Ep # 195: Customer Success Has Become a Consultancy ft.Elise Miller (VP of Customer Experience, Daloopa)

Elise Miller  00:04 When I say auditability, what I mean by that is every data point that we give to a customer, it's hyperlinked back to the original source document that we got it from, so they can have a clear audit trail. Intro  00:17 Welcome to Across the Funnel, where we dig into concrete go-to-market moves across sales, customer success, and account management, so you can build revenue that lasts. Brought to you by Hyperengage and Dextego. Adil Saleh  00:34 Hey, greetings everybody. This is Adil across the funnel. Yeah, like I said, I always mention I've kind of grown with this this podcast started four years back when I was you know young, wild, and you know single. Now got a daughter, like family, and a lot of lot of this AI. So much has changed, and thank you very much for giving it a rough listen always because we have always been candid. We've always been, you know, genuinely concrete about topics and all. And a lot of them went controversial with a lot of these these leaders that are super opinionated across what they're what they're thinking, what's right, and that's that's kind of right with us. So after talking about like so many customer success, like traditional customer success, traditional GTM tooling, like in customer support, sales, sales intelligence, marketing, and all, we were kind of thinking that how we can explore more topics with this big shift of AI, how things are transitioning into the financial investment, into you know thinking about like financial agents that how they're being deployed across larger scale enterprises. So that's why today we are going to talk about Daloopa. We're going to speak with the VP of Customer Success, Elise, hugely experienced in the customer success for the last, I would say, almost a decade now, and and I'm super stoked to have this conversation. Thank you very much, Elise, for taking the time. Elise Miller  02:08 Yeah, thank you for having me. Excited to to be here. Adil Saleh  02:12 Love it. So, looking at your prior background, Elise, I know that WalkMe, in a nutshell, is kind of a thinking of like data analytics infrastructure. If you think like that, they're like in the kind of the same category for someone like in a CS to transition from. So, how was your journey coming off of WalkMe, like almost two and a half years back, and now, I mean, leading a different team in a different maybe a category. So, how was that experience, and how did like what kind of norts you have? Elise Miller  02:42 Yeah, yeah, absolutely. So I mean, WalkMe and Daloopa are two very different companies, different sizes, different spaces. So WalkMe was more of I would say an in-app training and onboarding tool. So we would build out step-by-step walkthroughs, tooltips. We mainly worked in Salesforce and Workday, but then we also worked across any kind of HTML-based application as well. Versus Daloopa is a financial data company, right? And so, you know, for me, I was at WalkMe when the company was quite large. I I was there for seven years, but it grew a lot while I was there, and it was an amazing company, it had just gotten really big, and it had also gone public. And so, when I was looking for what I wanted to do next, I wanted to challenge myself more with joining a startup where I would essentially be building a function from scratch. And I was really drawn to the simplicity of the Daloopa product, and also, I would say the company's approach to product innovation. So one of our core values is that we always want to push ourselves to earn the right to do something new product-wise. So the company is really focused on getting the basics of the existing product down really well before expanding to to doing something new, and that really shined through during my interview processes with the co-founders, also. So I think the background that I had at WalkMe was helpful because I did have some experience building a team from scratch there as well. So we had a new services or implementations team at WalkMe, which I helped to build. So I had a lot of good experience understanding like how to recruit the right people, how to build out the right processes and playbooks, but you know definitely a shift where you have to be very scrappy coming into an early stage startup where there aren't really many processes at all in place, and and you're really building the team from scratch. Adil Saleh  04:41 Right, right. So now, you know, we like putting like GTM and customer success, you know, to be specific in perspective, and thinking about financial data, financial analysts, financial agents. How do you like for our audience? Just can you just explain like how financial agents. Like how skeptical a financial analyst has to be using AI or using platform. I'm sure like Daloopa has these AI capabilities to in this category. How critical it is, like mission critical it is using AI or leveraging AI, having AI or any tech set with AI as an augmented solution putting into this and how hard it is like on the GTM side to educate and you know then if people buy like the to make sure like they adapt to the platform they're potentially using it and all of that so how was the journey like from the GTM perspective? Elise Miller  05:36 Yeah, absolutely, it's a great question. I think you know our space is pretty. Our user base is pretty divided in terms of maybe their appetite or their readiness for AI, but there it's advancing very very quickly. So