#118 Inside RevOps at a $100M+ ARR Consumption-Based Business

Janis Zech: Hello, and welcome to another episode of the RevOps Lab Podcast. I'm here with Philipp, and our guest today is Markus Jaensch. Hey, Markus.

Markus Jaensch: Hello. Welcome.

Janis Zech: Yeah. Welcome. We actually met, I don't know, one and a half years ago. You lead RevOps at Aiven. You recently crossed a hundred million in revenue. Congrats on that. And today, we wanna talk about the complexity of consumption and RevOps. Just as a quick segue, right, like, obviously, consumption pricing has become huge with AI, and it's something that is overly complicated when it comes to revenue operations. Or it's, let's say, a lot more complicated than the typical bookings number that you might wanna forecast or track. So today, I mean, we wanna talk all about it from a RevOps perspective. So super excited to host you today on that topic. Before we kick off, I mean, maybe you can give a very brief introduction. Who are you? What do you do? And then we dive in.

Markus Jaensch: Yeah. Let's do that. Great to be part of the podcast already. Listen to a couple of episodes in the past, and happy to join here. Actually, as I said, I'm running revenue operations here at Aiven. What this means is that we have sales operations, marketing operations, compensation, sales enablement, and CRM and systems as part of the whole team as we want to be a revenue enablement engine for our sales teams. And yeah, try to bundle all the topics together to support our GTM teams as much as we can to help them do what they can do best, which is selling. And we try to figure out all the data in the back to send it to them.

Janis Zech: Yeah. So let's dive in. Maybe let's start with why is consumption ARR so difficult to work with? I mean, what are your real life experiences? And maybe also for context, right, like, I know you've done a big project towards consumption pricing. So maybe you can give some context on, you know, like, why you're actually here to talk about it, and then we jump into the challenges.

Markus Jaensch: Yeah. Absolutely. So the interesting part of consumption is, alright, we want to make it as simple as possible for our customers to start using our products, which means the easiest way is to swipe in a credit card and just start using our services. And as good as this is for the customers, as hard this gets to revenue operations and to our finance colleagues when it comes to what does it actually mean for revenue in one year from now. So once you buy a license, you go through a contract, you sign the contract, you have the payment, you pay for it, and then it's it. Or you decide for a subscription, you pay upfront, and that's it. That's pretty easy and pretty simple for a revenue operations function from a perspective of, okay, I know this is a booking. This money gets in fine. It's signed for the next twelve, twenty four, thirty six months. Here we go. On our end, like, we don't have this. We're not signing a contract up front where we have the payment upfront, and after the payment, the customer starts. No. The customer starts consuming whenever they decide. Now we have a service life. Of course, there's also ways to work with this. So commit contracts, for example, where a customer commits to spend over the next twelve months this amount of credits. But that's the part that makes it a bit easier. But at the same time, there's a combination with the pure consumption that still has a big amount of uncertainty into the forecast. So I know what we're consuming now. I know what the ARR might be in a month from now. That's straightforward. Three months from now, it's kind of visible. Like, the big customers are visible. But when it goes to six months from now, twelve months from now, this is the point where it's really, like, critical to say, okay, we're going into that direction or that direction.

Janis Zech: And just maybe to add to that, right, like what that means is, right, I might be in SaaS, I sign a twelve month deal, so I know this revenue is basically there for twelve months. Here, I don't know. So it could go up a lot. Right? Can grow like crazy. It could also go down a lot. And so if you run a company, right, I mean, that is pretty scary, actually.

Markus Jaensch: It is. Yeah. But at the same time, it's part of the business model. And I think everyone that's connected to Aiven and that's committed into Aiven, that works for Aiven, is aware of this that, yeah, this is how things go. But what we need to ensure is, like, also for our investors, for our company, for our future, we need to find a way to predict where we go to and to what direction. And this is where we work on forecasting topics a lot, where we work on predictive churn modeling where we try to understand, okay, how much churn will we have the next year?

Janis Zech: Yeah. There's a couple of things we need to consider in this process. So maybe high level and then we can jump into each of the topics. Right? So you already mentioned forecasting. Right? So, like, from more from a, you know, kind of revenue perspective, then you mentioned churn. What else would you say are the big blocks for consumption model from a RevOps perspective?

