C (4:47)

Yeah, and I love hearing that story because I think that just understanding as you have evolved, your understanding of how what the real problem is, right. So I think we are often in a role where our defined area of responsibility is, you know, in demand gen. For example, how many leads are you going to get, right. And how many of those MQLs are going to convert? And sometimes you miss the forest for the trees using that approach, right. Where you aren't working collaboratively and at that strategic level that enables you to say like, no matter how much we fill, it's always going to drain out at the bottom. So we better do more to prevent some of these things from draining out of the bottom. Like before we kind of dive into that high level piece. Let's talk about that why part. And I think that appeals to a lot of different disciplines. You know, we have a lot of UX researchers who are probably listening, product people, marketing people too. But across each one of those, talking to people is important. Those interviews are really critical. So what are those things that kind of, what are the aspects of talking to people that make it so essential for all of those Disciplines to make sure that they're being successful in what it is that they're doing.

D (6:22)

Yeah, it's such a good question. I was reflecting on how did this even all start and I think where it all began. Well, so first I will say it had never occurred to me and this sounds so wild because I've held a few in house roles at SaaS companies. Like I've been head of marketing before, I've been head of Demand Gen, both VC funded super high growth software companies. And when I went and started Demand Maven and I went out on my own, it had never occurred to me that behind every KPI that I ever want to improve, there's a set of conversations I can have about that. I can, I can make the number talk. There was a very good friend and mentor at the time who introduced me to the concept of jobs to be done. And I feel like every, I feel like Jobs beyond is like, it's like one of those things where it's like, it almost feels like an MLM sometimes. It's like, hey hon, like have you heard about Jobs to be done? It's like, I have not, but tell me more. But actually jobs have been done is amazing, but it does feel a little bit cultish times. But someone introduced me to Jobs to be done and then the more that I learned about it, the more that I realized that it was an incredible approach and way to understand like how someone thinks about making a buying decision. And what I didn't realize at the time was jobs to be on is one approach. But there are many other types of interviews and qualitative research that you can gather. And again, like it, it just hadn't occurred to me that anytime I'm staring at a KPI and I like want to make the number move, the way for me to kind of crack that case and maybe unpack. Like what's actually happening here is to actually have a conversation with either a customer or a prospect, an audience member. Like there is some qualitative component to whatever KPI I want to, I want to impact.

C (8:22)

Yeah, I think it's music to my ears partially because, you know, a lot of people kind of hit that data wall and get very frustrated because it's really hard for them to. And I mean, I just think that it's, you know, you're just throwing darts at the wall, right? When you just say, well, let's try this. Because data tells you that something happens and it's backward looking and it doesn't necessarily help you to pivot and I think that's where actually talking to customers is so powerful.

D (8:59)

Yeah.

C (8:59)

You know, and it kind of matters what you, I mean, you know, depending on what particular problem you're trying to solve at a specific point, the method that you use to narrow down is going to matter a lot. But you know, sometimes you can wander around a little bit and still learn. So. Yeah, that's helpful.

D (9:20)

Yeah. And I find too, I think a lot of the circumstances that I work with my clients under is usually they've tried everything, they're banging their heads against the wall and they know it's more complex than what they think it is, but they, but they don't know, like, how do we really isolate what the root cause is? And, and it, and it, and it just so happens too that they're usually very sensitive to waste. Like, like they don't want to keep trying a million things. You know, it's kind of like, okay, like we really gotta figure this out. How do we problem solve this in a way that is going to be efficient and isn't going to take six months unless it absolutely has to? You know, people want actionable insights fast and then, and then they want priority. So now that we get the actionable insights, now what's first? And that has been part of my mastery. That's been my thesis. The. If, if this were my PhD, like this would be my defense, like my dissertation, so to speak, of how do we, you know, Bob Westa constantly says, thanks to his mentor, you know, how do you, how do you make this as nimble and efficient as. And as fast as possible? Because I think there's a, there's a, there's a preconceived notion that research takes forever and that you're going to get a mess of things back and you're not going to know what to do with it. And I have been breaking down those walls pretty much ever since.

C (10:54)

I feel like you've been sitting in UX worlds. Oh, so that's actually interesting because I'd like to dig in a little bit on that point because it's getting to insights that matter quickly. What are some of the common misconceptions that founders especially have that kind of underlie their assumptions around why they aren't seeing growth? And how do you get them to, I guess really buy into the concept of one having those conversations so that they're, they can get beyond those biases?

