CX Decoded Podcast Episode 9: Amex CX Teams Take Customer Listening to the Next Level

Note: This transcript has been edited for space and clarity. 

Rich Hein: Hello, and happy to be joining you all on this episode of CX Decoded. And as always, I'm joined by my co-host, Dom Nicastro, managing editor of CMSWire. Dom, how you doing today?

Dom Nicastro: Rich, I am just fine. So happy to be here today, especially today because we've got yet another practitioner from the trenches, living and breathing CX every day. He's ready to share his tale with us about making some big pivots in his company's customer experience, Voice of the Customer programs. So happy to have him.

But let's get into it. Shall we introduce our guest with our guest rapid fire?

Rich: Yeah, Dom, nothing's more vital these days than Voice of the Customer. So let's jump right in.

Dom: All right, here we go. So what do we have today, Rich?

Rich: We have Luis Angel-Lalanne, vice president of customer listening at American Express.

Dom: Perfect. And why is he on today with us?

American Express: 150 Years of Customer Service

Rich: American Express, Dom, has been in the financial services business for more than 150 years. And according to Luis, serves as the core of everything they do. Luis himself has been with Amex for more than 20 years, and recently led an effort to take a critical look at how their team was measuring customer sentiment, questioning if the industry standard methods they were using and the metrics they were using were still relevant for the day-to-day things that they were doing.

Dom: Yeah. And I had the pleasure, Rich, of catching up with Luis earlier, and the result has got his teams shifting their voice of the customer paradigm, you know, process, which was formally defined by transactional survey programs will now be driven by modeled sentiment using NLP, natural language processing, and machine learning, which will provide the scorecard metric for their frontline care professionals. And Rich, basically, that sums up everything we've been hearing about where these CX practitioners want to be.

Rich: Exactly Dom. You know, there's a lot of interaction between customers and contact centers and technology like this is an absolute game changer. And with that in mind, we thought we'd bring in Luis to share with our audience, how this is going to be rolling out for American Express, and the biggest takeaways from the transition that they're making.

Dom: That's right, and enough about us. Let's get into it. Luis, how are you doing?

Luis Angel-Lalanne: I'm doing great, great.

Dom: Thank you for joining us today. Really appreciate it.

Rich: Yeah, it's definitely a pleasure to have you with us today. You mentioned in previous interviews, Luis, that your team has been powering their VoC programs with data from transactional surveys for years. Can you talk a little bit about the way that American Express was doing things and what was like the impetus for changing up this process?

Related Article: Voice of the Customer Paradigm Shift: Transactional to Sentiment

Customer Feedback Is a Core Part of Culture

Luis: I'm really proud to be part of a company and a CX program, where we've been asking for customer feedback, I think, since 2007. So it's really neat to have that history behind us and the fact that it formed such a core part of our culture.

And we started back then I think it was probably, this is before I moved into this role, but I think it was phone surveys. And then it turned to literally paper surveys. And then we pivoted to email and internet surveys.

But the basic structure is a survey goes out the next day or the day after you have some sort of interaction with American Express. And it could be a phone call, it could be a back-office interaction, where maybe you start a dispute case online, the charge dispute gets resolved completely online without talking to someone, and we'll still send you a survey. So anything kind of managed by the servicing organization.

And so we've been doing these surveys, like I said, for years and years and years, over time, we've rolled them out to all 24 countries that we do business in, and we try to survey every touchpoint we've got. What we'll do with them is we'll be sure to apply a suppression, you know, make sure we're not over-surveying customers. So if you've responded or not responded we'll suppress you for 30 days to make sure we're not overdoing it. And then we take these transactional surveys that we get back in and they become part of the scorecard for our frontline agents, and then it gets rolled up all the way to the top of the house.

Related Article: 10 Common Voice of the Customer Mistakes

Moving Toward Journey-Centric Lens vs. Transactional

Dom: That's so interesting, you know, it's funny, Luis, you have all this data, right? You've been collecting surveys for so long, collecting information, and that's great. You know, that's a great base, you have the data, right. But you know, you and I talked earlier, it's all about taking it to that next level and providing that true sentiment and getting at the heart of what customers are actually feeling. I mean, that's been really like the genesis behind a lot of this, right?

