Philipp Stelzer: Welcome to another episode of the RevOps Lab Podcast. I'm here together with Janis, and our guest today is Nik Garza from Kleer. So, Nik, warm welcome. Great to have you on the show.

Nik Garza: Thanks, guys. Glad to be here.

Janis Zech: Yeah. We first met last year in person actually in San Diego at the RevOps AF conference twenty twenty four. It was great to meet you there. And then shortly after that, you became hopefully happy Weflow customer. And now you're here in the podcast. So, they are really good. Back when we first met, you were busy with the merger. Basically, you worked at a company called Membersea and you merged with a company called Kleer. And now you're one company. And I think back then, one of the big challenges in twenty twenty four was that you led the whole also merger of two CRM systems, which I think thinking about, like, things to do when you work in RevOps, this certainly is in the top three, I think. So, just curious, Nik, maybe you can introduce yourself, you know, give us a bit of an idea of your background and, yeah, what's keeping you busy now that the merger is finally done.

Nik Garza: Yeah. So my name is Nik Garza. I'm the director of revenue operations. Called Kleer Membracy right now is just kind of like a stepping stone to our new rebrand that should be launching in a few months, but hey, you. So, yeah, the mega guys at RevOps AF last year, we had already been talking about, you know, working together. So it really nice to meet a vendor as we're going through the contract process. That's pretty cool. So yes, around that time last year, around May, we found out that we were going to be merging with the second biggest competitor in the dental membership space. So we had a pretty tight timeline for our goal on that, which was probably about four and a half months. So I had a lot of work to do as a team. One, I had to understand basically the data models for both the CRMs. Had to condense two tech stacks. You know, there was a lot of transitioning different team members. So there was a lot of work involved there. At that time, there were so many new go to market strategies that I was just really excited to implement. So just to kind of stay focused and hit my deadline with the merger, I kind of had to table those until we got to the other side of it. So here we are. The merger rolled out very successfully with very minimal downtime, so I'm super proud of that. Definitely the most challenging project of my career, but I definitely just set, like, a new standard for myself, so I'm proud of that. So, yeah, I guess just picking up this year post merger is is kinda just adding on some last go to market tools and tools for our CS team. But, yeah, I just you know, as things progress and we kinda get past some of those, you know, I'm kinda just starting to brainstorm around how I can enable my team internally now that I'm a team of two. I we just had a RevOps analyst join my team at the beginning of January, so that's been going great so far. So, you know, as managing, you know, two individuals now and trying to maximize our productivity, it's just really kinda getting into the nitty gritty of, like, what we can really make an impact on.

Janis Zech: Yeah. No. It sounds really tough, like, the merger of the two CRMs and probably constantly in the weeds and, you know, getting pulled in all the different directions. So now you have the time to actually think about, like, the cooler stuff, I would like to say, even though merging two CRMs actually, I think, should be considered cool. But, still, it's it's like a grind, and you have to grind through. And, yeah, I mean, it's hard to, you know, call that fun, but it is important work. So, yeah, I'm curious. So how do you think about this kind of, like, enabling this team of two to become more impactful, to become more efficient? Because that's sort of like what I'm understanding your key goal here is. So do you have sort of like different key areas that you're thinking about that you're focusing on that you think are good to start with and then go in a specific direction? How are you thinking about this?

Nik Garza: I think the way that we really kind of got these juices flowing was we invested heavily in a lot of different tools. And I just really wanted to, like, maximize adoption or utilization of those, you know, not only for my team, but what are those other specific use cases that I can apply for RevOps. Right? So getting specific insights from from my calls or my team's calls and storing those, you know, in Salesforce. But, like, I also have, like, a lot of automation tools at my disposal that I've used for, you know, bulk updating data and reporting and things like that. So things like Coefficient and Clay have been things that I've added to my stack over the last six months. And when you start just kind of piecing things together, you realize how powerful some of these tools can be outside of just go to market purposes of just generating pipeline and stuff like that. So yeah, I started to kind of figure out, like, how can I utilize these and really kind of just share some of these insights or just kind of save myself some more time so I can apply that in other areas for my team? So yeah, just getting to the point now where I can start to have those conversations with, you know, me and my team and my manager on how we can best utilize all that. It's a nice spot to be in now.

