#118 Inside RevOps at a $100M+ ARR Consumption-Based Business - with Markus Jaensch, Head of RevOps at Aiven

Speaker A: Welcome to the webhops Lab, a, uh, podcast exploring the art and science of revenue operations. To find more episodes and resources on scaling your revenue engine, visit getweflow.com webups hey, this is Philip, um, from WeFlow. Before we dive into today's episode of the Robots Lab podcast, we. Let's do a quick, real talk. We talk to RevOps leaders every week. I mean, actually every single day. And we keep hearing the same things. Our Salesforce data is a mess. Reps won't update Salesforce. They don't follow methodology. Everything is inefficient. Adoption is poor. And all of this is exactly why we built WeFlow's revenue AI platform. Because WeFlow automates Salesforce hygiene by capturing every customer interaction automatically. It locks your emails and meetings as activities in Salesforce and creates missing contacts automatically. WeFlow also records and transcribes your meetings and uses AI to update Salesforce fields, create summaries, and to sync everything back to Salesforce. Let's say you have some key fields like Matic or Next Steps. WeFlow can auto update them right after your meeting. Reps just review what we suggest to them and then make changes if they want to, and confirm everything with one click once. That way you get clean Salesforce data while your reps save time on data entry. If you want to see why hundreds of your RevOps peers trust WeFlow, just go to getwebflow.com to get your free trial today.

Speaker B: Hello, and welcome to another episode of the Revups Lab podcast. I'm here with Filip, and our guest today is Markus Jentsch. Hey, Markus.

Speaker C: Hello.

Speaker D: Welcome.

Speaker B: Yeah, welcome. Uh, we actually met, I don't know, one and a half years ago. You lead refops at Aiven. Uh, you recently crossed 100 million in revenue. Congrats on that. Um, and, uh, today we want to talk about the complexity of consumption AI and ref ops, um, 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, it's, it's, let's say, a lot more complicated than the typical bookings number that you might want to forecast or track. Um, so today, I mean, we want to talk all about it, uh, from a rough ops perspective. So super excited to, To, To. 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, we dive in.

Speaker D: Yeah, um, let's do that. Um, yeah, Great to be, be part of the podcast. Already listened to a couple of episodes in the past and um, happy to join here. Um, actually as, as you said, I'm running revenue operations here at Ivan. Um, what this means is that um, we have sales operations, marketing operations, compensation, sales enablement and CRM and systems as part of the whole team and we want to be a revenue enablement engine for our sales teams. And um, 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 uh, we try to figure out all the data in the back to send it to them.

Speaker B: Yeah, so let's dive in. Um, maybe uh, let's start with why is consumption ar uh so difficult to work with? I mean what are your real life experience? 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.

Speaker D: Yeah, absolutely. So the interesting part with consumption is um, or 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 uh, 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, you, you decide for a subscription, you pay upfront and that's it. Um, 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 next 12, 24, 36 months. Here we go. Um, on our end, um, 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, we have a service life. Of course there is also ways to work with this. So commit contracts, for example, where a customer commits to spend over the next 12 months this amount of credits. Um, but that's the part that makes it a bit easier. But at the same time there's a combination with the um, pure consumption has still 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, that's straightforward. Three month from now, it's kind of visible. Like the big, the big customers are visible. But when it goes to 6 month from now, 12 month from now, this is the point where it's really like um, critical to say, okay, we're going into that direction or that direction and

Speaker B: just maybe to add to that. Right. Like what that means is, uh, right, I might be in SaaS, I sign a 12 month deal. So I know this revenue is basically there for 12 months here. I don't know. So it could go up a lot.

Speaker D: Right.

Speaker B: And 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.

Speaker D: Um, it is, it is, yeah. But at the same time it's part of the business model and I think everyone that's, that's, that's connected to Ivan and that's committed into Ivan, that works for Ivan is aware of this. That, yeah, this is, this is how things go. But what we need to ensure is um, like also for 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 um, forecasting topics a lot. Where we work on um, predictive churn modeling where we try to understand, okay, how much churn will we have the next year. Um, yeah, there's a couple of things we need to consider in this process.