Luis: Yeah. So you know, with our survey program, we're really happy with it, obviously, it's performed really well for us over the years. But as time goes on, we really want to be able to pivot to journey surveys and take more of a journey-centric lens than then a transactional lens. And then we want to be able to provide better coaching tools to our frontline.

And so all of these ideas are things that we've been talking about for a little while and ideas bouncing around. But as NLP and NLP technology has gotten better over the last few years, we decided, hey, now's the time to let's dig into this. And let's see what we can learn.

And so it really started with just kind of my team as a side project, exploring NLP and saying, hey, can we model customer satisfaction using the phone call. We had a couple people on the team start this exercise, built a model we looked at said, well, the results look promising.

So then we, you know, we kind of set it aside because it was never a top priority, we always had our other priorities. And we'd come back to it, we listen to calls, where the survey is different from what the model says being like, oh, what's going on here, and we realize, about 90% of the time when the survey outcome and the model score differ, we prefer the model score.

So we started to get more excited about this capability. And last year, we decided to put some serious effort and make it a priority, to start to socialize this and see what operations leaders starting to think about this. And when we started to socialize it, we socialized sentiment, modeled sentiment, with the ability to pivot to journeys. With the theory being if we can move our frontline incentive management from off of the survey to this modeled outcome, then that frees up the survey to do different things with it, because we don't have to keep feeding the incentive machine. And that's where we can also sell this model sentiment hand-in-hand with the ability to pivot to more and more of a journey lens in our survey.

So it was really the two of these coming together that created the vision for the future for what we want to do with our customer experience measurement program.

Related Article: Foundational Steps for Customer Journey Mapping Initiatives

Don't Miss Out on Any Part of CX Journey

Rich: But looking at the old way you are doing things and this way that you're evolving to, obviously customer sentiment is important, but was that what you feel like you were missing from the formula that you had for so many years? And what I assume was working?

Luis: Yeah, it's interesting, I think from the survey program and listening to and understanding customers, as you look through that lens at what we were doing what we wanted to improve, or what we want to improve, it's really the journey view. We're serving transactions, we're getting a really healthy response rate like 22% globally, I think, we're getting good commentary.

So we feel really good about the feedback we're getting from customers today with our transactional survey, but it's a transaction, you know, so as I mentioned the beginning, we'll suppress a customer, so if they call in three times, we're not going to send them three surveys. So we realized things were missing is we might miss the followup phone call, you know where, where maybe something didn't go as well as the first phone call expected it to. But we're missing that bit of feedback.

And we're also missing just the end of the journey sometimes, you know, maybe we survey the setup of the event, but we don't survey the resolution, because again, we've suppressed that customer for 30 days. So it was really about wanting that journey lens and making sure that we're not structurally missing key moments in a journey, like we potentially are today.

Dom: Yeah, Luis, you know, we've seen some other articles and podcasts, you know, you're out there sharing your tales. And, you know, we've heard you talk about customer-obsessed culture when you began. I think we want to get there, I think there isn't a company that doesn't want to obsess about their customers, I would hope, I would hope. But put that into context of the work your teams do and truly build in that customer-obsessed culture. And kind of some examples of where you can shine in that arena.

Related Article: Developing a Customer Obsession Culture

Creating a Foundation of Customer Obsession

Luis: One of the most important things and something I certainly do not take for granted in my role is the fact that, like I mentioned earlier, we've been doing this since 2007. So this is a known, established program, no one could ever accuse it of being a flavor of the month. If you think of like, you know, call centers and employees coming into the call center, this is a multi-generational program. So I feel that forms the foundation of how Amex is so customer-centric. We've been doing this and listening to, and improving for our customers for so long, it's not some flavor of the month thing.

The other thing that's helped us is that it's been, again, in frontline incentive all the way up to leadership, for that long. So it's not, I mean, it's come and gone, like oh, maybe we're measuring, but we don't care about it. No, we've always cared about it.

And the third thing I would say that really helps drive this home and make it real, is the fact that it's not one of 40 metrics that we all have. It's one of a handful of metrics, so it genuinely matters. So that creates this foundation, I think of customer obsession, that we know it's been important, and we know it will continue to be important.

So I think that's the foundation, and then on top of it, we've been able to take advantage of opportunities to really shed light on what customers are thinking and have that transform and impact what we're doing in American Express.

And I think COVID was probably one of the best examples of how customer experience customer perception changed so quickly, and our listening post and being able to actually get verbatims back from customers really helped us understand like, how are customers responding and in the world is changing so fast. What do customers expect from us?