Markus Jaensch: Well, that's the two big blocks. Absolutely. Third one is, of course, then what's the internal implication for colleagues, which means the sales reps? How do we compensate in the right way if you have a portfolio that's completely uncommitted and you start with a portfolio that has, like, ten customers with three million ARR, and you give a sales target or growth target to the reps, but how big can it be connected to what's potential churn on your portfolio? Can we actually attribute the churn to your growth, or do we have to take it out of your comp plan, etcetera? So this is the third part of it. And then maybe a fourth — not so difficult part, but still needs to go hand in hand — is how do we build all of this into a system where it's visible for everyone, where it's available for everyone, and where we also have the reality that's out there with all customers in our internal processes integrated.

Janis Zech: I mean, you've run bookings forecasting organizations before. Right? Like, maybe let's start with, like, how does this differ? And also, like, how do you break down the forecast for new logo versus, for example, the existing book of business, which I'm sure is very different here. Right? So I'm just curious.

Markus Jaensch: Yeah. Well, like, there's a comparison to what I did before working for Aiven in the consumption world. And then there's also within my time at Aiven, we made different changes over time. So the difference to the SaaS is just like you can really focus on the booking. If you forecast a booking, then the question you have is, is it a real booking or not? Like, is it gonna happen or not? Is the rep — what he or she is having in the pipeline — is it real? That's a question where you need to focus on. And it's a good question. You could talk a lot about this. Like, how do you understand if pipeline is actually real? But this aside, understanding, like, there's measurements you can set in place with best case forecast, hard deck, how we also look into these cases. Then it comes to the consumption based forecast. And actually where we started a couple of years ago was we didn't really look into the bookings itself. Because a booking for us means, yeah, there's a customer that says, yep, this might be the future workload. But if they ramp into this workload or not, it's not clear. It's depending on the ramp plan, but it's also depending on the customer situation. So we had a customer saying, yeah, we commit, we start next week, and then we have all our engineering team to work on this. And actually then a major incident happened at the customer and they had their team blocked for four months. And they couldn't onboard at all. But we had it already in the books and it was like, yeah, when is it going to come? So what we first did was focusing on ARR, and we tried to understand how will this customer develop over time. And we looked into the named accounts, the big accounts that were handled by our field teams. And there we had a dedicated ARR forecast and didn't really pay too much attention on the bookings. So it was more like, what's the additional workloads coming in, but not when will this be a booking. And then we had all the mass of the long tail customers, done via a run rate forecast. And this together then gave an ARR number. But we learned that this is highly complex for our sellers. And we were really good in the ARR forecast on one hand, but the time we invested was so much — like, the time everyone in the company and the sales team invested into this was way too much — so we needed to change something. So this is where we changed then into the bookings forecasting, where we also had the conversation around the process even before with the ARR forecast. And actually what we want to understand is, what is the new business and what's the add-on business? And then the third part is, and this is now getting more and more important, what's the commit contract? Because a customer that's committing to a value, this is ARR that's in the books then. This is really important for us, but only a small part of our customers has signed actual commit contracts. So this is when we go into the forecast with the reps. We forecast the bookings. And this gets to a picture of where the business might go to in this quarter and the next quarter. This is like the bookings is the next two quarters where we look into. Or let's say the next four months, like this. And we extrapolate these numbers into monthly meetings with our finance FP&A colleagues, where we're then aligning, okay, this is what's on top of the setup that our customers have right now. And then we model the ARR development based on historical developments, based on future churn we know about, based on organic growth. And this together then creates a monthly ARR forecast where the reps are kind of out of it.

Janis Zech: Yeah. I'm curious about, like — so the reps are forecasting the bookings and, sort of, like, how deeply — just curious about the sales process here. So how deeply involved are the reps in, like, the onboarding and ramping up of the customer and sort of, like, what kind of — maybe you have, like, a specific sales methodology that you're using also to really have, like, a common good understanding across, like, the entire company what, like, a good customer looks like — because I would assume, right, this is not just about identifying, like, economic buyer and a decision maker. There's, like, a lot more that goes into this if you want reps to reliably forecast bookings here.

