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Speaker 2Hello, and welcome to another edition of the RedOps Lab podcast. I'm Philipp, and I'm here to talk to you about the RedOps Lab podcast. Together with Yanis. Hello, Yanis. Hello, hello. Hello. Good to have you. Good to be back myself. I think the last two episodes were done by you as a solo podcast warrior. Sorry, Yanis. And our guest today is like a repeat guest, actually. Colin Gerber from SoCure. Colin, good to have you back.
Speaker 3Yeah, great to be back. It was just yesterday.
Speaker 2It basically was. It basically was. At least. Yeah. About a month, I think. Yeah. That's when we last talked. Yeah. Colin, for those of our listeners who do know, who don't know who you are, who are you, and what do you do?
Speaker 3Yeah, totally. Yeah. So yeah, I'm Colin. I am leading revenue operations and strategy over at SoCure. I'm the IDV and digital identity and KYC space. So basically, any sort of digital transaction or any instance where you need to prove who you are, who you say you are in the digital world, we power the technology that actually enables that. So anything from e-commerce, traditional banking, a lot of fintech, public sector slash government, kind of e-commerce, traditional banking, a lot of fintech, public sector slash government, online gaming or gambling, you name it. If you have to say you are who you say you are on the internet, Soakier powers it.
Speaker 2Yeah, that's obviously a huge market. And I think it's only about to become a lot bigger as like, yeah, I think just faking identity is becoming easier and easier, I would argue, to some extent, to some extent.
Speaker 3Or even, you know, what we call first party fraud. You have a little buyer's remorse and you say you didn't do something that you did. And you try to, you try to pull a fast one. That's also another really big growing segment of fraud. All right. Okay. Not just, not just made up identities or people stealing our identities. People do it themselves too all the time.
Speaker 2Yeah. Yeah. My wife has an e-commerce business and yeah, there's some shady stuff going on. Like not with like the supplier on the e-commerce business side, but like some, some customers anyway, anyway. Yeah. Anyway, going on, it's getting, going on a tangent here. I think just, I think to, to bring people back up to speed and for those who missed part one, where we talked about CQQ, which is a huge topic and was really, really, really good episode. Can you just give us like a quick recap on how pricing is working at the moment at Soakier?
Speaker 3Yeah, totally. Yeah. So, you know, we are predominantly or majority a consumption based market. So, you know, we are predominantly we do have some, I would say repeatable or like contractual based revenue. Like that would be things like, you know, a nominal platform fee or annual support fee. But generally our, you know, I would say, you know, the vast majority of all of our revenue is based on API call consumption. So the way our kind of product works is a platform comprised of, you know, 35 modules and each module is a platform. So, you know, we are predominantly a consumption based market. So, you know, does a different thing. Say you are, you know, an e-commerce business and you want to screen the people coming to your website or your company as at the top of the funnel. So consumer onboarding, you configure a workflow. That workflow would be, you know, solving a use case consumer onboarding. There would be several modules below it in a sort of waterfall that essentially, you know, does different hits on different pieces of your identity or digitalized identity to ensure you are who you say you are. So our pricing is really based on the API calls throughput within those workflows within those individual modules. So that's how our pricing works. We're similar to like, I'd say like, you know, maybe like what, like an AWS or like a Databricks pricing. We do, you know, spend based discounting and different things like if you have a fairly, you know, even baseline of knowing kind of like what your volume is going to be. You can pre-payment um that also uh you know really drives what we call like roi based uh roi based pricing or like roi based selling so that's really our model kind of in a nutshell at this point
Speaker 2yeah so it's very much outcome based i think is like how a customer would look at it or yeah yeah that's
Speaker 3actually uh super interesting they bring that up i'll come right back to that but uh yeah it's uh you know what we're calling is uh decisions and the decisions themselves are basically a combination of a number of modules they have to go through if it's a riskier identity or say you know one demographic is like the 18 to 25s kind of like age bracket they don't really have like a lot of credit history or um a lot of uh you know actual like digital history on the internet like they've probably never taken out a loan they like you know they've probably done some bmtl stuff like that but there's like really a thin file um so like we might be calling more modules to ensure they are who they say they are um maybe someone with a longer credit history or someone who's you know maybe in more of a middle-aged bracket has a you know a lot more history on the internet a lot more history at these like different companies you might only call a few modules so at the end of the day um decisions yes are outcome based but uh each you know whatever modules are being used is really dependent on the consumer coming through and like how risky their profile actually is and how much it actually takes to um ensure they are who they say they are
Speaker 2okay just to get a follow-up question on that um like it sounds like when a customer makes one api call they actually don't know how many modules are being called or what the cost of that api call is is that a fair assumption or like i mean like give them give them the history right like um and and and and and then like question on that like how do you then like like give them a bit of reliability on like this not right like similar to like token usage actually right like where you sometimes just don't know how much you actually spend is that comparable and like how do you give reliability there or yeah the hardware is in place yeah that's a great
