Talking about the connectedness of FP&A and Rev Ops working together are all-star revenue leaders: Jeff Ignacio, Head of Go-to market Growth and operations at Regrow Ag, Arvind Chahal VP, finance and ops at autotrader Canada, and Drew Noel, VP Revenue, SCS Cloud.
First, what is rev Ops?
“Rev Ops is the discipline of aligning go-to market strategy and execution through four key pillars: process enablement, advisory and systems excellence” (Webster dictionary definition submitted by Paul Igancio).
In this episode:
- How revenue operations and finance operations need to be working together effectively?
- The challenges and worst stories when rev ops and Finance fail to click.
- The worst “bad behavior” in a go-to-market org
- Data and cultural issues with finance and Rev Ops
- Moving career from FP&A to RevOps
- How to ensure we are defining and reporting metrics in the same way
- The importance of a “data dictionary”
- How AI will impact the operations function
- Favorite Excel function or feature
Our guests:
Paul Ignacio, Head of Go-to market Growth and operations at Regrow Ag
Arvind Chahal VP, finance and ops at autotrader Canada
Drew Noel, VP Revenue, SCS Cloud
Paul Barnhurst:
And so I’m going to go ahead and start by introducing the guests I have here with me. As I mentioned, my name’s Paul Barnhurst. I’m lucky enough to host this event and have three fabulous guests with me today. So why don’t we start with Jeff Ignacio. Jeff, can you go ahead and introduce yourself to our audience?
Paul Ignacio:
Thanks, Paul. Name’s Jeff Ignacio. I’m the head of Go-to market Growth and operations at Regrow Ag. We’re a mission focused climate company, uh, in the agricultural supply chain space. I’ve been in the revenue operations space for a good 10 years, and prior to that was an FP&A at companies like Intel and at Google. .
Paul Barnhurst:
Thank you. Arvin. Arvin, shell, why don’t you go ahead and introduce yourself.
Arvind Chahal:
Yeah, sure. And first, lemme see how honored I am to be on, on the panel, and I’m just excited as our listeners to, to learn from the three of you as I am to, uh, participate Currently. I’m, I’m VP, finance and ops@autotrader.ca. autotrader.ca. It’s a marketplace where Canadians can come to buy and sell, uh, vehicles. Uh, my current role, I lead a team of six awesome people where we partner across the business to, uh, ensure alignment, um, between strategic, operational and, and financial goals. Uh, prior to joining Autotrader, uh, I was in the rev ops space for about six years. I spent about four and a half years at, at Shopify, leading a couple of different rev ops teams. Uh, and before all that, I spent the first 10, 12 years of my career in, uh, capital markets, quantitative finance. So I’ve been very blessed to have had a few different chapters and adventures in, in my career. So, hap happy to be here today.
Paul Barnhurst:
Happy to have you. We’re, thank you for joining us. Arvind. Drew, why don’t you go ahead and introduce yourself.
Drew Noel:
Absolutely, Paul. Uh, first off, thrilled to be on, uh, honestly, huge fan of yourself and also Jeff, and great to meet Arvind. Uh, just, uh, stoked to be here. Overall, VP revenue at SCS Cloud. We’re a revenue operations and financial operations consultancy, uh, doing implementations, upgrades, alignment across primarily revenue and finance teams. Uh, and that’s, that’s our mission. Seen a lot in terms of my background, uh, mostly on the consolidated go to market operations side. Um, originally come from, uh, marketing, PR and also as an AE. Uh, then bringing into, uh, marketing operations, uh, sales operations. And then most recently at MadKudu , and HashiCorp before that, uh, a certain amount of CS ops as well. Interacted heavily with Jeff as well. Uh, you know, in his capacity. Uh, you know, I I would consider Jeff, uh, a mentor to a degree. And, uh, stoked to be on the panel with him, uh, in his capacity at, uh, rev Ops Co-op.
Paul Barnhurst:
Thank, thank you Drew for that introduction. I thought this was interesting, uh, comment we got coming in. We’ll throw this up here. Kelvin’s letting us know he’s transitioning from rev ops to FP&A as we speak. So, uh, I think many on our panel have done that. So they can relate either one way or the other from fp and a to rev ops or Rev ops to FP&A. So thank you for sharing. Just to, uh, mention, we have guests coming from all over the globe. All over the globe. We got Canada, Ukraine, Romania, Madrid, London, Singapore, Mexico. So keep sharing where you’re coming from. Feel free to ask questions throughout our conversation. And where I wanna start, and I’m gonna guess, anyone who, especially has worked in a small company or spent a long time in FP&A, has probably seen one company define rev ops this way with these tasks and sales ops, with these tasks and another company differently.
Maybe you’ve seen Rev Ops in FP&A, maybe you’ve seen it sit in sales ops. Maybe you’re not sure what they are and how they all work together sometimes, because it can be very different from company to company. And I see some smiling from the panelists. I think they can relate to that. So why don’t we start by giving each of you an opportunity to just state how you define revenue operations. What do you think of when you hear the term rev ops? And for this question, why don’t we start with you Arvind on this one.
Arvind Chahal:
When I think of rev ops, I really think of that function that helps keep all the different go to market pieces aligned. And so go to market typically, you know, some combination of sales, customer success and, and marketing. And so rev ops sits at the center there, uh, and really make sure that those teams have the right systems, uh, the right tooling, the right technology, the right reporting, uh, the right operating rhythms, uh, to keep all those functions aligned and, and make sure that that go to market strategy is, um, how, how execution against the strategy is, is occurring. And, and also identify where there’s opportunities to, to identify, uh, new strategic opportunities.
Paul Barnhurst:
Thanks, Arvind. How about yourself, Drew? What would you maybe add to that or change from your perspective?
Drew Noel:
I agree with Arvin on the majority there. I think the operating cadence is central. And I think you’ll hear that probably from Jeff as well. The main point is, is that I would divide strategic revenue operations versus, uh, deep or entrenched revenue operations being, uh, a split between directional or advisory level information, strategic level information, forecast capacity planning, which is, which are often owned by FP&A organizations. I would consider that strategic rev ops. Whereas you have, um, the aspect of entrenched or systems level, rev ops being that data supply chain function that feeds those decisions, right? So they are truly interlocked. There’s no way for them to exist without one or the other. Um, but how you, how you organize them really depends on the organization that you are in and how that organization goes to market.
