E73: The AI Enabling 600 Customer-Facing Reps | Lauren Hughes, VP Revenue Effectiveness @ Justworks

You need to be a systems thinker. You need to understand technology and understand what's going on in the market and how fast each and every technology is changing.

That's Lauren Hughes, Justworks' first VP of Revenue Effectiveness. She runs RevOps and enablement under one roof, supporting over 600 customer-facing people across 20 different roles. And she does all of this with a very lean team, plus a lot of smart systems.

There's no excuse we shouldn't be able to give them more pipeline opportunity, more lead volume with all the tools and resources we have now.

In this episode, Lauren shares the operating system she built, which grew bookings, her AE by 28% last year. We also cover the traditional enablement roles she decided to stop staffing and how that work still gets done, plus the signals that tell her in just the first couple of days whether a new hire is actually gonna make it. All this and a whole lot more. Welcome to the Revenue Leadership Podcast. Today's guest is Lauren Hughes, the VP of Revenue Effectiveness at Justworks. Lauren is the first person in that role. Uh, John Bell created the function, Put RevOps and enablement under one roof, then hired her to run it last year. And Lauren spent 20 years in media and ad tech at Glam, NBCUniversal, Pandora, and Moby, and then crossed into SMBHR last June, which is quite a change. And she walked in 4 months after a layoff and then proceeded to take the enablement team from 16 to 6, used a bunch of that funding to move into RevOps, which had been understaffed. And we're going to talk about redesigning that operating system, the platform of this revenue effectiveness team. We're going to talk about redesigning the go-to-market operating system before then adding headcount to drive growth. And then we're also going to talk about tooling and so how she has decided on what to build, what to buy, when to wait on stuff, and what she did with the stack of 70 go-to-market tools that existed before she took over. So this is going to be a really fun one and I appreciate you joining, Lauren.

Awesome. Thanks for having me.

Let's go back to when you first started in this role. So, they bring together this function called revenue effectiveness. It's new on the org chart and you walk in like post-layoff and then have to, you know, right-size the enablement team, move the funding around. I would love to just like zoom out. And first hear about like your assessment. So, like, you land in this new seat. What did you do to assess what needed to happen? Think through the sequencing of events and just like come up with a strategy first.

Yeah. Yeah. I was kind of, I kind of knew what I was walking into. My boss has been forthcoming with me, John, as I was walking in. But, you know, he, I sat down with every single person. across the go-to-market organization when I started. So, I met with, I had about 20, 25-ish people on RevOps. I had 16 on enablement, you know, and I really made sure I sat down with every single person and found out exactly what they were doing. I also did a bunch of sales ride-alongs. So, I met with several SDRs just to understand what their process was, where there was friction in their process. I met with several account executives to understand their process, a couple people on customer support, customer success, onboarding, all that sit within the revenue function. So, I really tried to diagnose like exactly what was happening on the field through those ride-alongs, through the one-on-one connections with the people on my team, and also really through data. So, really dug in on the data piece. And one thing that became pretty apparent to me right from the get-go is we didn't really have a revenue planning and analysis function. You know, for me, like coming from building traditional revenue operations organizations at many companies for the last 20 years, I've always structured my team around kind of data-first decision-making and making sure that like a revenue analysis function, you know, Making sure that the data layer is clean and clear, and that we really use a lot of data to drive decision-making about the go-to-market org and operating system. And so, while there were a lot of hypotheses when I walked in through the door, my knee-jerk reaction was constantly to say, where's the data? What is the data telling us about exactly what's happening? So, the first 60 days were really just assessing what was happening on the ground through the data and the ride-alongs. Um, you know, the, the biggest piece that was pretty eye-opening to me is we had gone from 32 people on the enablement organization to 16 before I joined. And—

And how big was the company at that point? Like, it was 30-something people supporting how big of an org?

Uh, so, probably around that time, I wanted to say it was probably about maybe 450 in customer-facing roles. We're, we're over 600 now, so we've grown pretty substantially. Um, we have 600 customer-facing roles, um, or sorry, customer-facing team members across 20 unique roles. So, the enablement function, the level of complexity of the Justworks product was expanding. The level of complexity around the roles that we were using to service our prospects, partners, and customers was changing and also growing. And from an enablement perspective, we had to really figure out how to scale enablement quickly, but really in a personalized way, right? We have to meet people where they're at. We were, we were pulling out team members to have country launch training for over an hour, and they would never have to sell said country for maybe 6, 12, 9 months. months, you know, down the way, and they would not retain any of that information. So, we were pulling people, you know, out of the field to give them training for stuff that they didn't need to be trained on at that given point in time. And so, we really had like a pretty serious enablement issue on our hands. We had a lot of traditional roles like instructional designer and things that just, you know, LMS admin. We didn't really have an LMS. We had a tool that we were paying for that we weren't properly using for kind of a content library. And You know, I did these ride-alongs with, with the 16 remaining enablement people, and they were all working on the same thing. Not one person was working on something unique versus everybody else. And so, I had kind of 2 choices. I could like redeploy and redesign the existing team, or I could just kind of wipe it all out and start fresh. And that's really what I ended up doing. So, I made a pretty quick decision to take that team from 16 to 6. I eliminated, eliminated some pretty traditional enablement roles that I didn't feel were valuable or necessary anymore, especially given what we needed to build from a personalized learning perspective. Um, I, I saved that headcount in my back pocket and, and redeployed it in a way that I thought would be, uh, serve us a lot better for the growth that we needed.

And those roles were mostly RevOps focused?

Yeah, I mean, some of them were RevOps focused. I did hire a couple AI content, uh, creators. Um, so people who had, who had experience with AI tools. Um, I hired an awesome guy from JPMorgan Chase who just wanted to build stuff and had built within the, the confines of his current role as best that he could. But, um, I gave him the opportunity to unleash him without any, as many guardrails, and he's really hit the ground running and trying to stand up our personalized learning journey at scale across those 28 unique roles, and he's been amazing. And so, we redeployed a couple people that existed, hired a couple of new people who had more kind of technical chops and really systems thinking, and we kind of rebuilt the enablement team from there. I think we're at 10 or 12 now. We have a couple open roles. We'll be at 12, I think, when we're fully staffed, but the productivity is increased significantly for that team, and not only that, but the the recognition from the organization on the partnership coming from that team and their understanding of what it's like to be in a customer-facing role and how they're showing up every day. It's just the partnership has been amazing.

Okay. So, so you mentioned that you were eliminating a bunch of traditional roles. You mentioned instructional designers, LMS admin. What other roles do you think we've had in enablement teams that like we don't need today? And, and what are the specific roles that you think are the most important that people have in their headcount plans?

Yeah. Yeah. So we, um, we had another role in our, our headcount plan. We hadn't hired for it yet, but it was an onboarding coordinator. Like all of that was replaced with technology for us. So everything is, is AI administered. Um, it gets set up the right way in Workday and it trickles through every, every scheduling for the new hire, depending on whatever your role is and whatever your role is assigned in Workday. All of your scheduling for your first, whether it's 3 to 5 weeks in, in onboarding is, is done now straight up through AI.

So, the onboarding coordinator would've just been somebody doing scheduling and calendar management.

Scheduling and, and also measurement of, of their, um, onboarding progress. Um, so, whether we do, um, you know, uh, uh, uh, tests at the end of every week, um, for knowledge, like, you know, making sure like knowledge check tests and things like that, like all of that's sent out through AI and the manager gets automatic alerts, um, of how their, uh, their rep or whoever it is that we've hired on their team is progressing through onboarding.

And is that a tool you've built or is that something like out of the tool that you bought?