you know we're working primarily with hedge funds, mutual funds on the buy side, and then on the sell side we work with large global banks, so equity research, investment banking teams and more, and so anyone that has financial modeling as part of their workflow, it's really starting to catch on how AI can benefit them just from an efficiency perspective, being able to cover more companies, do more research. So that's been exciting to be part of, and I think yes, you could be educating a user who, you know, just got a license for their first AI tool internally or their first LLM internally, and so our CS motion has to adapt to that, where we're not only showing them how you know the Daloopa MCP can be plugged into the LLM that they're using like from scratch, but we're educating them on how to even maybe use the LLM to begin with within their workflow, right? Versus on the other end of the spectrum, you have users that are pretty advanced; they've already been playing around with it, and we're educating them more on how the Daloopa MCP can actually benefit what they're already doing in their more advanced workflows. So the edge that I would say that we really have as a company is that we have a very deep and trusted fundamental dataset. So we cover over 6000 global companies, and then what we also provide is auditability and a high level of accuracy of the data. Right. So when I say auditability, what I mean by that is every data point that we give to a customer, it's hyperlinked back to the original source document that we got it from, so they can have a clear audit trail. Which again, that just helps with the trust. When to your point, maybe in the industry that we're serving, there is a lot of that question of, oh, am I going to get a wrong number or something like that. Our our data helps to bridge that gap. Adil Saleh  07:45 Love it, you know. And you also touched a little bit on my question that I was also thinking, like on top of it, like we starting from 2020, I would say 2022, when GPTs came, like the real models that are that came outside of support. Like previously, it was just like customer support. At conversationally, I was like doing a lot better, and then we started having longer contextual windows, better reasoning. Then we had like models that came up and said like, "Hey, this this is something that we can trust in terms of like numbers and you know all these economical events. Then there's a lot of sales intelligence or GTM tooling came on in softwares and all these sorts. So now at a point where we're thinking that hey, there has to be some guardrails. There has to be some, you know, safety concerns and all of those. So now thinking all about this, where AI in terms of efficiency, in a good or bad or ugly, always going you being a CS leader, how you're thinking. Of course, Daloopa has has a greater impact on that too, because of course you guys are working with AI and your customers are working with AI. You have these AI capabilities. How do you see this industry moving with this like new bubble of guardrails and you know governance and every government is now thinking of like putting on some checks and you know even like all these like three generative AI leaders that we've been listening to Anthropic and all these these folks they're talking about like orders. So how do you guys see like in in the financial investment? I'm sure you're working with public equity firms. A lot of these these companies too. So that's a greater concern. So how do you guys thinking internally as business leaders, Elise Miller  09:22 yeah, absolutely. I mean, I think it's really our our job to adapt to that, right? It's pretty impossible to predict necessarily how these different types of you know potential regulations and things like that will affect things. But what we like to keep as a core value is that we're just meeting the moment in terms of both where our customers are, but then also what is happening in the market. And right now, what's happening is that there is this big push in the space that we're in to adopt AI, and so a lot of our roadmap is based around that. We recently went GA with a. A financial agent called Scout. That's actually an Excel-based model builder, and there's several other capabilities that our our customers can use it to also update their models and do other things as well. But you know we're seeing a lot of appetite for this type of thing, so we're shaping our roadmap around the assumption that that type of appetite will continue. Again, we have that edge because of the greater accuracy and trust that we're providing our customers. So, you know, impossible to predict exactly how it will how it will shape things. But if there are significant, you know, guardrails put in place or regulations and things like that, things may shift. And luckily, we do have bread and butter products that are kind of evergreen, like our Excel add-in for users that might not want more of that, like you know, LLM AI based workflow. We have other options as well to help them. So I think the business has a good long-term view due to that. Adil Saleh  11:01 Interesting, and just one last question on this. I know there's so much AI and buzz and everything, but at the end of the day, you talk, you guys talk a lot about data infrastructure, and when it comes when it comes to financial data and data analytics is altogether different when it comes to financial data. So, looking at infrastructure, like all of this, like how secure it is for all these these