Paul Barnhurst:
Thanks for that, Drew. I agree. Uh, Jeff, your thoughts on this? Yeah,
Paul Ignacio:
So I’m working on getting this in the Websters dictionary. I have an inquiry, so hopefully they’ll follow up with me. Uh, but I have to find it as the, uh, revenue operations as the discipline of aligning go-to market strategy and execution through four key pillars. And those four pillars are process enablement, advisory and systems excellence. So strategy and execution. PS in a pod, PEAS, process enablement, advisory and systems.
Paul Barnhurst:
Wait, say that again one more time. I gotta make sure I
Paul Ignacio:
Got it right. Enablement, <laugh>, uh, process enablement, advisory and systems.
Paul Barnhurst:
Got it. Process enablement, advisory and systems. I like it. It took me a minute, but I got there. So let’s go to the next question. We’re gonna start with, uh, one here. Now that we’ve de talked a little bit about definition. How have you typically in your career, and I use the term typically loosely, but how have you seen revenue operations, finance operations, kind of working together during your career? How, how have you seen that, you know, kind of the functions coordinate and work together? And Drew, why don’t we start with you?
Drew Noel:
Sure. Uh, well, I can speak to the first operations role that I took on was transitioning from a director of corporate marketing and a media firm to, um, to really like head of marketing operations. And I reported directly in, in that capacity to the CFO, which is really an interesting point. And I think, you know, whether that makes sense for operations to report directly to CFO or not, is really, again, dependent on the organization and kind of the, the, uh, I would say go-to-market savvy or maturity of the organization, right? Because in terms of a consolidated CRO role, that may make more sense, but I really enjoyed reporting directly to the CFO because of the accountability within campaign reporting within metrics, um, alignment to finance, uh, alignment to budget, right? And, and being able to really have that quick back and forth facilitated with the financial wing of the organization, and then report back on findings and results to the finance org.
I wouldn’t say that that’s typical necessarily, but I will say that that’s how I started my career. And I found that that was also a really fascinating aspect, um, that was echoed in a later point in my career, uh, when I was at HashiCorp. You know, if you look at the go-to market functionality that was, uh, in place there and the firm ended up going public and, you know, just really had a great scale, uh, period. The, the organization also was headed, uh, by somebody who had an accounting background. So the CEO there, right? Had an accounting background. And I think that rigor in terms of the go to market and also the finance organization was really deeply entrenched there. And we worked very closely with finance, you know, in terms of quota structure, commissioning, uh, you know, the alignment with, you know, we also rolled out CPQ during my tenure there. Um, and really making sure that the go-to market organization was aligned with the capability to not only meet targets, but also meet them effectively in terms of a, a degree of variance within, uh, the financial expectations.
Paul Ignacio:
Okay. Appreciate that, drew. Jeff, how about yourself? Uh, so I’ve been at different stage firms, um, series A to B, old way up to, um, you know, publicly traded companies. Um, and they’re, they’re quite different, um, depending on, on the stage. So your series A, your series B, uh, there’s a lot of, Hey, you’ve got this. If you don’t, don’t worry, I can cover you. So, you know, you have a left fielder and a center fielder to use a baseball analogy, the ball’s flying in between those two outfielders, you’re both probably gonna converge. Uh, so there’s a, there’s a lot of coordination that needs to be done between, uh, the two outfielders there. But, so several deliverables where we overlap resource allocation primarily around headcount planning, um, operational expenditures and tooling, looking at unit economics, trying to drive customer acquisition and payback period downwards. Looking at cost of retention, cost of retaining, uh, or cost of renewal as well for the ongoing customers.
So a lot of those areas we’re gonna work together. So board decks, QBRs, MBRs, a lot of that, uh, executive level reporting. Then there’s some tactical things that we might be working on. So closing the books, when a deal closes, you have your some closed one processes, so you’re talking about deal desk all the way to pricing, quoting, making sure we’re hitting the deal matrix, moving the closed won. And then once we closed won, you’re handing it off to finance for all the invoicing and, and moving into the billing platforms. Um, other areas, uh, might be, um, you know, tooling. I think we’ve gone through a period of cutting, at least in the SaaS space. So 20% discretionary cuts across the board, across software, maybe even people. And some of that, you know, that 20% mandate doesn’t really look at the specificities of how that impacts the go-to-market capabilities.
And so we’re trying to figure out as good stewards and how can we work with our finance partners to drive those costs downwards at the publicly traded companies. There’s, there’s a little bit less overlap, but it, we start to bleed over quite a bit more. Uh, once we’re doing, uh, large enterprise deals, we’re in the deal room together. I’m reviewing it from a go-to-market perspective, looking at the gives and the gets in terms of negotiations. Finance is looking at it from the perspective of making sure it’s kosher with all of our ability and our deal matrices. And then there’s also the annual planning side of the house. Annual planning side of the house is, you know, from August through January, <laugh>, uh, we are hooked at the hip. We are working together V one plan all the way to V 22 and V 22 final
Paul Barnhurst:
Only 22.
Paul Ignacio:
Only 22 <laugh>. So don’t get it twisted. If you get to 23, you’re taking too long.
Paul Barnhurst:
Yeah, no, you forget if you get to 22, you’re taking too long. But I didn’t say that. No. Um, uh, Arvind, anything you wanna add?
Arvind Chahal:
Great, great responses from, from Drew and Jeff there. Um, you know, when you asked the question, the, the word that immediately popped to mind is partners. Like I said, most, most of my time has been on the rev ops side, and now I’m on the other side of that FP&A. But I think of the two teams as working together and partnering together to ensure alignment between go to market and financial goals. Uh, when I was on the rev ops side, most of what we did, the finance team, uh, was involved in some form or another, you know, similar to like what Jeff said, whether it was more tactical things like weekly metrics reviews, you know, certainly finance was there, whether it got to more, uh, planning, um, projects like, uh, comp setting, uh, annual planning, uh, and budgeting. Uh, again, rev ops and finance, uh, usually, you know, partnering together.