Something that we bought, something that we bought. Um, and we were kind of one of their, their first customers. So we were able to, I think what's really exciting right now, um, in the industry is some of the tools that you're able to buy, a lot of the, the people that are building these tools wanna co-create with you and really wanna kind of roll up their sleeves and work with you, um, to make sure that their product is serving what the buyers actually need. Um, so we've been really fortunate with with several companies over the last year that I've been at Justworks, that they really wanna partner with us, and they really wanna co-create and build together.

Okay.

So, we hired what we thought was kind of like a Zapier integration tool, and we ended up building like AI roleplay with them. We have really good leading indicators for measurement of employees' onboarding success. We're able to have an early alert system when people are not progressing through onboarding as expected. We can have, it kind of gives us a forcing function to have a serious conversation with the manager of, is this person gonna make it or not? And so, that eliminated a traditional role right there that we never even ended up hiring for and we were able to replace with technology.

What's the, what's the tool you're using?

Tangello AI.

Tangello.

Okay. Yeah, it's a, a small company and, um, we're, we're one of their early adopters and it's just, they've been really flexible to build with us, which has been awesome.

Yeah. I, I mean, this is the same reason I partner with so many earlier stage companies. Like if they have the, if I'm really looking for like an alignment of philosophy, It's like, as long as we see the world the same way and we, we are like thinking about solving this problem in the same way, then being able to like shape, shape that product direction can be so valuable.

Yeah.

You just get so much out of it.

When I think they're looking like vendors are looking for that too, because now everybody has so many overlapping features, everybody's going after everything. Uh, you got the big conglomerates nabbing up all these companies all the time, um, just trying to consolidate as much as possible. And so I feel like some of these like Founders of these tech companies around go-to-market tech are just looking for somebody to say like, is my product viable? And if it's not, like, what other features and functionality do I have? And if I work with you, can I build it, um, you know, as good as the competitor, if not better? And, um, we've been really fortunate to have some great partners there.

Yeah. And so like LMS admin, where does that job get done now?

Yeah, we don't have an LMS anymore. Like we just didn't need it with moving to Tangelo. Um, Like how we've been able— we're also partnering with Spekit. So we have that just-in-time learning functionality that's kind of embedded within Salesforce. And we spend a lot of time cleaning up the content foundation, making sure that our Confluence pages are super crisp. All of that's done now through AI. And when we go and launch new things and we update Confluence pages and make sure everything is clean kind of behind the scenes, like everything gets served up either through Slack. We have like a sales Slack channel where they can answer any questions. All that's driven by the help center. We have the just-in-time learning built through Spekit within Salesforce. Again, all that's driven by our, our clean help center. Um, you know, and so we, we really didn't need like a traditional LMS. Um, all of our onboarding content's based in measurements, all based in Tangelo. So all of it's being served through kind of different channels. We just didn't need the traditional LMS anymore.

You're in back-to-back meetings all day. You're trying to stay present, but you're also worried you'll forget the decision, the action item, the important next step. And that's where Granola comes in. Granola is an AI-powered notepad for meetings. You jot down rough notes like you always do, and in the background, Granola transcribes and turns them into clear, useful notes when the meeting ends. No bots joining your calls, no distractions, just a clean notepad that helps you focus. Use code THEREVENUE for 3 months of Granola for free. So you can head on over to granola.ai/therevenue. The Revenue to learn more. Again, that is The Revenue for 3 months of Granola for free. Check it out at granola.ai/therevenue. And, and so there's nobody that needs to spend most of their time just like building stuff in Tangelo or, okay.

No, I mean, we have a couple people that are up with their hands in there, but it's all pretty self-service now. We spent a good, you know, we spent a good amount of time setting it up. Um, so we have different learning paths for different roles, um, and we're constantly improving those because we have such great metrics now on our onboarding pro— process. We can now go back and improve it. Um, when I first joined Justworks, our average time to ramp for a new sales hire was 18 months. Um, we have a very like compliance-heavy business, obviously HR is very confusing, health insurance plans, things like that. You're hiring a rep off the street who's never worked in, in HR compliance, health insurance before, and having to train them and onboard them. Um, you know, in, in a very unique way, um, just wasn't, it wasn't really scalable. And we've really focused last year on reducing that. We've gotten it down to 8 months and we have a pretty clear path to get it under 6.

Wow.

Yeah.

That's awesome.

And we hired 300 revenue-facing team members last year. 300. Okay. Um, so we had 300 people go through that program.

Wow.

So a little measurement, a little technology, high volume, um, but the results are, are in and it's been pretty exciting to watch.

And so you're measuring that on, on like time to full attainment or like how do you— how do you—

Yeah, time to average attainment of their cohort. So our cohort attainment is growing in terms of what they've been able to produce. So on average last year, our entire AE population has grown in terms of bookings by 28%. Over the last 2 years has been 44%. So our reps are getting significantly more productive. A lot of that's due to the productivity improvements that we've made and the decrease in friction in the operating system. But a lot of it is attributed to new hire and the new hire program that we've stood up as well.

Very cool. I'm going to bookmark that and come back to it because I want to unpack that a little bit more. I just want to run this sort of roles conversation to ground because I think this is a really interesting one that a lot of companies are going through. Everybody is trying to figure out like what roles are actually needed, what roles are are people gonna say are needed because like they're in that role today versus like, how can you, what can you repurpose? And, and so like instructional designer is an interesting one. So maybe, maybe just explain for people who don't know what that role is and like why you, you don't necessarily think that's like a requirement today.

Yeah, I mean, I think a lot of people were spending time, um, figuring out how to turn, uh, product launches or, um, learning experiences into classroom-type environment where they thought people learned the best. And now in this environment where we're learning from LinkedIn posts and podcasts, and we're learning from peers in the industry, and we're going to group dinners, and, you know, there's just so many different avenues to learn. Sitting in a classroom and being trained in an environment where with traditional kind of educational experiences just wasn't really landing for people. And everybody's on Zoom too, right? Like 5 years ago, like you have way more people, sorry, 6 years ago, we're 6 years out of COVID now, we had way more people like in the office and like walking up to people's desks. And now you set up a training with a traditional learning journey and people are on Zoom and they're like not paying attention. So, we really needed to move towards like bite-sized learning. What can you consume on the go? How do you tie that in what we're experiencing in the industry and how do you bring the outside in, you know, from a kind of external market perspective to what you're learning about? Another, you know, role that we deemed kind of like not scalable was like field enablement. So again, we have like 20 unique roles across 600 customer-facing team members. You can't have 20 field enablement people running around to these like specialist roles. So how can we replace those types of jobs with actual like personalized learning at scale. And that's where we went to technology and also our own internal AI abilities to say, okay, we're launching this product. It has to be, it has to be explained to the sales development team so they can talk about it in their outbound process. It has to be explained to account executives so they can actually sell it. We have to make sure the onboarding team is enabled so that they know how to onboard for whatever the product feature is. We have to make sure it shows up in customer conversations. So customer success and support need to know about it, but We now have AI that tells us, here are the profiles based on ride-alongs that we've done with the team members. Here are the profiles of the customer-facing team members that we have. Here's the thing that's launching. Tell us exactly how to enable these people. Um, and so it kind of cut, cut out a lot of that journey for us. Um, and now we just run everything through our, our kind of AI enablement planning system. And it tells us—

Is this all Tangelo or is this—

Uh, no, it's all like ChatGPT and Claude.

Yeah. Talk me through that system. Like, what, what is it? How have you, have you built the architecture? What are the things in there? How did you, how did you populate the context and skills?

Skills.

Yep. Yeah. Walk through it all.