Markus Jaensch: Exactly. So we're using MEDDPIC as the methodology. We're having onboarding plans. We're having mutual success plans that are mandatory at dedicated levels of, yeah, deal sizes. You can't progress to the next Salesforce stage if dedicated fields are not filled. We're measuring the MEDDPIC score, for example, also based on what's in the system, how mature our sales reps are with the opportunity. So if you're, like, in the last stage of the opportunity, but get a CRM score of, like, forty percent or fifty percent, then there's big red flags of, okay, is it really happening? Or is it just a hygiene topic? Or did you miss key parts? But are they included into the onboarding? Yes. They are included, but we also have, like, we have the sales reps and we have solution architects. And the solution architects are getting involved into the opportunities after they are properly qualified. And then we build together with the customer a plan of, okay, this is the solution we need. Okay. This is how an implementation can look like. Most likely, like, depending on the customer sizes, a smaller customer, SMB customer, they will switch on their plan and then the ramp is pretty fast. It's like four, six weeks at their ramp to the level of what we discussed about, and then they use their Kafka, their Postgres at this size what they expected. And if you have bigger deals, especially with what's like an OpenSearch, what we have, or ClickHouse, we also have that. It can take quite some time, and then you switch on the first plan, and then you upgrade, or you switch on another plan, you switch on another region. And this together, like, this is built with our sales team, which is the sales rep, the solution architect, and the customer together, where we try to identify as good as we can, okay, this is the plan. This is the first next couple of weeks. And with this, we kind of know what's to ramp up. But it can take up to six months, up to nine months that the customer is actually consuming what was the booking. So it's a future promise, but it's on us to understand how far in the future it is.

Janis Zech: Yeah. No. Absolutely. Understood. And, like, did you develop now, like, any kind of, like, leading indicators or, like, some critical events that typically signal, okay, this customer is gonna become, like, this or that size most likely? I don't know. Like, just in terms of, like, the data or, like, in terms of, like, their tech stack. Is there something like this also in place?

Markus Jaensch: Yeah. Yeah. It is. So we call it actually T-shirt sizing. And this is when the collaboration with the solution architects starts, there's a direction of is it an S, M, L, XL setup, or is it something completely out of the common ground. And in this case, like, this is giving us an indication, of course, then in this size, then there's adjustment over time. And how good are we with the opportunity itself? Well, we try to be as precise as possible once we close an opportunity that this has a size of where the customer will be in six months. But what also happens quite often, and I mean, it's a good case for us, you bring in a new customer, they're happy with what they see, they see it's faster than what they expected, which means they scale up faster than what they initially planned. So then we have a ramp of one hundred and fifty percent after six months. And you could ask, okay, was the opportunity sized in the wrong way or not? And this is where you have to go then into the details and understand, okay, what did actually happen? Okay. We should have had here a second opportunity after it was closed because it was ramped fast after six, eight weeks. Then we realized the customer's happy. They're thinking about new workloads that are like doubling their consumption. And we should have implemented another opportunity here that was then closed once new consumption starts. This is kind of the learnings that we take. We, for example, have it at the moment mandatory that reps cannot close-win opportunities. It goes via RevOps. The reason for this is we have two mandatory rules to close an opportunity. One is a valid payment method that's added, and two is that we have three consecutive days of consumption. We see when you have three consecutive days of consumption, more than ninety percent of the customers actually start consuming and stay. Before we had this, it was easy to close-win an opportunity. But as said, winning an opportunity doesn't mean there's revenue. So we needed to ensure that it's only a win when the indications are that this is most likely gonna happen. And the last thing you want is your CEO reaching out to you saying, why is there no consumption at this big customer that we just communicated last month? And it's actually one of the reasons why we decided, okay, these are mandatory fields, mandatory steps that we control. It's kind of bringing RevOps to a controlling engine that I don't really want to be. But on the other hand, it's protecting the business to understand where will we be in the future. And I know when I close-win opportunities, they're actually wins. And it's not just a customer that's telling us we want to spend, but we're not starting.

Janis Zech: Now, I mean, I think other companies call this also, like, deal desk. I mean, it can also be, like, slightly different. Right? But I think it's not that unusual. And, actually, I like the approach also, like — I'm assuming you do the same when you close-lost it. Right? Because then you can also dig deeper into sort of like the why it wasn't actually won or maybe not. Maybe you don't do that. You don't have it in place right now.