Give give you quick anecdote of, uh, what, um, something that’s going on right now for, for us at Autotrader. You know, our product team, uh, they’re thinking about making some changes to the product roadmap in, in the back half of the year. Uh, and, and that puts some of our revenue, uh, budgeted revenue at risk. So my team and the Rev ops team, we’re working to identify, well based on the changes in that product roadmap, you know, what’s the revenue at risk, uh, with the commercials, uh, uh, the commercials of the product changing. Um, and then what’s the plan to sort of plug, plug the hole? How are we gonna migrate customers from that one product to, to the new product? You know, how much do we think we will successfully migrate? Which ones might, might there be churn? Which ones can we give some offers to try and save, et cetera, et cetera, right? So, you know, there’s a, there’s a problem that the finance team has identified, and we’re partnering with the, with the rev ops team to figure out, okay, how are we gonna manage this, this risk? And, and is there even op new opportunity because of this change?
Paul Barnhurst:
Thank you you for sharing that. And so, you know, a couple things that we, we have heard in our conversation so far. One, it’s critical for rev ops operations, FP&A to be coordinating to work together. The second thing you’re hearing is, it’s gonna be different depending on size company and leadership. The tasks have to get done, but exactly how it’s distributed will vary a little bit. But the, the key message, and I think we’ll come back to this again and again throughout this conversation, is the importance that we’re all working together and that we’re all coordinating, and that there are some clear differences between the tasks, even though at times they can be blurred or a little overlap. I wanna ask a question here. So I had, I don’t know how many people may know him, but I had Scott Soffer, I think you know who he is, Jeff, right?
Right. So he is been A-C-E-O-I think about five companies now from beginning to public. And we had quite a conversation around rev ops, and he shared that he thinks Rev ops should be under FP&A, if, you know, analytics and deep analysis, really the focus is what you need from that go to market team. He did say though, if you have a really strong CRO, someone who’s more than just a sales leader, that a lot of times in small companies you see a CRO who really just manages sales. They don’t know marketing in the other areas. He said, if that’s the case or if your focus is really around the tech stack, it makes sense to have it under the CRO. So I’m just curious, we’ll start with you, Jeff. What’s your perspective on that? How do you, how do you think about that?
Paul Ignacio:
Yeah, I always think the org question is a little bit of a, a misnomer, right? So what you want go-to market to do is have the ability to push back and inform decisions grounded in data that tell a story and a narrative, but it’s informed by the data within your organization, or at the very least, bringing in third party benchmarks that are relevant to your peer set. And that’s from the data side of the house. From the process side of the house, you’re playing the role of more of chief operating officer to the go to market organization. So revenue rhythms, operating cadences, making sure that you’re running forecast calls, pipeline calls, win-loss reviews on a regular cadence that then bring in the right data pack, the revenue rhythm, and the right feedback loops that then allows you to adjust the organization in real time.
That can happen under both banners. It doesn’t need to be under the CFO or the CRO, I just think it has to be able to, one de-conflict the areas of interest and play a lot of offense on the areas where go-to-market can be go-to-market ops can be completely helpful. So on the sales process improvement, marketing process, improvements outta the house, bringing in the right data, the right data in real time has to be for some organizations like front and center on a daily or weekly cadence. Now, if you’re in the finance org facing with the go to market org, that daily or weekly frequency can be hard to come by because you’re not necessarily embedded deep within the Go-to-market team. So I’ve seen, uh, I ran a poll on LinkedIn recently, 300 responses. So N 300 and over 50% of responses said they reported to the CRO. Um, and then another 13% reported to cl. So you’re talking about, you know, five eights of individuals reporting to the revenue organization. Mm-Hmm. <affirmative>. And I think that works for many companies.
Paul Barnhurst:
Makes sense. And I think you make a good point, a little bit of a misnomer. It’s really more about do you have alignment than who should own what, right? I see everybody shaking their head. That’s really the most important thing. Like, I’ve also heard the, uh, conversation who should own data analysis? Like it can be in IT, it can be in finance. I can make an argument for both. I think there’s a real argument for finance, but I get totally sitting elsewhere. It depends on the leadership and what makes sense for your company. And so, you know, you bring up the key point to that whole question, and it’s really about are you guys aligned and is the work getting done? And are you de-risking the conflict and working together versus this is my territory, so to speak. Arvind, any thoughts you wanna add to that?
Arvind Chahal:
No, I am, I think, I think Jeff, um, you know, RA raised a really great point that it’s less about where it sits in the org and making sure that the, the function is operating whether to, to hit on its mandate. If I had my default way it would be in, in a CRO’s house. Um, to, to Jeff’s point, because I think it makes it a little bit easier for a rev ops team to get, um, you know, that time with their, their go-to market counterparts. And then they can still be partnering with, with other teams across the org, like the finance team for example. But, you know, every rev ops team starts somewhere. Uh, they, they’re not just emerging as some fully functioning, uh, ma mature group of people. Uh, I’ve definitely seen or heard of rev ops teams Yeah. Starting in the finance function or even starting in like a marketing function or something like that, right?
So they’re probably gonna start somewhere, and where they start is probably gonna dictate where a lot of the initial strengths and focuses of the team is. But I think the, the point is, as the org grows and matures and evolves, the rev ops function needs to grow and mature and, and evolve as well. And then you’ve gotta think about, well, where, whereas the, the place that where’s the right place in the org for this function to sit or, or these, you know, collection of functions to, to sit so that it can hit on its mandate.
Paul Barnhurst:
I appreciate that. You know, we have a lot of great conversation going on in the chat. I’m not gonna read through all of it, but I thought, you know, Ahmad interesting here also depends on what the CR owns. You know, this gets back to what I said. If the CRO only owns sales, for example, and not all revenue, rev ops may be pressured to be less neutral and more biased, I think, you know, there’s some validity to that. I like this one. If CRO only owns sales, they’re an inflated VP of sales, we’ve never seen that at a company, right? Because everybody nods their heads and
Drew Noel:
Laughs just, just chuckles.