customers, and in terms of not being having a lot more dependencies on third-party AI tech stack or any third-party resources. I know you you guys have like multi like company data that's coming from all different places, but there has to be some data. Actually, I've read some of some of the blogs that speak volumes about like data and infrastructure. That's that's one of your core product-wise, as you mentioned in the beginning. Like product is really infrastructure-wise, pretty pretty in good hands. Elise Miller  11:55 Yeah, yeah, absolutely. I mean, I think the way that we think about it is exactly what you said, right? So we are this highly accurate, deep, deep fundamental data infrastructure layer, right? That we're covering over 6000 global companies. It's fully auditable, as I mentioned. I think what are the way that we view the infrastructure layer is that it helps them with whatever tasks they might be doing that are recurring and maybe manual to give them some time back on those tasks. So, for example, typically our end users, when they're maybe new at their at their fund, they're having to initiate coverage on new models that they're covering, and then they're having to update those models during earnings, which would normally be highly manual and very time-intensive tasks that take away from time they could spend on other strategic tasks. So, being that data infrastructure layer, we get to not only give them the data that powers more efficiencies during those workflows because we're able to just get the data immediately instead of them looking for the data points manually. But then we're also introducing and have introduced other workflows that we that we help them with that are also very manual. So with the MCP product, if they're doing research on other companies they might want to cover in their space or they want to do some benchmarking exercises, our MCP data powers powers their ability to do that in a much more efficient and accurate way. So we are that infrastructure layer across several different product surfaces now, across our Excel add-in, across our MCP, across our Scout agent. Kind of however the user prefers to work, that's where we're serving as that infrastructure layer, right? Adil Saleh  13:44 Right. Depending on, of course, the use cases and depending on where they're playing, that what kind of data they need. Perfect. So now talking about post sales GTM, and traditionally we we call it customer success, and now it's being analog to many teams. Like it works with customer, like account management works with sales, and works as an analog. When we start this podcast, it was a new thing. Like there was like handful of even platforms like Gainsight and Vitally and all these platforms that people used to like say, "Hey, we're using this in regarding with HubSpot or sales for now. A lot of these customer success platforms are now being merged with sales support and CRM categories. CS leaders are thinking about like hiring forward deployed engineers, like technical resources more than like conventional customer success roles. Account managers emerging. So, how do you see this like a customer success being a being a VP yourself? Being is that a shift that is happening in a like taking it to a good leap, or or is something that you know a lot of it's taking things away from a pure play customer success, which was meant to you know not like on the economic side, like retain or expand customers or getting the you know good feedback, but it is it was more. To you know, help customers be widely successful with the product. So that was a core value of customer success when it all began from from the Salesforce times. So how do you see it now? Elise Miller  15:13 Yeah, great question. I think you know from a customer success perspective, it's really no longer enough just to be doing onboardings and QBRs and renewals. Customers are expecting more than that now, and with AI being so deeply embedded within the customer base, really, really in any space now, of course, to different degrees, but especially in our space, what customer success needs to do is actually be consultants and not just guiding them through a basic playbook of onboarding and and renewals. The reason that's so important is because, like I said, we could have users who don't know anything about AI and they've just been given their first license internally, and now they have Daloopa. So we're literally educating them on the ground up in a very consultative way versus users who are pretty advanced with AI. So it's no longer enough to just say here's a product guide on how to use Daloopa. Go, you know, go have fun and use it. We actually have to be trusted resources that understand how AI functions within our customers and advise them on that. I think you know you hear like forward deployed engineers (FDE) is this really hot term around tech right now in different spaces, and I think sometimes people do try to kind of force that in a sense. But there's but there is something to the forward deployed concept in general, even as it applies to CS, of like someone who can really go on site and be deeply embedded with the customer and truly understand their workflows and give that feedback back to product, even if it's not an engineer, even if it's not someone that can you know code per se, but someone who is really understanding the workflow of the business. So within Daloopa, that's the angle that we're trying to take within customer success is to not just consult on, you know, the product