And I think, in addition to having that score, go all the way through the organization for a long time, being able to pivot and really demonstrate what customers are saying in times of unprecedented change like the beginning of COVID, that's another thing that really helps create that customer-centricity where more and more people from across American Express are coming to my team asking us what are you hearing from customers?

So I think you know, things like that really help.

Related Article: Top COVID-19 Challenges: How Customer Experience Leaders Responded

Impact of COVID-19 on CX Feedback

Rich: In every one of the podcast episodes, we wind up talking about COVID in some fashion, and you just brought it up. So I did want for you to clarify just a little bit. How did the actual customer responses change, like from what you were seeing day-to-day? And what was American Express's response to those changes?

Luis: Yeah, so it's, it was really interesting, when COVID first hit, we saw our scores themselves go up and improve, which was interesting, we weren't really expecting that.

And we saw the commentary get longer and more emotional. So the longer was, you know, you can measure that quantitatively. But the more emotional was kind of qualitative assessment. I guess attribute that to a ton of uncertainty in the world. Lots of companies had a hard time just answering the phone and Amex did an outstanding job of pivoting to a fully virtual environment and still answering the phone.

So customers appreciate that we were there for them. And the verbatims, like I said, were longer, were more emotional. And so what we started doing was sharing verbatims every week with the leadership across all of American Express. That made it real for people and helped leaders understand what our customers experiencing, what are they telling us.

And then, as COVID wore on, if you got like two, three months into it, then the economic reality hit of people not making as much money losing their jobs, businesses being shut down. And then customer experience scores declined temporarily.

And again, through our listening posts, we were able to find out what what our customers telling us they need. We were able to pass that information to risk management in the credit organization. And they actually were able to adjust some of their short-term offers that we were offering for folks in financial hardship based on the feedback we were getting. And then scores started climbing again.

So while it was a really stressful time for all of us, it was you know, it feels weird to say, but it was really great to see how the listening program can bring to people making decisions, insights out what are the customers thinking, what are they feeling? And how can we respond to those needs as they change on what was back then a weekly basis?

Dom: Yeah, COVID almost forced us to listen better, to adjust better, to adapt better. And there were programs and they're like, Okay, well, we'll give you a free subscription for six months. But when the six-month mark came, organizations were wondering, do we keep something like this in place? Or do we just say, and we're not doing that anymore? No, no, you're seeing a lot of organizations actually change because of the quick pivots they made, you know.

Luis: It was a really interesting time. And I think one things I found rewarding from it, were the new partners who are reaching out to me and asking for information. So risk management, you know, like, every week, I'd send out, like I said, this weekly, verbatim file, and every week, a few more people from risk management would want to be added to the list.

And that was just terrific to see that folks from all parts of the company were interested in this and interested in, not just reading and understanding feedback, but responding to it. Yeah, it was inspiring.

CX Metrics Attempt to Answer Common Questions

Dom: I wanted to get to one as well, something earlier, you said you mentioned, your overall program right now is one of five metrics, not 40 metrics, which I think is very smart, obviously, you just can't have that many ways to measure things. Can you talk a little bit through those five metrics, if you have those off top your head? How you're looking at things?

Luis: We try to think through at a high level. And the exact metrics vary depending on the particular segment in the servicing organization, but we try to think through conceptually like, are we answering the phone? So you know, it's gonna be things like abandon rate, it's speed of answer.

Then you think of, are we resolving the customer's query on the phone, so some sort of first call resolution customer callback metric, and then obviously, their sentiment, the customer sentiment through the survey. And then we have quality measures to make sure that we're meeting the regulatory requirements for the servicing we need to deliver.

And then if it's a sales program, there might be a small sales incentive in there. If it's collections program, obviously, there's a collections metric in there. But that's kind of how we think about it. And we literally try to, I want to say minimize, maybe that's the wrong word, but we, we definitely don't want to maximize the number of metrics that are frontline have to balance for themselves. And so that's the way we think about it.

And obviously, customer experience is a critical part of that. And over the years, the weighting of these things might change depending on what the organization needs to drive, but the general concept of how we want to measure the frontline service and then incentive doesn't change.

Rich: In a perfect world, how does American Express utilize, make that data actionable, and make it valuable to the customer?