Paul Ignacio:
If you call, if you call a CRO glorified sales leader and you call, then you can call rev ops a glorified revenue IT shop. Yeah. Um, is a little facetious with labeling.
Paul Barnhurst:
Yeah, it, it is. But I mean, you know, you do see times when CRO is really just focused on sales versus the owning the whole thing. And so you just gotta think through all of it, I think is the main thing. I like that there. Kelvin spicy. I like it. Alright, so we’re, we’re gonna move on past that question to a, to the next one here. This one’s for you, Drew. So, you know, obviously as a consultant you work with a lot of different companies. What do you see as the key challenges to ensure that Rev Ops, FP&A, that they’re on the same page in their working together? Have you seen any kind of key issues or trends there when dealing with companies?
Drew Noel:
So I’ve, uh, you know, and, and I think it’s exacerbated too ’cause I I work largely in the mid-market, um, and you know, those companies, uh, a lot of private equity owned shops that are, you know, really trying to get their financial rigor in place, their accountability in terms of, you know, target attainment in place, things of that, things of that nature. Yeah, I mean, as Jeff spoke to the unit, economics really become a focal point. A couple of stories and I’m, you know, trying to anonymize them as best I can. But I had one interaction where I was speaking to A CFO and a head of fp and a only, and brought up the point of Rev ops, brought up the point of alignment to their CRM and also their sales and go-to-market organization overall. And it was the strangest response heard was, well, we don’t really care about the data quality on the actual CRM, we don’t care about, you know, how that’s all working for the sales organization directly because we’ve built it into the close that literally the close of the books and the model would just account for all of these errors.
And the person who had basically built the model on the FP&A side was just accounting for all of these random variables. And I was like, well that sounds really nice in terms of your direct mandate, but I wouldn’t wanna work at that sales organization either. And having, you know, myself, I mean, as a seller myself now, and also as a go to market person overall, right? Because I do care about marketing, I do marketing every day. I, you know, I’m also very involved in our customer success process. I just kind of went, that sounds like a real rough scenario for the actual experience of that data supply chain, the systems and the visibility within the go-to-market organization overall. So that was a very interesting experience there. Um, I’ll also say that I think if one of, one of the points that I think FP&A, uh, especially in consumer product and, health and beauty, that’s a strong category for us.
Um, they often own the allocation, right? And unless that’s well coordinated with the marketing organization and some of that actually sits within marketing those allocations, they can be misallocated effectively, right? Because the model might not be correct. And making sure that there is really strong, uh, I mean this goes back to my very early career was in consumer products and direct marketing was, you know, the aspect of being able to take the campaign performance and directly turn that around on a fast basis and get the new allocation established in order to optimize that budget and potential marketing performance as well, just in terms of your, your ROMI and your ROAS. So those, those points are really interesting, um, at the top of the funnel.
Paul Barnhurst:
Yeah. And one thing you said there really struck with me as you shared, Hey, we don’t care what they do, we just fix it all on the back end,
Drew Noel:
Right?
Paul Barnhurst:
If, if you’re doing that stop and have the conversations, you may have to continue to do it for a time. I’ve done it. I’ve fixed a lot of things on the back end. But you always need a long term, hopefully short term strategy to fix it on the front end. ’cause it makes no sense to be like, you could have all your data wrong, we don’t care. We’ll get it right over here. So they’re doing analysis on data that’s wrong. And you’re doing it on analysis that’s right. And then what happens, you get into a, a meeting and Joe says, I have 1.2 million for cac, and Pete says, I have 1 million. You spend the whole time fighting about right. Why it’s right or wrong versus accomplishing anything. And I can see Drew laughing. I’m sure Jeff can relate to that one as well. So that, that would be my comment there is always try to fix the, the data at the source.
I have been in roles, I worked in an FP&A role where my whole role was coordinating between sales OS and Rev ops and trying to help fix everything in Salesforce. And I was constantly talking to them versus I’ll just figure out how to fix this on the back end. So I appreciate you bringing that one up. So we’re gonna shift here a little bit. You know, Jeff, you mentioned this earlier, but I want to go back to it. You mentioned the four pillar, the four, uh, pillars for rev ops, right? Process enablement, advisory and systems. Why are, why are those the four pillars in your mind? Why, why are those the four key things? I
Paul Ignacio:
Think the original triangle that folks will say often, uh, with any sort of center of excellence is gonna be people, process and technology. I think we’ve gone away from using the word center of excellence, but a lot of what go to market does truly harkens back to those days of center of excellence and, you know, really borrowing from people, process and technology. However, I do think there’s something that’s missing, which is the concept of decision making. And that’s where I, I think advisory comes into play. And it goes beyond, you know, cleaning up the data that’s table stakes, getting to a place where you can perform analysis, deriving insights, not only deriving insights, but also marrying that with the business acumen to then be a revenue leader onto yourself and partnering with your sales product and marketing leaders so that you can, you know, achieve those outcomes that you’re looking for.
So it’s really racing up a data maturity and the decision maturity curve. And then there’s also the enablement piece. So I think a lot of times folks can make some changes on the process side of the house or the system side of the house and not necessarily communicate that effectively to the team. So change management, driving new behaviors, removing bad habits from the organization. That has to be part of, I think, what a qualified go-to-market organization, uh, can do, uh, for kind of the modern, you know, kind of hypergrowth phase of the company. And so that’s why I believe that enablement and advisory are two key pillars. And then process and systems are, have always been there. Um, I didn’t include people, I, I just figured that’s obvious. But now I was al I was also trying to fit a mnemonic peas in a pod. Uh, so that’s why also why I went with those four pillars.