Philipp Stelzer: Yeah. So maybe just to set the scene right, I think that most folks we talk to every week spend a lot of time actually automating repetitive tasks for either the marketing, sales, or CS team, mostly sometimes partnerships. Right? And, I mean, obviously, like, you know, we as Weflow, we are exactly in that space, whether that's, like, you know, automating activity capture, automating data retrieval from conversations, or, you know, like, enriching, you know, certain signals and insights, you know, to have better pipeline visibility or forecasting. Right? So, like, I mean, I think this is, like, a very typical thing, but the end users, obviously, not RevOps. Right? The RevOps is basically our partners in crime that, you know, we team up with to enable the other teams and drive efficiencies. What I really like about your thinking here is, like, okay. So how can you essentially like, what are ways to basically drive productivity for RevOps roles? So and I think you had, like, three very specific ones in mind. Right? So maybe we can go through them one by one and just, you know, you share a bit of what you're thinking of because I think this can be very interesting for many folks. I mean, I think the other common theme is, right, like, since a few years, everybody has to do more with less. You know? Like, teams are often, you know, very small, and the load of work is very broad. Stakeholders are very demanding, and there's a lot of firefighting happening. And so, you know, I think it's a very relevant and timely topic. So, yeah, I'm curious. Like, maybe let's start with the first one. Like, what's the specific use case you're thinking of?

Nik Garza: So I think the one that would just have the greatest impact overall is, so Coefficient is a tool that I onboarded during the merger. I was doing a lot of, like, consolidating data fields and things like that. And if you're not familiar with Coefficient and you're in RevOps and you haven't found it by now, I highly suggest you look into it. It's a huge time saver. But, essentially, you can pull from different data sources right into just Google Sheets or Excel. You can just make any, like, bulk changes and you can, you know, export it back into whatever systems you need to. But there's a lot of different features that are baked into it as well. It has some, like, GPTs, where you can just kinda run some information through, you know, LLMs on there. But I do think and there's also, like, Slack alerts. You can automate reports into different channels. But I think where I want to kind of just push the limit on that is you can set these schedules for, like, you have maybe, this is my closed won pipeline for this month. Here's a report on my closed loss reasons for this month. Here's a report on opportunities created this month, and those are just very specific, like, sales specific reports. But you can automate all those and you can send them into a folder on whatever cadence you want. So where anybody can just take those and run a manual analysis, you can go dump those into ChatGPT yourself and just have that, you know, get those insights and share in a Slack channel that you want. But I'd like to just, you know, take it even further than that. Like, I want on a monthly basis and a quarterly basis to automate QBRs, to put that data in a folder, then have a GPT look at the entire folder, basically prompt it to look at all the different reports and what the output is. So just kind of like a predictive or like a proactive advisory AI agent, right, that can just really give more direction on where you should focus for that next quarter. So I see that there's a ton of use cases there. But, you know, in RevOps, you typically own the reporting for the go to market teams, and sometimes just sharing, you know, a visual and a chart doesn't really kind of tie all the things together. You need that underlying context that maybe only your CRM is going to have, maybe that's in certain fields that don't really kind of translate into those visualizations. Since all the technology is kind of already there, it's just you have to put a little bit of time into the learning curve that it takes to kind of fine tune and tweak some of these things. But, you know, it's just exciting to me to know that I don't need a dedicated tool for this, although there are some very expensive ones out there that can do it. You know, I always kind of treat the company's money like it's my own and figure out, you know, how I can get the best bang for my buck. So I think that's going to be the best way just to update leadership, to just provide some more directionality there, but also just overall visibility into what my department is really kind of focusing on. All those efforts that we make for the sales team to automate things, they help that data hygiene, just overall those data governance processes. And so once you extract good data out of that, all you need to do is just get some additional context. And I really think once you tie that all together, it can be super powerful.

