Janis Zech: Hello, and welcome to another episode of the RevOps Lab. I'm here with Rhys Williams. Nice to meet you.

Rhys Williams: Good to see you again.

Janis Zech: Good to see you too. How are you?

Rhys Williams: I'm doing well. I'm doing well. We're just getting started. It's morning here in the States, but things are going well. Things are going well. It's been fun to get to know one another, meeting at kind of these different RevOps AF conferences and getting to follow along what you and the team are doing over there as well. So things are going well. How are you doing?

Janis Zech: Yeah, doing very well. Actually, holiday today, so Philipp is not with us. But yeah, very much enjoyed meeting you at both last year's RevOps AF and this year's RevOps AF. For the audience, who are you? What do you do?

Rhys Williams: Yeah, great question. My name is Rhys Williams. I'm the founder of a RevOps agency called Domestique Consulting — for all cycling fans out there, Domestique's a French term that got popularized in road cycling. Road cycling is a team sport and individuals kind of sit on the front of the Peloton and bury themselves so the leaders can come out and win the race at the end. So we are a full stack RevOps agency. We've a team of about fifty today. Got all the different RevOps people that you would think of. So marketing ops, sales ops, CS ops, all the major admins, HubSpot, Salesforce, Clay, Marketo, content enablement, paid media. So I, like a lot of individuals, didn't set out to get into operations. I'm curious about you, but I've yet to meet somebody who's like, oh yeah, I was really trying to get into RevOps. I landed a job in twenty ten in the financial crisis in sales ops just because I needed a job and figured out I was kind of good and I liked the ops. And from there, kind of built my career and that kind of led me to build Domestique a few years ago.

Janis Zech: Well, congrats on creating your company. I'm a founder myself, so I do many things, very few very well. Today's topic is how to empower executives to make data driven decisions. I think it's a topic that comes up a lot. It's really challenging and I want to lead with a stat. Gartner did a study on sales operations and found out that fifty three percent of the go to market executives don't trust their data. And whether that's fifty three or sixty five, I think we've all seen this, data quality being a huge issue. So let's maybe kick off in your experience. You see a lot of setups. We see a lot of setups. Why don't the executives and often also RevOps trust their data?

Rhys Williams: You know what's wild? We're sitting here in twenty twenty five and the vast majority of executives, to your point, whatever the number is, don't trust the data. The amount of money that is getting spent on the tech stack, people are managing the tech stack, and they don't trust it is wild. Just to provide some context — I would probably say, and I actually have to go back and look, but if I were a betting man, I'd bet somewhere between thirty to sixty percent of all the engagements we do at Domestique start with some sort of use case around, I don't trust the data, I need help finding it. And what's interesting is when we start to dive into that, it actually never has anything to do with the quote unquote report or the dashboard, right? Oftentimes the executive thinks things like, oh hey, I don't trust this data, so can't you just build me another report or another dashboard? Very rarely does it ever have anything to do with that. What we normally find is there's this whole kind of infrastructure — and this kind of gets back to how we think about RevOps. So just taking a step back, when we at Domestique talk about RevOps, we think across the customer journey: strategy, process, tools, data, enablement, in that order. And if you think about it, data in what I just laid out is intentionally fourth. And the reason why a lot of executives don't trust the data is because they haven't spent the time flushing out their go to market strategy, updating their processes, building that into their tech stack, and understanding what the data is. So to give a concrete example around what I specifically mean there — if you haven't spent the time, so like strategy, right? That's things like identifying who's your ICP. Are you using account based marketing? What's your customer engagement strategy? If that is not documented or written down, it doesn't exist. So think about it like, if you say, all right, our ICP is these companies that look like A, B, C, and D from a firmographic standpoint, they have these other traits — what is then the processes you need to do to make sure that that is all codified into your tech stack? Do you have account rating? Do you have lifecycle stages? And lifecycle stages, for those of you who don't know, it's like stage advancement criteria but kind of broader. It's essentially the customer journey. And it really helps you kind of navigate the marketing, the sales, and then sales into the CS. But a lot of times people will say, hey, something only triggers as an MQL if they fall into our ICP. So thinking through that strategy, process, tools, data, enablement — if you haven't documented your ICP, you don't have lifecycle stages, which is a process in terms of how you define it and codify it into your CRM, you then technically don't know what an MQL is, right? And then how do you codify that into your tech stack? And usually when you're talking about lifecycle stages, you're marrying between the marketing automation platform and your CRM. It's being able to kind of define in both. And then from a data standpoint, one of our big tenets about data is only measure what you can make a decision around. So using this example I'm using here — if you want to be able to say how many MQLs do we have, in that scenario I just laid out, you need to have clearly documented who is your ICP. You need to have flushed out what are your lifecycle stages. You need to have built those lifecycle stages and trained your team on it inside your marketing automation platform and your CRM, and then built a single source of truth report that is the holy grail report around MQLs. I know that sounds like a lot, but the reason why most companies don't trust the MQL data, or reports like that, is because they haven't gone through all those steps upfront. And typically what then ends up happening is Mr. and Mrs. Marketing leader or Mr. and Mrs. Sales leader have two different definitions about MQLs. They each have their own reports. Then they get in this meeting and they spend half the meeting complaining about whose report is correct, things like that. And that's where the executive then comes to us and essentially says, I don't trust the data.