Speaker 3question i mean that's really something we've um you know really been doubling down on on the last you know 18 months is um really that you know real-time billing or real-time visibility into your consumption against like what you're actually contracted for from a price perspective um i'd say like go back maybe like four years um when our infrastructure was uh you know still kind of being built a customer would get an invoice they would see all the calls obviously they they made there would be some explanation but they really wouldn't have that real time but um you know even prior to signing a contract um when we do like a offline like pov or poc um we like run a sample set of their customer data like a representative sample set so they do understand when they're configuring the modules and the workflows about what percent of people coming through any given workflow are going to hit you know say the the first step or top of funnel versus ones who may hit like the second or third steps because you know there could be like seven to ten modules maybe in a workflow um those are probably divided into you know two to three steps and then you're like oh i'm going to do this and i'm going to do this and so they're not calling one one one one one it's usually like three or four maybe another two and then maybe a final one if there needs to be like a step up and usually for a step up that's like uh actual like document submission like you take your passport or your government issue id scan it and like that's really like the last way for them to like actually tie everything together so we do do these tests so they do kind of understand their populations and like who's actually going to be hitting what and like what percentages so they're going to be able to like understand their populations and like what percentages so they're going to be able to like somewhat of a baseline of what to expect and they do once again have the real-time visibility within the platform to what they're actually consuming from the workflow use case workload to the module level yeah super
Speaker 2interesting i mean it reminds me very much of the agent builder we launched where you also have essentially different type of agents you can build and depending on the workflow runs and the complexity of the agent you're including in the workflow you have different kind of consumption you know um and um yeah okay that makes it makes a lot of sense okay yeah thanks for clarifying yeah definitely all right i think so it's great to have you colin because i think consumption-based forecasting or just you know consumption-based pricing and as a result also consumption-based forecasting is becoming more and more prevalent uh like it's popping up everywhere right like traditional industries or like just companies that have used seed-based pricing before are now like shifting either to some kind of like hybrid models or trying to go all in on like consumption-based pricing i'm not saying that customers always love it i would say as a fair as a fair assessment i think if you come from a seed-based pricing and switch to consumption-based pricing can be a tough one can be a quite tough one um but um i mean it's happening right and like especially like new companies um initially um or like when they start when they launch their business they immediately start with consumption-based pricing uh more and more i think it's just like something that is like driven by ai which is the accelerant here um but um yeah i think uh you know speaking from a revops perspective i think it's interesting because it it puts you sort of like in this position where you know like it's not like what's closed one that is defining you know what actually can be forecasted on but there's like you know something else that you need to untangle uh in order to you know get into the position that you actually can can do a proper forecast so um i'm just curious sort of like what your current setup is overall um what enables you to be in that position i think sort of like what would be interesting to talk about is like um you know along those different lines of you know how does like a typical forecasting session look like at the moment and so cure and then you know we can talk about the tooling the ownership uh the processes behind it i thought that would that would be a good approach
Speaker 3to it yeah definitely yeah um yeah a signed contract that's once again uh i would say potentially what the revenue could be there has to be a lot of i guess you know evidence and supporting pieces to actually substantiate it um and actually you know one thing about consumption based pricing like in a typical like seat base or like sas world um kind of the engine or the kind of uh i guess the robots machine kind of stops at closed one typically it's like okay we booked it we know we're gonna get this revenue um being in a conceptual based model really extends it out and like extends out kind of um i'd say the responsibility and like the controls and structures you need in place after closing because that's really where most the work is at that point uh so you know when we typically go through and forecast um obviously looking at like sales stages or looking at um the funnel itself this the more mature deal is the more we're gonna know about it one thing we did um you know have implemented in the past year is what um we're referring to is like a solution readiness document or like a solution readiness process um and i think i might have touched on this a little bit on our last call but essentially like what this is is um basically spelling it out exactly like what is the uh i guess business use case how are we solving it what's the solution and like how is that solution comprised like what product what modules are using uh the second part of this which is like the actual substantiation or the really really valuable part when it comes to forecasting and what we actually you know gut check our like bookings our pipeline amounts against and this is something that happens um a little later