Janis Zech: Yeah. It's so funny you say this because, I mean, I think we talked about it in the past, but, right, like, many times the, let's say, charts that come out are not the fundamental problem. Right? This is kind of a solved problem. The problem is more the underlying data source. So assume you fixed all the pipes. Right? Pipeline hygiene is there. Right? Data structure is there, and it all works well. You would basically, you know, use Coefficient as almost like a data warehouse, BI analyzation tool, right, to automate reporting. And then in addition to that and I think this is actually also really interesting because we are recording another session later tonight with the VP of GEA, and I talked to him. And, basically, what he said is, I mean, in the preparation of QBRs, right, like, one role of RevOps to really shine is to essentially sit back and think about what are the things that really move the needle for the business. Right? So it's almost like developing an, I don't wanna say agent because it's not an agent, but developing basically, right, a, you know, LLM analytics functionality that lets you analyze, you know, what's actually happening and, you know, have a conversation to identify gaps and do that in a much more automated way. Right? So it's actually funny because, like, we started building this into Weflow as well. Right? Like that. But it's a very different purpose. Right? So this is more for the sales leader side where, like, sales leaders can basically say, look. I mean, analyze this specific pipeline view, and then you get insights on, you know, opportunities at risk. Right? And specific suggested actions and why they're at risk. Right? Which is, you know, a very different use case, very different way of doing. But what you're basically suggesting is to take the aggregated level and build yourself a system that spits out these recommendations, you know, identification of risk or revenue risk, right, and then essentially sit down and, you know, analyze and either, you know, bring to leadership, you know, or, like, have a conversation about it or also not. Right? Is that fair?

Nik Garza: Yeah. Because there are, you know, there's some really good tools like Sigma that, you know, just kind of these more modern BI tool replacements that you could just go query whatever you wanted to. But I think where you really kind of save yourself time is doing that proactively by setting that on certain schedules. And if you really wanted to really push the limits of things, you could just set up, like, more of like a vector library to where all these monthly ones are actually captured in like a memory of it. So now you're starting to build on those previous insights too over time. So if you really want to start getting into that, you know, agentic side of things, you would kind of build off of that. And I think that would make it even more powerful. But just for starts, even just doing it by looking at segmented data for a specific period can already just provide you with so many more insights immediately. And you can always just build onto that afterwards. So yeah, I'm just trying to not have to go ask for another expensive tool or anything like that, you know, but there are ways that you can connect directly to Salesforce and query your data exactly out of that. There's tools like Myco dot ai that kind of does that. But yeah, just trying to just be self sufficient with what we have. And then if there's any need outside of that, you know, we can explore that later on.

Philipp Stelzer: Yeah. I mean, I think another part that also I think is really good about, you know, like solid LLMs. First of all, they're adding the reasoning models now, right? Like the reasoning to it. So that's very helpful. I tried it last week with DeepSeek and felt really good. And I'm excited for this to be added to ChatGPT as well. But I think a big problem with data in general that I've constantly encountered in the past, less so at Weflow certainly, in the past, is that people are scared of data. They're scared of tables and also sometimes of charts and being able to properly analyze those and share what they think the data means. And here, I think LLMs can help a lot when they're accurate. And I think they mostly are nowadays. Then you really have like a partner in crime that can help you kind of like give you a solid written explanation of what like those ten thousand rows of data actually mean. And that is something that makes it suddenly tangible. It's similar to Janis' point. Like, the problem is not to create a chart. The problem is to create the underlying data. And this is essentially what this is doing then. Right? It gives you, like, a solid explanation that you can understand. And if you don't get it, then ask it to explain it like you're five years old. And not trying to talk down to anyone, right? Like I do this, right? So it's just helpful sometimes to get these different angles and get them presented to you. Are there outside of analysis, is there like another automation or like, yeah, approach that you're thinking about, like a different use case there?

