Ian: 0:07

If one word could sum up the modern utility-customer relationship, I think collaboration and engagement might be tied for first place. Adoption of newer technologies like electric vehicle charging equipment, connected devices, and solar plus storage systems give customers greater insight and control of their energy consumption patterns, and they're demanding better service from their utilities. Still, it's interesting that many customers remain passive. They're unaware of programs and resources available to them, and utilities aren't always effective in reaching customers that should be prime candidates for their programs. And as energy costs rise across the US, proactive customer engagement and education is more important than it's ever been. Hello and welcome to the Energy Beep Podcast. I'm your host, Ian Perderer. And today we are joined by Alex Corneglio, Vice President of Product Management at Brilliant, and he's here to discuss the importance of precise and proactive utility customer outreach and the role that data plays in those communications. Today we're going to answer questions like how can utilities take advantage of data to better segment, target, and communicate with customers? And how do more effective customer connections benefit both the customer and utility? So with that, let's get started. Welcome to the show, Alex. Tell us a little bit about your background, your current job, and what role your employer Brilliant plays in today's energy market.

Alex: 1:34

Absolutely. First off, thanks for having me, Ian. Excited to be here and excited to talk about this stuff because it's a topic that I've devoted probably the last decade of my life to. So I've got a lot to say. Hopefully it's helpful. And let's let's jump into it. My background is engineering. I'm an engineer at heart, and right out of university, I started working in energy and just got hooked. So my journey in the broader energy space has lasted for, at this point, 20 years. Hard to say that. And I've done everything from oil field services to solar and now very much focused on utility software, particularly for about the last decade. I'm the co-founder of Energy X Solutions, and we started that company about 10 years ago to really try to solve the problem of utility customer education. Our vision was that we saw that on-site building energy assessments were really the best way to characterize energy potential and give customers the information that they needed to ultimately take action. And we really wanted to scale that. We wanted to scale energy assessments through technology. And then Energy X was acquired by Brilliant. And now, as you mentioned, I'm the vice president of product management. And my job here is to take those same sort of ideas that I was working on before and really expand them. And now instead of just being focused on customer education, we have access and purchase across the entire customer journey. So we can take customers all the way from engagement through fulfillment, and we can do that for almost any utility program and offer. So I think where my focus is and what Brilliant is really trying to do is to take some of the solutions that historically maybe were really point solutions. They were focused on certain types of programs or certain types of initiatives and really give utilities and governments and efficiency organizations the opportunity to apply that technology across their entire customer journey. Because I think, as you mentioned in the intro, we are really looking at a much more holistic energy landscape today, now more than ever.

Ian: 3:43

Yep. So you mentioned the customer journey and let's set the stage a little bit for our conversation. I like to think of utilities as unique in some ways in the marketing sense. And yet at the same time, they still have to adhere to a lot of the same rules and constraints and realities everyone else is dealing with. But I think the utilities space and its journey with customer engagement is a bit of an interesting one that would probably leave most marketers in the for-profit or tech sector scratching their heads a little bit. So can you describe the old style of customer engagement we used to see in the energy space and then catch us up to like the now? So maybe that last three to five year interval, how does how does that old style contrast with what we're seeing today in the utility space?