stage probably in parallel with like our kind of like contracting um stage or like tour fairly late stage but this is what we call like the kind of the phase two um this is essentially uh you know custom object um my team had built out in sales in the last couple of years and we've been doing a lot of um my team have built out in salesforce which basically takes like uh you know the use case and like we have like one like record per use case and then like what are like the number of decisions you're making on a monthly basis um what are the modules which comprise that use case um and like i was saying we could be in step so you know modules can be grouped together like one two three um what percentage of the decisions are actually going to hit those modules so you know say the first step is going to be 100 second step could be 50 third step could be 25 basically based on the pricing that the customer has like in their contract um times the number of decisions times that percentage for each module that is basically the number that we use to substantiate the bookings number so like those numbers need to be pretty close or the same um because like that is a solution that our solution consultants have worked on with the customer using kind of all the contextual information they have um through discovery like understanding you know for i keep using consumer onboard it's kind of the easiest one but like how many like new customers are they signing up each month like really understanding what that looks like is it you know if it's an online gambling site it might be a little bumpy it might be a little seasonal um if it's uh like a bmpl um could be a little smoother but a lot of these companies especially a little more mature ones should have an idea of like what their actual you know net new customer acquisition is a month so it's really like that mass times the number of decisions a month times the percent that each module gets hit times the price of the module and then we substantiate that against a booking so that really gives us our best uh i guess kind of our best guess or our best uh kind of foot forward onto like what the actual booking number should be and like that's the number that we actually book and close when the contract is signed so that's like really the the main control um prior to
Speaker 2booking yeah is it so i grew up in uh performance advertising sounds very similar it's similar complexity would you say like um do you have like a minimum contract term is it always 12 or 24 months so you can just fill out the number or do you take into consideration the seasonality of the you know like customer data that they give you right like often there's like you know specific quarters that have specific ramps yeah um is that something that you take into consideration or in the new booking site right like
Speaker 3yeah yeah on the new booking side um we book out an annualized number at like what we call full ramp so um you know if it's like if it's an existing service or company or we see this a lot in like state and federal government like you know say it's for um you know the internal revenue service in the united states say it's like for taxes like they know how many tax payers they're gonna have they know how many people like each year are gonna log in and like file their taxes but say it's like a pre-launch or very early like fintech uh they don't actually really know like that's where we tend to be a lot more conservative and maybe only book like what they actually are contractually obligated to kind of in the middle ground like say we have um a fairly robust and mature um bmpl or like i said like online gambling like sure there's some seasonality in there but they generally have an idea of how many new people they're signing up and we kind of bake that in into the annualized number at full ramp um really when you know kind of a breakdown of that and like when we're talking about what we call internally as like our bar bar booked arr realization um that's really where it really does depend on like what months where they're actually implementing and then what that realization looks like month over month based on that time in the year so like say if we were um i keep i keep going back to like bmpl online gaming we know these companies have code freezes in uh i guess like typically like september october you'd be a four like nfl season in the u.s or um black friday but we know they're going to see a huge spike in like november december for bmpl for all my gaming uh september october and then like playoffs like january february those are the big spikes and that's the seasonality but that's what we model into the ramp for these each individual opportunities against these customers that
Speaker 2we're booking do you have a gap between closed one and go
Speaker 3live yeah yeah so um um with our platform and the way we've been doing implementations um we mostly do what we call like a live trial now so typically most of our new customers are actually fully implemented uh by time that we actually closed one them even if they're just setting uh test data through but their their workflows their modules um everything you know their their uh their kind of like data model that they're using like everything's kind of been like just checked since checked tuned um so at close one at least from our end like for the majority of our customers at this point they should be ready to start using typically when we see a lag in um any sort of implementation or uh utilization it's it's typically kind of based on the customer's timeline that's something that uh the team has gone a lot better about like kind of sussing out like is there going to be you know some sort of like internal change or internal delay like are you not launching this specific product that we that you implemented us for to like xyz day there's usually like externalities we need to be aware of it's not typically our own kind of implementation because implementation delays because we're doing this up front prior to close now via live trial where typically before we had done like what was like an offline toc or kov um which was you know demo test environment demo test uh information but now we're actually running them in the live environment prior to like signing them up or signing the contract