Yeah. I mean, so on the enablement side, it started with just ride-alongs, like making sure that we, that we as an enablement team have very deep understanding of like who our population is of customer-facing team members at Justworks and who their customers are. So, we support everybody across prospects, partners, and customers. So, really needed to have a very deep understanding of who those people are that we're serving at any given point in time across the customer journey, and then why these unique roles were set up at Justworks to begin with, and what exactly they need to know to be successful in doing their jobs. So, the purpose of the enablement function at Justworks is that we really need to make sure that every customer-facing team member is ready and equipped and with the right skills and the right empathy and resources to be able to talk to prospects, partners, and customers at any given point in time in the journey. And so, really trying to like get back to bare bones and understanding, you know, what those conversations need to look like. And that kind of helped teach us how we should better enable these people in a more personalized way. And through that, it would resonate better, hopefully, and it has. And then we could, we could go and, you know, enable these guys at scale with hopefully fewer, fewer people and more kind of technical capabilities. So, so it started with ride-alongs.

Like, what was the output from the ride-alongs then?

Yeah, so we, um, so we recorded all of them. We record everything with Gradola. Um, so we have like really sufficient notes kind of coming out of that and we could, we could load it into, uh, one of the AI tools and say like, you know, help us build these personas of our people. Um, not only just our, our people who are talking to prospects, partners, and customers, but of our prospects, partners, and customers and what they need to know. Um, and so we, we actually, this was like a good, like, um, we tested it and it failed. We tested again and it failed. And then like third time's a charm example. Um, so I had somebody that that wasn't— that was on the enablement team that wasn't necessarily like skilled or equipped technically, didn't have the right vision for the future. She had done a bunch of the ride-alongs. She tried to build something in ChatGPT and it just kind of like fell flat. The personas weren't perfect. Too much slop. Yeah, just a lot of slop. Yeah. Yeah. And so we tried it. We got— we were kind of earlier adopters of Clogged Code. We got back in like February and we moved really fast across the revenue operations organization to start building stuff. And, um, we tried again in Claude, um, and we got much better results. Um, and then we really like, we really worked at it and perfected it for a while. And we met, went back and circled back with the personas that we did the original ride-alongs with. And we said like, does this sound right? Um, you know, have, have we nailed your persona? Like if you were, you know, receiving an enablement session on a new product launch, is that, are these the things that, you know, work for you and your team? Um, if we gave you, manager, a meeting in a box, uh, concept, like, you know, 'cause we or, you know, 6 enablement people now, we can't scale and be in every single person's team meeting. But if we give you a meeting kit in a box to be able to go and enable your team on whatever the topic is, like, you know, does this work for you? So there's definitely a huge feedback loop involved there too. But we used to, like the former enablement team, when I was first doing those run-alongs, there were Excel spreadsheets with project plans for days and days. There were tons of PPTs flying around, lots of Word documents. Like now we have just like our you know, system built in Claude. All the personas live there. We can improve the personas. It can, it can learn every time we do a new training. We say this one really landed well, this one's measuring actual results. You know, this methodology worked. Let's keep doing things like this for this population of people. Um, and that's how we've really been able to nail it.

And so when you say like in Claude, that is like a Claude enterprise account with shared chats or shared skills, like what's Yeah. And everybody can update the skills.

Yeah. Yeah. So our entire revenue effectiveness team, actually our whole company has access to Claude, um, Code, Cowork, Enterprise Claude. Um, and so we're all in there building the skills, um, feeding, feeding the skills back. So, you know, technically if I was a manager of a new customer success team, I could come in and, and change the skills if I wanted to. But, um, for the most part, we're trying to do that through my team.

So, so essentially this revenue effectiveness system is basically like a set of Claude skills that you're managing centrally and then deploying out. And every, all of the feedback loops basically come back to like, hey, the results were good. This moved the needle. This didn't like update the skills, update this skill with this, with this feedback. That would be like—

Yeah, that's on, yeah, that's on the enablement side. On the revenue operations side, we're running things a little bit differently, but, but on the enablement side, that's kind of what we're mostly using.

Okay.

Interesting. We can, we have Tangelo connected in there. We can feed in the onboarding results. Um, so all of that gets updated in real time.

Okay. And those would be like as workflows?

Yeah. And, and, and connector, like it's all connected in there so we can prompt it and say, um, you know, we just got through this onboarding class. These are the things that work. These are the things that didn't, um, you know, based on the latest data, like how would we adjust? Yeah. And how would we adjust the program going forward? Okay.

Very cool. And then how do you centrally manage the context of it all? So like the personas are in the skills, like, you know, uh, much of the time you would keep those 2 things separate. But I guess to make it work in Claude, you just, you've combined context and skill in sort of one artifact.

Yeah. Well, the, the content is still essentially updated in Confluence. So, okay, based on the product needs to update content, if customer success and support, like our customer education team updates content. So, you know, everybody's kind of touching the content, updating a little bit. In the past, the revenue enablement team was The only ones updating Confluence. And I said, like, that's not a job of an enablement department. We need to make sure that the field teams are ready to speak to prospects, customers, partners. We can't be updating content all the time, so you guys got to figure out how to automate that. So we do have connection between product, product marketing, and customer education now where content does get updated more fluidly. We have measurement on all of the content so we can tell how often it's getting hit, how often it's been updated. If it's stale, it refreshes and alerts people that it hasn't been, you know, hasn't refreshed in a while. It gives us suggestions on how to refresh it. Um, so that, that content baseline is still incredibly important, but like one that can be so easily updated and maintained now that we have AI.

Okay.

In the past it was literally a human like, oh, I, I don't know if this has been updated in a while. Let me just check that it's accurate and I'll ask 15 people if it's still accurate and I'll come back and like rewrite it.

It's crazy that this is how we did, like did this like 20 years ago. Yeah, yeah, yeah.

We, we needed 32 people to keep Confluence updated.

That's, that's gonna be the quote of the podcast.

Um, but it's true, but it's true.

Yeah. So, so then, um, going back to creating the system, so you do a bunch of these ride-alongs. One of the first biggest artifacts is the personas. And then I think the thing that I, I want people to take away is like this feedback loop, cuz, uh, like the reason people are like, ah, like we use AI for stuff, but like, I don't know, it's not that good. Is because people aren't iterating. Like you get the first, you just like cram a bunch of crap in there and you get an output and it's like, you know, if you spend a lot of time in AI, you're just like, it has bot breath.

Okay.

It sort of said the same things 3 in like 3 different ways, but it's very repetitive. It's like overly, it's just like got that tone of voice. It's not this, it's that. And people don't spend the time to be like, okay, it's like like pretty good, but it's not at the quality bar to actually like build something off of or put in front of my team. And then all of your field teams or your leaders, like they just start ignoring everything because they're like, oh, like the, like that persona doc was twice as long as it needed to be. And it was like really confusing and they sort of like missed a bunch of stuff. But I think like going and being like, okay, this isn't good enough, this isn't good enough. And like running it to ground with that level of, of, uh, like rigor is, is so much of the magic to making this stuff work.

Yeah. And that's Like, was so— I mean, we had no choice. Like, we, we took the team down to the studs and we said, we're, we're growing so fast on our customer-facing team members, like, in terms of quantity and role, um, personalization. We've got to move fast to figure this out. And so, the feedback loop for sure, um, is huge, and it certainly helps to be able to go talk to somebody in person and have Granola in the background taking all those notes and just feeding the Granola back into Claude. I mean, that's— been a huge productivity unlock for us at Justworks is just being able to use Granola and, and have that. Um, and then the measurement, like being able to measure the results and the efficacy of the training programs that we're rolling out, like that never existed at Justworks before. Um, it was—

Interesting.