Nik Garza: Yeah, I would say that one is just more for strategic gain, that first one. But I think when I look at my day to day and trying to maximize my output and productivity, it's like, where am I spending a lot of time that isn't necessarily tied to a specific tool? With all these, you know, projects, rolling out new tools, having to keep everybody informed, updated to meet deadlines, and just stay on track with things. There's a lot of communication involved. And I will say that when I got into RevOps, I think I was mostly drawn to seeing everything as, like, this was, like, a kinda, like, a puzzle for me. I've always just kinda generally, things that made me think. So building out all these systems and the data pipelines and all that has always been fun to me. But, you know, as I've kind of moved into more of this director level role and a lot more just leaders have joined our team, there's a lot more communication directly to the managers, other directors, to the end stakeholders, and then also just the leadership above. So, you know, a lot of my day is kind of just doing this admin type of work, just setting these updates via Slack or email alerts. And so I'm like, how can I get some more of that time back? So, you know, for a while now I've been using different, like, AI notetakers connected to Notion, which is where I kind of just manage my own self projects. By pulling in those transcripts and being able to just auto process them, AI summaries isn't anything new, but taking the latest from a project and then automatically updating your overall project plans. Are there any new tasks or any new deadlines? Automatically. So with as many updates being had, that's a lot of admin work itself. So where I want to take that is I want to use more of that kind of like a RAG system to where I'm saving all of my meeting notes, but I'm also just telling this logic of the project plan to constantly refine itself. And then also generating whatever sort of communications like, hey, once this new project plan gets updated, generate a new Slack alert and automatically send that to this channel. So just trying to completely remove myself from all that to where I can just show up to the meeting with the agenda that my AI already prepares for me. You know what I mean? And then just asking all the right questions, recording that, taking that transcript, and just kind of just feeding the loop. And so that's kind of where I'm at right now, which is kind of just building out what the structure of that looks like. Is Notion that tool? Or do I just need to, you know, do I need to go to Google Drive and store them all there and build it separately? So to me, I know if I can get at least, you know, five to ten hours back a week from all that work, you know, you extrapolate that through the rest of the year and that's a ton of time that I'm spending there. So, I mean, at this point, nothing is out of the picture. Like, anything that I know that I'm putting a lot of time and effort in, I'm really asking myself, can I automate this? And so far, there's very few things that I don't think there's any no's. I think it's just kinda like, I don't know if I've quite cracked how I would do that yet.

Philipp Stelzer: But I I already see your next time showing up as an avatar here. Yeah. You know, fed by, you know, all your conversations over the last twelve months and then giving really precise and, you know...

Nik Garza: It might be still, like, the basics. Sending my AI to, you know, to do my work for me and just, you know, sit back. Like, maybe that's the end goal. Right? But, you know, it's all fun stuff. I think it's I'm just constantly building on this skill set that I, you know, I apply for my team, and I can repurpose it for myself. And so it just really makes me wanna just keep learning and soaking in. There's just so much exciting stuff coming out, like, every week.

Philipp Stelzer: And I think this is sort of, like, the key thing now, right, with AI is I think, like, I think people are starting, like, becoming willing to, like, experiment a lot more and put it out in the open. I feel like last year, this was a bit more tainted as an overall topic in that regard. And now I think it's just fine. People have accepted it. It's okay. You could just go ahead and do it. And people accept, you know, that's like, it's not like crazy to do this stuff anymore. If you want to prepare interview questions or things like this, when you talk to candidates, yeah, sure. Like you can ask ChatGPT to come up with like ten good questions and those questions will be good. Right.

Janis Zech: That's fair. I think, like, this is also something that, you know, when when you're new to, like, a higher role in a hierarchy, like, you're suddenly sucked into all these meetings, and it just takes up so much time. Right? Communication suddenly takes up so much time. It can be so draining. And but then, like, you're still like this team of two, so you need to be operationally efficient as well at the same time. And, you know, people still have all these expectations around your output. So, yeah, I totally get where you're coming from. One thing that you focused on here was the manager or leadership relationship. Are you thinking also about, like, alignment across departments or teams that you could automate there? Also, the communication, like, you know, outside of you yourself, kind of like turning this into something that can be valuable for the whole organization?