Related Article: Customer Journey Moments That Matter: 3 Key Investment Areas

What Are the Moments That Matter to the Journeys?

Luis: The picture I'm trying to paint for my internal partners at American Express is, we should have this modeled sentiment running on every transaction, every phone, call every web chat to really understand each moment, what's going on, what are we delivering to our customers.

And then we should have a layer above that of journey surveys, where we survey the customer at the end. And that's probably more traditional old fashioned surveys of an email survey. You know, at the end of some event, we send a survey that says, what was your overall impression of the event? And like I said, I usually use the dispute experience at American Express, you know, you call to dispute a charge that could take a few days to resolve. That's a perfect example of where we want to put, or where we have already, a survey that goes out at the end and asks about the entire dispute experience.

And then above that, at the top of the pyramid, we'd have the product NPS scores. And so our vision is, if you understand the transactions in the journeys and the product, you can start to do the correlations between all of these and understand mathematically, what are the moments that matter to the journeys? What are the journeys that matter to the product? You know, some of this understanding right now is gut intuition from experienced executives and leaders. And you know, we know gut intuition is often right, but not always right.

So I really like being able to get these three tiers of data, do the correlations understand mathematically the moments that matter. And then hopefully, we get to a world where we can be more precise with customer experience improvements and business-outcome improvements. When we say this is the business outcome we want to drive. And we know the correlations, we know the connections, therefore, these are the moments that matter that we really want to focus on and improve.

So I look forward to that future where we can be more precise about where do we invest to deliver specific outcomes.

Rich: So regarding your VoC programs, you've talked about Artificial Intelligence, Natural Language Processing. Can you talk a little bit about the technology that is powering these processes, whether it's a specific product you're using, or what the underpinnings of all this is?

Luis: So today, the way we've set up our sentiment model, is we take a sentiment score from our call recording system, so our call recording system records the calls, creates a transcript of the call, and gives us a sentiment score. And we then take that transcript, that sentiment score, and then some other call metadata, like, you know, length of call, dead air time, cross-talk time. And we put that into our model. And we model that against our overall satisfaction score from the survey.

And the reason why we have taken this approach, and not just using the sentiment score that comes out of our call recording system, is because we really want to model against the survey outcome, which is, you know, the survey outcome is really the customer's recollection of the experience. Whereas the sentiment score, is the average sentiment of the call. And we know our your recollection of an experience is not the average of that experience.

There's a good theory that I like that says it's what peak-end theory that your recollection of an experience is based on the peak emotion and the end. And we see that in our model, you know, our model weights, the sentiment at the end of the call higher than any other part of the call. And so that's part of why we've taken the approach to model it using the transcript and the sentiment coming out of the call recording system.

And the other reason I like this approach is if we ever switch call recording systems, we ever get a new transcription service, a new sentiment service, then great, we can point our model at any input that gives us a sentiment score and a transcription.

So I think we built a model that's portable, and that better meets the need that we're trying to achieve of modeling the customers perception at the end of the call.

Rich: That's really interesting. And it sounds like it takes quite a bit to get that done. I'm curious to know, is that a department within American Express? Is that a team? How is that built internally, as far as manpower goes?

CX Teams Get Access to Data Experts

Luis: You know, one of the advantages my organization has is that we're part of the servicing organization. And we're actually part of the MIS [management and information systems] and analytics group within the organization. And on paper, that might be a strange place for a customer experience team to fit, but I think it creates this great advantage of, we've got access to terrific modelers and all the data experts.

So my team actually built the model, we've got the skill set on my team and a few people like I said, it started with just a few people on my team kind of exploring this on the side. So we were able to do it with the people we have in the skillset we have within the team. And now we're in the process of making sure it's industrial strength. So we're working with like the model governance team at American Express and some of the experts to make sure that the model that we started with is going to be industrial strength, and it's ready to run for the over the long haul.

So that's the way we've been interacting with American Express. And we're balancing this really nimble approach of having built it on our team with the needs of a big bank and to make sure that we're meeting all the requirements and the governance around you know, using an NLP model in decisioning, in this case would be incentive management for our frontline people.

Rich: The areas that you're working in, are very in demand areas I think across organizations, we see these giant acquisitions where they're spending millions of dollars for an organization that hasn't even launched a product yet.

So I guess my question is, you said you were able to do it while with the skills you had in house. But what are the really specialty skills you need, for instance, like data analysts and algorithm builders? And I'm just curious to know, what you think is like the critical underpinnings for that?