Arvind Chahal:
I think. Yeah, I think those, those pillars can apply to finance as well. Um, you know, finance has processes, um, that just help people understand how the business is performing. Uh, the FI finance team can partner with other groups, product team, marketing team, like whoever enable them with a little bit more fi financial, um, uh, fluency help help them understand how, how their business is per their part of the business is performing and, and affecting the overall company. There’s, there’s obviously that advisory piece of it, uh, where, where your, the finance team is working with, with leaders to, to help them make strategic decisions, decisions. And then of course you’ve got, uh, you know, systems all over the place to, to collect and consolidate data to, to help you with your analysis. So I love, I love that that framing that Jeff has developed for, for rev ops and, and I think really astute finance leaders will actually see their function in a very, very similar way and figure out how, uh, how their function, however their finance function can add value in those four pillars as well.
Paul Barnhurst:
Yep. Alright, so I’m gonna go here to a few questions. I think that’s triggered some questions from people. So Bill asks, and this is shifting a little bit, and then we’ll go to the next one. He asks, what are the typical bad behavior you have seen in a go to market org? And I see Drew chuckling a little bit. Um, I’m curious, you know, Arvind, maybe your take, what are some of those things you see that you’ve seen done that you’re just like, why are we doing it this way outside of a messy salesforce? Right? Does anyone have a clean sales force?
Arvind Chahal:
I was gonna say that, yeah. The first one that came to mind is just like bad reps habits of, of, uh, entry and stuff. But hey, that’s, that’s like on the rev ops team then to figure out like, yep, Hey, how do we reduce the, the overhead of, of data intake? You know, I think one bad habit of, of any go to market org is start doing like finger pointing. And, you know, the sales team is pointing at marketing saying, ah, you’re not giving us enough leads or doing enough demand gen, or blah blah, blah. Our marketing is saying, Hey, we’re throwing you stuff, but you’re not doing anything with it. At the end of the day, uh, we’re all responsible for, for revenue. And, and yeah, we’ve got, you know, our part of it. Marketing got their part of it, sales got this part of it. Customer success got their part of it. But like, if, if the leads that are coming through and sales is feeling like they’re not quality, like marketing and sales has to figure that out together, that’s not a marketing problem. That’s not sales problem. That’s, that’s a go-to market org problem. So that’s, I think that’s a, a bad habit that I’ve seen a few times where people just try to point fingers and, and forget that you are all one go-to market team.
Paul Barnhurst:
[Datarails ad] You know what? It is, like 13 different spreadsheets emailed out to 23 different budget holders, multiple iterations, version control errors, back and forth updates you never really feel in control of the consolidation and collection process. Yep, I’ve been there. Stop. Breathe.
Data rails is the financial planning and analysis platform for Excel users. Data rails takes data from all your company’s disparate sources. No organization is too complex, consolidating everything into one place, secured in the cloud. Now all your data finally talking to each other, everything is automated. Back into your report in Excel. Cashflow, FX conversion, intercompany transactions now automated and UpToDate drill down and variance analysis in seconds. Don’t replace Excel, embrace Excel, turn your Excel into a lean mean fp and a machine. Find out more@www.data rails.com.
I see two really good points in there. One, the data that’s often, you know, you see bad habits around data and who’s responsible for it, inputting it. I think the second, which is a major cultural problem, and I’d say is more important, is when you start getting into finger pointing, right? If people aren’t a line and they’re not working together, it doesn’t matter if your data’s good or not, you’re gonna have a lot of problems, right? You gotta have that cultural understanding that I am responsible for helping the team be successful. Jeff and Drew, anything either of you would like to add to that?
Paul Ignacio:
I’ll name a few
Paul Barnhurst:
All right, go for it, Jeff.
Paul Ignacio:
Yeah. Uh, I’m sure this has never been done by any of the folks, uh, who are on the channel, um, selling outside of the menu. So <laugh> typically your business, it doesn’t operate like in and out, I’m assuming there’s no secret menu. The gummy bear milkshake, uh, bypassing discounting policies, right? Just willy-nilly handing out discounts without any checks, focusing on the wrong channels, not following up with leads, not tracking renewals, focusing on the wrong segments, paying first class
Paul Barnhurst:
<laugh>. Uh, that’s a good list. So I’ll, I’ll, I’ll share one. I ran into at a, we had one where our Salesforce had been set up in such a way I had to approve as the director of FP&A, any deals over a certain percentage. And I just got promoted. I got my first deal, one of the first ones I got, and I denied it ’cause we were gonna lose it, like made no sense. I’m like, why did you sell it at this price? And I immediately get a call from the sales guy, well, the customer’s already signed the contract. I’m like, wait, wait a second. I haven’t even approved it. He’s like, yeah, we can print ’em before you approve it. I’m like, so why am I rubber stamping this? Like, what’s the point here? And I immediately went to our ops team and said, okay, we need to fix Salesforce.
And I told the salespeople, yeah, we’re not, we’re not doing that game anymore. But I did it in a way where it said, come to me if you need to do a discount and let’s discuss what makes sense. Help me understand what you’re doing with this customer. Not just, okay, it’s 15%. No, you know, not just trying to be the no person, but really working with them. And it, it ended up being a really good thing in the long term because I developed some great relationships with our sales team and they started to realize I was a resource to help them. Not a hindrance, but initially they weren’t, they were pretty mad at me. They were not happy ’cause they were being allowed to pretty much do whatever they wanted. And I’m like, yeah, not, not a good idea, right? But I tried to not do it in a finger pointing way, like, what are you guys doing? It’s like, how do we solve this together? So that was a good learning for me. Just when you said that, Jeff, it reminded me of that experience. Alright, so if we we move on here, I wanna ask a kind of a little bit of a different question for you, Jeff, because I think you’re the one here that went from FP&A to rev ops. Why’d you make, what, what, uh, brought you to make the change? Why did you switch?