Nik Garza: Yeah, I do think beyond just the revenue team, I work very closely with our data analytics team who reports under our P and E team. So outside of just, you know, sales, marketing, customer success, you know, even product too, especially like, you know, onboarding tools and you have all these different stakeholders that you have to keep informed. So not only is it just the cross functional alignment, but it's like the different tiers along in those different departments and functions as well, which makes it even more complex. And you know how you kind of have to distill kind of technical information down to some people. You also have to keep it high level when you're speaking upstream. So there's a lot of nuance to it where these AI tools can really just kind of help you out and streamline that. I also probably say at this point, I probably couldn't live without them just with, you know, how quickly things move. But, yeah, I think it's very important on the alignment side of things to, you know, have the right communications to the right different teams and the right channels for it as well.

Janis Zech: Yeah. Before we started hit record, we chatted about a third topic that is actually probably one of the biggest topics in RevOps. That's customer support. But here, I mean, you could argue it's basically, like, supporting the teams and, you know, also creating a knowledge base to be able to do that. So I'm curious, like, that's an area you're also looking into. How do you think about that? What do you think is feasible there?

Nik Garza: I think that just kind of ensuring that there's, like, as few knowledge gaps as possible across, you know, the teams that you support is super important. And just to really make the most out of those, you know, different processes that you're defining and creating documentation for, you just want to make sure that you can put them right where people need to look for them. So sometimes people might, you know, ask certain things or they might put certain tickets in where there is, you know, that's already resolved in some sort of article or something like that. Once again, you know, there are a bunch of different tools where you can build these kind of AI chatbots, which are further investments, you know, and so being able to connect Confluence or Google Docs through things like Innate or Zapier, you know, so I can kinda just go query that data and answer some of those questions. A cool example of that was late last night. I was probably working way later than I should have, but I was trying to build, I was trying to finish this agent, that AI scraping agent with DeepSeek, just to save on Clay credits. And I got, like, to the very end of it, and the webhook back was adding new data on a different row. So I went to the Clay channel in the Clay Slack under support, and I just asked the question. And it queried that data and gave me like a very human response exactly with instructions the way that I needed. And so that's different from somebody just going and asking and then like chatting back with it, like for it to respond with exactly what I needed. Like, I need to be able to provide that level of granularity to my teams at the end and to do it in Slack in the support channel as well, you know? So just see the value that I get from that. I would rather avoid going through an email ticket or having to call in, you know? And it's just overall just more efficient. So it's implementing those beyond just kind of customer support, but just for overall just enablement, sharing knowledge, and also just to kind of protect your focus in RevOps. Sometimes there can just be so many distractions outside of just Slack alerts, people pinging you for all sorts of things. So it's like, it's just another way that, you know, you can protect your focus.

Janis Zech: Yeah. I mean, one thing that would be, I mean, for sure amazing. Right? Like, you can just if the bots or, like, the chat would actually know, like, you know, what kind of, like, level of hierarchy you have, what kind of, like, permissions and access you have to different pieces of information stored in, like, you know, some secure backend or some database. And then you can ask any kind of question, how is this deal going? How far are we with the preparation of the QBR and whatnot? And it would just basically know it and would solve a lot of, like, the kind of, like, annoying just updates, communication that you constantly need to have. And I thought sort of like, this is what you were going with. Right? Like, all this, like, stuff that you have to do, it adds value, but only for one person, basically for them to catch up to what's going on and not, not their fault. Right? Like that they need to catch up, but it's not really helping you. It's not really moving anything forward.