Speaker 1hey philip here are you enjoying this episode well good news because you can find more free rev ops and go-to-market resources on getmeflow.com slash rev ops you that will help you become a better revenue 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 slash rev ops so basically
Speaker 2you model out the new logo bookings number you have ram you have pretty clear guardrails around like how do you convert from kind of a trial into production like into into to re-revenue uh how do you do the like like actual revenue recognition and like in life right like existing customer forecasting because i assume that is super important right like that's where you have the extra data you see probably also discrepancies between you know what was planned versus what's the actuals how do you deal with that and and how does that feed into the the forecasting yeah yeah
Speaker 3yeah so we um we're on a monthly forecast cadence um typically you know after we close these each month we have a um a series or a meeting series that is curated by my team and our fpna team um our realization series uh essentially that's exactly what's looking at um it's a very specific cohorted view of our booked opportunities by customer um looking at the actual revenue realized against the ramp forecast that was provided um from there you know we either move stuff essentially um you know upgrade or downgrade you can be like upgraded off the list like hey like they're on their ramp they're hitting their number um there could be um we need a way too early to see um or there's some explanation needed with the discrepancy and then we adjust the forecast um that in a very small i'd say not so typical there is a also a thing where like hey this customer signed they didn't use it they don't intend on using it that's marked as red and that ends up being like a write down and we actually back out the bookings um that doesn't happen too often but you know it's either like hey they're doing great we can move them off this list like they've graduated from the realization like what they're using they're using there's ones that are like yellow it's like uh they are below but there's still a path to get there and we need to monitor and we need to adjust the forecast accordingly then there's the ones the small population was like this is not going to happen it's not going to materialize it needs to come off the backlog um but that's really like you know the the account executives our csms speaking to each specific opportunity and speaking to the forecast that they provided based on the customer ramp at time of booking and what's actually realizing okay
Speaker 2okay yeah that's super interesting one question that pops up in my head like so you mentioned already like some custom objects right so i'm assuming you try to do some of this in self force but what kind of like other architecture have you deployed since uh just to you know control it because like from the way you describe it right like it sounds like okay there's a lot of alignment going on and you just sort of like need to pull in information a bit manually also uh from different sources and just discuss things also um i'm curious if that is true like my my my my understanding of this or yeah yeah uh
Speaker 3i'd say majority of it is actually systematized the pieces that we don't have systematized um like uh basically the uh what we call the monthly like ramp realization forecast like we use like basically prepare a seed file which like feeds into tableau because in tableau where we do this run this meeting or do this reporting um or whatever bi you choose you name it um essentially is marrying uh you know sales force for all like the i guess pipeline slash that srd um we have jira for our implementation tickets which does factor into the bar realization piece um we're pulling in like met suite data obviously into tableau as well for the actual you know what's realizing and then the actual forecast piece since it is on a rolling basis um and it is cohorted uh we've tried to build stuff in salesforce not a great like custom object use case there so that is the one piece we are using a flat file or a c file to actually put in the ramp schedules to mirror all the uh the revenue against and then um cross reference all the like workflows opportunity specific information booking information um just into one single pane it's really simple as that it's really like four sources going into one kind of like visual there and like those are the
Speaker 2main pieces okay
Speaker 3got it
Speaker 2got it so the the weekly forecasting meeting or like the the monthly forecasting meeting however you run it um that basically is everyone sitting in front of a big tableau report um yeah or yeah yeah yeah yeah and
Speaker 3i was gonna say there isn't a there's a fair amount of pre-work as well so it's not really like we're discovering all the context prior to the meeting um like the week before we have it um all the kind of uh i guess opportunities or line items in question go out uh the csm ae they gather all the context so that's actually in line and then it's really only a discussion um between them and the leadership as to like um you know progress remediation plans like against a specific set of opportunities um and yeah this one's only monthly because we do it after we close the book so we actually have our finalized revenue um we obviously do like a fairly robust or um like several cadences of like you know pipeline or like actual opportunity forecast and this is really specifically to the realization piece and like we do it monthly because like that's you know once we actually get the revenue for the month because we build our