and the onboarding, but to do it within the ecosystem of the AI tools that they already have. Adil Saleh  17:14 So, you know, you know, trying to work where they work, like integrating. I remember back back in the days when Grammarly came, and I was just listening to her. I mean, that's we were using Grammarly when everyday life, but there she was like, I think she still is a female led. Grammarly is a female founder, right? So there was an advertising, and I was listening, and she said like, "We work where you work. So it's it's it's it has to be like that. And and at the end of the day, yes, data plays a part. Like you see the product analytics, you see the exceptional behaviors, or some lack of behaviors, or some triggers that you might set as it's a customer success starting off, maybe post kick off. But at the end of the day, there's so many other variables that come into play as well. When when when we build this platform, Hyperengage, by the way, it's it's sort of a customer success platform to begin with, and now it's more like a GTM all function. So what what we did it was like in the beginning it was more product analytics and you know building data and all of that. Now it's more qualitative as well, like you know, seeing like how customers are engaging with the content, how they're getting knowledge, and you know how they're engaging with support, how they're engaging with sales, and what kind of commitment sales have made, and all the business goals that they shared in the first place. How we, as a platform, as a customer success manager, as partners, need to evolve with them with their goals to make them win. So, what kind of data? Like, getting back to the question, like how? What kind of data that you guys are looking at this point? Maybe talking about some mid-sized hedge fund, mid-sized public equity firm. Elise Miller  18:57 Yeah. Adil Saleh  18:57 So, Elise Miller  18:59 yeah, absolutely. I mean, just to comment on what you said, I really agree with that. Where it used to be very easy to just say, "Oh, you can look at you know the number of model updates they're doing with the tool, or you can look at the usage of the tool alone, and that's enough. But now, to your point, it's like this much more complex thing where it's how does the tool fit into their existing tech stack, like you said, and it's a little bit more qualitative in terms of how they're getting value rather than just you know the number of actions or the number of events with the product. So, the way that we look at ROI and usage has definitely shifted over time. When we previously just had our our Excel add-in it was pretty simple from a sense you could really look at yeah how often are they updating models with us how often are they logging into the platform are they consistently doing that usage over time versus now we have multiple products so it's spread out across a larger surface area. I think the key principles of the usage still remain intact, right? It's frequency and consistency of yeah, how maybe a hedge fund is using us for initiating coverage or model updates or research with our MCP. But what we are looking for now more is breadth across the whole product suite. So, how many surfaces are we actually providing value on, rather than just ideally than just one product? We want to look at are they using us across the whole product suite and all of our capabilities. But then also, what I'm pushing the team towards is the product usage is not just enough to look at ROI. We also have to understand did they get value on the original use case that they purchased us for. So, did we give them time back in their day on what they were looking for, so they could do something else, maybe even more strategic with the time that they have? So, yeah, it's it's shifting as you as a company goes multi product. It's it's not just enough to look at you know one product and and usage. You have to look at like the whole ecosystem. Adil Saleh  21:03 Interesting, and building the layers and success metrics around those for CSMs, of course. How many book of business on average your CSMs deal with? Like so, Elise Miller  21:17 on the on the buy side, I would say so we sell to end users directly, kind of on a one one to one license seat for the license basis for the most part. I would say each CSM has maybe 250 to 300 users. Adil Saleh  21:32 200 to 300. So it's it's it's super. It's it's super of course super crowded. But I mean, if you have success metrics laid down for each segment and segment them, and then you have like value pass starting from first value pass to you know. Of course, ultimate goal is to initial goal is to retain them, and once they see the value and see then. So, is expansion also owned by CS or is it owned by sales? Elise Miller  21:56 It's owned by CS for some of our smaller accounts, more SMB kind of mid market, you could say, but then it's owned by sales for our what we call like expansion and strategic accounts. But I would just emphasize it's it's such a collaboration across the board. So one of the things that we incentivize our CS teams on is if they can identify potential leads for you know someone new at the company that we haven't sold to yet, that they find out from our existing user base because that that happens quite a bit. If they're able to do that, that's actually something that we do incentivize and compensate them on as well. So it's not. I don't really believe in this. You know, CS owns or sales owns or not, I think it's always the two functions have to