I recently had the chance to sit in on a conversation between Chris Warren, contributing writer at Utility Dive’s studioID, and Bidgely’s Director of Innovation, Maria Kretzing about increasing the effectiveness of demand-side management (DSM) program marketing using AMI analytics. Their discussion centered upon how energy providers are changing the utility-consumer dynamic to increasingly rely on their customers as partners in ensuring a resilient and decarbonized grid.
“It was not that long ago that electric utilities routinely referred to customers as loads or ratepayers,” said Warren. “It’s not that utilities haven’t always cared about their customers. After all, the fundamental mission of utilities has long been to reliably deliver the electricity that homes and businesses needed. But they focused their time and resources keeping the grid working – because when they did, customers were happy.”
But now, as the power system dramatically transforms to become more distributed and support electric technologies like electric vehicles, the significance of energy customer engagement is evolving as well. More and more customers are feeding electricity back onto the grid and their energy usage patterns are changing.
“Let’s think about what these trends mean for utilities. At one level, a large and quickly increasing number of distributed energy resources makes customers a very important part of the grid. For instance, the time of day that EV owners charge their vehicles can have a significant impact on a utility’s ability to meet peak demand,” said Warren.
For customers to play an integral role in the energy transition requires a deep understanding of how customers use electricity, which technologies they’ve adopted, and the types of utility programs that could benefit them. These granular insights make possible more effective communication, deeper engagement, greater customer satisfaction, and ultimately, a true partnership relationship.
Tapping the Power of AMI Data
Approximately 75 percent of US homes now have a smart meter. In the past, when a utility read a customer meter once a month, there wasn’t a great deal of insight available about how a household used electricity or what appliances they had installed. But the level of insight into customers that’s available now is enormous. Instead of monthly reads, smart meters provide data about customer electricity usage every 15, 30, or 60 minutes.
“Smart meters can provide utilities with the granular information they need to understand customer electricity usage,” said Warren. “If that information is analyzed and understood, utilities can proactively communicate with customers to give an incentive to become partners in creating a grid that is decarbonized and reliable.”
“For utilities interested in a more personalized approach to communicating with their customers, this data is invaluable. It’s an opportunity for utilities to understand their customers and how they use electricity in a way that has never been possible before,” added Kretzing. “But in most cases, utilities haven’t changed their engagement models to take full advantage of the customer insights available.”
A large part of the disconnect between the now-available data and data-informed customer engagement is that useful insights don’t flow automatically out of the smart meter. The raw data has to be analyzed and understood before energy marketing teams can use it effectively.
That’s where Bidgely’s patented data science comes in – applying AI-powered algorithms to smart meter data for all customers to produce an accurate and continuously updated appliance-level view of how every household consumes electricity.
“We’ve developed a solution called Analytics Workbench that can help a utility understand when a house is running its HVAC system, pool pump, or washer or dryer,” explained Kretzing. “Our AI-powered analytics can also identify which homes have an EV and when they charge it, and the impact of solar generation at a household level. For instance, someone may be running their air conditioning unit while their solar is more or less offsetting that load. We can see that in AMI data, and with Analytics Workbench, utilities can easily discover those insights as well.”
She went on to emphasize that “all of this household and appliance-level information can be gathered without having to ask a homeowner a single question. It can be deployed quickly and at scale. And it can be used to both better serve customers and achieve utility objectives around customer engagement.”
Making DSM Program Marketing Outreach More Targeted, Personalized and Effective
“One of the big challenges marketers have faced is that they have had to craft and deliver messages to a broad cross-section of customers. Some of those customers may be keenly interested in the program or incentive you can provide, while it may not deliver any value at all to others,” explained Kretzing. “For example, an EV charging rate or an incentive for a Level 2 charger is of little use to someone who doesn’t have an EV. AMI data and analytics allow utilities to move away from using a single message for all customers to specifically targeting only those customers who will get the most value out of a given offer and program.”
With AMI data and analytics, it’s possible to quickly segment customers to identify those who are best suited for a special rate or offer. For example, it can be helpful for utilities to identify customers who use the most air conditioning at times of peak demand in order to prioritize them for enrollment in a demand response program.
“It’s not just about targeting the right customers,” Kretzing emphasized “Marketers can also take advantage of the customer data they collect to personalize their communications to make them more effective and engaging. For example, a utility could educate a customer about exactly how much energy their household is using during times of peak demand and quantify how much it is costing them. Then they could point out what a customer would save if they enrolled in demand response.”
Warren and Kretzing went on to talk about the urgency of putting AMI-informed segmentation and personalization to work in response to the increasing adoption of EVs.
“Once you have used AMI analytics to identify who your EV owners are, you can do a little more digging to understand their behavior better,” said Kretzing. “As we know, EV charging represents a big opportunity for utilities to shift their load. What AMI and analytics can reveal is when EV owners are charging their cars, and more specifically, which customers are charging during peak times. Marketers can then craft messages to those specific customers who are charging on-peak to let them know about the time of use or EV-specific rates, and include personalized insights about how much a customer would save by shifting their charging to off-peak times or taking advantage of a time of use rate.”
Enabling DSM Program Marketing Flexibility with Real-Time Data
“Precise targeting and personalized messages are critical to effective marketing, but AMI data and analytics also allow marketers to more or less measure the effectiveness of their initiatives in near real-time,” said Warren.
“That’s exactly right,” agreed Kretzing. “Think about the limited capacity marketers have to measure the effectiveness of their work today. For the most part, it comes down to the conversion rate — in other words, the number of consumers who actually enroll in the programs and incentives they are marketing. For example, after defining a segment to target for a demand response program, marketers can deliver personalized messages to that group of customers and then use Analytics Workbench to monitor customer behavior on a daily basis. If the enrollment isn’t what a utility hoped for, marketers can do a couple of things. One is they can adjust their communications to the target audience to boost engagement. Or they can select a different target segment to see if those customers are more receptive to the outreach. Utilities can even take advantage of insights about past customer engagement. That can be used to define new segments so that utilities are reaching out to their most engaged customers.”
This real-time measurement and verification is a powerful tool to layer onto traditional M&V studies, which can be robust but tend to be done only after a campaign is complete. What Bidgely’s Analytics Workbench provides is a tool for marketers to continuously assess the effectiveness of their campaigns and adjust them based on what they are observing, ensuring more successful outcomes.