Janis Zech: I mean, so obviously, right? So it's like touching on the data dictionary, the data definitions. Let's maybe stay a bit with what are some other reasons you see that people don't trust the data? I think your take is, okay, the strategy isn't defined, the processes aren't married, maybe the documentation isn't there, the alignment across the different lifecycle stages from marketing to sales to CS isn't there. You can't really track everything on the bow tie. Then there's the tooling part, right? What do you see on the tooling part?

Rhys Williams: Just curious — it's a great question. I think oftentimes when we see people try and attack this issue, right, and the issue being I don't trust the data, they stop at the tooling part. They just essentially say, oh well, we gotta fix the HubSpot to Salesforce integration, right? Well, that's only a component of it. I wanna kind of make a comment here — I'm not suggesting like, oh hey, you gotta go do a year's worth of work in order to kind of get this right. There's very much a crawl, walk, run iterative approach here. But half of this, and you started to kind of lay out all of it, is trying to understand, first and foremost, what is it you're trying to measure. And we believe, as I mentioned, only measure what you can make a decision around. You don't need five hundred thousand dashboards. You need the dashboards that you're fundamentally managing the business against, right? And how, in a perfect world, that reporting and that dashboard should kind of layer up — meaning there's an IC version of a dashboard, right? Sales rep, BDR, CSM. Then a leader version of it. But that leader version is just a roll up with the same data at a higher level. And then there's an executive version. But it's not a bunch of disparate dashboards and things like that. So if you're currently dealing with the classic, we don't trust the data, the first thing I would do is sit down and say, what is it we are trying to measure? And the north star that we try and use is essentially what is the business's goals for the year. If you're an EOS shop — Entrepreneur's Operating System — you should have laid out your annual vision and traction document that essentially says these are what we're trying to do. Other shops out there may be using an OKR system, things like that. And this is a question we ask a lot of our clients: what is your big goal? And oftentimes we get this answer of like, oh, we wanna hit the number. That's not it, right? The point of what we're asking is, what are the core goals? All right, if you didn't hit the number last year, why didn't you hit the number last year? Oh, well, we didn't hit the number last year because we didn't have enough pipeline, and we didn't have enough pipeline generation because we only have two pipeline generation sources — marketing and partners — and we want to set up an outbound motion. Okay. So maybe one of your goals this year is standing up an outbound motion. All right. If you want to stand up an outbound motion, what is then the data you're using to measure the efficacy of how effective is standing up that outbound motion? And it could be things like how many contacts are you adding to a sequence, what is the number of meetings you're getting per sequence. You can start to kind of do this, but what you're then doing is you're aligning the data you're trying to measure not only against how you're managing the business, but equally as important, against the key goals that the business is trying to achieve for the year. And most businesses should have somewhere between, call it two to five core goals for the year. And then you can start to filter down — all right, what are the data and dashboards we need to make sure that we're on track for that, as well as kind of managing the business. So once you understand what you're trying to measure, then it's starting to reverse engineer what exactly we're just mentioning there — all right, what is it that we don't have that people aren't trusting? Do we have lifecycle stages? A data dictionary, as you mentioned. Stage advancement criteria is one of the classic things. On the customer side, right — we always forget the customer — is there a clearly documented customer engagement strategy? And your customer engagement strategy doesn't have to be this big, long, complex customer journey mapping exercise. It could be like, hey look, we've got two segments, in our SMB segment with a one to many model, and the customers go through onboard, engage, and renew. And in this, there are these few different touch points and we're gonna measure a customer health score based upon usage. Something simple like that. But understanding what it is that you're measuring and then saying, where are we missing the key foundational pieces underneath?