Speaker B: So maybe high level and then we can jump into each of the topics. Right. So you already mentioned forecasting. Right. So like more from a, you know, kind of revenue, ah, perspective. Then you mentioned churn. What else would you say are the, the big, the big blocks, um, for, for consumption model from a reference perspective?

Speaker D: Um, well that's, that's the two big blocks. Absolutely. Uh, a third one is um. Of course, then what's the internal implication for colleagues which meets the sales reps? Um, 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 10 customers with 3 million ARR. And you give a sales target, a growth target to the reps, but how big can it be, um, connected to what's the potential churn in your portfolio? Can we actually attribute the churn to your growth or do we have to take it out of your comp plan, et cetera? So this is the third part of, 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.

Speaker B: 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 already like is, is very different here. Right. So I'm just curious.

Speaker D: Well like there's a comparison to what I did before working for Aiven, um, in the consumption world and then there's also within my time at a, we made different changes um, over time. So the difference to, to, 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 going to 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, um, like how do you understand if pipeline is actually real? Um, but this aside understanding like there's measurements you can set in place with best case forecast, um, hard deck how we also look into these cases, um, then it comes to, 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 is 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, um, 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, ah, when is it going to come? Um, 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, um, 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. Um, so it was more like what's the additional workloads coming in but not when will will this be a booking. And then we had all the mass of the long tail customers, um, done wiring, yeah, profit forecast and, and this together, then gave an ARR number. But we learned that this is highly complex for our sellers. And we were really good, um, in the ARR forecast on 1 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, um, into the uh, bookings forecasting, where we also had the conversation around the process, um, even before with the ARR forecast and actually what we want to understand 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, uh, um, 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. Um, like this. And we extrapolate these numbers into monthly meetings with our Finance FP&A colleagues where we're done aligning. Okay. This is what's on top of the setup that our customers have right now. And then we modeled the AR 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.

Speaker C: I'm curious about the reps, uh, forecasting the bookings. How deeply. Just curious about the sales process here. So how deeply involved are the reps in the onboarding and ramping up of the customer and sort of what kind of maybe you have a specific sales methodology that you're using also, um, to really have like a common good understanding across like the entire company. What like a good customer, um, looks like because, um, I would assume. Right. So 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.

Speaker D: Exactly. So um, we're using MATPIC as the methodology. Um, we're having, um, onboarding plans, we're having mutual success plans, um, that are mandatory at dedicated levels of deal sizes. Um, you can't process to an Excel stage if dedicated fields are not filled. We're measuring the MATPIC score, for example, also based on what's in the system, um, how mature are our sales reps with the opportunity. So if you're like in the last stage of the opportunity but get a CRM score of like 40% or 50%, then there's big red flags of okay, is it really happening? Or um, is it just a hygiene topic or did you miss key parts? Um, but, um, are they included into the onboarding? Yes, they are included, but we also have like, we have the sales reps, um, and we have solution architects. And the solution architects are getting involved into the opportunities, um, after they are properly qualified. And then um, 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, um, SMB customer, they will switch on their plan and then the ramp is pretty fast. It's like four or six weeks that they're ramped to the level of what we discuss about. And then they use their Kafka, their postgres at this, at the size, what they, what they expected. And if you have bigger deals, especially with products, uh, like an OpenSearch that we have or Clickhouse, but also Kafka, 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 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, we kind of know what's the Rambo. But it can take up to six months, up to nine months that the customer is actually consuming of what was the booking. So it's, it's a future promise, but it's on us to understand how far in the future it is.

Speaker C: Yeah, yeah, no, absolutely, 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, customer is going to become like this or that size, most likely, I don't know, like just in terms of like the data, ah, or like in terms of like their tech stack. Um, is there something like this Also in place, I'm assuming. Yes.

