Janis Zech: Hello, and welcome to another episode of the RevOps Lab Podcast. I'm here with Philipp, and our guest today is Spencer Hardy. Spencer, great to have you.

Spencer Hardy: Great to be here. I'm a fan of the podcast, so I'm happy to attend.

Janis Zech: Yeah. We actually just met, and then you mentioned you know us already, so you know our voice. We just had a little intro chat. Really excited about today's episode. We're gonna talk about the data dilemma, you know, how bad data breaks go to market, and really try to dive into the ripple effects of bad data. Before we do that, you know, for the audience, who are you? What do you do?

Spencer Hardy: Yeah. My name is Spencer Hardy. I'm the VP of operations at HG Insights. I've been at HG Insights almost ten years, which is a very long time in the tech world. I did work for another tech company previously for about five years doing consulting and implementations. And then before that, I did about five years kind of doing finance and analysis in investment banking. But I've been focused solely on RevOps at HG for about five years, but also have a background in finance where a lot of those responsibilities lived before RevOps really kind of came its own centralized team.

Janis Zech: Yeah. Awesome. I mean, it's so interesting to see all the backgrounds in RevOps positions. Right? Various different ones. This is a very good one. We also had a bunch of episodes talking about the alignment between FP&A and RevOps and how important that is. But to dive into today's topic, I think we've all been there, data being incomplete, maybe outdated, duplicated. I think it's a topic we also talk a lot about on LinkedIn, and I think it's a very common problem, I would say, also having talked to, I mean, now probably hundreds of RevOps teams in different various organizations. So I don't think the topic itself is that new. Right? But what I think you have a very interesting view on, okay, what does this actually mean for the day to day, you know, strategic alignment execution of the go to market team? So maybe let's kick off with the first topic. What do you think are some of the major ripple effects? And then let's dive into those in detail.

Spencer Hardy: Yeah. I think this topic is a really good one for especially if you are a RevOps leader or aspiring RevOps leader to shift your team from being, you know, viewed as tactical to really being strategic if you understand that. I'm not talking about like, oh, this field in my CRM is incorrect or, oh, we have some bad job titles in some of our lead data. It's how I think about the cost of bad data. It really starts with alignment across the entire company and your go to market team on a standardized understanding of your addressable market and what your ICP is and where your competitors are present, and then leveraging that through clearly defined definitions and data to fuel your go to market strategy all the way from your planning through to your execution and having the underlying data in your CRM, in your data lake, wherever you operate, all support and have the same defined segmentation and prioritization of what accounts fit and don't, what audience fits and doesn't for your product market fit. Where I see the biggest costs to organizations when that is not clearly defined is three areas. I call it the executive tax and that's time spent in the boardroom, in QBRs, mid QBRs, just debating reporting, debating what's happening in the market, maybe even saying the same thing but with different underlying definitions of where you're winning and where you're losing. That I think is the biggest cost of bad data and misaligned definitions. The second one is what I call operational drag. And that is when your internal resources are misallocated, maybe from poor territory optimization or just inefficient handoff and definitions between marketing and sales. And it's really RevOps' role to come in and be the team that unifies sales, marketing, CS, and product on those definitions that reporting to be efficient internally. Things like defining your ICP, capacity planning, account scoring, lead scoring. And then the third item is really the valuation gap that happens for a company. If you get into diligence or M&A activity, portfolio companies have their own whatever, if it's a CRM expert or analyst that comes in and looks at your processes, your segmentation, your success metrics, your valuation as a company will take a big hit if it doesn't look like you have predictable scalable revenue through the underlying data and reporting and segmentation and definitions in wherever you do your reporting.

Janis Zech: Yeah. Great. Okay. I think this is super helpful. Thank you for that overview. I mean, like, if I may kinda repeat that back. Right? I think a key thing for me here in the beginning that you said is essentially, there is no alignment on the terminology and the schema of the data, how it's used in the company, and this then has huge ripple effects other than the data not being there. Right? I mean, that's obviously also like a huge problem. So I'm curious. Right? Because I think this can also happen at companies that are quite mature, that may think they have the data that they actually need to make decisions, but then they still have these, you know, endless boardroom discussions where no one is seemingly aligned. So I'm curious, like, in your experience, what are some very early signals to help spot that misalignment on the data?