Janis Zech: You brought up something very important, right? Once you get the data infrastructure correct — and the infrastructure is that strategy and the process, the process being the data dictionary, the lifecycle staging — it's very important to think through how you operationalize the data and align people across organizations. Because one of the classic mistakes of why companies don't trust the data is, let's assume that you've got all the strategy, the process, and the tooling correct. One of the then big reasons why companies don't trust the data is people are operating in a silo and they're speaking from two different sheets of music — meaning Mr. and Mrs. Sales leader is operating with their own set of reports, they have very limited interaction with the marketing leader, they have their own set of reports, there may be a filter or two slightly different. And then they get into those classic meetings and say, well hang on a second, my MQL number says a thousand, and the other person's saying my MQL number says two. And they spend seventy percent of the meeting complaining about whether it's a thousand or two. So there is this concept about how do you operationalize the data once you get the infrastructure set up correctly.

Rhys Williams: Exactly.

Janis Zech: So what I really like about the concept you're laying out — first, make sure you know what we're going for. I think that's obviously part of the annual plan, part of basically every company's annual strategy. But more than that, really understand the strategy and then what you need to essentially track the strategy. So it's almost like a KPI tree, right, where you have different layers of KPIs. And I think in SaaS, right, you typically have these KPIs like, for example, Rule of Forty, that consists of various different metrics. And this can actually be broken down into the volume metrics on the very bottom of the funnel, right? So you could say, how many — if I want to be very simple here — how many meetings should we have to actually achieve the Rule of Forty, in different stages throughout: how many customers do we need to win, what is the value of the customer, what's the sales cycle length, things like that. So a variety of different metrics that then lead to the next KPI, that then lead to the next KPI. And so I think one challenge is always that the executives spend a lot of time thinking about the aggregated metrics versus the RevOps team being ideally the glue between the executive team and the day to day volume metrics. And then you have the finance organization with FP&A that also has a different view often, coming more from a top down versus bottom up kind of way of looking at metrics. And so I think why I'm saying this is it's always good to know where you are in that pyramid of the KPI tree to understand whether this is actually something that matters to the executive layer. I'm sure you see this a lot, right? I don't know who you typically speak to, but I would assume you work closely with RevOps folks and also the executives. So I'm curious — what do you think about how you package that story to the executives versus how you unwrap RevOps so that it's actually well understood?

Rhys Williams: I love this question. So two comments on it. The first comment is we tend to think of data in two very simplistic buckets. One of our big tenets in general is simplicity drives execution — we don't need to overcomplicate stuff. But if you think about the data, there's output metrics and there's input metrics. Output metrics are things like, what was our bookings number? That is something executives care about. But bookings number is not an input metric. An input metric — how do you get to the output metric — is something like, one of our favorites, sales velocity. Sales velocity: the number of opportunities in a given period, times close rate in a given period, times ASP, divided by sales cycle. Because you can start to sit down and say, hey look, does this rep, does this team, does this segment — do they have enough opportunities? Oh hey, their close rate seems to be falling off. Why is that? Let's dive into that. You can start to understand, hey look, what are the things we can do? Because our general tenet at the end of the day is, you take care of the input metrics, the outputs should take care of themselves. So it's just understanding what is what. Output metrics are fine — I'm not saying anything against them. But if you only have output metrics, you can sit there and say, hey, we're behind on bookings. Then the question is, what do you do about it? And that's where the input metric becomes very valuable. So making sure, to your point, RevOps has this understanding of input metrics and output metrics and is helping to manage the organization — BDR, sales, marketing, CS — via the input metrics. Because if you manage that well, you're constantly evaluating, this is off, what can we do about it? The second comment I was going to make is, you need to find moments within your operating cadence — your meeting structure — and this is how you get data into the organization's DNA. One of the things I recommend to everybody is, if you're not doing this on a quarterly basis, you unequivocally should: take a look at every meeting, figure out who should be in it, what is the objective, have a clear agenda, and then link any reports into that meeting so that everybody's speaking from the same sheet of music. But the comment I was gonna make is, inside of your operating cadence, make sure that there are clear moments where there's cross functional alignment and the business is working on the input metrics. One of our favorite meetings that we recommend to a lot of clients is something called a demand council. What demand council is, at the end of the day, is essentially a cross functional weekly meeting with the heads of marketing, BDR, sales, and CS. And what you do is you look at week over week pipeline generation, but the pipeline generation we work up the reverse waterfall. So it's like MQLs, SALs, SQLs, and closed won. And you compare it against what your annual plan is. And the whole point of the meeting is to essentially say, are we on track or are we off track against the annual plan? And if we're off track, where is it? And you can break these down by segments — enterprise, mid-market, SMB — geo, product, whatever it is. And we always set them up based on pipeline source. So not as granular as channel, but things like marketing generated, AE outbound generated, and so forth. Because in these meetings, you can quickly say, oh hey, we're off track. It seems to be that we're off track primarily in the enterprise segment and marketing generated. And then based upon that, it seems to be both a top of funnel demand issue as well as a conversion rate issue. Our ASP seems to be on track. You can see this all super quickly. But then the point of the meeting is to essentially say, all right, if this is where we're off track, what are we doing about it? And let's hold ourselves accountable. So this is again working on the inputs, not the outputs. And you document some sort of action items — oh okay, we're gonna set up another webinar event to target the enterprise, and we're gonna attend this conference that we weren't gonna attend because there's gonna be a lot of customers there. Then you come back the next week and you say, hey, how's it going? Have we seen any change? Okay, this other thing is off. But you're constantly pulling levers here and there, working on those inputs. Because the whole goal is, if you have enough pipeline coverage coming into a quarter and assuming nothing materially changes on your conversion rate, you should theoretically hit the bookings number — which again, the bookings then hit the output. So thinking about where in your operating cadence you can find that cross functional ability to work on inputs.