Getting onto the next activity. Let's just roll out the training program and get onto the next one on our list and roll it out, roll it out, roll it out. And there was no measurement coming back into the system. And if you roll something out and it falls flat and there's no adoption and we're not selling the new product or people don't know how to sell it, or, you know, where we've— we also kind of didn't really do well about a year ago in Justworks was conversational intelligence. Like, we have rich conversational intelligence data. It's firsthand from our partners and our prospects and our customers. I mean, it's amazing, amazing information, and it's so underutilized. If you just feed that back into the system and help figure out if this stuff is actually landing, it improves your training by You know, so much. Um, and so you just have to, like, to your point, you just have to like really take the time and feed it back. Um, but having that, that measurement piece is, is huge and is like super critical to be able to feed it back into the system.

And so what are you measuring on now? So you do a training and like, you know, it can be sort of ephemeral to figure out if this training landed. So, so what are you doing to really like discern what's working and what's not?

Yeah, I mean, I think it's like, you know, leading indicators is just like conversational intelligence. Is the, is it, if it's a new product or feature, like, is it mentioned on calls? Um, so that's kind of like step number one. Um, you know, is it materializing in the pipe? If it is mentioned on calls, is it materializing in the pipeline? If it's not closing in the pipeline, do we need to do further training about kind of details of the new product or feature or whatever it is to be able to explain it better and make sure that it actually gets closed? You know, and then of course booking results. Um, but those are, you know, and then of course any, of course any closed loss reporting too. Like if we're actually losing more business, um, trying to really understand why. And I think even like velocity also, like, like for new product attach rates, like if we attach those to deals, does it help close faster?

So what, and like what skills did you actually go build? So you built these personas, you built this, starting to build this like context layer that everything's going to plug into. Then what were the skills you actually built for your enablement team to help them go so much faster?

Yeah, I mean, I think it was like, um, a couple of people really needed to be upskilled on AI and we had to bring in more technical talent. And this is where like the revenue effectiveness vision really came to play. A lot of companies run revenue enablement and they run revenue operations, and those teams don't talk to each other. And I came in as truly a revenue operations professional saying we need to run enablement with systems, technology, measurement, and that's the only way we're gonna scale. So, I kind of, by marrying those 2 teams together and saying, okay, revenue enablement, you're, you know, the new hat you need to wear is you need to be a systems thinker, You need to understand technology and understand what's going on in the market and how fast GTM technology is changing. You need to be looking outside just as much as you're looking inside, and you really need to partner with revenue effectiveness on measuring the efficacy of your training programs and making sure they're resulting in revenue impact to Justworks. And so, marrying those 2 teams together and really showing them how they should be working together, I think was like the true unlock for the revenue enablement side. Um, cuz they would've just kept going on producing their programs and checking the activity off their list. High activity, low results. Um, and now the, the results are really embedded, um, and really it even brought forward, um, before the activity. Like, we know we need to improve results. Where do we need to train? What results are those? Like, how are we gonna measure the efficacy of those training programs and then produce the results that we need?

And so in, in this like Claude library, what were the specific skills that you've built that your team is now using to like accelerate this whole program? Because it seems like that Claude skills library is like the key thing.

Yeah. So the, the, um, the skills produce the project plan, which a human used to produce, um, basically says like, here's what your training program should look like for whatever particular thing is. Here's what you need to get from various team members in order to build a structured training program, whether it's what we need, data we need from product, data we need from PMM, What do we need for marketing? Um, to really kind of put together, um, a cohesive training program. Um, and then we, we, it's, you know, it spits out that, it spits out the content for us. So, if we need to go do a PPT or we need to go do a podcast, like recording, um, we use Synthesia for videos. If we need to build an AI roleplay, all of that gets created and spit out.

Right.

Um, so, there's still a human in the loop element, like an enablement, uh, professional has, we're calling them readiness professionals now, um, revenue readiness professionals. They have to go in and still review like, okay, AI and my skill is going to build me an AI roleplay for this particular training or rollout. You know, is it, is it asking the right things and saying the right things? So there's still a human-in-the-loop element of it, but it's pretty close. And a lot of those AI roleplays that take a lot of time to build because you're, you know, having to feed it exactly the right things to say and the right, the right things to pressure test with the rep are getting built so much faster. Like, we don't have to write out the narrative or anything like that. It's all being driven by AI.

Okay.

But the—

as a baseline, you have to have, I guess, your methodology documented in there and the core narratives for everything. So like, what are the input foundations or input pillars that you had to get in there to let you spit out? Like when you say, oh, it spits out the content that for many people, that's going to seem like a dream or like something that's not necessarily attainable. So maybe unpack like, what are the things you had to get right in order to get that outcome?

So in addition to the personas and the confluence maintenance and making sure that the underlying context was all accurate and confluence, we had to partner really closely with our PMM team to develop these playbooks. We didn't really have the right playbooks written for every single go-to-market launch that we're trying to do. So working with PMM to say, okay, like what is the, what does the marketing activation look like against these launches? What is our, how do we launch against prospects? How do we launch against partners? How do we launch against customers? 'Cause those are all very different.

Mm-hmm.

Personas that we have to speak to on a regular basis. So making sure that we have that right kind of methodology agreed upon with our product and marketing friends. And all of that gets fed into the model as well so that when it spits something out, like we say, like, is this an internal launch for an internal productivity tool? Is this an external launch? If it's an external launch, who is it targeting? If it's targeting, you know, customers, we treat it a little bit differently than if it's targeting just prospects. Um, you know, is this a, is it a product expansion? Is it a feature expansion? Is something totally new that we're going to market with in a very different way? And if so, like, what does that look like? So, all of that's in partnership with our product marketing team. Um, and we use all that information to go back and, and, and feed the model as well.

And, and then have you documented as well, like your sales methodology so that it's, when you're building stuff, it, it resonates with like, oh, we, you know, we use Challenger, we use Sandler, we use Miller Heiman.

Yeah, yeah. We're like, uh, in the process of revising our sales methodology, I would say. Um, we've tried a lot at Justworks and none of them have been thoroughly adopted. Um, and we have a new leader in, in seat who's got some new ideas, so I think we'll be revising that. Right now it follows my pick. Um, but we probably will revise that and improve that over time. I feel like sales methodologies are like those things that fall flat if you don't have the right, like, leadership reinforcement of them.

Yeah.

It's kind of built into our process, so it's a lot of people use, use it differently just based on what we've enforced and what we haven't.

Yeah, we had to create our own from scratch. It was, you know, like, especially, you know, in a high velocity, in a high velocity environment, there's not a lot of great methodologies. Like you can get stuff off the shelf, but, you know, it's like Jordan Belfort or Jeremy Minor and like, you know, very like a lot of good stuff in there, but a little cringe. Like it's too, B2C, like we're selling you an insurance plan vibes, but then there's, you know, like way upmarket stuff, you know, command of the message that's also not resonant with like the—

Multithreading, like—

Velocity of these deals. And so ultimately we just like built one from scratch and we built it based on, you know, all of the pieces of the methodologies I like, but then also what actually works like in the field with the teams. And so we were, pulling specific words and lines and approaches that we see worked in the field. And so the output was very much purpose-built for our environment. And also what we found is the reps responded to it because when we did the training and we showed clips of people like, okay, this is how we're going to do This is how we're going to offer evidence to a customer. And then we would play clips of people actually doing it in the wild. We're like, this is what we based it on. You know, this looping, like, you know, this is here as a rep doing looping really well. And it wasn't because they were executing the playbook. It was because the playbook was like built on them. Working for them. And so it's, it's much easier to feel like, okay, yeah, like, you know, that's the best rep on my team and they're at like 400% quota. Cool. I'll do that.