D (11:36)

Yeah, yeah. So the first is one of the most frustrating ones, but it is so just demonstrably common. I don't know what it is, but there is a very common belief that the problem that they're facing is unique and no one's ever fixed it before. That's the first. The second is that there is no process to solve it. So my problem is special. There's no process to solve it. There's no preexisting process. And I will say 10, 15, 20 years ago that that probably was true. Um, so that's number two and then number three, that it will take forever. And yeah, like, it, it certainly does take time in enterprise contexts to like, once you have insights and once you have action items, it might take a quarter or two just to like execute it. But if you are not in an enterprise SaaS company or a software company, chances are like you can probably start executing sometimes as soon as, like the next month. And I do think that there, sometimes there's a little bit of like scarcity mindset around time and timing and sometimes that prevents founders from taking the next step, which is crazy because the longer you wait, the worse it gets. And I think that's the other problem too. But those are my big three. If you're open to seeing maybe a different way of executing, implementing, moving quickly, which is what people want, at the end of the day, I think like you're just going to have, you're going to see better success. You are going to get to answers faster and hopefully implement them faster, which ultimately addresses the problem. But yeah, hopefully that answered the question.

C (13:19)

It does. And you know, I think it's really fascinating because maybe the biggest thing or the thing that was the most resonant for me is the you can, you can decide to move fast.

D (13:32)

Right? It's a choice. It's actually a choice. Yes. Yeah.

C (13:38)

So even in large companies, I think some of the assumption comes from the perception that you have to move the whole ocean and rather maybe just take an eyedropper and try it and see. And you know, I think that's some of what people are trying to do when they're saying let's, let's go out and innovate. It is, let's just decide to move faster.

D (14:03)

Right, right. And obviously, like, I think a lot of this is driven top down. Like I do think you need the right executive support to make, to make that happen internally. But it is certainly a choice. And I feel like, you know, to the CEOs and executive leadership listening out there, I would be reflecting a little bit on what are some of the levers that you can pull in that way, because that is where the magic happens is actual implementation and execution. We can decide to move quickly. Research can happen extremely fast. I've proven that several times over. It can happen as little as two weeks. I can get people on the phone in 24 hours. Qualified prospects. It can happen fast. But I think implementation is where we see the magic happen. So yeah, yeah.

C (14:52)

Well, actually to that point, how do you decide? So you said obviously research can happen quickly. I agree. How do you decide who to interview? And does that look different for companies that might be at different stages in their growth?

D (15:10)

Absolutely. It's a great question. So this is where we get into research, design and strategy. Wah wah. Which is not anything anyone's usually ever that excited about. But maybe I actually have a. No, I'm, I'm, I'm appreciative of the fact that there probably are people who are like, yes, tell me more. It all starts with what is the problem that we're ultimately trying to solve? Is it poor long term retention? Meaning like maybe our monthly retention rates are great, but like Maybe, maybe the 12 month retention, net revenue retention at 12 months is poor. So that would say, or tell me at least. Okay, we probably are looking at, we want to speak to people who either have churned after 12 months or people who actually who have stayed for longer than 12 months. I always think about two, two things. The first is what is the actual problem that we're trying to solve and who is the most directly connected to that problem. So that's the first layer. The second layer is what's my control and what's my variable? So now we're getting into, gosh, what is it like the scientific method here? But your control is pretty much always going to be the positive version. Like, like the success case. Like, so what does a successful customer look like or a successful scenario look like? And now what are the different variables? So in the case of retention, for example, especially like long term retention, that's what we're solving for. Then your, your success case is going to be the customer who stayed for 16 months or 20 months. And your variable is going to be the person or the different types of folks who churn at 12 months or maybe even a little bit before. And what you want to understand is what creates a success or like a positive case and what creates a churn case. And there likely will be various segments that you look at. And then from there it really just becomes about sample sizes. Although I will say we really don't push for statsig unless we are looking at a business to consumer case that has like, that's like a very horizontal product. So think like the Netflixes of the world or sometimes there are like subscription based companies that have similar like requirements like a SaaS company, but they have other like operational needs but their markets are generally huge. So that is where we might push for a larger sample size. But for most SaaS companies, especially in the PLG space, most, most of us don't need more than like five interviews per segment, sometimes even a max, like a total study of 10 to 20. We can do, we can make so much progress just with that. And that doesn't have to take six months, that could take just two weeks a week if that. So I will say there certainly are scenarios like. So for example for Freshworks we conducted about 100 interviews. Very, very. But that was across four products. So to be fair, 100 interviews across four products. But we really didn't need like hundreds of interviews in order for us to get what we needed to make progress. And that I think is the key here. Now if we were trying to do like an industry report, different story, but we're not, this is for internal use. We're just trying to unblock our teams, make decisions quickly and prioritize what needs to happen first.