Janis Zech: Yeah, yeah. So I think it's often referred to as leading and lagging indicators of success, right? So the booking number is the lagging indicator of success, or revenue year on year, month on month growth is the lagging indicator of success. And what you should measure and manage actively is the leading indicators of success — for example, pipeline generation by source or channel. And I think the other piece you're alluding to, and I really love this, is making sure that you speak with one voice, right? And you bring the people together who actually can have an impact on those numbers, on the leading indicators of success, so that you actually change them and you're not just basically knowing that something is off but nothing changes. Are there any other specific ways how you can operationalize data driven decision making throughout the process?

Rhys Williams: One recommendation for all RevOps professionals out there. I think one of the classic issues that we see with a lot of RevOps individuals is most organizations — meaning companies themselves, even if they have RevOps internally — actually don't understand what RevOps means. Meaning they're still treating you, if you're an in-house RevOps person or an outsourced RevOps person, as Sales Ops two point zero, right? Or a glorified CRM admin, whatever it is. And because of which, oftentimes what happens is the RevOps person is sitting there just waiting for the next executive to come to them and say, hey, pull me this report or do this report. So it's very reactive, right, instead of being proactive. So one of the things that we love and recommend is RevOps professionals scheduling what we call a RevOps readout. And it's essentially a monthly meeting with key executives — and this should really be the C suite in a perfect world, depending on the size of the organization. And the whole goal behind it is to essentially say, all right, let me, being the RevOps individual, present to the executive team where we are from a data standpoint across the customer journey. This can't just be sales data. It should include things like — one of the things we used to do at the last company I was at is we used to look at product feature requests, meaning what are all the features that we're getting requests for on the sales side and the CS side so we can help inform the product planning. That type of stuff. Summarize key data across that customer journey, particularly tied to the key goals, to essentially say, hey look, I know we wanna stand up this outbound motion — this seems to be having some success. However, our total pipeline generation goal is off. And because it's still off here, I recommend we do A, B, and C. Hey, let's spend a little bit more money on digital. This allows RevOps to be more proactive. You're actually showing the data that the executive team may or may not be looking at and helping them to make decisions — doing your job. And one very important note here is, don't just show the data. Show the data, give your analysis, and provide a recommendation. I think a lot of RevOps professionals just show the data and they don't take the next steps of critical thinking to essentially say, and this is what I think we should do about it.

Janis Zech: Yeah, I really love this. So we had Samad from Rubrik on the show and talked a lot about data. And I think one key thing that we in ops can do very well is we can be very abstract on the lagging indicators, but then we can drill down into the leading indicators. So if you do this meeting, you can basically come with an explanation of why things are good or bad. And I think even good and bad is very interesting because the executives, they don't know it most of the time. So they basically fly blind. They know something is off on the lagging indicators and they want to know why. So you first answer, why is it off or why is it going really well? Both is interesting to them.

Rhys Williams: Yeah, exactly.