Speaker D: But, um, yeah, so we call it actually T shirt sizing. Um, and this is when the collaboration with the solution architects start. There's directions of is it an smlxl m ah setup or is it something completely, um, out of. Aware of the um, common. Common ground. Yeah. And in this case this is giving us an indication, of course. Then in this uh, 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 one an opportunity that this has a size of where the customer will be in six months. But what also happened 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 150% after six months and you could ask, okay, what's the opportunity size 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, um, after it was a close because it was ramped fast after six, eight weeks. Then we realized customers 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. Um, this is kind of the learnings that we take. Um, we, we for example have it at the moment mandatory that reps cannot close one opportunities. It goes via rev ops. The reason for this is we have two mandatory, um, 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, um, more than 90% of the customers actually start consuming us and stay. Before we had this, um, it was easy to close one 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 going to happen. And the last thing you want is your CEO reaching out to you saying, why is there no consumption at this big cost? One that we just communicated last month and um, it's actually one of the reasons why we decided, okay, this is mandatory fields, mandatory steps, um, that we control. Um, 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 there actually wins. And it's not just um, a customer that's telling us we want to spend. But we're not starting.

Speaker A: Hey Philip here. Are you enjoying this episode? Well, good news because you can find more free revops and go to market resources on getweflow.com RevOps access over 20 cheat sheets, reports and guides that will help you become a free better revenue, uh, operator or join over 2,000 subscribers who already get the latest resources right into the inboxes with our free newsletter. Just go to getmeflow.com web ops

Speaker C: now. I mean I think other companies call this also like deal desk. I mean can also be like slightly different. Right. But I think it's not that unusual and actually like I like the approach also like if you close.

Speaker B: Yeah.

Speaker C: I'm assuming you do the same when you close lost it. Right. Because then you can also dig deeper uh, into sort of like the why wasn't it like uh, actually one or maybe not. Maybe you don't do that.

Speaker D: You don't have it in place right now. So you have to like, you can't just do close lost an opportunity. You have to select reasons, you have to explain why. Um, but I mean we all know what's going to happen if you, if you don't want to fill it out, you, you select other and just type in other and then you close loss. And it is what it is. Um, but we're now like we just started four weeks ago to really go into detail also with a couple of AI tools to analyze our closed loss opportunities. Also to understand um, 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.

Speaker B: Super interesting. I mean I think obviously we could talk a lot about, you know, how do you better analyze closed loss opportunities because that's typically based on you know, just the drop down field in the note. Not uh, really useful. But obviously now you can query all the related data, uh, transcripts and everything and just dive a lot deeper. I think it's happening a lot um, throughout this output for your customers. But um, um, I'm curious. So let's Assume like you close one, right, you agree to close one. Um, and there is a bookings value. Um, like how long is the bookings value? Is that 12 months? Is it 6 months? And then in live what are the data points you pull to look at the existing ARR? Ah, and what are some of the indications for churn or growth, uh, um, um, or any other things that influence the AR number?

Speaker D: Um, I try to go through all the topics like it's just, just remind me if I, if I miss some. So um, what is the indication for what are we going to do um, after it's a close one. So um, in combination with our finance colleagues, so we, 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 AR forecast for new customers. Um, with existing customers we're way faster. We're like four to eight weeks, um, that we're ramping. So um, the assumption is it ramps to that point in that time and then there's a growth that's depending on the region, let's say at 5%, um, on top of this until the end of the 12 month mark. This is how we usually take it into the ARR forecast. Um, from a rep perspective and from, from a sales leadership perspective, um, 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 um, does it ramp? We um, just take an average assumption here when we, when we go, Sorry,

Speaker B: just a question on that. Like, but the rep does indicate a number for the like ah, that it should reach after six months, probably together with the solution architect or.