Alex: 4:34

That's it's a really interesting question. And I think what I'm what I'm coming to the conclusion of is that in in some ways, you know, things have changed completely. But in other ways, it it's still the it's still the same game. And what we have today are maybe better tools, maybe a deeper understanding of the of the impact that we create and the outcomes therefore that we're able to drive and achieve. But I I think in a way, and maybe you'll see this too, you know, the the story is the same. And if we go back way back, I mean, it's always been about mass marketing. If you go back, you know, 10 years, 20 years, utilities have been tasked forever with the challenge of effectively serving everybody. And that's a complicated order, no matter what tools you have at your disposal. So I think if we go way back, the strategy of the utility was that they had a couple of things that they really wanted to talk about with their customers beyond just the please pay your bill and there's an outage and the the regular transactional communications. And the idea behind mass marketing is that you you push a message out to everyone and you try to get that one to two percent conversion rate. And I think what we've really seen over the last, in particular, maybe it's happening a little bit before this, but now in the last three to five years, I would say this has almost become table stakes across most of the utilities that we work with at Brilliant. It's it's the rise of modern digital customer infrastructure. So almost all utilities now have a customer information system or a CRM in place. They're all utilizing cloud infrastructure. AMI data is available, it's properly warehoused, it's being properly processed, and it's almost expected that that information is going to be used in a variety of customer engagement strategies. Other tools as well, that maybe 10 years ago people were saying that this is going to be the future. Well, now it's here. You know, everyone or many customers are paying their bills online. Almost all utilities have customer portals, they have billing offerings that range from budget billing to flat billing to a fairly at least robust understanding of what income-qualified customers may need from their utility. There's lots of ways to pay, there's different rate options. And I think that we're also now seeing that messages are turning more into notifications, if you will. Like we're already using that wealth of information and those technology tools to be more proactive with customers, letting them know in the middle of the month if we think the bill is going to be high at the end of the month. These are normal things, allowing people to interact with their utility across a whole bunch of different media. Calling them up is definitely a high cost channel, but it's certainly no longer the only channel for people to get in touch with their utility. And so I think that what's really come about is that we've gone from this era where we were using just the basic information to be able to communicate with sort of everyone all at once. And now we have a plethora of additional information about our customers stored in new tools that are able to really drive proactive service management. And I and I think that's exciting.

Ian: 7:41

I 100% agree with that. You know, you've been in the customer journey marketplace for energy for about 20 years. I've been in clean energy marketing for about a decade now. And, you know, in some ways, marketing and customer engagement, it's not unique in energy, in the sense that data is going to make or break your efforts. But I think what is unique is the kind of data I think nowadays that is really truly valuable isn't something you can find in your Google Analytics or your Instagram dashboards. You know, it's it's harder to access. And it's something that you have to take care to set up. For example, I know one of the things that you're doing at Brilliant as you look at property data to better understand and target customers. So let's take property data as an example. How does that help utilities hone in on the right customers better than the data that they are traditionally collecting?

Alex: 8:36

Right. Property data is our way of going beyond the bill, if you will. To summarize, sort of my last answer, the the customer engagement game in the utility space has always been, to a certain extent, about mass marketing. And what we've realized recently is sort of full utilization of all of the data and technology tools that the utility inherently has at their disposal. And so if we're gonna ask the question of how do we go, how do we go further, how do we drive better results on top of that, I think, or at least Brilliant's answer is that we've got to bring in additional context. We've got to be able to also understand what's happening, as I mentioned, beyond the bill or or outside of that utility customer relationship. And if you think about it, property data makes perfect sense. So we're talking about, you know, information about the building that has the meter attached to it. This is what's driving the bill, this is what's driving the consumption. And if we can understand the building and the consumption, then we have a much better picture, not only to proactively suggest things to customers because we know what their building needs, not just what would benefit their usage, but we can also start to better unpack the impact that we're generating. We can start to sort through all of the noise that comes with consumption data. There's estimated billing, there's occupancy changes, there's weather, there's market factors, macroeconomic factors, rate changes. All of these things are encapsulated in the data feed that you get from a bill. And it's just great to have data about what the energy is actually being used for, i.e., the building, the property, to be able to put that in deeper context and make more targeted usage-independent recommendations.

Ian: 10:33

So when we say property data, what are what are we really talking about? I mean, is it beyond and more granular than maybe what you would get in an MLS listing? So things like the year the house was built, how many bedrooms it has, or is it even more granular to the point of like, okay, well, based on a house this size, it probably has a water heater of like this capacity, which draws this much kilowatt hours and da-da-da-da-da-da-da-da.