Paul Ignacio:
Prior to finance, I, I was in sales, so I wore the bag. Uh, I wore a bag and, um, you know, I thought for sure I was never gonna go back into supporting the sales organization or be anywhere close to go to market. So I ended up gonna business school pivoted to FP&A. And sure enough, at Google I was the sales FP&A partner, and I remember asking myself, how did I get here? Oh, yeah, okay, well, you know, it’s fine. I started enjoying the role and I partnered so much with the sales operations team that quite frankly, we were doing a lot of, we had a lot of overlap. We talked about this earlier within the hour. And, uh, quite frankly, I, you know, found a lot of joy in what I was doing. So, um, made switch to, uh, sales operations a year later at a hypergrowth company there, I, you know, brought a lot of what I did in finance, so a lot of data analytics, um, taught myself some, some hard skills like SQL BI, um, you know, really getting the data out of, uh, you know, raw database and then surfacing those insights and working face-to-face with the, the sales organizations to get closer to the customer.
I actually wanted to get more towards the strategic side of the org and the acquisition side of customers. And I thought sales operations was a natural extension, but there’s not a data that goes by even now, 10 years later where I don’t leverage a lot of the skills that I developed during my FP&A days.
Paul Barnhurst:
Great. Thank you for sharing that. And I’ll, I’ll add two thoughts here. You know, one, I I’ve said this a number of times on LinkedIn. I encourage anyone working in fp and a to try to get at least at one time during your car career, a role outside of fp and a. I think operations is a great place. It just gives you appreciation for the rest of the business that if you spend your whole time in finance, you often don’t get. And I imagine Jeff could tell some stories having seen both sides of that. And the second thing he’s mentioned a couple times in others have the importance of insights. And there’s a quote I love from, uh, former, uh, Jim Cook, former CFO of Netflix. He said, remember, your product is not a spreadsheet. Your products are the analysis and insights you provide. And I think sometimes we get so focused on the, the spreadsheet and what we’re building that we forget the purpose. And so I think you brought up some really good points there. So next question I have here is for you, Arvind, you know, something we often see, I think a real challenge we see is making sure we’re all defining and reporting metrics the same way, right? You see, sales may have one, thought marketing may have another customer service, finance, et cetera. Anything you can share with us? Any advice on how you go about making sure they’re aligned? Yeah,
Arvind Chahal:
You know, when, when we’re talking about a metric, I think there’s sort of three, three who’s to it? Uh, there’s, who defines it, who reports it, and who’s accountable to it? And ideally it’s not the same team or same person that’s, that’s doing all three. Um, uh, you know, you hopefully there’s a little bit of a, a separation there. Uh, but you know, I think the defining it and, and reporting it, if that team’s the same, that’s fine. But, um, I think the first step is to define on those sort of three, you know, three areas. Who’s, who’s the owner, who’s, who’s the person who’s, who’s gonna be defining it, who’s gonna be reporting it? And, and ultimately who’s accountable to that metric? So, you know, super quick, simple, simple example, something like, uh, I don’t know, weighted, weighted pipeline. Um, you know, maybe it’s the rev ops team defining, uh, how the weighted pipeline gets calculated.
Uh, and then it’s the, maybe the rev ops team or the data data team. They’re actually, you know, building up the reporting, uh, infrastructure around it. And, and then ultimately it’s, it’s the sales team that is sort of accountable to, uh, the size of the pipeline versus revenue targets. So I think having that common understanding of, well, for a metric, who’s the person who’s, who’s actually owns the definition of it? Uh, who who are we entrusting with, giving us accurate reporting on it? And, and they’re keeping on top of the, the data systems, uh, or the data supply chain. I think Drew keeps saying, I like that term. Um, uh, and, and ult and who’s responsible for how that me’s performing? Having clarity on those I think helps keep everyone aligned on, on ensuring you’re looking at and defining reporting metrics the same way.
Paul Barnhurst:
I appreciate that, Arvind. I really like how you broke that down into the three buckets. The defining, the reporting, the accountability, and the importance of not having it all sit with the same person, right? Having what we wanna call separation of duties in finance often, right? Drew, I see you nodding your head a lot. What, any thoughts or anything you wanna add to that?
Drew Noel:
It’s actually a cross between systems or process systems And the enablement aspect of things is really having a structured data dictionary within your organization. I mean, really at an actionable level, that is something that I find missing all the time or something that’s grossly out of date. So much so that recently, uh, from an advisory perspective, I engaged with a client and, and it was the first thing I brought up, like their pre-revenue. They’re, they’re actually pre-processed to a large degree, but they do have a PLG function. And I immediately said, look, what’s, what’s your data dictionary in terms of the product tracking and the tags and making sure that, you know, your naming convention is tight, you know, your actual definition of what all of those things are so that you can do aggregations and rollups and actually start to track product-led growth metrics accordingly, right? And then that builds into, of course, your modeling later on, right? Because you’re looking at the bridge between product-led, uh, growth overall to self-serve conversion, uh, or, or self-serve pay to, uh, eventually a product-led sales model, right? Where you’re bridging to the enterprise motion after that. And I think all of those things, I mean, really one of the most critical steps is just data dictionary, making sure that you have those data points tagged, aligned, mapped out, and everybody knows what,
Paul Barnhurst:
Since you’ve been, uh, our fun ones to discuss data dictionary, we’re gonna throw this question your way, drew. Should the data dictionary be like Wikipedia or Webster’s, if the latter, who owns it? Any thoughts on that?
Drew Noel:
Yeah, I, I mean, I, I think it should be like, it should be a, a combination of the two. So I would say that, you know, if you have a relatively decentralized organization, it should be like Wikipedia with a re with a review function, right? Where you have somebody who’s the ultimate arbiter of acceptance or, uh, qualification, right? And I would say that that needs to be probably at the C level, right? Because I mean, you know, it finally rolls up there. But of course, like tracking all of those specific points. And I think it could also, you know, a strong vp rev ops, um, could definitely take that role and say like, no, this is what everything means and this is the tie breaker and this is how we’re gonna adjudicate the final definition. So there needs to be an official final document, but it could be crowdsourced in terms of the, the base level documentation.
Paul Barnhurst:
I agree you could do the base level, but at the end of the day, you need somebody who owns it and says, Hey, this is the final definition. So I think ultimately it’s more like Webster and that someone has to own what that true definition is. But I think without having a little bit of that Wikipedia where people can comment and provide their input, it’s hard to get buy-in
Drew Noel:
A hundred percent.
