Structured (YC S23)’s Post

Transcript

Hi everyone, Welcome back to the Structured Podcast. We have a very special guest with us today. We have Rahil joining us from Carta. Rahul, would you like to kick us off with an introduction? Like you just mentioned, my name is Rahul. I've been in the Rev space for a couple of years at this point. Started off in sales, moved over to sales OPS, and then transitioned over to revamps. Awesome And tell us a little bit more about like, you know, how you view like sales and Rev OPS like and sales OPS like what does the landscape of these different roles look like and you know, how do you see your place in an organization? Yeah, I think sales was a really good foundation as far as like getting a picture of the broader go to market landscape because it started off as an SDR. Obviously you have to do some direct selling, get some exposure to marketing and then the. Need to being an effective SDR, in my opinion, was this organization and good operations. So that naturally pivoted into sales operations. I took a data science boot camp in between, which was pretty helpful as far as like helping me understand how to better use the data at my disposal. And that's kind of been the foundation of how I use any type of operations methodology. I think as of now, like Rev OPS basically just rebranded sales OPS. So I usually prefer go to market OPS. And the reason for that is I think that's more holistic of like the entire funnel as opposed to just paying attention to the sales aspect of it. So even though I'm technically doing sales OPS right now, my general methodology is like looking at all operations and seeing how we could maximize revenue throughout the funnel. That's awesome. And what like unique insight do you get from looking at like GTM OPS versus sales OPS? Like how do the additional like functions that are included in GTM like color the story that is told in a way that's like just sales focus lenses cannot? Yeah, I I think the pipeline just gets a lot bigger because then you have exposure to like marketing operations and like the top of the funnel as well. You, you get to clearly analyze like handoff metrics and make sure that there is an inefficiency between like marketing or equals and sales qualified leads. And then similarly at the bottom of the funnel, you have a clear handoff between sales and CS and make sure that's a smooth process. So I guess like in my head go to market OPS like the key underlying. Foundation for it is just like good pipeline operations and making sure you understand your pipeline inside and out. Got it. Makes a lot of sense. And like you talk a lot about metrics like what are the key metrics that are the most important to the organizations you've been in? Yeah, I, I think either places I've worked or companies have consulted with the general methodology is like less is more. I, I think directionally as long as you have good volume metrics like number of leads or opportunities that you have conversion metrics, So clearly defining stages and seeing what percent moves from stage one to three, etc. And then velocity metrics just like how long a deal is sitting in a certain stage that gives you the insight you need to. Double click into basically anything you really need to and you can definitely use the foundational data elements that you have and basically mix and match and come across any type of report that you need. So I think if you have those three or four core core metrics, you can do just about anything you want. You say like double click on a metric like how do you do that exploration or drill down today? Like what's, what is that process like a lot of Excel and that's kind of like where the data analytics and data science program came in handy. So I've modeled it out in SQL. I've done some Python work. Honestly, for quick and dirty analysis, I just use Excel because it's like the easiest. So typically it's just exporting out of your data source. Salesforce is usually where most of your data is going to live and then. Put it like all of your data into specific categories based on stages. So what one type of analysis I like doing on a regular basis, especially for consulting clients, is I like to see whether they have stages defined. If they don't, we like to define them. Then we take all of their historical data and we take a look at what their historical conversion rates have looked like. And then we start cohorting. So we cohort by like quarters, years. And then we take a look if like directionally things are getting better or worse. And then if you see like a steep decrease in conversion rate. Between stage two and three over like the last six months for example, you should probably take a look at that and see what changes you've made. So then you could overlay like a timeline of changes you've made as far as your sales, motion, products, etc, and double click into that. So a lot of this sounds like it's dependent on like the data quality of the of the organization going into the engagement. What what do you recommend for businesses that maybe have not been keeping track of this in the most clean way so far? I would say that's like most companies I've worked with, I don't think I've ever come into a consulting arrangement where they have good data already. So I think that's like 80% of the consulting. I do basically, hey, I need to get to these three or four metrics to actually tell you what's working well and not well in your company. And most of the time they can't answer that using the data, which usually transitions into, hey, let's take what you do have and actually change your system so they capture good data that's usable to answer these questions in the future. And then sometimes you can transform the quote UN quote junkie data that preexists, but sometimes you can't, in which case you're kind of just flying blind and using like best. Mass metrics that you could just find online. Got it. And like what? When is it too early to start investing in like a good revenue architecture? Like is there such a thing as too early? I think you should do it as soon as possible because it's five to 10 times more expensive to fix it later than if you just like took a couple hours and just set it up right to begin with. Because the minute you set it up right, you have rich data that you could pivot off of and less truly is more. So basically all you really need to do in the beginning is define your stages, define exactly what I just mentioned, like the core metrics that you need to capture and make sure you can answer that with your data architecture. And then you could always add on more later. But if you make things overly complex and make it so you can't. To actually answer the right questions, which happens more often than you think, then you basically have to rip and replace. And that becomes very costly because over time you start adding new tools, new processes, new systems and everything becomes like in jumbled up a little mess or just like a ball of string that you have gone tangle. And that takes a lot longer than if you just did it right the first time. Businesses are also not like static, like especially if you're very young like you maybe you're adding new GTM motions or maybe like your sales process looks different and it's not yet repeatable. Like how do you how do you recommend designing this revenue architecture in a way that is extensible enough and modular enough that it can adapt and change