Alex: 11:00

All of the above. All of the above. And so this is kind of where where we go back to this idea that, you know, if you want to, if you really want to understand a building and how it operates, what do you do? You get an expert to come in and do a 200-point audit and run all of those data points through building modeling software, and you can really start to calculate and understand the usage of the building. And so we we really want to do the same thing. We start at sort of the most fundamental, which is basically geographically, where is this building located? And believe it or not, there are patterns that exist between buildings that are located in a similar area. We see those buildings, regardless of their makeup, performing similarly in certain ways, and and that's really helpful. On top of that, we then start to layer things about the structure. So when was it built? What's its vintage? How large is it? What's the what's the house type? Is this a town home? Is it a is it a single detached building? Is it a pre-manufactured home? Then we start to layer in information about the the equipment, the major pieces of equipment that are inside that building. What sort of heating fuels does it use? Does it have multiple heating systems? Once you start to get a more holistic picture of, you know, where the building is, what its main structure is, what are the main pieces of equipment, then we start to model all the various ways in which those factors, if you will, could could operate together. So we're looking at, you know, if this building replaced their current heating system with a high-efficiency heat pump, how would that change things? How would that change heat loss through the building? Should we do an insulation upgrade before a heating upgrade? Which one would be more beneficial? And how do these characteristics relate to other homes around them? The last piece then for us is bringing back in that consumption information and understanding, you know, okay, we understand how the building could operate. Let's now look at how the building is being operated. And you can imagine that between those two poles, there's a whole lot of information. So we're we're going we're going very deep. We're we're going all the way. We want to understand everything that we can about the building. We look at a whole bunch of different publicly available data sources to get this information. It goes way below way beyond, you know, real estate data, as you mentioned, MLS information. And we're also, you know, like an energy auditor would, we're trying to model the future state of the building and include that in our understanding as well.

Ian: 13:21

You know, we're talking about the customer engagement, but then I think you also touched on some other applications that just having that data would have, which brings me to the next thing I actually wanted to ask you about, which is, you know, once you have the right data, you need to put it to its best use and put those insights into practices. And at UTIs, the larger the utility, I think the more difficult that can be. They are famously siloed, and I know there are utilities out there who are doing some really important and great work to flatten out their organizations and make more integrated teams, but it's still a work in progress. So how can a utility, either on their own or through working with a partner like Brilliant, solve some of these integration challenges to ensure like when they're really getting this piece of data, it's being squeezed for all the juice it has, so to speak?

Alex: 14:13

Yeah, that's a that's a really interesting topic because I think the the answer there, at least from my perspective, is maybe counterintuitive. Just because you have a lot of information about a customer, whatever that may be, consumption information, property information, demographic information. It doesn't mean that you have to use it directly in the communication, or it doesn't mean you have to use it directly in the engagement. We look at data fundamentally as just another tool to be able to understand how current engagements are going and make sure that future engagements are being optimized in the right direction. So let's give an example here about how maybe you might find yourself in a situation at a utility where you have to work across silos and maybe that'll put what I'm talking about in a little bit better context. So we had a challenge recently where we were working with the utility and they had three goals that they were trying to drive with a single communication. And each of those goals involved a different part of the utility. So one of the goals was to increase customer satisfaction. Common one, this was driven by a representative from the communications team. We also wanted to reduce call center activity. So something was happening, people weren't necessarily satisfied by it, and what were they doing? They were going to the call center. So that then involved the call center team, and they were trying to look for ways to systemically lower call center activity. And then, of course, there was a program manager involved who had some efficiency goals, and they were hoping that these communications could also promote certain programs and help manage energy usage. How do you design a communication that does all of that, that speaks specifically to each of those points? It's really difficult. What we ended up doing was focusing on what we felt was the core issue. We started targeting customers, looking at their consumption information that were having unusually or particularly high bills. And we included messages that generally in past campaigns were shown to drive positive customer satisfaction. And the hunch was that if we included the right type of information from the bill and we put it in a format that was already consistently driving improved customer satisfaction, that that would check the box. So part of the strategy is just looking at what's worked before and using that plethora of information to try and provide a educational experience that's really outcome focused, right? We we took the information from the call center, we included information that would directly address the concerns that people were calling in with, and we embedded that into a format that we know is, I don't know, it's just a funny word to say, but it's pleasing to customers. But then what about the efficiency goals? Well, the interesting part is that that's where all of the rest of the data came in. We were, after three months of sending out these communications, able to use that background data to segment the campaigns and find customer segments that were actually high performing in terms of lowering their bill. There was a group of customers that, if they received three of these communications in a sequence, they would actually systemically reduce their energy use by 1.5%. So all of a sudden, now, because we have all this background data and because we're leveraging communication methods that we can properly study, what we have running in the background is almost like a behavioral energy program. So I think like the way that you cut across silos is first you understand the outcomes that everybody is looking for. And then you make sure that there's sort of a component of your communication design that's going to address all of that. But it's not to say that you have to use all the data. Certainly, customers were not getting personalized messages on the basis of because your home is like this, we're sending you this message and we think that's what's driving you a high bill for you. Instead, we use tried and true methodologies to drive outcomes where we could, and we use the data to help with the analysis of the third.