Paul Barnhurst:
I can still remember one company I joined and we were trying to switch to be more of a SaaS business and having the conversation of just trying to align with the CFO and the CRO and a bunch of people what a booking was. Yeah. You think something as simple as that would be easy? No, there were all kinds of definitions for a booking. And so it’s really important to have those conversations and make sure people are heard. ’cause it can be a real challenge. But, you know, one thing it leads to is the data dictionary and good data is even more important today as we see more and more AI coming because AI relies on lots of data. And so I’m curious to get all of your guys’ thoughts on the panel here. How do you see AI impacting the operations function maybe today? What have you seen, you know, kind of in the last year and in, how do you think about it in the future? And then if you’re using it, maybe just a little bit of how, so I guess on this one we will start with you, Drew.
Drew Noel:
I actually think that, you know, somebody who’s, who’s dug in on this more than any of us is actually Jeff. Um, I saw some interesting posts from him recently, uh, on this exact point, but I will, I’ll speak to one point, which is enablement. Enablement is probably going to shift in a direction of using, you know, taking that brain trust of the organization if it’s codified at a reasonably structured level. And you would probably see more and more enablement level GPTs across an organization, right? So that pillar of revenue operations, I think will largely move in that direction. Um, and I think that’ll be great in terms of just data distribution and democratization access to that information overall. Um, but that’s, that’s probably the primary point that I see becoming largely automated in the near term with, uh, generative AI at least. Okay.
Paul Barnhurst:
And we’ll go to Arvind here next, then we’ll finish with Jeff Arvin, what are your thoughts on this?
Arvind Chahal:
AI is gonna help just open up, um, time and capacity, uh, for, for, for people where you can get a lot of the, the lower value activities outta the way through, through AI, and, and you can start, uh, leveraging AI to, to answer some, uh, some insightful questions. So I gave the example of, um, earlier where, oh, our product team, uh, is changing the roadmap in the back half the year and that that introduces some revenue risk. So, um, I can, I can see, you know, leveraging AI to be a bit of that advisory function that Jeff’s talked about before, that, hey, we’ve got this revenue at risk, these are the, some of the parameters of the situation. What are, what are some potential options that the team, team, team team should explore? So that’s, that’s one. And then, um, one, one area where, where I’ve been pushing my team, uh, on leveraging AI is just skill development, where I’ve got them to develop some scripts in VVA and, and Python folks who haven’t done that type of work before.
Um, and, and I’ve challenged them and, and I’ve said, Hey, you know, here’s a couple of, you know, relatively, uh, against low, low, low value tasks that, that I think you, you folks do, um, that could be automated a little bit more. Uh, just go to chat GPT, describe what you needed to do, ask it to, to produce some code in Python or VBA, uh, and, um, you know, start automating some of your work. So, um, that’s how my team has been leveraging it is, is in just, um, short, short circuiting the, uh, the learning curve of, um, uh, developing some, some new skills, uh, where when they identify some opportunities for, for automation or, or some need for some code development, um, uh, they, they get, they get 80% of the way there. They like half an hour by leveraging, uh, like chat GPT or something like that.
Paul Barnhurst:
Yeah, no, that’s a great way to use it, is definitely to, uh, shorten the learning curve. It can definitely help with that. Jeff, what are you seeing? What’s your thoughts?
Drew Noel:
Yeah, so I’ve had the
Paul Ignacio:
Pleasure, pleasure of actually interviewing quite a few founders in the AI space, particularly for my newsletter. But ultimately, I think what this leads to is increased worker productivity. So, for example, the amount of a ARR CSM can support the number of accounts that a sales rep can prospect into, um, the amount of lines of code that an engineer can produce and push into production. So you just think about those ratios. What we classically viewed as, you know, heuristics for whatever work use case that is, those ratios are probably gonna start moving up. Now the question is, is it gonna be incremental or is it gonna be exponential? And those are the curves that just, I’m not quite sure, but you can make the argument that certain engineers today with the, with the aid of AI and their GitHub repo or in their code editor, you could probably be four x more productive.
You could probably push more codes than ever before. And maybe we see that up and down different roles in finance and, and rev ops. And I know right now we’re talking about LL out as a model, but at some point these models are gonna turn discreet and you’re gonna be able to do real math in a meaningful way at scale in a lot of these models. So a couple of use cases that I’ve toyed around with, um, content creation, both internal external internal documentation training, knowledge basis, um, even something as small as description help text fields inside of your Salesforce external. A lot of folks are now trying to prospect with ai. In fact, when you go into a chatbot for customers service, you’re probably not going through a decision tree anymore. You’re probably interfacing with some sort of, uh, AI that’s now taking the context of what you said and blending it with the right data points, cookie tracking for example, and then sending it down to the right knowledge base or channel.
Uh, next research and context. The hardest working intern is a term that I’ve thrown around quite a bit. It may not get it right, but hey, it does the amount of research and a fraction of the time <laugh> and 70, 80% good enough. Um, I’ve used it for an account scoring project. I can’t use, you know, data providing tools to get the data insights that I need to score my accounts. And so I’ve just fed it PDFs and URLs and it’s done the work for me. I’ve mentioned copilots. So debugging, if you’ve ever wrote a complicated if statement and side of Excel, or if you’re doing a, you’re writing a complex query on a data editor, um, it could debug it and probably shrink it by a factor of two or three. And then at the application layer, there are tools being built that are not just sprinkling AI on top of there tooling, but you’re talking about unlocking true natural language processing, the ability to speak out loud and then have the AI listen speech recognition, and then turn it into the results you’re needing and feed fetch the reports you want. And then not only that, I think these, um, no code, low code seamless workflow builders are only gonna get built better.
Paul Barnhurst:
Thank you, Jeff. I think there’s a lot of great points there. And the reality is it’s here, it’s here to stay, it’s making us more productive. It will continue to do so, whether it’s rev ops, FP&A marketing, sales, engineering product. And if you’re not learning how to use it, you’re missing out. I think that’s just the reality of it today. So we’re coming up near the end of our time. If anyone else has any questions, please throw those in the chat now. We’ll try to get to a few of them. So I have two more questions for each of you. And the first one here we’re gonna ask is, what is your favorite function or feature in Excel? This is something we ask all our guests, so you’re all gonna get it. Hmm. Should I pick on first? We’ll start with you, Jeff, on this one.