Yeah, yeah, yeah. I mean, like, like with your population of customers, like very similar to ours, like we exist to support small business founders and we try to make, you know, their, their time is really precious. We try to make HR compliance benefits like really easy for them. And, you know, to your point, like following like some of these bigger, more enterprise level sales methodologies, like just don't work. Like there's not a lot of multithreading when you're talking to a founder who has one employee and is like looking to pay them payroll for their first time. Like it's that kind of stuff doesn't exist. So, um, so I, you know, I, I I feel like we might end up in a similar place as you, just trying to create our own at some point. 'Cause I just think what's, it's all fallen flat 'cause to your point, it just doesn't resonate.

Yeah. It took a lot of time. There was a lot of, a lot of people.

Yeah.

That's, and a lot of like important people, like a lot of people who don't have a lot of spare time were all working on it. Like the AE leader, head of enablement, BDR leader, me, You're pulling a bunch of managers and reps into it. Like it, you know, obviously multiple people from enablement. So it was a massive investment, but when we rolled it out, we literally saw win rates like climb overnight. Like it was like almost instantaneous because I think people were generally excited about it. And the enablement team did an incredible job on the rollout. Like it was like top, top, top notch. And so much of it was made so much easier because it was this internally built thing that was like from the reps outwards, not like, you know, done to the team. It's like, oh yeah, this is how we're gonna, everything's gonna change. You're gonna do everything completely different now because of this like book that the CRO likes.

That's cool. Did, um, did you build it into like your entire operating system too? Like, are there like checkpoints throughout the process that they had? Yeah. All in.

Everything. Like it's all woven in. And so like from, so the entire onboarding program is built around it and the way we assess deals is built around it. Like our language, I get like overly picky about like semantics.

Yeah.

And so like an example is we used to call, like when we were doing like ROI math with a customer, we would call it napkin math because we sell to restaurants and like We're not doing a big ROI analysis. We're doing like napkin math for them and like doing some whatever. And, but in the new methodology, we called it impact math because we needed something that starts with an I to make the word profit work. And so it was like impact math. And so people would say, somebody would like default to saying napkin math and I would always correct them. It's like, no, no, no, impact math now. And it seems dumb and it seems like overly semantic and to just like pick at it, but the Like the shared language of a team matters a lot. And then everybody got on board and, you know, you have fun with it where, where like everybody's checking them, checking each other. And like, I would say the wrong thing, I would say the old thing and somebody would be like, ah, da da. And that like, that, that has this reinforcing effect. Even like the fields in Salesforce, like calling them the right things, even though it doesn't totally matter. I think just like we, every, every every opportunity you have to weave that thing into people's memory, the better.

That's how they, yeah, that's how they remember and how they make sure that they remember to act that way.

So I want to go back. I made a note of this time to ramp thing. Going from 18 months to 8 is like absolutely game-changing for a company. Like it changes, it's an impressive big number, but like, I think oftentimes people underestimate the impact on the spreadsheet. Like if you can just like drag that 18 months 18-month thing, uh, 18-month result, 2-month, 8, your, your, the economics of a business change pretty materially.

Yeah.

And so like, talk me through how, like, what were the biggest changes? And then we'll talk about like how you implemented it.

Yeah, I think, I mean, you know, a, a couple things are human-centric. Um, so one thing that we decided to keep from a human-led perspective is in classroom training for onboarding. So we still put our reps through a couple weeks in the classroom, um, to start. Um, so I do think that that was something that, um, you know, we, we found out that it worked. Um, we wanted to maintain that. Um, it, it's certainly helpful for the managers if you're staffing out an entirely new team and, um, you know, you're, you're running basically your entire book of business by yourself and all of a sudden you have a couple new hires starting and don't know what to do with them and it's, it's kind of crazy. And so at least they kind of go in the classroom, they learn about Justworks, they learn about our mission, our purpose, our vision.

Yeah.

Um, and all of the kind of basic stuff about our product, how we make money, how we like to treat our prospects and partners and customers and what our values are. All of that is kind of, you know, still maintained and done in the classroom. And I think that was really important to put a lot of structure around that and to make that very cohesive for all of the revenue employees. So, everybody is brought on the same way. And then from there, you go into your unique specialized learning paths. And I think, you know, the biggest thing that we were able to unlock with those specialized learning paths is not only being able to create that content for those learning paths at scale in a personalized way, but also being able to measure those. So, you know, with lack of measurement, you can't report out on progress. So, to be able to say, you know, the learning path for the SDRs is going really well, and we're finding out by week 3 that they're like ready to hit the phones, then awesome. Like, why are we holding them in training to week 4? Let's just let them go at week 3. You know, or if we're finding like, you know, 10 out of 12 AEs are passing and like 2 aren't, and we can go back and diagnose it that those 2 maybe didn't work in the industry before, this is their first sales job or something like that, then that helps us to be able, like that, just those measurement and having that data helps us to be able to go in and adjust the programming if needed or work with the direct manager for more ongoing training and more kind of attention during the nesting process. So, really having that, that the systematic setup and the measurement in place has been wildly helpful for us. We also changed the way we hire.

And so what are you measuring like in, in an onboarding program? Are they, is it the like quizzes and checkpoints and, and mock, uh, experiences? Yeah.

Quizzes, checkpoints, and, and the AI roleplay. Yeah.

Oh, okay.

Yeah. So we have a lot of AI roleplay woven into the whole thing. Um, and you know, of course, even like attendance, like every, like a lot of people are onboarding through Zoom now. Um, some people don't show their cameras. Um, so yeah.

Which baffles me.

Yeah, well, it's a, it's a mandated requirement for us, but, um, yeah, we can, we can tell, uh, if somebody's not gonna make it just based on how they show up the first couple days. We changed the way we hire too.

Oh, right.

Um, so yeah, so we're trying to, we're trying to do like more profile assessments of the type of, um, you know, if we talk, talk about just sales reps for, for example, um, the type of reps that we hire and, and how, um, we see them ramp better. So, you know, first and foremost, what's, was proven time and time again at Justworks, I'm sure this is the same for you, is like the best reps are from the SDR organization. Um, our best reps, our best managers, like full stop, come from the SDO. So, um, we really doubled down there. Um, and we found a way to move people through the program, um, that kind of AE readiness program faster. We invested in building a better AE readiness program. Um, and so we, we kind of took our, um, our seriousness in hiring on the SDO. So that we could really feed that pipeline. We also looked at kind of the correlation between onboarding, whether they have prior PEO experience or not. So PEO is our professional employer organization. That's what Justworks is. And so if people have prior PEO experience, they come from a competitor potentially, do they ramp faster than just a rep off the street who's maybe worked in SaaS or worked in a different type of business before? You know, we find, um, sometimes, uh, adjacent—

Did they?

Yeah. CS for the most part. Yeah, for the most part. Um, usually like adjacent businesses, like, um, we found success, like people who've worked in sales at like rental car companies, um, sometimes work really well. Yeah. So we did a whole profiling assessment to try to determine what, what the right profile is of people. And I think that also helped with ramp time.

What do you think was like the biggest needle mover? 'Cause it seems like you put in a measurement system, You made some structural changes, but like 18 to 8 months is such a big change. Like there's gotta be something else in here that I'm not pulling out.

Yeah. I mean, the productivity that my revenue operations team has brought, like the reduction in friction in the sales process has been pretty significant too. This is pretty specific to our business and our industry, but we have to do health insurance census process processing to be able to get health insurance quotes for new companies, prospective companies. And that's like a very much Excel Uh, spreadsheet exercise. It's like, your name is Nick and on your, your legal name is Nicholas. And if we put your name as Nick instead of Nicholas, we can't process, you know, the quotes for you and your dependents. And so that makes like, it's a bunch of like Excel manual paperwork processing, um, that our reps were doing that was taking them time away from prospects and time off the floor. And so just introducing like intelligent document processing so that AI does that for them instead of them having to do that themselves in Excel. Some of them not even knowing how to use Excel because there's a lot of people out there that still don't know how to do like lookups and things like that, um, were that, you know, just removing friction in their process made it so much easier for them.