Ian: 18:33

So talk to me a little bit maybe about how this data-driven targeting, when it's being applied in this way, help utilities increase the impacts of their investments. I would imagine the applications are myriad.

Alex: 18:47

Definitely. You know, at the face value, once we start providing a lot of this data to utilities, even if it's not, as I mentioned in the last example, even if it's not meant to enhance the communications or make the communications even more personalized, the first thing that utilities often get from it is just a new view of their service territory. It's an opportunity to sort of scratch your head and say, oh, I didn't realize that all of the older buildings that can benefit from certain improvements or have certain consumption profiles all happen to be located in these specific areas. And maybe if we just targeted communications to those specific areas, we would see a much greater return on investment from those types of communications. So there's something to be said for just good old-fashioned uh segmentation and targeting. But I but I think that the piece that is often missed, and that's kind of the punchline of the last story as well, where you know the energy savings component actually came from the analysis. We really have to think about designing communications to be able to find all of the benefits. I think that utilities and governments communicate a lot with their stakeholders. And those communications are generally trusted. And with just a little bit of personalization, you can get great engagement around those communications. The question then is: is that engagement working for you? Are you going to be able to look back and determine that, oh yeah, there was this group of customers located in this area with a certain type of building that showed outsize engagement or outsize savings, or vice versa. Is there are there segments of my population that are really underperforming and pulling my averages down? Statistical analysis combined with data and combined with really robust communication design, I think uncovers some of these hidden impacts. And that's really, I think, the big secret moving forward. It's that there's so much that's happening. If you think about a typical utility service territory, let's just say, let's just say they're communicating with 100,000 meters every month via a bill. It's hard to imagine sending out 100,000 messages a month and that not having an impact. And I think when you open your eyes to that potential, there's a lot that's hiding in plain sight almost.

Ian: 21:10

There are certainly segments of the utility customer base that are very much accessible and receptive to the traditional digital marketing messages when they're tailored. And then there are also, however, portions of the utility customer population that are considered difficult to reach. And I think sometimes it can be easy to forget that there are communities that utilities serve where they maybe don't have you know reliable access to an internet-enabled device in their home or they work non-traditional hours. So I'd like to talk maybe a little bit about that. And can you tell me some of the challenges around getting the customers engaged and then actually enrolling them in programs? And how does that data that we're collecting, how does that assist or transform engagements for those hard-to-reach populations?

Alex: 22:21

This is maybe controversial, but we don't necessarily think about hard-to-reach populations. You know, our our goal is to our goal is to understand every building, every consumption profile. And and one of the things that we've learned that's really interesting is that the that data is often independent from engagement. You know, so hard-to-reach customers may be hard to communicate with, but we may actually have a lot of information about where they're located, what their buildings are like, and and what potential they have for programs and utility offers. And and vice versa, you know, the the opposite can also be true that sometimes it does work as you would think. But when it comes down to it, you know, what we try to do is come up with strategies that help us understand what's happening in particular buildings. And then it's just a sort of a tried and true process of cycling through communication strategies and messages and then having the ability to figure out which ones are actually working. You know, there's one thing I've learned is that there's no silver bullet and and there's no paradigms, I guess, that that can't be broken. An example here would be probably a home energy report program that we ran recently. And the sort of the adage about home energy report programs is that they work best when the home energy reports include a certain type of message and are targeted at a certain population. So if you use social normalization and you target your relatively high-consuming customers, HER programs give great results. This story is about us not doing that. We we targeted the entire customer spectrum. And what we actually found is that when we tried different message styles, we actually got our low-consuming customers to outperform the high-consuming customers. So the customers that weren't supposed to be the ones that generate the savings actually generated the majority of the savings, the most of the savings. And I think this gives people hope that, you know, these customers that we thought were going to be hard to reach, these low-consuming customers, they're gonna be hard to reach with a home energy report message, and they're gonna be, it's gonna be hard to generate savings from that population. Well, what we found out is that if you focus on fundamental information, information about what's happening in their building, you try different message styles, you combine that with other tried and true methodologies, like we talked about in the last example as well, you can, you can actually flip that paradigm of which customers are hard to reach and and which customers are conversely high potential. I think that all depends on, you know, what are you talking about, how are you talking about it, and how are you trying to measure the results?