Nik Garza: I think a cool use case, like just what you're talking about there, is we just signed with Attention, basically just like a conversation intelligence tool for go to market teams. And they update Salesforce automatically after every call. So not having it from a sales ops perspective, having to chase down your team to keep it updated or just shutting it down with a bunch of validation rules. But aside from that, those insights that can come directly from those calls and be able to query that from your implementation calls, your customer success calls, and all of that, I think just providing more of these modern tools to your team will kind of reduce the overall, you know, hand raisers for trying to chase down resources or insights.

Philipp Stelzer: Yeah. A hundred percent. I mean, I think this is a great example, you know, as Attention is actually a happy competitor of ours on the call recording side. And, I mean, I look. I think we see this all the time. Right? You look through the Salesforce instances. The next step field might be populated. But as soon as you come to MEDDIC, MEDDPICC, right, they're just most of the times empty. I mean, there's two sides to this. Right? One side is you want the team to live by the methodology and really apply it in the conversations. Right? Super important. And the best reps do this, and it really helps to close more deals. Right? But the other side is you actually wanna make sure that you, you know, take that data and actually, you know, use it to inform deal insights. Right? Mix it with your activity data. Mix it with your contact or buying committee intelligence. Right? Mix it with your, you know, MEDDIC or MEDDPICC data you have in your CRM. And then holistically have that data lake so that you can let an AI make sense of this, right, on a deal level, on an aggregated pipeline level, a forecast level. So this is what we obviously, you know, live and breathe day in, day out. I think it's so, so important. And, you know, like, I mean, I think this episode is a bit about, like, okay, like, dreaming a bit. How can you apply those use cases also for RevOps, right, to help, you know, reduce noise, improve focus, you know, reduce tasks that, you know, can be automated. And I think that's a really good cause. And so, I mean, in sense of community, right, like, you know, this is, I think, still, you know, we're further along than last year, but I think in reality, all this isn't deployed widely. Right? I think there's a lot of hype. I think the deployment phase comes now. And so, you know, if anyone is excited about this topic, Nik, where can they find you? And, you know, I'm I mean, I know you're super busy. Right? But I feel like this is a topic where probably, you know, people want to chat with you, so, yeah, curious where they can find you.

Nik Garza: Yeah. You can find me on LinkedIn. It's Nicholas A. Garza. I'm also pretty active in, you know, RevOps Co-op, RevGenius. I just joined a new go to market engineering group with Unifi last week. So, you know, just learning from those channels, and I'm pretty active in there as well. But, yeah, directly reach out to me on LinkedIn. If you wanna talk about anything that was discussed here, I'd be happy to do that. I love nerding out about it.

Philipp Stelzer: Amazing. Thank you. Nik, always one final question for our guests. What book would you recommend to our listeners? One exception here is Revenue Architecture by Jacco is not allowed as an answer, but anything else goes.

Nik Garza: You know what? I'll be honest here. I'm not the biggest reader, but I soak up way too many YouTube videos. And I can point you at some YouTube channels there, but go for it.

Philipp Stelzer: Go for it. We didn't even go to a book that I'm not, like, fully behind.

Nik Garza: Well, I'm sure everybody knows, like, you know, Eric, I'm gonna butcher his last name, Nowak's Laszewski. You know, he's definitely one of the pioneers of, you know, Clay cold emailing. There's another guy, Lee Zin, that I follow. Those would probably be the two main ones. I could give you partial names of the other one, but I don't want to disrespect them by mispronouncing their names. Yeah. Sorry to put me on the spot like that.

Philipp Stelzer: Yeah. No. That's great. I will put the links in the show notes, then it doesn't matter how they are pronounced. All good. Right? I think it's too much to ask of us to pronounce every name right. I mean, basically, Janis and I get it wrong every episode that we're recording.

Janis Zech: Getting wrong. We rehearsed every three weeks. That's for sure.

Philipp Stelzer: Okay, perfect. Nik, thank you. Thank you so much. I think there was a lot of great inspiration here for our listeners. So we hope everyone enjoyed the episode. And yeah, have a great rest of the day.

Nik Garza: Thanks a lot. Yeah. Have a good one, guys.