C (18:38)

Yeah, that actually leads very well into the next question that I have.

D (18:42)

Perfect. Nailing it.

C (18:44)

Love it. You are, you're just anticipating everything here. I think obviously we all go in with a hypothesis, right. That helps us drive our experimental design. But we've all also been in that situation as researchers, people who are actually doing interviews where things aren't going quite as you expected. And it can happen really two levels. It can happen in the context of an interview or as you take that step back to say like where do we go next? So when you say you're learning from every interview, right. They're all gold in some way. What tells you that you need to pivot and how do you make that decision? And you know, what are the sorts of things that you're weighing when you do need to switch things up?

D (19:36)

Yeah, great question. So I'll start with in general, how do we make sure that we get what we need out of the study? So I'm actually experiencing this right now. I'm running a project right now. We are, I'm working with a client. They are, they are testing two new offers to their customer base, specifically for their customer base. These are upsell opportunities. They are two new, we'll call them like products that don't currently exist. And they're, they're developing them, developing them, thinking about like what is their go to market strategy. Although interestingly enough, like they actually don't want to go to the larger market, they just want to sell it to the existing customers. We went into it with an hype, with a hypothesis that was not a, it was an unknown known. It was one of those hypotheses that we were like, oh, I guess we didn't think about the fact that we were kind of assuming that this was the right subset or segment of customer to talk to. So I'm going to give you an example. So for both of the products we made the assumption that the best people to talk to to upsell would be a super active like long time paying customer. Like that was the hypothesis. So we conduct the first five. We've got 15 interviews booked. Yesterday we conducted, we conducted the first five interviews, the rest are happening next week. And by the fifth interview we were like, oh shit. We've made this assumption that the best possible person to upsell these offers to are going to be these high, highly engaged customers. But what we're hearing is the opposite. So we have a couple of options here and thank goodness we don't have all 15 interviews at once. So I actually, I pitched to the client. There are, we have a few options. We can continue the course. We can, we can, we can do nothing. That's, that's certainly an option. And we can kind of hope and you know, cross our fingers that maybe we do talk to someone who is like, oh yeah, I love these ideas. Like I love these offers. So far that has not been the case. We haven't talked to anyone who's been like hell yeah, like sell this to me. It's been the opposite actually. So we can continue and maybe we will stumble upon someone who is like the perfect fit. Unlikely. But it's possible. We could also though instead pivot the offer. And because these don't exist yet, it's not like they're building this in the background. This was more of an idea. So it's not even an MVP version, it's just like a landing page with the idea on it and kind of getting feedback about it. So what if instead we crafted a separate offer based on the feedback that we've heard and what if we instead float that in front of the rest of the customers to see if we're closer, like if that's a better offer. Now two things about this. The first is, well, we kind of are sacrificing the original idea but I think if we're good product designers and good like product thinkers. I think we're okay with that. I think we're okay with pivoting a little bit because we're still going to get a ton of information about what this batch of people at least would care about. Now to be fair, we probably shouldn't have booked all 15 interviews at once. That was, looking back, I'm like, I don't know why we did that. That was kind of dumb. Let's not do that again. I'm very lucky that I have a client who is very much like, they're very much into like testing product ideas quickly. And so we are going to like pivot a little bit. Oh gosh. It's all about trade offs. Management. We're constantly managing trade offs. And I think the trade off here is it's actually better for us to learn more than to continue course. But there certainly might be scenarios where it actually may be better just to continue course.

C (23:18)

Well, you know, that's, I really love that answer actually because it kind of like brings up several points for me. And I mean if I take anything away from that there, the one is very tactical point which is give yourself the time and the space to pivot.

D (23:33)

Yes.