Um, and so you built an in— like built a tool out of your function to do that?

Yeah, I mean, we usually— we started by using like a wrapper tool, um, and then we moved into doing something internally, and now our product team is building it into our product. So it was an evolution that we've gone through, um, and, and a, and a fun one because we've learned to work really closely with our product team on, on what's You know, how we should approach like build versus buy. Um, and, and then when we talk about build, who should build it? Should it be revenue operations and go-to-market engineering, or should it be like actually built in our product? Um, and so I think we've, we've had a little push and pull there to, to figure out, um, what's, you know, what's the best way for that to land. I think for the most part, when we, we think about internal productivity tools, they live with revenue operations.

Mm-hmm.

Um, but for some of them, that also makes sense to seamlessly live within our product, especially in a self-service experience, we've kind of taken the stance that revenue operations will stand up something in the short term to increase immediate productivity on the floor, and we'll buy air cover for our product team to develop that the right way over time, which is kind of what we did in this particular example. We basically stood up something last year. It worked for us for a year, and now it's time for it to be built into product, and it's going live tomorrow.

Oh, cool. Good luck.

So, very hopeful. Yeah, we're very hopeful. Um, phase 1 at least is going live tomorrow. So, um, you know, it's, it's, it's a, you know, it's been a learning journey of how we, how we partner with our product team on stuff like that.

So it sounds like the biggest changes for reducing ramp are just the basics. Like you, you, you probably just like didn't have the right basics and you got, got that in place in a, in a very different way. Is that a fair assessment?

Yeah. Yeah. I mean, with Justworks, it was like, oh, it's always about like dotting the i's and crossing the t's. I think a lot of the things we've done, we've done by accident. Like, our bookings growth is great. We are hitting our revenue targets. Like, everything looks great on paper, but then when you, like, you know, 20-year experience revenue operations professional, I get in the room and I'm like banging my head against the wall. Like, I can't believe that we like do things this way.

Yeah.

And if we just dotted our i's and crossed our t's and brought a little bit of precision to some of the stuff that we're doing, like how much more productive could we be? So, I definitely think onboarding was, and ramp time for new hires was certainly that. It's like, let's just like, Like peel back the onion, figure out exactly like what we're missing here. Some of it's like very basic, but let's stand it up quickly with the right, the right people, the right technology, the right systems thinking. Um, and then, you know, let's run at it and see if it's improving. And it certainly has improved and it continues to.

So, so I wanna transition and talk a little bit about the, the like redesigning the operating system. You, you mentioned that there, there used to be a ton of friction in the sales process and, and you, Um, talked about this in your intake, like revenue leaders oftentimes just like overhire. They just like throw bodies into a system to, um, to drive growth where like just improving the system could have such a bigger impact, which very much resonates with me. So talk to me about how you like assessed the broader go-to-market system and what you would, what were the big changes you made? Yeah.

I mean, for me, like it's all about like the ride-alongs and living what the reps live. Um, Like, probably the best example is what really what happened with the SDR function. Um, so when I joined Justworks a year ago, I sat down with a bunch of SDRs and my favorite story is, is one, um, who I sat down with and I spent 30 minutes prospecting one contact. And he was a previous Justworks customer. He had moved to be a CFO at another organization. Um, we were double-checking him in LinkedIn, double-checking him in Salesforce, double-checking him in Six Sense, like going through all these kind of like disjointed systems to figure out like, is this guy legit or not? Um, we manually crafted, um, a note to send to him about him being a former Dressworks customer and congratulations on his new role. And I had to run out to, to my next meeting. And no sooner did I sit down in my next meeting and open my laptop and the rep emailed me and said he wrote back, no thank you, I'm not interested at this time. So, um, it was like really an aha moment for me, um, by just like studying 30 minutes, going through, living what the reps lived through. Um, and, and just seeing that firsthand, I'm like, God, like, there has to be a better way to do this. Um, so I set out to, to fix things that were within my control at that point in time. I tried to first form a standup, a signal system. I was like, there has to be a way to better prospect these guys and to do it through signals that tell us when is the right time to talk to them and what to talk to them about. Um, and, and so we, we kind of worked on building that, um, behind the scenes for a couple of months for, for the SDR team. And, uh, in the background, I had some talks with my boss about taking over the team and just really You know, we're talking about, talking about the operating system, like outbound pipeline generation should be a systematized function at this point. We have the right tools, we have amazing best-in-class technology. Justworks is great about investing in the best, the best tools. And so, we have an amazing tech stack and we just needed to like make the tech stack work for the team. We had bought all these amazing tools, we hadn't adopted a lot of them well. I think I mentioned that we are recording like 50% of Gong calls, were going unrecorded, 50% were getting recorded. We're missing out on so much conversational intelligence. We were using some outdated technology we needed to upgrade. We had done a pilot with one of the parallel dialers and it kind of fell flat because there's a lot of overlapping features with other tools that we had bought but hadn't stood up properly. And so we finally made the decision about 4 and a half months ago to roll the SDR function under me. And the first thing I did was inspect the tech stack and figure out what we can turn on for these guys quickly. We got them a dialer, we had that that in a POC within 4 days of me taking over the team, which showed huge results for the team. Like, there's no world that we should have gone that long without having that. We migrated over to a new engagement platform, which has been awesome for the team, and we've got the signals stood up. And so, when you think about like, kind of like rebuilding this operating system, like it starts to me with like diagnosis and like really living the pain and the friction that the reps are are going through on a daily basis and trying to figure out like, is this something that we can better train on? Is this something that we can better stand up through technology? Is this something better that we can improve with better reporting and better feedback loop? And then kind of in combination with the sales development function moving under revenue effectiveness and improving just the technological support that we give these guys, I was also able to rebuild the AE readiness program and really truly build an amazing experience for feeding pipeline for the account executives. Um, so we've recently promoted 6, uh, SDRs to account executive roles. I'm moving 2 over to partnership acquisition. I moved one into a manager seat and one into, to revenue effectiveness, all within the first like 4 months of being part of that team. And so showing them—

Oh, cool.

Building motivation and showing that career path for top performers has been, um, you know, amazing and also very rewarding. Um, but all of that was part of this, this idea of reinventing the operating system. Um, it just, it, nobody had really looked at this function and the importance of this function in a long time, and, and nobody had figured out how they need to be served and enabled to do their jobs really well. So, for Q1, our Q1 starts in June, we're running at 2x productivity, uh, pipeline opportunities, logo count, 2x productivity of pipeline ARR, and I'm down 4 in seat just from attrition of people moving to AE roles, not by, by intention of losing 4 heads, but—

Wow.

Um, but the productivity results are there, and it's a great example of just, fine-tuning that operating system and, and having the right systems thinking put on a problem, removing friction for these guys, and just like, you know, really running at, at, um, improvements there. It's, it's been wildly successful, and, and the team's happier. They're like so happy that they have a playbook and they—

Yeah, of course.

Have technology that exists. They have a path to growth, um, for their own professional career development. It's amazing.

Yeah, it's, it, That term removing friction, I think is so critical. There's this book I really like called The Human Element by Lauren Nordgren, and it's sort of like in the behavioral economics canon, I would say. And his whole thesis is basically like, we spend all of this time on fuel, on like encouragement, pushing, comp incentives, like Threatening somebody's job with a performance plan. There's all of this fuel that we try to jam into a problem to make it get better, but then so infrequently do we look at the friction that we can remove to move somebody towards the objective we want them to. And it goes back, the sort of canonical nudge theory experiment was when they, in the British health system, They moved people from opt-in to organ donorship to opt-out to organ donorship. And it went from 25%, I'm just making up the numbers, but it was like 25% of people opting in to 75%, all because you've switched the friction.