Spencer Hardy: The most apparent signal and maybe the most frustrating from a RevOps perspective is when you get into meetings and each functional area are kind of bringing in their own reporting, maybe even using different underlying data sources. Like, that is the number one red flag that immediately everyone's challenging each other's reporting, internal friction gets created within the company, and that's for the RevOps persona, that's the worst scenario to be in. You don't wanna be the person in the middle trying to keep the peace and kind of managing political debates within the company. That is not the funnest position for RevOps to be in. So the second that people are using different data sets or different reporting, that's the number one red flag. I think another one is you should be able to go to your CMO, your CRO, your CEO, your head of customer success and say like, hey, can you define how we segment our prospects and customers and how do we define our ideal customer within those segments. And if those leaders have different definitions and there isn't a documented definition that they immediately all point you to in a shared space, you know the company's misaligned.

Janis Zech: Yeah. It's so funny. I actually talked about this today for two hours, and I mean, we're still early in our maturity, but I think ICP governs a lot of the energy of a company, but it's obviously something you have to learn over time. Where do you win? Where do you lose? Why do you win? It is in context of product. It's in context of your TAM. It's in context of your competitive positioning. It's a very complicated thing to get right. And then it has a lot of ripple effects from that perspective. So I really like hearing you talk about it. Any other signals — you call it the executive tax. Right? Like, what are other signals that that exists, and what is the tax specifically? Like, if you play this further. Right? Like, okay. So the CMO comes with their data analyst, the CRO comes with their own data analyst, and then they come in and they fight. What other signals have you seen that indicate that something is not right?

Spencer Hardy: Yeah. Some other signals to watch out for is one is within the sales org, really drastically different participation or quota attainment rates. That could mean that you have some reps who have a territory of a good amount of accounts that really are a good product market fit in your ICP and some reps may have a territory that really doesn't align to your value prop and your ICP. So looking out for basically a small number of people who do overachieve their quota, then you have a lot who are not and the participation rates are low, or attrition on the sales side, is a good signal that your territories may not be optimized and aligned to your ICP. At the executive level, I think it's another big signal is when basically whatever meetings or MQLs or however you wanna define it, that marketing's handing over to sales and sales is coming back and disqualifying or saying a lot of those aren't a fit, that's a great signal that there's a misalignment between the two orgs about where you should have a high win rate and with what types of accounts and personas. That's a really good signal. And then I would say as well similar friction between the AE and CSM handoff where CSMs are coming back and saying, like, this account is not a good fit for the product or what you sold. We're not gonna be able to implement them or get time to value in the first ninety days. That's a signal that there's misalignment across those orgs on what is your ICP and who you should be selling to. But ultimately, maybe the number one signal besides the individualized reporting by function is a bunch of, say, during a QBR or a mid QBR or annual planning, a bunch of urgent fire drills of reporting requests or model analysis that comes out of those meetings that gets handed off to RevOps. Those fire drills are usually because the team is really having a hard time getting alignment on disparate reporting or disparate definitions, and then all of a sudden, it's like, oh, we have to solve this now. Fire drill, RevOps, can you do this analysis over the last twelve months for us?

Janis Zech: Yeah. Yeah. Great. I like — so basically, let's say I'm in a company, mature or not. Right? So I talk to five, six people. I join two, three meetings. Probably I know if there is a problem. Right? Like, it feels like something you would quickly, very quickly be able to tap into if you really wanted to, by just talking to the right people and asking the right questions. So let's say our company has a data problem. How would you then go about and try to fix it? What's sort of like your first step when you, you know, walk out of that room where everyone has a different source for their individual reports?