C (23:34)

Like assume that it's going to happen. And in your experimental design, if you give yourself that space, then you give yourself the universe of options which can be a. Yes. Pivot and continue down the same path or some other variety of options. The other thing, and this is totally not related, but I was reading this quant researcher just saying, like, use that Bayesian approach because essentially, you know, you're adding on to what it is that, you know, as you learn it. And again, Bayesian math enables you to essentially say let's calculate the probabilities. Right. Of what those trade offs really mean.

D (24:20)

Yes.

C (24:21)

Make it really tangible to people. So it's not just, you know, qualitative, but actually say like we've got statistical significance here. The probability is, you know, this percent and it's something that you find to be persuasive. Then you've got enough data to make a choice.

D (24:43)

Yes, yes. And I, I do think that going back to one of your early questions about like, what are some of the myths or preconceived notions or things that hold people back from like making progress is that one of the other ones is that they need a huge volume of data before they can move forward. And that's also not true. Not, not in the, not in our world. Less than a certain amount of ARR. So. But yeah, totally.

C (25:12)

Yeah. Well, actually shifting gears a little bit. So I find that one major accelerator is actually having founders and other decision makers in the room when you're doing interviews.

D (25:24)

Yes.

C (25:27)

How did you get there in your head? Because I'm assuming that you, like me, have had some experiences that showed you why. I'd love to hear more about what those were.

D (25:38)

Yeah. So this actually was standard practice at Demand Maven. So the opposite. So we would be like, founders, executives, don't come to the calls, don't attend. We don't want you there. It's going to throw it off. And I remember I used to be so, like, adamant about this. And I was this way for, I want to say, like five or six years, like a good portion of me running Demand Maven. I took that mindset and I remember, like, you know, we had challenges, of course. Like, one of the biggest ones was we would go off into a hole. We do our research and we come back and be like, look, guys, what we found. Here's things you recommend. And we. Sometimes we would get a very, like, okay, yeah, like, this is awesome. Thank you so much. It's very thorough. But most of the time we'd kind of get this like, blank face of like, great, sounds awesome. But then when it came to actually implementing some of the recommendations, there was always hesitance or pushback or, okay, like, yeah, like that, that sounds good. But like, there was always like some level of, I don't, I hate to say distrust, but just like a. It wasn't distrust as much as, like, it was almost like a lukewarm level of being bought in. Like, there wasn't actual buy in into what we were presenting. And I had always wondered like, well, is it, is it because of how I'm presenting the data? Is it because there aren't enough, like, pretty charts and graphs? Is it like yada, yada, yada, like, why that seems weird. And for years, you know, me and my team, we tried to figure out, like, what are the different ways that we can get people to buy into this. It wasn't until the man himself, Bob Moista. I actually, I actually took a, you know, first person jobs to be done class with him. Class one. He was like, you gotta have the executives in the room when you do the interview. It's a critical to have the leaders, executives, contributors, individual contributors, like having those people on, on those types of calls when we. So everything that we do is virtual. But since we're virtual, everything is on zoom. We just have everyone join the Zoom call, but everyone turns off their cameras. Similarly, whenever we run UX interviews, it's the same thing, but we really want, like whenever we do UX interviews, we have to have the head of product or the chief product officer or the VP of product on the call watching those interviews. One, because they are painful as hell to watch, but in the good way. It's good to have that uncomfortable watching someone struggle, bust through your product. It's necessary because that is, it's, it's the, it's the level of cringe that you can't unsee and you can't just be passive about. And I think that that was the difference for us. So once we started getting like CEOs, founders, executives, individual contributors on the actual interviews themselves, that's when there was a switch moment that happened for them where they could no longer be passive about what they were hearing. And I think that that was like the magic. Now you still have to condense what you're hearing and you still have to translate a little bit. So we would actually bake in debriefs after every interview. Let's talk about what we saw or let's talk about what we heard. Our clients could no longer ignore. Maybe like the, like we were making it too passive for them. Now it's active. And now, to my great delight and joy, almost immediately after interviews, they will start executing like, especially with ux. UX is particularly powerful for product people, I find, and also founders, because as soon as they, after like the third or fourth interview of some of watching someone struggle through something, the person is already like coding fixes and like implementing changes like on the, on the spot. And it's just, you can't, once you see it, you can't look away. And I, and I think the way that we did it before, it was just really easy to look away and kind of be like, okay, yeah, I guess I get it. But I really didn't get buy in until people joined the calls. And so now I'm a believer. I'm a believer.