Markus Jaensch: So you have to — you can't just close-lost an opportunity. You have to select reasons. You have to explain why. But I mean, we all know what's gonna happen. If you don't want to fill it out, select other and just type in other and then close-lost and it is what it was. But we're now — we just started four weeks ago to really go into detail also with a couple of AI tools to analyze our closed-lost opportunities. Also to understand, did champions move? So do we have an indication that this might reopen, or did blockers move away? Or what's the reason behind this? We're not perfect in this, but yeah, I think, like, this year, so many positive implications with AI are happening. So we can make much bigger steps at the same time to help our sales teams to identify gaps that we haven't seen in the past.

Janis Zech: Yeah. Super interesting. I mean, I think, obviously, we could talk a lot about, you know, how do you better analyze closed-lost opportunities because that's typically based on, you know, just a dropdown field in the note, not really useful. But, obviously, now you can query all the related data, transcripts, and everything and just dive a lot deeper. I think it's happening a lot throughout our portfolio customers. But I'm curious, like — so let's assume, like, you close-win. Right? You agree to close-win, and there is a bookings value. Like, how long is the bookings value? Is that twelve months? Is it six months? And then, like, in life, what are the data points you pull to look at the existing ARR, and what are some of the indications for churn or growth, or any other things that influence the ARR number?

Markus Jaensch: I'll try to go through all the topics. Like, just remind me if I missed some. So what is the indication for, or what are we going to do after it's a close-win? So in combination with our finance colleagues, we assume a ramp depending on the size of four to six months, and we make it easy. We take a linear ramp, and this goes into the ARR forecast for new customers. With existing customers, we're way faster. We're, like, four to eight weeks that we're ramping. So the assumption is it ramps to that point in that time, and then there's a flat growth that's depending on the region, let's say, five percent on top of this until the end of the twelve month mark. This is how we usually take it into the ARR forecast. From a rep perspective and from a sales leadership perspective, we're just looking at if they close and if they build a pipeline that creates opportunities that will close this quarter and next quarter. So we don't discuss with the reps and with the first line managers how does it ramp. We just take an average assumption here.

Janis Zech: Sorry. Just a question on that. Like, but the rep does indicate a number for the — like, that it should reach after six months probably together with the solution architect?

Markus Jaensch: Yeah. So this is the opportunity size. So this is the opportunity size. There's also cases where you can't really say is it four months, six months, or is it twelve months? But you have the customer talking about, okay, this is what we need. And this is the size where it will go. And as a rule of thumb, we say, let's assume for six months. I'm not too strict here, but it's helping us quite well to get the direction that we need to get. That's fine. So with this, we see, okay, the rep has this size of the pipeline and this part of, yeah, what will be forecasted for this quarter. So what we measure the rep against — like, we have QBRs, like, always at the first two weeks of the new quarter, where the rep has to set a commit. Okay. This is the bookings that I commit to. There's this many new logos, this many expansion deals, etcetera. And then you measure at the end of the quarter against the commits the rep gave at the beginning. On top of this, of course, every rep has a bookings target. They're not compensated on it because in the end, a bookings target is a sanity metric. Like, what counts is ARR that gets in. So we want to ensure that the ARR actually gets through. But the booking is the big indicator. And if you don't have any bookings, but your ARR is growing, then it's also a question, okay, where is this coming from? We do have customers that are growing nicely that say, hey, we don't need any interaction with you. We're happy with what we have, and they grow sixty percent year over year. And fine, these cases exist, but in general, we need to ensure that a pipeline is built and that there are bookings every quarter. Because only with this, you can ensure that you really hit the number, and, like, we don't just want to hit the number. We want to overachieve the numbers. And like every seller is highly motivated to overachieve the numbers. And we set good accelerators for this. So it's not about getting there. It's getting above. Like, that is really, really the hope that we have with the plans that we create.

Janis Zech: And are the sellers active in life for the existing customers or is it only the expansion opportunities, or do they also account manage the account then throughout? Because, obviously, there's — right. The way I know it from my previous company, which was also consumption basis, like, it's actually a huge challenge in terms of what you set as targets because you might be lucky. Right? Like, you sign up this one account, and it's just ramping like crazy. And, you know, you're done basically for the year and next year. Right? But you don't have to do anything anymore. Right? And like, so how do you deal with that?