Yeah.

You've now made people move their hand with the pen to opt out. And it's such a funny, silly example of the importance of friction. And you wouldn't imagine that allowing your organs to be harvested would be so trivially moved. You'd think most people would consider that more, but now just changing it to opt-in to opt-out. And there are so many examples of this, especially in BDR where 25% of their leads aren't very good and then they have to go place and that, or even just like marking a lead DQ. Like how much effort does it take to, for you to mark that this lead doesn't have the right information and to like send it off to wherever it needs to go? And, and if you can remove the friction for a rep to do the thing you want them to do, you'd just be like blown away by how fast you can get them moving.

Yeah. Yeah. There's like, there's so much diagnosis that has to happen. And that's like where I find myself spending like a lot of my time as like a revenue effectiveness leader is just like really trying to diagnose like what is, why are they stuck? Because we do have amazing technology and we are doing really cool things with AI. And I do have a pretty amazing revenue effectiveness team supporting at every stage of the customer journey from the minute that something becomes a lead all the way through, you know, retention and renewals and upsells. And, you know, it's, it's just about like listening to the people who are doing the actual work. To your point, making sure that we have a very clear understanding of what work we want them to be doing, making sure that that's very clean and clear. And then just like really trying to be creative and solution with them on, on how to bring them, you know, automation to their day and how to make things, you know, be just be less effort. We had our revenue kickoff for the year. Fiscal year starts in June. So our revenue kickoff was a couple, a couple of months ago. And I promised 3 things to the field. I promised we would give them more time back to spend with prospects and customers and partners. I promised that we would give them more capability than they've ever had before. That capability would be personalized to them and their unique role. And then I promised them more opportunity because there's no excuse we shouldn't be able to give them more pipeline opportunity, more lead volume with, with all the tools and, and resources we have now. Um, so those are the—

More career opportunities.

Yeah, more career opportunity. Yeah, yeah, totally.

That's cool. And so you've come back to this like multiple times, just like ride-alongs, like spending time listening to people, like being in the weeds. Is there anything else about diagnosis that you think is important that you've done well that's helping you figure out like root cause of these problems? Yeah.

I mean, I think it goes back to what I said in the beginning too, about just about data. Like Everyone walks around with hypotheses. My boss gave me a hypothetical thing that he wants to stand up a couple weeks ago, and I was like, cool, this sounds great, but like, I'm not gonna say yes until I go and I look at the data and I find a way to like, A, validate if your hypothesis is accurate, and then B, use the data to tell me the best way to do this. Ultimately, I try to arrive at what his vision is, but I try to do that by leading with data. And so, I think like, you know, it's really easy for people, especially people who've worked at similar companies for a really long time, people who have years of experience of revenue operations to just basically kind of, you know, flippantly say like, oh, well, this is like a lead scoring problem. But like, is it a lead scoring problem? Like, have you looked at the data? Is that what the data tells you? Just those are things that I always go back to is like, you know, listening and learning contextually to the human that's doing the work and then that mirroring that with like the, what the data story actually tells me. And between those 2 things, I can, can arrive at probably the right hypothesis and the right solution for what we need to do to remove the friction and improve the process.

The other thing on the data piece that I find is helpful, but oftentimes tricky is like getting beyond the first layer. Like oftentimes the first data that you pull, it's like, oh, we have a conversion rate problem. So let's go like teach people to be better closers or whatever it might be. But then it's like, okay, well, have you controlled for mix shift? Like, all right, we were 70% inbound and now we're 60% inbound because we were developing more outbound pipeline with all this effectiveness. So like, have you controlled for channel? Have you controlled for X or Y? Or it could be, and I find that it's oftentimes the analysis is just like too superficial, but you haven't gone like multiple layers deeper.

Exactly.

And you need good data science partners. And, you know, I was really lucky In the earliest innings of my time at Owner, I had a head of biz ops and data who was like really great at this. And so oftentimes at that company stage, you don't have that type of like resource or brain because he would take something away and be able to like control all the confounding variables and be like, okay, we thought it was X, but when I control for Y, it shows up as B. And to like, I think That's a piece that is really difficult to stay diligent on because you just want the answer and it leaps out at you. You're like, oh, it's conversion rates. And then once you start to really get into the details, which is way more effort, especially like, oh, just throw it all in Claude and see what Claude is going to tell me. It's like, okay, but how do you really do that analysis? And there's a whole Claude Giving you weird output.

Yeah, false negatives.

Yeah. Yeah, confident, super confidently wrong. Yeah, yeah.

No, we have great data science partners also, which is awesome. The resources on our data science team are hard to get, but we're paired where we need to be with them for the right outputs that we need. So, I'm totally with you on that. It's like, we used to have this, when I first joined Deskworks, we had this like 120-page PPT that went out every week, and it was like every single metric and every single cut of every single metric across every single channel that you could ever imagine. And the only one person who worked, looked at that every single week was my boss, who's the CRO. And he kept telling me, Lauren, all I need is somebody to put AI on this and tell me when anytime something deviates from the norm.

Yeah.

Yeah. Then we need, then we need to double-click on it. Yeah. The acceptance report. Exactly. Yeah. And then we just need to double-click on it. So, um, so we stood that up, um, and we're running at that, uh, through— he's got this kind of CRO skill tool that we built him, um, and so we're able to kind of alert to him. We've, we've got to do better at it. And I, I gave the team, um, my revenue planning and analysis team, like a list of like 50 KPIs a couple weeks ago. And I was like, these are all the KPIs that you need to like manage because we can't have this 120-page deck anymore that nobody has time to look at.

Yeah.

Um, but, but really we have— that's another good example of where you have to like really spend the time and give the feedback loop, like make the AI work for you. Because having access to that data and that trending and really understanding like what's exactly going on in the business at any given point in time and, and how to have early monitoring system to help you act on those things is so important. Um, and, and AI should be able to help us do that better.

Yeah. Yeah. One of the difficulties is like you, if you just If you just pump a bunch of data into the context window and it's not framed the right way, you can really get sideways and get all sorts of the wrong outputs out of it. I've seen this lots of times with leaders around who were like, oh, Claude did this analysis, here's the output, here's the answer. And then it goes to the actual data science team, they're like, oh, well, no, it's completely wrong. The hypothesis that the solution presented is wrong because of X, Y, and Z. And so I think there's a bunch of companies that are trying to answer this at scale now. Like Vasco is the tool that I've spent the most time in. And I've spent time with the founder as I was evaluating it and getting to know the problem. And so he was He was explaining to me how much you have to do to torture your data to get the right context into the model to give you the answer and to torture the model to basically not lie to you or give you an answer when it doesn't know.

Yeah.

And so I think that's a space that is going to be more and more of a topic of conversation pretty soon, which is just like data harness. How do you Because, you know, it's great. You give everybody a ChatGPT or a Claude account and they're wired into whatever data sources that they have and they're like, oh, I did this thing and I have this answer now. But how do you make sure that those are right? Because it's potentially more harm than good, people going off with confident wrong opinions.

Well, yeah. I mean, and that's where business context is so important too. We can have our research team or our FP&A team even sometimes like pull directly from our data sources now, like everything's available, it's so easy to access. We could even have our IT team pull stuff, our HR team, and like without any go-to-market context, the assumptions that they could come up with could be like, you know, fairly damaging if they're not wrapped with contextual relevance that we as go-to-market leaders have. So, that's a constant internal battle that we're always having too, like where, you know, My boss has basically said nobody can, can produce any sort of statement about go-to-market analytics without running it through me first. If you're outside of my team, because we don't trust that the context exists in other departments.

Yeah.