C (29:46)

I'm laughing. But yes, I've seen that. When you get people to really understand and then I think the real kicker is saying, oh, and by the way, looking at the analytics, this is costing us x million dollars a day. And then they really get it.

D (30:02)

Yep. Yes. Yeah. And then when you monetize it or, sorry, when you quantify it. And that, and that's also part of why I don't believe in just doing qualitative. Like, I do think you have to have quantitative. The Both of them have to talk. But I do think that you can't just look at one, you have to add the other. And that that just. It tells a different side of the story. But, yeah, when you quantify it for people, that also gets people moving.

C (30:31)

Yeah. Money's motivator.

D (30:33)

Yeah.

C (30:33)

Oh, yeah. So, you know, we talked about Churn a little bit, and I wanted to go a little bit deeper there because we often find it very uncomfortable to talk to who don't love us anymore. And one of the big things that we have to do in kind of embracing that pain is just really understanding, like, what went wrong. And so how do you help customers to get beyond that discomfort in that interview process to uncover the reasons behind churn?

D (31:09)

Yeah, yeah. So there are a couple of approaches that we take. So the first is, when it comes to a Churn interview, we tend not to do like a full hour. Usually we keep it short. We keep it like 30 minutes. And part of that is because the churned customer usually has a perception of, like, if it's longer than 30, they feel like they're going to get grilled or something. Like, they feel like they're going to be like, I don't know, put on the spot. We do compensate pretty heavily for churned interviews. We compensate in general. That is actually a recommendation that I also took from Bob is to incentivize interviews just to ensure that you get a variety of people. What I highly recommend is when you conduct that interview, to get a quick read on, is this person upset and. And do they want to vent about it? Because sometimes you just gotta let the person get on the phone and just let them vent, just let them get it out. It doesn't. Like, you, like, you almost kind of have to, like, pause your personal script and just listen. And then the second thing is, I want you to imagine that you're a dispassionate analyst. You are a dispassionate analyst who is compassionate, which I think is different. It's perfectly okay that they churned. It's perfectly within their right to. Your job is to almost be like an omniscient, enlightened being. And I know that sounds wild, but that's kind of who you have to be and resist the temptation to dig into. Oh, but did you know that this feature exists? Or you're using that wrong. That's why it doesn't do the. Like, you kind of have to resist that a little bit, and you really have to dig into the psychology of what happened. And usually what you find is it's never just one thing that makes someone turn. It's usually like five to ten things and they just stack. And your job is to make observations, but to never force that person or make that person feel like they're being urged to do something or to change who they are or their behavior or what have you. Because they've turned. And if they want to come back, it's going to be because that, the perceived value of your product is now, it's now worth it to them. Again, like, that's, that's going to be what ultimately makes them come back. But your job is to make note of, oh, they didn't know how to use that feature. That's a note for product marketing and for product and possibly ux, depending on the context. Um, so your, your job is to kind of make those notes and to make those observations and then gently clarify if you really, really need to, but without making it feel like you're trying to sell to them because you ultimately want to protect your brand perception as well. Usually what we find though is if, if you've done a really good job, that person ultimately may or may not, may not come back like that. Like, it would be cool if they did, if they reactivated. But usually what ends up happening is they end up being like, wow, that felt so good just to talk to someone and just tell someone about this. And that's how you know you like, did a good job because that person expressed themselves and they didn't feel judged. It's. And they feel like that feedback is actually going to go somewhere and do something. And hopefully it actually does. It doesn't just like end up in a void. But yeah, that's how we think about churned interviews. They are different. It's definitely a little bit more therapy. Like, and I think like being the dispassionate but compassionate analyst is how I recommend folks think about it.

C (34:36)

Well, you know, it's interesting. So I'm gonna go back a question because I want to pick up a thread that I have definitely lived, which is around the fact that it's more than one thing that usually causes people to churn. It's usually more than one thing. And there's a laundry list of, you know, situations where that's the case. Prioritization is difficult. How do you figure out the trade offs in order to recommend, like how you prioritize addressing some of the things that you're seeing that contribute to Churn as an example? Right. Could be whatever it is. But Churn, I think is What a lot of SaaS people focus on.