Janis Zech: And then I think you can go back and say, look, and this is essentially where we can improve — whether that's leakage, whether that's investments, whether that's product prioritization. But I think that is something that is really powerful and it will elevate the conversation quite a bit. It should be a meeting that — and I think you have to earn it. If you have a RevOps team of ten, fifteen people, not everybody will hold this meeting. The VP will hold the meeting and you have to earn that meeting. So you probably start with your direct report, which ideally sits in the C suite, and then test that meeting with that person and say, hey, why don't we do that across the board, the entire executive team? And I think once you get to that meeting, and then ideally the goal should be that the executives really look forward to it — because then suddenly you'll hear the CEO, the CFO asking questions. It is probably one way to get into the C suite at some point in time, right? Like if you do this really, really well, because I think that is something that is often not done very well in the executive suite. There's nobody who can basically navigate the leading and lagging indicators and actually have a very clear, Swiss neutral understanding of why things are happening. Obviously there's always politics among the executive layer, so you have to read that and take it into consideration. But I really love this. Any other recommendations or comments on operationalizing the way to really use data to help executives and the entire organization to make better decisions?

Rhys Williams: Don't be afraid to hold executives — in particular executives and other ICs — accountable to speaking from the same sheet of music. And what I mean by that is, if we've created a single source of truth report for MQLs or whatever it is, and an executive comes in with a different report, you need to say, hey look, I know we all agreed on this MQL report. This report looks off here because of A, B, and C. Why don't we, if you want to dive into that, I'm happy to take this offline, but why don't we come back to this. It is our job as RevOps — and this is one of my fundamental beliefs — the best indicator for RevOps is the ability to lead through influence. And this is one of the keys to it: to be able to hold executives, even executives who we don't report to, accountable to making sure we're speaking from the same sheet of music. Because at the end of the day, the data is kind of like your Rosetta Stone. And getting everybody speaking that language will fundamentally help you have more thoughtful conversations about what you can do. Because again, data's only powerful if you can make a decision behind it. So you need to make sure everybody's speaking from the same sheet of music so that you can ultimately make those decisions.

Janis Zech: Yeah, I love that. I think that's so important, right? And I think we've all been there where suddenly a spreadsheet is pulled up or you have a different report or somebody created that Tableau or Looker report and it comes into the meeting. I think it comes back to what you said initially — what are the five to ten things on the lagging indicators you should track, what are the influencing factors, and is everybody aligned on that? And so to your first suggestion, the demand council meeting or the pipeline meeting or the renewal meeting or whatever meetings you have where you have different groups of executives and leaders in one room, you always refer back to the same thing.

Rhys Williams: Yes, exactly. You can write a data dictionary — nobody will read it, right? So you have to incorporate it into the meetings and then you have to show one set of dashboards and you have to refer back to that and make sure that everybody is aligned. And if they're not aligned, they need to speak up. Then you can solve that and you know there's a problem and you solve it. But if you don't do it, basically the organization will take control and everyone will create their own set of KPIs that are supporting their story. And that's dangerous. Then yes, everybody's speaking like seventy two languages and you spend all your time trying to get everybody on the same page.

Janis Zech: Yeah. Great. Okay. Look, I know you have to run. This was awesome. Thank you so much for sharing all this insight. I think we have to invite you back for a longer conversation on a different topic. But maybe before you dash off, any book you would recommend to our audience, or a report or any source of material you'd like to share?

Rhys Williams: Yeah. So one of my favorite business books that I use on a regular basis is something called The 360 Leader by John Maxwell. He has a pyramid in there and the pyramid talks about the five levels of leadership. And one of his whole principles in there is like, anybody can lead regardless — you don't have to have people reporting to you. And this kind of gets back to my comment about the ability to lead through influence. This left a profound impact on me, particularly thinking about it from a RevOps perspective of how do you get other people to follow you? Because if you think about it, the vast majority of what we try and do on a day to day basis in RevOps is all around change management. And you've got to get people bought into what you're saying. So I think that's a phenomenal business book. On the more fun stuff — I read The Daily Stoic last year, which if anybody's into stoicism and things like that, it's a good one page a day type of thing. Very much like, control what you can control and let go of outside factors — which, again, if you think about it from a RevOps standpoint, oftentimes the number of fires and bombs that happen during the day, you can only control what you can control. So anyways, love it.

Janis Zech: Thank you so much for joining. Wish you a great rest of your day.

Rhys Williams: Thank you so much. I always enjoy it. And feel free — if anybody has any questions, I'm always happy to do a sounding board session or anything like that. Much appreciated. Have a great day.