Related Article: The Skills Your Customer Experience Team Needs to Succeed

CX Practitioners Need NLP, Unstructured Experience 

Luis: Thanks for that question. We've been recruiting on my team for a couple years now to recruit folks who have NLP experience, unstructured data experience, modeling experience, just knowing that this was the future, knowing that we're going to need this to be successful. And so we've been recruiting that both from school and from other companies. So we've been, like I said, actively looking for that combination of NLP experience, generic unstructured data, and then some like machine learning skillset, and modeling skillset.

The final skillset we've been looking for, as well, is just making sure that as we're recruiting, that skill set, we're trying to find people who understand what it means to bring that to bear in a corporation, you know, which I think is very different from doing a cool modeling project in college. In a corporation, you've got to be able to explain it to people who aren't modelers; you've got to be able to work with like I said, we're working with the model governance team, to make sure that we're meeting all the needs that they've set aside and the standards they've established for a machine learning model at American Express.

And so those are kind of four things we've been recruiting for for a couple years and continue to recruit for on my team, just knowing that we wanted to be in this space of Natural Language Processing and machine learning, even before we knew, hey, the outcome is going to be a sentiment model that's going to replace the survey for frontlinel we didn't know that, but we knew we wanted that skill set.

Rich: So the last follow up I asked regarding your team building is around your internal staffing, as you were doing this. Did you find with some of these more specialized areas that you were able to retrain people or upskill people within the organization to take on that? Or was it all just we have to bring in these skills because either, A) we have to get it done in a certain amount of time, or, that was just your approach?

Luis: It's a little bit of both, the highest end modeling skillset like that's something I believe, is really hard to upskill and train that's, I think a career, it's an education. So some of the hardcore NLP modeling skillset has been something we've recruited. But the ability to do some unstructured data analytics, NLP, that's something that's been a blend; we've hired it, and people on my team have upskill themselves and learned it.

And we still have a great need for regular analytics, the day-to-day bread and butter for our team for the analytics and insights piece of my team is traditional analysis. So we've got a broad skillset on the team, which I think is totally appropriate, and I think all of those skills are equally valued in the work we have to do today.

But in terms of like this NLP unstructured and machine learning skillset, like I said, it's been a little bit of a blend, where a handful of people have upskilled, around NLP unstructured data, but that ultimate modeling skill set is something we recruit.

Rich: You know, we've covered the digital workplace for years at Simpler Media Group. And that is always a common theme is to how important upskilling is and creating these career paths. But I just think these other organizations are just so far ahead that organizations are being forced, as you say, just for these top level roles, really bringing in talent.

Luis: Yeah, I guess I think what I try to think through is, what is a new skill you can learn versus like, what's something in education that you really should have been in school for four years learning about? And that, you know, that's what I say, like, I think modeling is that second tier, whereas, some unstructured data analytics, like yeah, you can expand what you're doing to pull that in.

Related Article: The Pandemic Is Changing How Consumers Feel About AI in CX

How Does Change Management Impact VoC Programs

Rich: Okay, well, we are getting close to our time here. There's a couple more questions I had, I know you guys are a gigantic ship. I'm just curious what change management looks like within your organization, how that impacts your VoC programs?

Luis: That's a great one, we started exploring the sentiment model, as a side project might have been 2019. At the very end of 2019, we start exploring, 2020 was about like realizing, hey, this has some some legs to it.

Last year, our goal was to make it inevitable to really start to socialize what this sentiment model means, start to socialize the vision of sentiment plus journey working together.

And then this year is the beginning of the rollout. And then next year will be the completion of the rollout. So it's going to be a really long change management process. And so far, everyone I've talked to is completely okay and happy with that long change management process. We want to make sure we're doing it well. We want to make sure we're bringing everyone along with us.

So last year, we did a bunch of socialization info sessions. And then we did some tests and some pilots where we gave it to teams not as part of their official scorecard, but as an extra element to coach on. And we got a lot of positive feedback, and a lot of team leaders discovering really powerful ways to use this new model for coaching. And so what we're hoping to do this year is roll it out, and we're actually we're planning to at the end of first quarter, roll it out to just one team, one group within American Express, and it will be part of their incentive.