Speaker D: Yeah, so this is the opportunity size. So this is the opportunity size. Um, there's also cases where you can't really say is it 4 months, 6 month or is it 12 month. Um, 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 the first six months. I'm not too strict here but it's helping us quite well um to get the direction that we need to get. That's fine. So with this we see okay, the wrap has this size of the pipeline and this part of what will be forecasted for this quarter. So what we measure the rep against like we have um, QBRs, um, 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. This many new logos, this AR, this many expansion deals, etc. 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, is a sanity metric. Um, like what counts is ARR that gets in. So we want to ensure that the ARR actually gets through. Um, but the booking is a big indicator and if you don't have any bookings, um, but your AR is growing, then it's also a question, okay, where is it coming from? Um, we do have customers, um, 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 60% year over year. And fine, these cases exist, but in general, um, we need to ensure that a pipeline is built and that there is bookings every quarter. Um, because only with this you can ensure that you really hit the number. And we don't just want to hit the number, we want to overachieve the numbers. And like every seller is highly motivated to, to overachieve the numbers. And we, we said we set good accelerators for this. So it's, it's not about getting there, it's getting above. Like this is really, really the hope that we, that we have of the plans that we create.

Speaker B: And um, are the sellers active in life for the existing customers or only the expansion opportunities or do they also account manage the account then throughout? Because obviously there's. Right the way I, and the way I know it from my previous, previous company which was also consumption basis. Like, uh, it's actually a huge challenge in terms of what, 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, like, you know, for the year double. Right? But you don't have to do anything anymore, right? And like, so how do you, how do you, like how do you deal with that?

Speaker D: I mean we're 11 years old and in those 11 years we, 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. Um, and then you realize, okay, it's working in these cases. It doesn't work in these cases. So we um, changed then to um, to the setup of um, reps own accounts plus new business. And then actually we 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 the rep needs 18 to 24 months to close a deal. Even if it's a big deal. Um, how do we pay the rep until this, like if this is a big deal, that, that is two times the size of the comp, um, of the target. Um, 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 in Inside Sales Motion where our Insight sales teams are like their target is um, fast deals, quick wins. And last year we said okay, field reps own all the named accounts, the bigger accounts and Insight Sales owns all other accounts and all other prospects except 50 prospects that every field rep has selected. What this led to was we realized it was maybe too big of a size of a uh, prospect portfolio. Our inside sales team board. Um, so we now um, have named territories now um, for feed reps that contain more customers. But out of these big um, territories they have to select a small group. And with this we now ensure that we have inside sales reps that purely hunt. Um, we have one farmer inside sales rep per region that has the role of the smaller accounts, grow these into bigger ar and then we have the feed team where we're saying, okay, you own five to 10 customers, your goal is to grow these. But at the same time, depending on if it's enterprise or commercial app, you also have a net new target on top of this. And the net new target is combined with a booking. And we actually like the compensation is paid once the size is hit. Um, so we said we have dedicated sizes for an enterprise rep for a commercial app. And we say 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 a new ARR. Um, of course it goes into your overall ARR target. Um, but last year we had the situation Feed team was primarily um, compensated on um, AR growth. And what happened was we had a big change into our existing customer relationships which was good, like you want to see your NRR growing. Um, 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, um, it's a learning curve. Um, and the years I'm with Ivan, um, 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, um, to go into that direction, not that direction.

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

Speaker D: Yeah. So we use Salesforce, um, and the key thing is the connection to our data warehouse. Um, and we try to have all the relevant data in there. Um, our AR is calculated out of the consumption. Um, but what we realized also over time, back in like a couple of years ago, ARR was on time consumption. So if you have a customer, like we have customers that optimize their services based on their customers. So if there is 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 um, fluctuation in the consumption. And if this fluctuation happens last day of the fiscal year, you uh, don't want to have this 2 million drop. That's just for 12 hours. But this really has an impact on the fiscal year ending. And with this we changed the AR metric. It's still based on consumption, but it's an average out of the past 30 days of 24 snapshots a day that we are kind of connected to what's happening but still way, way more stable. And, and this flows into Salesforce. So we know exactly in Salesforce, um, where we are, um, how it's developing, et cetera.