work together to make that happen. Adil Saleh  22:45 Together, yeah, together. So now thinking about you mentioned touched on a little bit of this this data point, like a lot of these, a lot of your like I would say deep down users, not on account level, like user level, they're shifting companies like six months. Yes, like you have triggers set for because they can be a real good advocate for you guys going into this domain. That's how a lot of a lot of customers we rolled in for Hyperengage. By the way, you know, profit. So all of those datas that are like not coming from the systems or tech tools, like per se, like quantity, but they're external data sources, like their data points that are signaling growth, might be sharing a lot of things. So, do you guys have those you know signals in place for CSM that are managing 250 for a big number? Elise Miller  23:29 Great, great, great question. So, you're absolutely right. That happens quite a bit with our end user base. I think, especially on the buy side, but on the sell side as well, where it's pretty common that they're that they're jumping between funds, or they, you know, have another another fund that they're going to. And what we actually do is we do provide them with a transition account access while they're in between the two roles. So what that does is it's a little bit more of a limited access than they would get if they were a full-paying user, but what it does is it keeps Daloopa fresh and it keeps us top top of mind while they are looking for their next role, and we actually find that we have very good success with then converting that user and having them come back to Daloopa at their new company. So that's something that has worked very very well for us, just in terms of you know a lot of organizations will say hey you you you know churn so to speak right but it's like they didn't they didn't really churn they had a life event that happened and maybe they loved using the product so that has been very high ROI for us in terms of the signals you know honestly we do find that out in different ways. Sometimes it is finding out from other stakeholders at the company, or you know, we get an email bounce back or something like that. But we do have certain tools that we use that will let us know kind of big. I would say just more changes or or things happening in the hedge fund ecosystem, for example, where we. Can get some some of that information as well. Adil Saleh  25:02 Yeah, Elise Miller  25:03 but yeah, that's one of the best systems I think we have in place at Daloopa is this transition user flow that that has really worked well for us. Adil Saleh  25:13 Love it because of course a lot of times people leave and they like take months, like a quarter to put up a LinkedIn post. So that's not a good like good accurate signal coming off of social media because they when they settle in only then they you know make a pretty post about their new position and everything. Elise Miller  25:31 Yes, and what we what we aim to strive for is that actually you know they they often will let us know voluntarily that that's happening. So rather because to your point, in our space, LinkedIn usage is actually not even that consistent within our space. Especially, you know, someone posting, "I left here and I'm going here. It doesn't. It doesn't always happen, or it happens very delayed. And so, what we rely on is that we're providing so much value during their initial experience with Daloopa that they reach out to us saying, "Hey, I've left. Could I get this this type of transition access? So oftentimes, it's organic from the users themselves. Adil Saleh  26:08 Okay, perfect. So now, talking about overall culture at at your success team and overall as as team, of course, you guys are Series C, right? How big is it? Like around 100, 120 people, 120. Elise Miller  26:23 Yes, with GTM, with the GTM team. Yes, yeah. Adil Saleh  26:25 Okay, so overall, because you'll have like I know that it's always good to have things written on the wall and put in the DNAs and operating principles and all, but all it comes down to what you live with every single day and how people feel and made felt when they work at organization. So, what kind of those values that you guys truly live every single day, and you guys are known for? Like, I go into an episode next week and I talk about Daloopa, and if they know you, what is what that one thing that they will think about Daloopa? Culture, team values, all of that. Elise Miller  26:58 Yeah, yeah. I would say our one of our biggest values is no BS internally, and what we mean by that is, you know, when I started, one of the things I really loved about the culture was that, sure, people have different, you know, titles and and whatnot. We have our our co-founders and we have VPs, et cetera, but it truly does feel like an extremely transparent environment to work in. So there was never any kind of hesitation of, oh, I can't speak to our our CEO Thomas Li, or I can't speak to our our other co-founders. In fact, they so welcome feedback, and they so see it as okay, you know, maybe this is something that I didn't think about, and I should change my perspective based on what I did before. And I think that's actually kind of rare sometimes when you're talking about the C-suite or or various co-founders. And so the way that that ties back to the no BS value is that they ask for no BS when we're giving them feedback, and then we also accept, or excuse me, expect the same back from them. So we