And then hopefully halfway through the year, we'll roll it out to a bigger chunk of frontline agents. And then next year will be the kind of the big bang of you know, the remaining 75% of our US English speaking agents will hopefully have this in their incentive. So it really is about going slow learning as we go doing multiple check ins with the same group over and over again, to make sure everyone's good.

And I would say the final piece of what's really helped with the change management is when you do call listening. And what we do is we set up called listening sessions where we, and I mentioned this at the beginning, where we listen to calls where the model and the survey differ. And 90% of the time everyone agrees with the model. And that really helps bring it home, and help sell it for people.

Because as you would imagine, the survey picks up a lot of experiences outside of the phone call; it picks up your overall impression of Amex, it might pick up something about fulfillment, that happened after the phone call, or maybe you called three people in a 24-hour period, and you're answering for some blend of all three phone calls when the surveys really allocated to just one person.

All of these things that certainly influenced the survey, that don't influence a phone call, is kind of what why when we do call listening, 90% of the time we agree with the model sentiment. And so that's been probably our best tool to get people to understand this and get people to be excited about it, because right there frontline people say, hey, this is even more in my control than the survey outcome today.

So it's going to be a long change management process and with lots of different problems, but like I said, I think the call listening is probably our most powerful tool in the toolbox.

Dom: Luis, we're getting close to the end here. And I wanted to wrap up with something that every CX practitioner listening to this really wanted to know in this whole thing. Do you have a history as a yacht designer?

Luis: Oh, yeah, my undergraduate degree is Naval Architecture and Marine Engineering. So I did that for 2 1/2 years. There were parts that I loved, and lots of parts of it I didn't love. So I decided to go to business school. And that's what got me to American Express. And I always tell people that like saying you're yacht designer is a lot cooler at cocktail parties.

Dom: I analyze phone calls and customer service. Oh, wait, no, I'm also a yacht designer, too.

Luis: Yeah. But I gotta say, like this industry, in financial services, and customer experience, in particular, are much more dynamic than the yacht design industry. So that's definitely personally why I find it so rewarding, even though yeah, it doesn't sound nearly as cool.

Dom: I love it, fascinating. Rich, how we did not dominate the podcast with that topic?

Rich: If I had only known earlier Dom, I definitely would have dove into it.

Dom: It's one of our channels, you know, digital experience, customer experience, yacht design, Artificial Intelligence. So hey, let's bring it all home, give us like the 1-minute takeaway that you know, you feel listeners who may be wanting to do something like this implement sentiment in their organization, what's the one takeaway you want them to bring home?

Grassroots Project Gained Momentum on Its Own

Luis: So, you know, I think what's been most successful for us or really beneficial for us is, we started this on our own as a grassroots project, and we started exploring it. And we started socializing it and we let it build its own momentum. And like I said, then when you do call listening, it continues to sell itself.

So I'd say if anyone wants to go explore this, I would say get started now, and start building your own momentum. And I think it's been really great and really healthy, that we've done it on our own, we don't have external timelines; there wasn't some mandates that I want you to go launch this by next Tuesday. And because we've been able to do it on our own, kind of organically, I think is the way I want to describe it, it's felt good, it's not been threatening to anyone, we've given everyone the chance to come along with us.

So I really think that's been a really powerful way to start to create the momentum to make this huge change possible, is just get started on your own and start to bring people up to speed with you as you go.

And then you start to make more and more advocates. So now when we go present, a lot of times, we'll bring our strongest advocates to do the selling for us, so I don't have to sell my ideas. I've got partners who have been using it on their team, and they're selling the idea.

Rich: Last question here. Every month when my American Express bill comes, there's an angry woman who threatens to cut my American Express card in half. And I was curious to know if customer support could help me with that.

Luis: Yeah, I don't think we've solved that one yet. I think I think you're on your own with that one. Sorry.

Rich: Fair enough. Fair enough. Luis, before we let you go, we always like to give the guests an opportunity to tell where we can learn more about you where we can share and follow. So if you wouldn't mind?

Luis: Yeah, I'd say LinkedIn is probably the the best place to reach out to me. I try to be good at responding particularly to, you know, requests where there's a question, there's some sort of engagement, you know, my own little commercial, like, if it's just a random reach out, I usually am not interested. But if there's some engagement, then that's always interesting. It's one of my favorite things about being in this industry, is that we have an industry, like we can engage with peers, we can talk, we can commiserate when it's useful. And I always find that rewarding.