Ian: 25:06

So I think, yeah. So if I could sum that up, it sounds like what you're saying is that the avenue or the vehicle for the message really is secondary or almost given what really, you know, from a data perspective matters more is that the message is really aligning with what you are seeing on the data side in terms of both in terms of the reality, but also maybe what is possible. Is that correct?

Alex: 25:31

Yeah, definitely. And I mean, we when we think about it, we're we're thinking about, you know, what is the outcome that we want to drive? We we want to drive, in the case of the HER program, we want to drive maximum behavioral savings. And so we went after customers where we thought there was high potential. And high potential is not necessarily related to whether or not a customer is easy to reach or not. And and so I think again, by adding that additional context into the picture, it can help program managers and marketers to rethink what it is they're trying to do and be more outcome focused as opposed to worrying about I don't know, the makeup of a customer base, for example. Okay, that that definitely makes sense.

Ian: 26:12

I think it's a helpful, different way of looking at it. And then engagement is obviously, as we're talking about right now, is a big part of enrolling customers into a program. But the customer education component, maybe not the final pitch, but just getting them aware whether that's aware of a program, aware of a rate, or it could even just be simple as aware of their usage. They may not even be aware of their usage. What role does that play? Are and are there other factors in customer education that come into play that has an impact on these messages that you're sending out, even if you have all the right data? Are there other things we should be considering?

Alex: 26:57

I'm gonna sound like Charles Dickens here. Like education is critically important. Ultimately, like that's that's really what we're trying to do. And and and we define education at Brilliant as inbound customer experiences that are designed to drive customers to take action. The ironic part here is that I think that sort of like data, these educational experiences don't have to be obvious to the customer. And more and more we're effectively seeing these edge educational experiences probably going away. Or maybe it's better to say they're going to become more and more embedded in the short interactions that we have with customers. And I think that's just a sign of like the modern times, right? Everything is getting snappier. Everything, people, people's attention span is not minutes, it's it's seconds.

Ian: 27:45

And it's sometimes more like integrating that micro learning concept into all the communications that you're doing.

Alex: 27:52

Exactly. And how do you do that? Well, you you do that by understanding your customers and anticipating the types of reinforcement they're gonna need, the types of objections that they're gonna have, and that therefore you need to eliminate. And that's almost what I think modern personalization is really about. It's certainly not about parroting back to people, oh, this is your name, this is your address, this is the type of heating system we think you have. I think it's more about understanding, because of all that, what is this person likely to need? What are they likely to have questions about? How difficult is this customer journey gonna be for them to walk down compared to other customers? And then how do I embed in my communications things already that are filtering out the ideal customers from the people that are really gonna struggle to participate? Because chances are we don't want them as participants necessarily. They're gonna be more costly to serve and they're likely to have a more difficult time with the program overall. So this process of education is really just it goes back to again the outcomes that you're trying to drive. Understanding who your ideal participants are, using the data to get ahead of the concerns they're likely to have, and making those educational experiences embedded in these microcustomer journeys that we create.

Ian: 29:12

You know, where I think that really helps is you said utilities and you customers generally already have, for all intents and purposes, a high level of trust in their utilities. And I think being able to play that role of providing the relevant information at the right time, in the right context with the right reinforcements only serves to really kind of reinforce that. But have you seen, whether it's quantitatively or qualitatively, have you seen any impacts to that trust level between the utility and their customers when you start using the data in the way that you all do this? Have you seen any impacts in that area?

Alex: 29:57

Yeah, man, I'm so controversial today. You know, like I I'm I'm what what the thought that pops into my head is like we don't care about trust either. You know, we don't we just care about the outcomes that we're trying to drive. And so if you're trying to drive improved customer satisfaction or reduced call center activity or cost savings in some other part of the business, then those metrics become a proxy for, you know, do our customers trust us? In general, there's lots of great information about how this type of approach, a data-driven, direct, focused approach, builds trust. We we look at that when when we do certain like rate explanation communications with customers. And we'll ask them, do you feel like you now understand the rate? Do you feel like you need to call into the call center and ask more questions? And in some cases, we'll even ask, how has your perception of the utility changed because of these communications? And and simply providing timely, relevant communications that are targeted to the customers that need them the most. I mean, the how that drives satisfaction and perceived trust of the utility is I was gonna say it's immeasurable. It's not, it's perfectly measurable. I mean, you're getting 20 to 50% improvements in those scores after very short communication cycles. But again, I would I would challenge that what we really mean by trust is if we present an offer to our customers, are they willing to accept it? Like that's trust in action. And we don't have to necessarily have the causation there that, oh, they did this because they trust us. We're happy with just the correlation that we reached out to this customer and then they took this action. And I think trust, you know, is it's an important thing to think about in there. But again, we're in a place where we have a lot of data, we can get ahead of the needs of customers, we can present them with very relevant offers at the exact right time. And trust is then evidenced by us achieving our targets.