Markus Jaensch: I mean, we're eleven years old and in those eleven years, we figured out different things that worked and that didn't. So when I started four and a half years ago, we had a clear farmer-hunter split. And then you realize, okay, it's working in these cases, it doesn't work in these cases. So we changed then to the setup of reps owning accounts plus new business. And then actually realized, well, that's quite a stretch. And do reps really focus on where they should focus on? Is it the right size? Because what you don't want in a scale-up is that a rep needs eighteen to twenty four months to close a deal, even if it's a big deal. How do we pay the rep until — like, if this is a big deal that is two times the size of the comp of the target, it's fine that year. But what are you doing the year before? You're not earning anything. So this is where we realized we can't just keep it like this. We need to set it up in a different way. So this is where we implemented last year an inside sales motion, where our inside sales teams are like, their target is fast deals, quick wins. And last year, we said, okay, field reps own all the named accounts, the bigger accounts, and inside sales owns all other accounts and all other prospects except fifty prospects that every field rep has selected. What this led to was we realized it was maybe too big of a size of prospect portfolio for our inside sales team. So we now have named territories for our field reps that contain more customers. But out of these big territories, they have to select a small group. And with this, we now ensure that we have inside sales reps that purely hunt. We have one farmer inside sales rep per region that has the role of growing the smaller accounts into bigger ARR. And then we have the field team where we're saying, okay, you own five to ten customers. Your goal is to grow these. But at the same time, depending on if it's enterprise or commercial rep, you also have a net new target on top of this. And the net new target is combined with a booking, and we actually — the compensation is paid once the size is hit. So we said, look, we have dedicated sizes for an enterprise rep, for a commercial rep, and we said, okay. You need to close this, and it needs to ramp there. And once it ramped to that size of ARR, then you get paid on new ARR plus. Of course, it goes into your overall ARR targets. Yeah. But last year, we had the situation where the field team was primarily compensated on ARR growth. And what happened was we had a big change into our existing customer relationships, which was good. Like, you want to see our NRR growing, but we didn't have enough new customers compared to what we needed to. And you need to balance, like, the ratio of new and existing business to a degree that you have a healthy growth that's coming also from new customers into your company. So, yeah, it's a learning curve. And the four and a half years I'm with Aiven, I think we're now at a point where we really understand what's our sweet spot of customers, what's our sweet spot of hunting profile, and how can we motivate the different teams to go into that direction, not that direction.

Janis Zech: Yeah. One thing I'd love to spend, like, a couple of minutes on would be just, like, the stack implications. So I'm not sure which CRM you're using, whether you use Salesforce or not. But most CRMs are, like, on, you know, like, just sort of, like, fixed amounts, like, not really, like, for volume or, like, consumption based pricing, like, at least not optimized for. I mean, you can make everything work also in Salesforce, right, if you do a lot of custom coding. That's also the beauty of Salesforce, of course. But I'm curious, like, sort of, like, where you are there, like, in that stack, and what changes were made, like, in the last couple of years in order to make it work for your pricing model?

Markus Jaensch: Yeah. So we use Salesforce, and the key thing is the connection to our data warehouse. And we try to have all the relevant data in there. Our ARR is calculated out of the consumption. But what we realized also over time — back in, like, a couple of years ago, ARR was on real-time consumption. So if you have a customer — like, we have customers that optimize their services based on their customers. So if there's a time on during the day, a retail customer has no traffic, they shut down services and they spin it up in the morning. So then you have fluctuation in the consumption. And if this fluctuation happens the last day of the fiscal year, you don't want to have this two million drop that's just for twelve hours. And this really has an impact on the fiscal year ending. And with this, we changed the ARR metric. It's still based on consumption, but it's an average out of the past thirty days of twenty four screenshots a day that we are kind of connected to what's happening, but still way more stable. And this flows into Salesforce so we know exactly in Salesforce where we are, how it's developing, etcetera.

Janis Zech: Yeah. So, like, how many months of data do you have in Salesforce? Like, is it, like, forever data or, like, just last twelve months, last twenty four months? And why do you think you need it?