Speaker 2arrears okay let's take a bit deeper into that because i think that's that's super critical to understand right so you do the opportunity forecasting still so you still basically ask reps like okay which deal is real which one would you commit um and then those basically go into the opportunity forecasting into like okay like a ramp evaluation um or like do you already say like hey okay hey look at we have those like 10 deals that are like being committed um and historically we know that like 90 of those will actually like uh properly convert and use like the the budget that they like promised to spend with us um or how do you do that piece right because i think that like then is like new booking realizations also um that also needs to be fed into the system somehow yeah i'm just curious how you bring it all together yeah
Speaker 3it's like i think it's a common misconception in um conception-based models that like you know bookings are sort of irrelevant or it just like comes down to revenue because it's just it just doesn't matter because like at the end of the day like you're kind of booking a i guess a made-up number but that's not really a case because that does inform essentially your forecast and like understanding like how much revenue do we expect and how do we track towards this so like that's the goal line um as far as like booking and at like the top of funnel or in the pipeline like we understand um you know based on historical like based on you know even customer type or customer size like what our realization will be at like the 12 month mark and that's usually our goal line so when we like report up to a board report up internally and when i was talking about these co-works we basically look at like quarterly cohorts of like book customers looking at like the average ramp within each cohort and like that's really you know we have an assumption on there we don't want to like inflate like our actual bookings target because like we have like a really low realization of it's why we want to be like really accurate we want to be like 90 accurate we don't want to be like realizing 60 on like a rolling 24 month basis that would mean our bookings number is uh you know either wildly inflated or we need to wildly inflate it up in order to get to 100 so like now those are great answers that's why those controls at like the top of the funnel or within the contracting process are very important so it's like as close to reality as possible so that we can get to like that 90 realization by month 12 on average on a cohorted basis yeah just let
Speaker 2me maybe play this back because i think it's actually really really important so in the end there's like three motions going on right there's new logo bookings where you basically take away the amount field from the reps because you actually have built a custom object that essentially has input factors and we use it uh
Speaker 3we use it to compare the rep still does call their number but if the number is way off from what the srd is spitting out then that's a discussion to be like hey like something's not right here we need to figure this out before we book it kind of thing okay okay
Speaker 2but i think that's actually really really important because like obviously as a sales leader right what i want to do is i want to focus my reps on you know certain customers that have you know core metrics that are very healthy where the product we offer is very attractive and will generate a lot of revenue and where the likelihood of ramp success is high right so like by customer segment you have certain assumptions of ramp you have certain assumptions on kind of if you put these things in this is the amount and that gives guidance to the rep right and i think that is very very important because if you don't do that it will be all over the place yeah and it's in and and then okay you already solved the gap problem the gap problem you know once it's closed one often in consumption base if you don't immediately start production the ramp can be delayed for three four five six months we had that at fiber but we had a technical integration process and we had huge customers and sometimes we didn't get to the roadmap and then we got to the roadmap three or six months later and you know suddenly your forecast looks very different right so like i think that's like one motion then the second motion is like how do you like do the kind of ramp like actually like execution right so like i think that's like one motion then the second motion is like how do you like do the kind of ramp like actually like execution right like where uh you have essentially you know the actuals versus the forecast and you know my question there is like who's involved in actually managing the ramp is that the aes and the ams it's only the ams like who is actually managing that and who how do you like how long are people calmed on the new logo side into the ramp side that's super super interesting
Speaker 3yeah yeah yeah uh so really like if we look kind of like at our account teams right now we're looking at like the at our account teams right now um it it is kind of a on the direct side it is like a heavy technical team um predominantly uh those responsible for the ramp the ae is always going to be the quarterback on it but um you know like the main like dri the main person responsible for ensuring that um the customer stays on track it really is like you said it is our csm but we also have um you know post sales solution consultants or post sales platform solution consultants those are the ones actually actively working on the ramp side of the ramp side of the ramp side of the ramp side of the with the customer on their deployment or implementation um as well as uh tweaking and tuning things to ensure like the performance they are getting is kind of like what we um presented or what they had prior to close um but then there's also like i said there are externalities um for us a lot of it is you know you know maybe a customer said that we were going to be at the top of the funnel for like their flow but we're you know secondary to it like how do we get to be able to be moved up to the top of the funnel like you were saying um you know with a technical sale like are we correctly like do we understand where we are on the roadmap do they have engineering resources to actually do the integration like that really is that relationship piece and the pieces that we need to be very clear about to like actually accurately forecast um these uh net new bookings or these new customers even with upsells and expansion