D (35:17)

Yeah. So I do find that there are different types of churn. So holistically speaking, like I think we all probably collect churn reasons. I think we all generally have like, you know, the list of typical churn reasons. So like maybe not enough features or not the right features or I don't need it anymore or what have you. And usually what happens is we almost ignore those, to be honest. We kind of just like walk through the process of churning like what, what were all the things that led up to churning? And usually what we find is there it breaks down into controllables and non controllables. Like there's contextual things that you cannot control. Someone loses their job, someone changes job, someone like there's like a million things, someone starts a family or there's just like a number of contextual non controllable things that there's not really anything you can do. Then there are the controllables. And this is where I encourage teams to focus. And this is where it gets some folks might say hairy, I think opportunistic. I think there's opportunity here. But then it comes down to usually why people leave is because the product satisfied an initial job to be done or set of jobs to be done. There was some job that they came to the product for and it satisfied it. But then new jobs popped up and that is where there's opportunity for product to expand or to grow in some kind of way. And some of those new jobs are very in line with product vision and business business vision. And some of those are not. And so this is kind of where you have to one, tally those opportunities up, like actually quantify them if you can. Like how many times did this come up? Over 30 churn interviews, for example. And, but then it now becomes a question of, and this is where I think executive leadership certainly needs to be involved at this point. But then it really becomes about, okay, where do we steer our ship From a business and product perspective that helps create better long term retention. On the flip side, there's going to be stuff that's just easy to fix. And like, so I like to separate these by there's value generators, which is what we just talked about. So new jobs to be done to tackle new ways to keep people with our product longer after we satisfy that initial job value generator. But then there's also quality of life improvements. And you might have like a list of 50 quality of life improvements. Like that button doesn't work exactly the way that people think it should. Or that feature is great, but it needs like these three extra toggles for them to actually get more value out of it. That type of stuff. Or that's kind of where you get into. You, you, you have to, you have to start thinking about but what ultimately is going to make people feel like, yep, this was worth fixing and my life is improved and it's worth for me to stay. So that's how I, that's how I think about those controllables versus non controllables value generators versus quality of life improvements. And then when you get to the value generators, that's where you, you, you kind of really get to put your business vision and your product vision to the test and then also market opportunity because there's so much potential for expansion and growth, but also solving jobs in ways that other products just don't. And that's what gets me excited about the product strategy side and also go to market. But I digress. So that's how I think about it.

C (38:58)

That's really interesting because I almost think of it as you create a menu for yourself of here's the top five things in each one of these pillars that you could potentially address and, and put a number next to it. And it helps you to just like understand like really, what are those trade offs?

D (39:17)

Yeah. Especially if, especially if you can, you know, churn surveys are great for this too. You tend to, you do tend to get more responses than just interviews alone. But if you can categorize those and then also quantify them like here's how much MRR or ARR we've lost because of these reasons, that also makes that discussion more compelling as well.

C (39:39)

Yeah. Oh, that's so great. Anyway, I really, I'm so thrilled I'm going away from this conversation today. Thank you so much. With a ton of ideas that I can take back to even my teams to think about. So thank you so much.

D (39:53)

Absolutely. All pleasure is all mine.

C (39:57)

Well, that's awesome. We'll have to continue the conversation at another future point but for the time being, how does someone learn more about you and your work and your thought leadership and demand maven and all of those things.

D (40:09)

Yeah. So you can find me in another. In a number of places. I'm demandmaven IO is my website but I do livestream on LinkedIn. I really have been enjoying signing up for new products and then live streaming the experiences like what that actually looks like and pointing out some of the things that are really successful design and UX wise and other things that could be improved. So it's. It's been a lot of fun. LinkedIn is probably the place where I'm most active social wise. But yeah, I also do have a newsletter called the Work and I'm about to kick that off again pretty soon. I tend to take seasonal breaks and then I also have a podcast called In Demand that I recommend that you listen to. Not a very unique name though. There are a million in demands. But I'm sure we can include a link to the POD below or something.

C (40:54)

Yeah, I'm sure we will. And that's awesome. That's so great. I just am thinking, huh, A live streamed cognitive walkthrough. That's what I translated in my head. So that's great. Thank you again and I will close this out for the day. Awesome.

D (41:10)

Thank you so much.

B (41:12)

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