Speaker B: Yeah, sort of like how many, how many months of data do you have in Salesforce? Like is it like last forever? Data or like just last 12 months? Last 24 months. And why do you think so we

Speaker D: did a big change back in 21 and since then, um, the data is quite on point. And then in that time what we changed is then definitions of ARR, for example, et cetera. But since then, whatever data point I'm trying to figure out, um, I can find it, um, like AR related and billing related.

Speaker C: So the data warehouse in combination with Salesforce, that is your like source of truth for everything or. Um. Okay, great. Yeah, that's the key thing.

Speaker D: And whenever, whenever something happens, um, I like the data analytics team, um, directly going to the root cause and then like doesn't take longer than 24 hours usually to figure out, okay, there's something missing. And then it's changed to data warehouse. And then it's like it's taking minutes and then it's back, back and production back to normal.

Speaker C: Yeah. Amazing. I mean so hard to achieve. Uh, congrats on getting there. I mean I'm sure there's still like plenty, uh, of things to do.

Speaker D: Absolutely.

Speaker C: As always. Right. Like it never ends. But uh, sounds like you're in a really good, really good spot. Any more topics you want to cover, Yanis? One more.

Speaker B: I have one more question. Anything else the listeners should know about this topic? Um, just open ended question here, but uh, I mean any other learnings that are worth mentioning?

Speaker D: Yeah, um, well, regarding consumption based forecasting and ar, um, well, you need to figure out, um, how do you measure your success and how do you measure, um, the future? Um, ideally you have a dedicated, 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, et cetera, we're not having this. Um, but this is definitely something that's also differentiating just pure consumption. Um, where it gets easier for Revops colleagues to understand. Okay, that's the future. And for the finance colleagues as well. Um, I think this is something. Think about um, how you measure, um, ARR and how this goes into the forecast. Um, this is something I would say here. It's maybe not the most detailed answer, but.

Speaker B: Yeah, no, I mean, look, I think I fully copy Philip here. I think uh, it's a great illustration of the complexities of consumption. Like little things like you cannot just rely on the daily ARR that is calculated, but you need to take a 30 days average.

Speaker C: Right. Like those things matter.

Speaker B: If you had 100 million scaled setup and you want to do an IPO because uh, yeah, I think it's just. And the details matter as always and especially here. And um, this hits the, the finance teams, the investor reporting, everything right. So it's, it's extremely important.

Speaker D: And, 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, um, 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, to be, to help my life to, 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, and we can, we can handle this. Um, but yeah, bringing this in one direction and making it in a way that it is also easy to understand for our sellers, um, that's a challenge.

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

Speaker D: Yeah, um, there is um, actually not really revops focused but um, I actually bought this for my team a couple of years ago um, because I thought it's, it's pretty eye opening um, and helping to shift a bit around. So it was the Cafe on the Edge of the World, um, from um, Strelitsky. Really, um, interesting book. I mean it's 150, 200 pages. Um, there's I guess four or five um, full up books. Maybe I'm wrong and it's only three but there's a couple of books around this. And yeah, I, 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, um, have them reading to also understand. And, um, don't always go the same direction just because I tell you.

Speaker C: Yeah, great. Okay. Love it. I gotta look that one up. Thank, ah, you for sharing. This is a new one. Uh, uh, yeah. Appreciate it. Thank you, Markus. Have a great rest of the day. And thank you for joining.

Speaker D: Thank you.

Speaker B: Thank you so much. Awesome.

Speaker A: Thank you for listening to the RevOps Lab podcast. If you enjoyed this episode and would like to support us, share it with a RevOps friend or drop us a five star, uh, rating right now. And if you have feedback, questions or guest ideas, just send a message to Yanis or me on LinkedIn. Thank you and see you next time.