Markus Jaensch: So we did a big change back in twenty-one. And since then, the data is quite on point. And then in that time, what we changed is then definitions of ARR, for example, etcetera. But since then, whatever data point I'm trying to figure out, I can find it, like, ARR related and billing related.

Janis Zech: Yeah. So the data warehouse in combination with Salesforce, that is your, like, source of truth for everything?

Markus Jaensch: Yeah. Great. Yeah. Congrats. And whenever something happens, the data analytics team is directly going to the root cause, and then it doesn't take longer than twenty four hours usually to figure out, okay, there's something missing, and then it's changed in the data warehouse. And then it's taking minutes, and then it's back in production, back to normal.

Janis Zech: Yeah. Amazing. I mean, so hard to achieve. Congrats on getting there. I mean, I'm sure there's still, like, plenty of things to do, as always. Right? Like, it never ends. But sounds like you're in a really good spot. Any more topics you wanna cover, Philipp?

Philipp Stelzer: One more. I have one more question. Anything else the listeners should know about this topic? Just open ended question here. But, I mean, any other learnings that are worth mentioning?

Markus Jaensch: Yeah. Well, regarding consumption based forecasting and ARR, well, you need to figure out how do you measure your success and how do you measure the future. Ideally, you have a dedicated way of committing to something or of having some spend that's decoupled from consumption. If you get there, it's way easier. So talking about platform fees, etcetera. We're not having this, but this is definitely something that's also differentiating just pure consumption where it gets easier for RevOps colleagues to understand, okay, that's the future, and for the finance colleagues as well. I think this is something to think about — how you measure ARR and how this goes into the forecast. This is something I would say here. It's maybe not the most detailed answer, but yeah.

Janis Zech: No. I mean, look. I think I fully copy Philipp here. I mean, I think it's a great illustration of the complexities of consumption. Little things like you cannot just rely on the daily ARR that is calculated, but you need to take a thirty days average. Those things matter if you had a one hundred million scale setup and you want to do an IPO because the details matter as always, and especially here. And this hits the finance teams, the investor reporting, everything. Right? So it's extremely important.

Markus Jaensch: And maybe to add one thing here as well is it's a thin line of how do you ensure that your internal teams are happy and what does the customer actually need? In the end, yeah, it's extremely complex for me, but it's the best for the customer. So I can't just implement something that's making my life easier, but the customer experience worse. So then it's on my team to figure out, okay, what do we need to change that we adapt to this? So I don't want to charge a customer something just to charge something to help my life to get better. So it needs, like, the customer focus always needs to be at the beginning. What's the best for the customer? And once this is figured out, like, we need to build the process around this and figure out that it's on point and we can handle this. But yeah, bringing this in one direction and making it in a way that it is also easy to understand for our sellers — that's a challenge.

Janis Zech: Great. I really love it. Thank you, Markus. Thank you for sharing this. I think we haven't had, like, a lot of — I think this is maybe, like, the first episode we did on consumption based pricing. I think it's a topic we should dig more deeply into as the market is, like, shifting more into that model. So, yeah, maybe we'll get you back on to go into a couple more details here. But thanks again for sharing. Always one final question. Is there a book or, like, you know, like, a read that you would recommend to our listeners?

Markus Jaensch: Yeah. There is. Actually, not really RevOps focused, but I actually bought this for my team a couple of years ago because I thought it's pretty eye opening and helping to shift a bit around. So it was the The Cafe on the Edge of the World from Strelecky. Really interesting book. I mean, it's one hundred and fifty, two hundred pages. There's, I guess, four or five follow-up books. Maybe I'm wrong, it's only three, but there's a couple of books around this. And, yeah, when I was reading it, I was like, yeah, it's actually a good way how you can look at business challenges at different thoughts, different directions. And I thought, yeah, give it to my team and have them reading to also understand. Don't always go the same direction just because I tell you.

Janis Zech: Great. Okay. Love it. I gotta look that one up. Thank you for sharing. This is a new one. Yeah. Appreciate it. Thank you, Markus. A great rest of the day, and thank you for joining.

Markus Jaensch: Thank you. Thank you so much. It was awesome.