Ian: 31:53

Yeah. So certainly from your perspective, you know, if you're doing the right things, trust is kind of a given outcome of what you're gonna get from the right data being implemented in the right way to the right audiences. It's a natural byproduct, it sounds like.

Alex: 32:10

Of course, yeah. But but certainly I'm not saying don't look for distrust, right? Like you have to, you have to monitor, you have to be, you have to be careful, you have to constantly be measuring things. So we, whenever we do communication campaigns or engagement campaigns, we're always looking at, you know, how many people are complaining about this? You know, how many calls to the call center did we get? Was there any negative sentiment? How many people opted out of digital communications? We're we're looking at reputation scores when we're sending things out. The fundamentals don't go away, obviously. But absolutely, when when things are going right, reduced complaints, I mean, it should really go almost down to zero. You'll you'll see these numbers doing what they're supposed to without you having to necessarily focus on it because you're focused on getting the right message to the right customer, measuring all of the outcomes and achieving your targets.

Ian: 32:58

And many of our listeners are involved in the implementation and management of utility customer programs, and they may be responsible for the outcomes. So, what you know, what's one final piece of advice you can give to those listeners for how they can better help their programs connect with the customers?

Alex: 33:21

I I think I would really go back to that outcome focus. Right. I think that we we have so many resources available these days that it's tempting to fall into patterns from taking it full circle here, I guess, going back 10, 15 years where mass marketing communications were really the only way. And I think that what we have now is an opportunity, the systems are in place, the infrastructure is in place. I think even the utility mindset is shifting. Utilities more than ever recognize that they need to provide their customers with choices, and they also recognize that their customers' choices can have big impacts on things that the utility cares about. Customers are not just consumers now, they can generate their own energy. Customers can make choices that significantly change their consumption patterns. On the commercial side, we're seeing data centers rising. On the residential side, we're seeing electric vehicles and all these things that are really expanding, not just contracting the usage that customers are wanting. And as you mentioned in the intro, we're now facing challenges across the industry of demand actually being greater than what we can readily supply. And so I think now more than ever, it's absolutely critical that we think carefully about the outcomes that we're trying to drive. And then we let data and tools be in service to those outcomes. You know, we need to spend more time, I think, understanding the processes and the actions that customers need to work through. And this stuff is this stuff has nothing to do with technology or or data science. It has everything to do with energy expertise, engineering knowledge, building science knowledge, program management knowledge. And so the experts within your organization are more critical than ever. So when we think about how should we be implementing and managing utility customer programs today, it's really about breaking down those silos that we talked about before. Let's get all the stakeholders together and let's deal with everybody's concerns and outcomes all at once. Because as we talked about, you know, there's definitely ways to serve all the stakeholders with single focus communications. And you can't do that if you don't know what the outcomes are that are most important. And then once you know those outcomes, then you can look through all of your technology resources. You can look at companies like Brilliant, and there's lots out there like us who are here to provide you with new technology and services. You can look within your own team, and I think you can start to find that, again, the tools that you need to drive your outcomes, like the outcomes themselves in many cases, are hiding there in plain sight. The problem that we have today is putting all the pieces together quickly and intelligently to achieve our goals. I don't think that the challenge that program managers face today is like finding the next new thing. I think we have the tools, and it's just a matter of taking advantage of that opportunity.

Ian: 36:30

Well, Alex, thank you so much for that. I think that was a great final word of advice. I appreciate you uh stopping by and talking with us today. You really helped underscore, I think, the importance of the utility-customer relationship, how the right data delivered at the right time in the right way really can fundamentally change the outcomes for utility programs. And, you know, with with energy costs rising for the foreseeable future, this is gonna be more and more important as people talk more and more about affordability and efficiency. So I want to thank you for stopping by and talking with us today. That's it for this episode of the Energy Beat Podcast, and we will see you next time.