Paul Ignacio:
I don’t even use Excel. I use Google Sheets, <laugh>, um,
Paul Barnhurst:
Come on. You’ve used Excel in your career though. Oh, you did work for Google. Fine. What’s your favorite feature functioning Google Sheets?
Paul Ignacio:
Uh, I have a few. So, um, I’m a big fan of the query function, big fan of, um, filter, uh, and then comp, combining it, but filter unique and counts.
Paul Barnhurst:
I use unique and count a lot. Not so much filter, but yeah, that I use filter a fair amount, just not that combination. That’s a great one. Appreciate that. And yes, uh, we have Ezra saying pivot tables and concatenate. Feel free to add yours in the chat, what your favorite one is. We always like to see ’em. Arvind, how about you?
Arvind Chahal:
I know a lot of your guests like to, to mention like a lookup function. Um, so I won’t, I won’t repeat that, but if, if you remember, I said I started my career in quantitative finance and a lot of quantitative finance is built on, uh, a form of modeling called, uh, stochastic modeling. So trying to model, uh, random processes. Um, so I’ve, I’ve actually built a lot of stochastic models, uh, in Excel. I, I wouldn’t recommend them for any production, uh, purposes, but you know, for little hack jobs, uh, just kinda getting some directional analysis of, of things or, or just getting a, even a production process off the ground, Excel is still pretty reasonable for building stochastic uh, models. And so, you know, just by using even, uh, the Rand function and, uh, I can’t remember if it’s nor Normas Inverse or Normas dis, uh, but, you know, combination of those two and, and you can basically create a stochastic, uh, model all on your own, all in Excel. So I’ll go with, uh, uh, yeah, the stochastic modeling functions, um, in Excel.
Paul Barnhurst:
Yeah, that’s the first time I like it.
Arvind Chahal:
You can, you can do the same thing Google Sheets too.
Paul Barnhurst:
We’ll cover both of ’em. Drew, how about you?
Drew Noel:
About, I think he is not necessarily a favorite, but I will say like, something that I, I kind of
Used again very recently, and I think it’s underappreciated, is just trim. You know, I like honestly like, was like so annoying that I was like, oh, good trim, okay, fine. Thank, thank goodness. Um, so I, I kind of wanna just like, you know, uh, call out the, the humble trim function. And then, uh, also, um, I think a feature of Excel that, you know, I I think if you’re trying to do sort of pilot level, um, pilot level dashboards or, or design like a pre preliminary dashboard or something like that is, uh, spark lines are a great function. So, you know, I, I’m a big fan and, you know, it takes, it gives you a little bit more visual candy than just having, you know, a bunch of numbers in a row. Um, and so I, I call that up.
Paul Barnhurst:
Spark lines is a great one. Trim I love. The other day I was dealing with one and used a trim, didn’t clean it up. You know, you’re thinking it’s a space and it’s a special character. So wrapping trim with clean, yeah. Around the whole thing to get it to work. So it will take out those special characters. So that’s one of my favorites. Wrapping things with trim and clean and converting it. So it’s actually a number all in one, one formula. Everybody’s nodding there. I think they can relate to that one. All right, so we’re gonna give Drew one question here that we got asked by Christopher, and then we’re gonna go to our last question. We just have three minutes left. This one was specifically for you,
Drew Noel:
<laugh>. That’s a great one and I gotta
Paul Barnhurst:
Read it. So, because we, we’ll, you know, not everybody will hear this. So he, he asked, what’s the harder metric to improve? GRR Are watts per kilogram. And I’m sure there’s something behind this.
Drew Noel:
Well, I mean, my, my second job as a competitive cyclist on, on my weekends, um, is what it’s referencing. And, and watts per kilogram of course being the, the amount of energy output according to kilogram of body weight. Uh, and you know, I’ll say, uh, on a micro, let, I’ll give you a and of it depends answer. Uh, so, uh, it
Paul Barnhurst:
Depends. ISN allowed on a podcast, you gotta pick a side.
Drew Noel:
So then if I’m gonna pick, if I have to pick the, the harder one to improve in a short term, in the short term is GR, but the harder one to probably maintain in the long run is Watts per kilogram. ’cause we, uh, as an athlete, you, you lose fitness three times as fast as you gain it. So, uh, I actually was just at our company onsite this week. It was great, you know, totally awesome in terms of the relationships and, you know, our, our overall strategy for the year, effectively wrapping up an operational kickoff with a, with a, uh, sales kickoff. But yeah, I’ve, I lost some fitness this week, so, uh, I’m gonna call that out. So,
Paul Barnhurst:
<laugh>. Alright, well we had some fun with that. So we have just one minute left. So I’m gonna quickly go around with this, uh, last question here. We’ll start with you, Jeff. If you could give one piece of advice on how FP&A and operations can work better together, what would it be? Just one piece.
Drew Noel:
Communication.
Paul Barnhurst:
Communication. Arvind,
Drew Noel:
Uh, empathy for operators.
Paul Barnhurst:
Operators. I like it. Drew.
Drew Noel:
Uh, I think building on top of those two is consideration. Um, and what I mean by that is really just really trying to stack on top of empathy and communication, that reality of how it is within that area. Um, so, you know, taking it maybe one step further and that cross-training, cross-functional aspect is super critical.
Paul Barnhurst:
Thank you, drew. I appreciate that. I love that. The communication, the empathy, the, you know, building on top of it there, the things that were mentioned. There’s a lot of human element to all this. And keep that in mind. You’re dealing with people, not just systems. And again, thank you for joining us, Jeff Drew Arvin, if our guests wanna get in, get ahold of any of ’em, uh, you can find ’em on LinkedIn. I know they’re all out there, so feel free to reach out to them. And thanks again for joining us today.