same thing and then kind of like on that piece um as far as um you know compensation um uh generally um outside the ae team uh 70 percent of folks like variable compensation is based on revenue um and that is you know either what we call like gtm pod or segment based revenue um that could be like you know whatever kind of like revenue you're servicing it could be at an executive level or leader levels like a company revenue or like commercial versus public sector but 70 percent of everyone's like you know on the revenue um majority of that revenue is going to be uh you know existing customers or existing implementations that's still like you know we signed them before 2026 most of that's going to be ramping up customers from last year there is a small piece which is like ramping up new customers there's just kind of a compounding effect um and that's just all in your plans the aes due to like this being you know kind of a sometimes a slow burn um we want them to be uh motivated to get these customers ramped up and we they're basically paid out on like a contract over 24 months so eight periods um and like that starts basically at go live which is the very lowest threshold it's like a small amount of calls in a given month basically starts the clock and then they get eight quarters from there on that opportunity to get paid out so like there are the most motivated and show the customer ramps up the quickest to get that revenue capture
Speaker 2yeah yeah all right it's also good to get like a steady uh stream of income um no big rolex purchase um you know at the end of the
Speaker 3quarter depends on who you're who you're closing how long you've been here so like you know if you're a rep and you have you know what people like to refer to internally as a stacked annuity you just kind of stack yours this like overlap on top of each other so like you know we have reps who are being paid paid out like um three within three different plan years in the same period depending on like when they were actually booking stuff how long they've been here so like there's there's i think it's actually in effect i think it's
Speaker 2actually really tricky because like uh most aes will probably not spend most of their time the next 24 months but actually the csm's and the solution consultant will spend most of their time but the aes get most of the revenue right so like like most of the commissions so it's like it's a very tricky thing right like uh like um it also depends obviously on the size and how involved they are but yeah yeah it i mean it
Speaker 3depends on like you know your risk tolerance like i don't think i'd ever want to be an account executive but like you know high risk high reward um folks on like a solution consulting plan or a csm plan they're gonna have shared kti targets but if you're an ae you're really only uh you know i guess what they say you're really really eating what you kill so i think it just depends on what you're actually looking for and uh what motivates you but it's a very it's very different profile role to role you know a revolve profile is not you know the same as a csm profile not saying it's an ae profile different people want different things and you know certain people that resonates with them and they want to be an ae so they like the risk and they like
Speaker 2the high reward yeah yeah yeah no and and it can be both right can be both um it's just trying to move us on like a little bit here because uh we only have like a few minutes left so um uh last time uh like one one piece in the last podcast that i really like was like just uh the top three mistakes um you know that you would kind of like warn others about like uh when they move into consumption-based forecasting um do you have like uh i mean if it's just two or if it's four yeah um that's also fine um but curious if there's you have any like um just recommendations some advice for our listeners here yeah
Speaker 3i i think my my two top main ones and ones that like you know are into my brain just over the last like six years i've kind of been in this world um first one being um if it's you know fully consumption-based and you are selling into small companies i would say be very conservative on how you book uh if it's like a pre-launch startup and they think they're gonna have like crazy growth or crazy users probably be like that's great we'll you know maybe book a follow-on deal when that happens and then if it's like a pre-launch startup but only book about what they're actually contractually obligated to otherwise you're gonna have a huge hole in your forecast and you're gonna be answering to everyone for the next 24 months on that one and why that hasn't realized not to mention you will miss revenue targets if you do that enough times and then the second one um in order to i guess avoid a perverse incentive um you really you want bookings to be important and drive you know some compensation um some performance management but you do not want the bookings to be important and drive that to be the main focal point for your teams and how they're compensated because that will end up with the same problem as well is um overinflated bookings say you paid out on it that person leaves you have a hole in your remedy forecast again as well um that it ends up being very unreliable and people are basically motivated to do the opposite of what you want them to do you want them to be there for the long term grow the revenue you don't want them to you know book it and a bunch of insane bookings that don't end up realizing and you know despite the fact that they're not your controls you end up with the same thing and missing
Speaker 2forecast yeah that's the worst yeah that's the worst yeah those
Speaker 3those those two are when i'm thinking about realization revenue like those are the two big ones that stick out of my
Speaker 2mind so here in germany we had a moving company and they incentivized the sales people the wrong time and they almost you know went bankrupt because of it it was like high very high velocity uh very yeah very different story but i think obviously um you know having guardrails in place and you know ensuring that the company won't suffer or you incentivize wrong behavior is always super super important especially with such a long time frame of right i mean yeah i