Spencer Hardy: Yeah. Great question. I think the default for most people in ops who are very good problem solvers or like to solve problems is to immediately jump in and say, okay. We're gonna get the company aligned on what is our centralized source of truth for account intelligence or firmographic data. We're gonna get really defined on our processes and handoffs and immediately jump into execution mode. But I would advise against immediately going in to do that. The first thing you need to make sure you do is if you really wanna solve the problem, the executive tax at the boardroom level is you need to get those stakeholders all aligned on some standard definitions of what your ICP is. Even like Janis, like you said, the first time you do an ICP is not a one time thing. It's an iterative process that's ongoing, especially as the market changes and your product changes. You should always be redefining your ICP and that's actually the act of doing that project is what gets the exec team in alignment and speaking the same language. It's almost worthwhile to proactively a couple times a year revisit your ICP definition with the head of product, the head of sales, the head of marketing, the exec team because that ensures that that group stays in alignment on the strategy of the company. So I would honestly start there and your first ICP doesn't need to be perfect. You're gonna get things wrong. It's the iterative process of refining that constantly that really brings the team in alignment. Segmentation is the other item. Once you're aligned on your ICP, defining your segmentation is gonna have ripple effects throughout the entire organization. When you go back and do a win loss analysis or a loss analysis for customers, that reporting is gonna be based on your segmentation cohorts. So how you define that segment — it's always a mix of like, it could be size of company in some form, it could be a maturity model that you layer in as well. It could be what products or how much money they spend with you as another variable. Geos are kind of not really as important with the segmentation anymore, but however you define that, that's gonna be kind of the base cohort of all your reporting on conversion rates, on renewal, gross retention, net retention, all that stuff. So getting your segmentation aligned with your ICP and aligned with the exec team would be the next thing I would do. And maybe within that is where you talk about, okay, to do the ICP and do the segmentation, we need to make sure that we have a unified data source that the entire go to market org gets agreed on. And whether you have that already or you have to go obtain that, that's what kind of the data piece fits into those use cases. And then after that, of course, it would be kind of maybe benchmarking the status quo with that new ICP and that new segmentation and provide that reporting back to the exec team.

Philipp Stelzer: Okay. That's crazy. You're saying we shouldn't buy a new tool, but we can actually solve this by just using Excel or Google Spreadsheet and just looking at all the data? That's insane.

Spencer Hardy: It's the most insane thing I tell you.

Janis Zech: But sorry. Interrupted you, Philipp.

Spencer Hardy: Yeah. I am a fan of before I immediately go out and try to purchase a solution to solve something, I really wanna make sure that I understand the problem the company's facing, have definitions and success metrics with all the stakeholders. And usually for me, it's a, okay, let's do some amount of manual work to figure out where we're at today and really understand where we're at and maybe even test or trial something with a little bit of elbow grease doing it internally before we're like, okay. We're gonna scale a solution with a new tool.

Philipp Stelzer: When you said about let's not jump to action, but actually ensure that the executives are aware, they're interested, they are in the consideration stage, they have the intent, and then you basically — it's almost like a funnel. And I think sometimes that gets forgotten. If you are aware that this is a problem, it doesn't mean your execs are aware. If your execs are not aware, it doesn't matter if you start acting on it. You'd only drive change management by ensuring that the people are very problem aware and they feel strongly about solving the problem and they want to get into a better state. And so that's essentially the first job, right? Before you then go out then.

Janis Zech: Right. It's obviously — I mean, on the ICP segmentation discussion, that is not something you can do alone if there's no buy in. Right. So you need that strong buy in. And then you can think about how can you automate it and scale it. But I think it really starts with that. And I think that's quite universal in RevOps, I would think. It's the same with product. You don't wanna jump to the conclusion. And sometimes it's hard actually because you're aware, and you actually feel very strongly about getting started. But then there's this, you know, fun discussion with the CMO and the CRO and their data analysts about getting alignment, which, you know, obviously is — if the groups are running off different siloed data and doing their own reporting, it's a little, you know — well, your reporting looks really good for you, but it's actually not true. And the company reporting looks better for the company, but not for you. How about that?

Spencer Hardy: Yeah. That's a perfect example of why RevOps has developed into its own department and kind of created this whole new function within organizations because it's really at the end of the day, the number one priority for a RevOps org is to bring alignment across the go to market team and I would extend that to the product team for the product road map. And the only way that's gonna happen is if you have someone who is fully dedicated on doing maybe some things that might get overlooked in the other responsibilities of those orgs, which is like the single source of definition, the forcing a revisit of your ICP on a regular basis, really making sure that your underlying data collection and reporting supports the analysis of your segmentation and your ICP and the reports that eventually the board and exec team are gonna be looking at on a quarterly, mid quarter basis.

Janis Zech: Yeah. Yeah. I like it. I like it a lot. I think it makes a lot of sense. You mentioned something that is very, very interesting to me. It's like — and I would have never actually thought about this, but company valuation. Right? I mean, there's a lot of SaaS companies these days that are having conversations with growth equity investors, with PEs, with potential M&A partners. And so it seems to be one of the ripple effects being that if you don't have your house in order, you know, those folks will find out. We do a bit of work with a bunch of private equity partners that recommend our software into their portfolios. And I would say, you know, if you ever worked with private equity companies, they're very detail oriented compared to the growth equity guys. So they definitely know what they're talking about when it comes to go to market efficiencies. Curious, like any specific pointers there, what are things they think of or look at and what do you need to get in order to ensure that you don't fall into that valuation trap?

