for-performance approaches are mostly in pilot phase, but as the industry shifts toward that model, it’s going to be really critical to have that targeting,” said Hale.