oftentimes, as a leadership team, have debates around how things should be done differently, and nobody takes it personally. It's very just open, but people will say what they think. You know, they'll they'll very directly say, "Hey, I don't think this makes sense, or I think we should do something differently, and so that value is very helpful because it doesn't feel like there's this political layer that kind of makes people afraid to speak up. Adil Saleh  28:32 And I love this. I have some questions coming from the team, and that was also related to this. But I have one more question on this. Like with this AI and all of this, everybody can be factual and logical, and you know a lot of these these opinionated things. And I would not put it as conflict, but when it's on the one side, there's one thing that you want to do, and there's another side. There are people that think that that might not there might be a different approach to it, you know. And they are they are AI equipped, as so as you, and they are also doing their own research, and their effects and logics, and these scenarios, how you guys are coping up and being leaders, and of course, you've got to be a bigger person in the room, being co-founder, being a bigger person. I, being a being a leader, have like five, six people working for me, and always like if something opinionated comes and I have an opinionate, but since I have this, like maybe they think that I'm the boss, I don't think I'm the boss, but I gotta I gotta see it this way. Like I don't want to press myself my title on it and let them be free with whatever they think, whatever is the reason they're using Claude or AI, whatever being recommended. So how do you deal all of this? There's one question on this too by the team because this there is no monetization to this podcast, right? So ask my team to hey, this is a guest. This is the team. This is the leader. You can get a lot from this this girl. She has this experience. I'm gonna learn. You're gonna learn. You come up with your questions. Maybe whatever is makes you curious on the bedside or whatever. Put in the question, and we'll ask these questions to you. So I'll bring in that this question too. But this scenario, quite. Often happens. What do you think? Elise Miller  30:02 Absolutely, I think yeah. Everyone's going to have a different opinion. One of one of the things that we try to hold as another value, I would say, is disagree and commit. And so we will have some kind of debate about yeah, like you said, how we should use AI internally or how we shouldn't, or a new product that we're building, we have those debates constantly, and I think that's healthy. But one of the things that we do well is we say, okay, we've spent two weeks talking about this topic, right? Who is ultimately accountable for making a decision here? And that person, we say, hey, this is most related to your team or your function. We ask you to make a decision, and we're going to all move towards that, right? So even if maybe I have a different opinion from what we decide, I think we've built a good culture in that way of saying disagree and commit. We're doing it, and and we'll pivot. If if we if we fail, the worst that happens is is we pivot from there, Adil Saleh  31:01 it's it's it's it's just like any sport, right? Yes, you know, athletes they do the same way. Like there's so many people they bring in their intuition, they're bringing their options and all. But there is one captain who's a leader who's the owner of that decision. He's going to own it, so it's going to be a team failure, or of course, a lot of lot of talk about like team failure and team success. So not many people or many athletes that I've seen they talk about like team failure because at the end of the day it's failure for the whole team if something doesn't knocks out and other way around. Perfect. So there is one question related to this from the team. My team that they're young enough, like they're back in Pakistan. We are more than 70 million people under the age of 32, so between 18 to 32, there's almost like almost 18, 19 million people. So 60% of them are AI enabled. They're they're just like fresh grads. They're one, two years into their experience. They know how to build skills, automation, using Claude and all of that. So they are so much, maybe sometimes overly reliant on AI, so that's why they have this question. The question is like, how do you deal with the the first default impression that you get from your boss or somebody senior on any given test that gives you know impression that hey, this is being you're too over reliant on AI or you didn't put you know more thoughts into this task and let AI to do all of this and and it's just like like what they think is it's just like a somebody engineer that doesn't read the code using AI and building products using AI. You remember those days when vibe coding was big thing like lovable and all these rapid things like not too long ago, but now they're like people not with with coding background. They can still build the product, but they cannot read the code. They cannot decode a lot of things. So that's the first default impression that they think that they get and how they can overcome it. Elise Miller  32:59 Yeah, yeah, that's a great question. I mean, it's funny you mentioned because even internally at Daloopa, sometimes we'll give each other a hard time of like, hey, you wrote this memo with AI, and it it comes across that you wrote this document with AI, and we can tell, right? I