with businesses as they grow? Yeah. I usually keep things super vanilla. I think every company I've talked to whether we. Ended up working together and not the resounding theme has always been like, hey, our go to market motion is very, very unique and here's why it's unique. But then when you like really dig into that and ask like 5 layers of why or dig into like their responses, it usually comes down to it not being that unique. Most go to market motions nowadays are relatively standard. So if you just look at like a classic pipeline and instead of calling it like unique stage names to yourself, you just put like one. 234567 And then after those numbers, you could put in whatever stage name you want. So you have the best of both worlds. It becomes a bit more modular in the sense that you can keep adding to that. You can also remove it. And then if it's in your system and it's just like a classic sales process already, you can choose what stages you do and do not use, which is super helpful. Yeah, I think at a high level, flexibility is just the key. I know that right now a lot of businesses are feeling like SAS tool overload is the root of the problem that people are using Salesforce and HubSpot and like something else like is it the? Is the data getting fragmented because of the? Like breath of tools or is there another reason? Yeah, I, I think like a lot of folks have a knee jerk reaction to wanna like buy a tool to solve a problem. Like sales are down. Let's look at new tools. Like let's look at a new engagement tool. Let's bring in a new CRM. MQL are down. Let's bring in a new marketing automation tool. Oftentimes it's just like foundational work that needs to be fixed. Like a hot take I have is you don't need the fanciest. Tool as long as your foundations are good. Like it's better to get your foundations right in a tool like Air Table or Excel first before investing in the most fancy like new sales automation tool that exists. Because once you're foundations are good then it makes sense to scale but you don't want to scale inefficiencies. Yes, that that absolutely makes a lot of sense. Have you seen like variances in terms of like the cultures different organizations have around data? Like how do you convince your team that it's worth like tracking these things in the first place? Yeah, for basically everything I've talked about through this episode, I've been relying mostly on the consulting experience. And I think the reason is, is allows me to be agnostic and not biased as to like, hey, this is a great culture or bad culture. And honestly, there's no such thing as bad culture in my opinion. It's just different. So to that point, when it comes to like culture around data, the biggest thing I've seen when consulting is everybody seems to think their data is good except for like a couple people that that. Have tried to solve problems with it and haven't been able to. So I think the big thing is just making sure every core business stakeholder sees value in data-driven decisions. So I keep going back to the pipeline example, but the reason is that's honestly like the main data use when working with go to market leaders. Like that's usually what like every marketing leader I've worked with cares about. That's what every sales leader, CS leader cares about. So it's basically showing them like, hey, here's what the numbers. Like show right now here is like the situation you're in here is how you can be like more predictable in the future and like. The numbers can get you there like you can build backwards from a goal if you have the right data, but you can't just fly blind and hit a goal most of the time. Can you give an example where like the data like maybe contradicted intuition? Like how if there's like let's say a problem with like top of funnel or like demo conversion rates, like is there is that intuition already in the organization or does the data expose something that people already don't know? Yeah. I, I think oftentimes the data usually reinforces intuition, but the few times it hasn't is I think when it comes to like booking meetings top of funnel like the SDR AE handoff. I've oftentimes seen like intuition says, hey, if I have like a lot more rapport and like longer calls, I'm more likely to actually book a meeting and people actually show up to the meeting. What we found is sticking to a type process. Where you keep authority or and this isn't any specific organization, just more broadly speaking, I found this like less report, reports still matters, but not to the point where like 90% of the calls report. Like from a sales standpoint. I found like if you keep like the first like 2-3 minutes to report and then keep a tight agenda on an SDR call, you're more likely to actually set a meeting that folks actually show up to as opposed to just saying yes because they like you on the call and then hanging up and then never actually showing up and talking to your A. That's really interesting. Yeah, I think it's, it's a good guardrail because even if like people have intuitions, like being able to like confirm them or like see the picture, see the world as it is, it's can be very valuable. So what is like your like top of mind right now? Like what do you think is like the most important problem to be solving in the Revo's world? I think right now we're we're at an interesting place just from an economic standpoint where the, the grow at all costs like mentality I think is diminishing. So there's more of a focus on like thoughtful growth and like being very considerate with where you're putting your time and money. So I'm focused heavily on like conversion rates with all of my engagements. And the reason is previously if you put $100 in and you got, I don't know, 200 back, that was great the goal. Nowadays like put $100 and get three $400.00 back and the best way to do that is conversions. So minimize like a leaky funnel. How once you see the data, like what is the action that you take? Like sometimes the thing that can be tricky for companies is you can see that hit the conversion rate is bad, but it's hard to know what to do with that information. Like how do you actually improve that? Yeah, the, the numbers just tell like one part of the story. It'll tell you like, hey, here's where the issue is. But then that's where you kind of have to put on like the RTAT because that's the signs where it's like a mix of science and art. So science is like directionally finding out like where you have to look. And then the art is like digging into like actual notes, call recordings like etcetera, etcetera, actually talking to like the reps. So what that could look like is that I think the example I gave before is you have a precipitous drop off between stages two and three. So in that situation. They'd probably take a look at like most opportunities have been coming in, group it by like the actual Rep that is having like the steepest drop off, listen to their calls, compare that to like reps that may not have as big of a drop off, drop it like down differences and that becomes the new enablement project. So a lot of it is just using data to guide you directionally, but not being so fixated on like, hey, the loss reason is X. So it has to be this like oftentimes that might not be right. Keep it is to get to the root of the problem. Well, thank you Rahul for this amazing take. Like really enjoyed this conversation and yeah, hope everyone enjoys enjoys hearing this podcast.