Spencer Hardy: At the end of the day, the equity funds, it's all about predictive revenue or predictive results and the ability to potentially scale the business. That's the whole reason they're investing is to scale the business, to ramp up revenue, but also, I would say more so than ever, especially in the last two years, cost efficient growth is very important. We're no longer in the world where it's growth at all costs. It's really about efficient growth, and that's where you're gonna get the highest valuation. You have to be able to — an outside person who knows nothing about your business — be able to really clearly explain your top line financials, and the best way to do that across almost every part of the business is through segmentation. I think segmentation is not talked about enough about the importance of having really good segmentation that supports business decisions and actions. But if you look at, say, your gross retention is maybe below where you want it to be, say it's seventy percent gross retention, but if your segmentation then can call out that, yeah, in our primary ICP and these three primary segments that we know are really good product market fit, in those three segments, we have ninety percent gross retention. But then our gross retention is getting dragged by this segment or that segment and this unique thing which we're testing there that we haven't fully flushed out, that's really a growth bet that we are doing market validation on and our core ARR use case is really strong with a good gross retention rate. Being able to talk like that to an outsider requires really good underlying data, really clearly defined ICP and definitions, and that totally changes the conversation and your valuation as a business. So even when — we've done a lot of M&A activity at HG Insights since I've been here. And whenever you get into diligence, it's those type of details that become critically important. And you should — depending on your size of company, but if you're anywhere in like a SMB to mid market tech company, you should be prepared at any time to potentially be having M&A discussions. And you want to be able to tell the story that's real about the business with the underlying data and how you track it, and bad data immediately sends a signal to an outsider that either their internal operational efficiency isn't good, that you don't understand your product market fit yet, or that there is misalignment within the team of what the product vision is. And that's kind of the assumption that gets made if you aren't able to define your entire customer and prospect journey through your reporting.

Janis Zech: Yeah. So this resonates so, so well with me because, you know, I think that fundamentally, right, the modern go to market playbooks are very much from how do you generate pipeline, all the way from new logo expansion renewal and then product market fit per segment or product. Because also I think the reality is that a lot of companies are actually multi product these days. And you don't have one product market fit. You don't have one ICP. And these are all trade off decisions you're making, basically. Right? So if you think of 2026 strategy, those are the fundamental blocks you need to basically align on. Because if you say we're an ABM play, one to many, then that leads to your LinkedIn marketing budget because you upload your list to landing pages, to then basically calling. And that go to market engine might be very different to your product led engine, which you might be also running. So I think getting a really good understanding of that end to end customer journey and the positioning and messaging is another point that actually feeds into that. Right? So this is obviously something I really like talking about here because I think it's often in RevOps — I feel like this is something that the marketing and sales team often discuss about. But then RevOps needs to be part of that conversation because you can really back it up with data and you can really bring more science to it. Because I think it is, to a certain extent, what are the fundamental signals where you know, okay, this is actually the segment and ICP we target where we always win. And so we should do more of that, but should do less of other things. And this can be actually quite complicated to slice and dice. And yeah, I mean, we're living through it at Weflow right now.

Spencer Hardy: What is — I think every company is constantly living through this. I mean, we work with the biggest Fortune 50 tech companies that are out there, and they're constantly redefining their ICP and their segmentation. And they're constantly releasing new products to new markets or geo expansion. That's where this type of analysis — it all comes down to trade offs about you have a certain amount of resources for FY26. You wanna allocate those resources where you're gonna get the best ROI. And the only way you can really make those decisions is if you're really defined — like you said, you made a good point that honestly, it's not like an ICP is one thing. You have multiple ICPs. I kinda think about it like you should have an ICP per product or feature or solution that you sell to different personas. And if you don't have all that stuff clearly defined and have that implemented in wherever you're doing your reporting or your CRM, it's guaranteed that at planning time, at doing your FY26 plan, ninety percent of the time is gonna be spent debating, and that really delays execution into the next year.

Janis Zech: Yep. Yep. Great. Yeah. I just wanna add to this. I think even within one product, you can have different ICPs for different features and parts of it, right?

Spencer Hardy: Yeah. Absolutely.

Janis Zech: So even there — I think it goes back to, you know, we quoted it multiple times, Hillary Hadley, who also said, like, you add a market, you add a product, you multiply the complexity of your revenue operation setup. And I think this discussion is another great example of exactly this happening.