Speaker 3mean think about like yeah think about like the uh you know like the gig economy like the ubers and lifts of the world when they were doing this the ride subsidies for the drivers and basically the fair wars it was great as a consumer i was paying like you know five dollars to go like 12 hours a day and i was like oh my god i'm gonna have to go miles but like it wasn't sustainable and like it was a perverse incentive overall to like a lot of drivers and they would be like waiting till the surge hit so like the company was was like losing money because the drivers like caught on to like how they were actually like doing things and like no matter how many times they like kept changing it was just kind of the race to the bottom yeah
Speaker 2100 yeah that that was crazy that was actually a fun story just sorry quick tangent here but like uh just recently recently at the birthday party and one of the guests told me um so when when uber came to berlin and germany they started handing out promo codes of all the different startups to get like early adopters and the promo code was always something like and then company name is always the same so what he did is just like he just like went and like typed in all the promo codes he could come up with just with all the different startup names and then had like i don't know like hundreds of like you know like dollars of like free credits didn't go didn't like take his bike or like whatever public transport to work anymore just like used uber for like half a year until he got burned through all those credits so it's like
Speaker 3thousands thousands of stories like that i'm sure
Speaker 2yeah completely insane yeah all right um colin thank you so much um actually we love the episode hopefully well i'm 100 sure that this was very useful for all listeners i think a lot of companies are thinking about consumption-based forecasting it is just like one of the like the hot trending topics at the moment for good reason um to close things off last time we asked you about a book recommendation and you said you had a book recommendation and you said you had a book now to spice things up uh we want to ask you what is your most controversial or what is like a very controversial opinion yeah that you have on revenue operations
Speaker 3yeah like i said this one is uh yeah somewhat inflammatory but uh you know i i stand by it um you know rev ops shouldn't own the forecast accuracy they should own the forecasting system and the guardrail so like i always say we can lay the tracks and put up the guardrails but at the end of the day it really is the sales management the ones who are closest to the deals and the ones who actually sign off on everything that are actually in charge of the accuracy of the forecast um no one wants their rev ops person going to all their reps to like figure out the forecast is when they're not on any of the calls from the deals like we can set up the system for the visibility and how it's done but ultimately it is the sales management's job to ensure forecast accuracy and hygiene
Speaker 2yeah 100 with you yeah i mean i guess uh somewhat related to 18 months tenure of the average cro currently where i think a lot of you know to me like often the forecast discussion is it's not just like okay what's the number but also how do you get to the number yeah if you display you're not able to actually you know know what's going on because that's a bit like you're actually not controlling the engine well enough uh it basically displays kind of to the board that you might not be able to be in control and i think like it i'm still like baffled by that 18 months number because i think if you're in a company as an executive for 18 months you achieve literally nothing and typically it hurts the company more than it helps definitely because you you typically come in you do a lot of changes you change a lot of teams and then you're out again and that typically takes like 6 to 12 months and then you have maybe six months to the way you see the effects and then you're already out again then you but somebody comes in and it's actually really really terrible i
Speaker 3was going to say like on forecasting um or even like your forecasting methodology it's really only as good as long as you've been using it and you actually have something to look back on a big red flag for me is when uh you know a sales leader joins a company and like their first thing is like oh we need to redo our all of our sales stages we need to change our entire forecasting methodology so you're basically starting at square zero and like you don't really have anything to look back on there's probably a lot of other things they could be addressing and like coming in with like your playbook or like i forecasted here though my last place i was at is not really you know in the top 10 of things they should be addressing it's like let's you know see how it goes let's have some historical data so we can actually get some predictability instead of you know flying completely blind probably for like nine nine months
Speaker 2yeah yeah 100 percent oh awesome as philip said thank you so much colin uh i'm sure there will be a part three i really enjoyed it uh it seems like you know you're going to be able to you're like you're like an ongoing guest now if you ever want to host a bit like let us know you know feel free yeah i suppose with me or philip sometimes but that was awesome really really
Speaker 3enjoyed it yeah absolutely guys i enjoyed it as well this one was uh this one was smoother than the last one this was great
Speaker 2oh i think um i think equally smooth equally smooth in my book yeah thank you so much colin really appreciate it yeah
Speaker 1you for listening to the rev ups lab podcast if you enjoyed this episode and would like to support us share it with a rev ups friend or drop us a five star rating right now and if you have feedback questions or guest ideas just send a message to janice or me on linkedin thank you and see you next time
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