think my philosophy on that is that if it's a good quality output, then like, who cares? To be honest, of course, your examples about coding and all that. Yeah, you have to have the skill sets to be able to do the job long term. But I think internally at Daloopa, the way that we look at it is really, if I write a presentation or a memo using AI, but it's good and it actually helps me in my research process and it helps me make it more whatever visually appealing, more accurate, or whatever. That's something that I encourage with my team, and I think that our leadership team is very open to that. It's just that there's a there has to be a guardrail of where it's just sloppy work, you know, potentially. So if you didn't double check the numbers that are in your presentation or something, then that then that is going to come come across as very lazy. So I think for folks who are young, absolutely lean into the AI you know parts of your skill set because that is the way of the future. It's happening. It's it's already happened. But I think just keeping in mind you can do that in a way that's still tactful, and as long as you're double checking things, and there's kind of that middle ground of, well, does this actually does this output that I got actually represent my full thoughts, or is it missing something? I think as long as you're doing those types of checks and asking yourself those questions, then it's a great thing to continue, and hopefully also that that issue you described of like bosses or or folks that are more senior not being accepting of it, I'm sure that is the case in a lot of industries. But I think in tech, most leaders or go to market leaders that I've seen are like, "All right, I want my team to use this because it makes us more efficient. Adil Saleh  34:58 Yeah, if as supposed. To help us do better and smarter, then what's the question, right? You know, if somebody comes back with in like three minutes and comes like back with a killer deck or presentation or some proposal or ideas and anything, who cares? Like it's just as simple, you know. So perfect. So last thing, I'm sure you have a bunch of roles, and you talk about your team, like no BS, no politics, straightforward people that are hungry, they have the appetite and teachable that they're good enough. What else do you think? Like a lot of these customer-facing roles, there's one that you have in here in SF as well. I think that's more for strategic sales sales role, so could you tell to these all these folks that are looking to switch to Daloopa, listening to you know this episode and knowing more about your team culture and you specifically, what is that? A couple of things that you would tell them to do before applying. Elise Miller  35:58 Yeah, yeah, absolutely. So we are hiring within the customer success teams. Actually, our buy side and our sell side teams primarily in New York, but we're open to speaking with candidates who are remote as well. It's just our offices in New York, so we have a preference for that. But I would say for people who are interested in applying, you know, when you are going about the interview process. We're we're looking for people that do have at least some experience with some of the personas that we sell into, whether that be hedge funds or banks. I think that with where the company is and how quickly we're growing, we do need that type of experience from the people that are coming into the CS org right now. But when you're going through the application process, I think really, really thinking about how does my experience relate to the role, and how would I uplevel and maybe suggest new ideas for the product and and new workflows that should that Daloopa should be looking at. And I think that goes for if you're applying to any kind of you know startup company, what really makes you stand out from other applicants is have you done your homework on the product, understanding and coming with a pitch of how your experience relates to the product, but then also even coming with some suggestions for where the product could go, or from your experience, what what else do you think we should work on? That really distinguishes applicants in my mind, and then someone who can go very deep on, you know, answers to things and asking us questions where it's not just like we were just talking about AI, you know, the the pre-canned questions that you might go into a CSM interview with, but where you're truly asking things that are that are thought provoking and show that you've done your research on the company, and those are those are tips that I think apply to really any any space and any position you get into. Adil Saleh  37:50 Yeah, love it. Perfect. It was you know quite insightful conversation. Love the energy, and I love the way that you approach all of these questions that are sometimes kind of naive because we only had like three or four platforms in in the financial space, so that's why I wanted to explore more. And it was good to learn more about you. And thank you very much for giving me this all of this knowledge and all that you carry and keep doing this. Elise Miller  38:13 Yeah, thank you so much for having me. It was great. Thank you. Adil Saleh  38:17 Have a good rest of the day. Elise Miller  38:19 Thank you too. Outro  38:21 Thank you very much for listening to Across the Funnel. If you got one useful GTM idea out of this show today, please share this with a teammate and hit follow. Explore Hyperengage at hyperengage.io and Dextego at dextego.com.