Spencer Hardy: Yeah. It's exponential. That is a great call out. I completely agree with that. There's also the — we start to talk about the who you integrate with, who you partner with, being able to track that within your underlying CRM, and that should be involved within your account scoring and territory optimization. Whatever it is you do with the underlying data — you get your definitions, you get the team aligned. I have seen the ability to go from a market analysis and ICP to then drill down to the underlying accounts that make that up. So whatever my recommendation is, whatever you're doing to do your go to market strategy and market analysis, rather than using maybe some static market reports and compiling all those together to understand your market and the competitive presence — if you can get to a point where you're building bottoms up the data that you use to define and analyze your market and where you wanna prioritize resources, if that's built bottoms up with the accounts, then once you define the strategy and where you're gonna invest and where you're gonna plug marketing dollars, where you're gonna apply territories, if you could from the same dataset get down to the underlying accounts and then start to understand first and third party signals against those accounts, that's a really good position to be in because then you can centralize the reporting and talk the same language from the exec level to the rep level and be aligned on which segments and which areas you're winning against your market strategy and your financial plan and where you're falling behind. I see a lot of friction gets created if you're defining your go to market strategy and your product roadmap using some market reports that don't have the underlying accounts that make up that market. You know, it's like, oh, the BI market in North America is spending x amount of money and that's gonna grow by six percent next year. And you make decisions on investing your product based off that, you really wanna make sure that you know who the underlying accounts are that that market study is pulling up, that you're making decisions off.

Janis Zech: Yeah. Like, on the accounts. Right? Like, I think sometimes I ask myself the question, okay, if I had access to all the information of an account. Right? Like, maybe what call recording software they're using, what activity capture tool they're using. Right. In our instance. And I would actually know all of that. And I could then slice and dice the accounts. What competitors are using. Right. And I could slice and dice these accounts that way. That would be amazing.

Spencer Hardy: Right. The more you can get to that across your entire target addressable market, and if you are able to do that at an account level, you should be able to roll that up to a market view and use that to do your go to market strategy and alignment with the exec team and the product strategy.

Janis Zech: Yeah. And then you basically use that in your performance marketing budget. Right? And you produce that to then the signals for your warm bound or all bound. Right? Or however you call them. Signal based. And you then funnel it into the right channels, and then your win rate should be higher and your NRR and GRR should be better. Right? So basically, it goes downstream very much. It is really hard to do, I think. But I really believe in that approach very much.

Spencer Hardy: I mean, I may be a little bit biased there because that's really kind of the use case that HG Insights is trying to provide a solution to — and is providing a solution to — kind of really the strategy and RevOps community on a single revenue growth fabric or account intelligence fabric for the B2B tech landscape that allows you to do that from strategy to execution in one place. But however you're trying to do it, I really believe that as a RevOps professional, if you don't get ahead of aligned definitions on your ICP, multiple maybe aligned market analysis of where you win and where you don't and who your competitors are — you'd be surprised at how often there is misalignment about who actually are our competitors in the market and who are they engaged with and which one should we be trying to displace or compete with. Those type of items, if you don't get aligned across the marketing org, the sales org, product org, even in CS, you're creating a situation for yourself as a RevOps professional to have a lot of friction between those teams to make it really hard to align those teams and prioritize projects, which ultimately, my recommendation is get ahead of those things to make your life easier when you try to go and scale the business from the RevOps side.

Janis Zech: Beautiful. Yeah. Thank you so much. I think this is a perfect moment to end. Always one final question though that we have. We didn't warn you beforehand. But essentially, you know, you are a listener to the podcast, so you should know — what is a book or a good read that you would recommend to our listeners, for this topic or any other topic that you're really passionate about?

Spencer Hardy: Yes. Great question. For this specific topic — oh, what's the name of the book? It's the HubSpot founder. Do you mean like Mark Roberge? The Sales Acceleration Formula. Yeah. That's a good one to think about how to scale the go to market with these types of use cases. I'll have to look up — I don't know if I've read a book that's specifically focused on ICP and market analysis and aligning that across the team, but I would be really surprised if there's not a good one out there. So that might be a good follow-up — find a book that really dives in for RevOps on these topics because I haven't read one yet, but I've just learned through working with a lot of other very smart RevOps people on these types of use cases, and it's kind of what we've learned over time.

Janis Zech: Yeah. Great. Perfect. Spencer, thank you so much. Really appreciate it.

Spencer Hardy: Yeah. Thank you. Have a good day. Bye.