So it's a pretty, a pretty big piece. I would be really excited to test some of those tools and to be part of some of those conversations that are evolving because, you know, there, there's no reason if you set it up the right way that everybody across the organization should have the right context and to be able to pull data at any given point in time and understand the story. But we have to make sure that We are very clear with the story, 'cause what your story is, my story is, could be totally different based on our, our business context and, and exposure that we have to, to different parts of the business. Yeah.

Well, I'll introduce you to Guillaume from Vasco because—

Yeah, I would love to.

They're, they're probably the most interesting tool I've seen in this, uh, in this category. It was, which is like solving for correct answers.

Yeah.

Which, uh, you know, and then you can build like, you know, I built a, like a CRO daily in there and you can have like your exception or you can do whatever you want. But the, but it all comes back to, do, does this tool understand the tables and the construction and what's in like out of date, out of, at not, how does it reconcile things across multiple systems? It's, uh, awesome.

That's great.

Not a very exciting problem to solve for most people, but it was for me.

But you have it, you have to have it. It's like there's no excuses because otherwise everyone's walking around with these random hypotheses. that may or may not be true.

Um, so I wanna go back to this BDR thing and doubling productivity in a very short amount of time, which is cool. So, um, we talked about the, the how, like the diagnosis, validating with data and time and field, the ride-alongs. What do you think were the things that moved the needle the most? Was it the dialer raising, um, the number of calls made? Was it connect or conversion rates? Like what, what, when you actually pulled it apart, what were the things that actually made the biggest difference?

Yeah, I mean, definitely for sure the dialer was a huge productivity unlock. We saw a significant increase in pipeline opportunities, qualified pipeline opportunities pretty quickly. Um, we did like a couple week trial.

Just because of volumes.

Just the volume. Yeah. Um, so that was great. Um, we, I think just like honestly being part of the revenue effectiveness team, this was the, the SDR team was part of the sales org. They were not really like being served, not only from a leadership perspective, but just from an investment perspective. Um, and so just just reassuring them that we're, we're investing in them from a tech stack perspective. We want to give them the best-in-class tech to do their jobs most efficiently. We want to train them to become account executives, and if that's not what they want to do because they're not in the sales org anymore, we can find other homes for them. We unlock them on Cloud Enterprise like we did for, for the entire Justworks organization, and we did some pretty custom training for them on, on list building and things like that. So that I think was, was certainly helpful, but I think it was a combination of All, all of the things. Like, I don't think it was like one smoking gun to say, oh yeah, turn on the dialer and your team will be like 2x more productive tomorrow. I think it was, it was a, it was definitely a combination of, you know, pulling all the tools. I mean, I, I changed their compensation. They hadn't, their compensation and their job description hadn't been refreshed in 3 years. Um, and so, we had to really change it to meet the market. Um, I got additional compensation bands approved for people who have preexisting technical skills, um, to try to test if we could hire a different profile of SDR. Um, you know, we were kind of going the more traditional route, hiring like college athletes and recent grads. Um, we've now, uh, have approval from our HR team to hire in a higher comp band if we have somebody that's worked in, in a different industry, adjacent industry for a few years in some sort of technically, um, uh, you know, first sort of technical first role. Um, maybe people with a little bit more systems thinking so we can test if like a different profile brings like a different kind of productivity unlock for us. So, I think it really was a combination Of, of a lot of those things.

Dotting i's and crossing t's. This is the key.

Dotting i's and crossing t's. I know. I'm, I mean, I wish, I wish I could say some like big aha thing, but it was really just, um, this team needed focus. They needed somebody who understands firsthand the friction in their day-to-day work. Um, and, and they needed us to, to build the system for them and show them how to work that system and how to make that system work for them.

What else did you change in the comp plan?

Um, so I added the pipeline ARR component. So they were just strictly on logo count of qualified pipeline opportunities, and I added pipeline ARR. Um, so we are, we are focused on small businesses. We like to kind of get, uh, companies at their point of inception, um, and they typically grow with us. A lot of companies stay with us for, for over 7 years, um, and we have a really high retention rate. Um, but we also have opportunity to go upmarket and to, to take business from competitors. And so giving them that pipeline ARR component kind of helped us to balance logo acquisition and pipeline ARR. And so we've blown those targets out of the park for this quarter to date, which is awesome. Yeah, so the team's making money again too, which obviously helps.

Yeah, and BDR is such a momentum game. Like when people are successful and getting promoted, then you get good talent in, you get people's best effort because they know, It's like, oh man, if I really crush, I can be into the next seat. I always jokingly say like the number one job, the number one objective of a BDR is to become not a BDR. And so giving them that promo path, I think is so critical.

Yeah. We just had our President's Club trip to Costa Rica a couple weeks ago. It was my first President's Club at Justworks. And our CEO gave a speech at the closing party. And we have 54 qualifiers across, um, sales, partnerships, customer success, and support, and 24 of them came from the sales development organization at Justworks. Um, so it was just so awesome to be able to come back and, and tell the team that, like, look where you can be, um, if you just, you know, do your job effectively and, and you follow the playbook that we've written for you and, you know, we, we can get you there. Um, it's pretty awesome.

Very cool. So what's next?

What's the, what's the thing you're most excited to build I mean, I am excited about the data piece, um, and really trying to figure out like, what, what is, what does an analytical layer look like for us? Um, you know, given the data that we have, I'm like excited about this concept of like customer 360 and can we figure out the journey that any given customer has had with Justworks across all the different touchpoints, marketing, sales, now that you're a customer, the support pieces that you call in, the quarterly success reviews you had with us, like that's something that I'm really looking forward to stitching together. We've got pieces of it in pockets, but we need to like bring it, bring it together under one roof. So that's something I'm working on and I'm super pumped about.

The Holy Grail.

Yeah, the Holy Grail. The other place that I'm going to hang my hat on the next quarter is just customer support automation. So we've, we've made some really great strides there. We were early adopters of, of an AI tool. We've done some really great work, to your point about the opt-in, opt-out. We did a great like deflection work, but that's an area that's moving really fast. There's a lot of amazing AI-native customer support platforms out there, and I've been so focused on fixing the top-level problem. Now that we're running pretty great on outbound and making some pretty big strides diagnosing our inbound flow, I feel like I can kind of now balance my time with like the post-sale side a little bit better and trying to bring the same type of focus on removing friction.

That's awesome. Um, let's move to quickfire before we run outta time. Tell me what you think it makes like a great, I mean, like most companies don't have a revenue effectiveness leader, but somebody who is in this like supporting function, RevOps enablement, sort of the, this like partner to, to the revenue leader, what makes somebody like world-class at that?

Yeah, I mean, I think like my, um, my CRO would tell you that like if he's not in a room, I, he has 100% confidence that I could be in the room making those decisions for him. And so, there's a huge trust element that I think is so important, and a lot of that comes with, you build that through data, you build that through delivering results that are directly, like, tied to outcomes for the business. And I think I was really able to quickly prove that to him, that the work that I do really drives business results, and therefore, you know, we built that trust layer, and now, you know, he's pretty confident that I would be the one in the room as his right hand and his replacement if he's not around to, to solution or answer certain questions.

What's the piece of advice that you find yourself giving the most to new people in this role? They're like a first-time VP of RevOps or enablement. What's the most common advice you give?

Yeah, I mean, I think it's go— I hate to say it, but it goes back to like the ride-along. It's like you can't just sit there and try to improve process and remove friction and buy a bunch of technology if you don't really have a A firsthand understanding of like what the person in the role is, is feeling and facing and what their day-to-day challenges are. So, spending time with AEs, spending time with SDRs, spending time with customer support representatives, CSMs, onboarding, like really truly understanding like what they go through on their day-to-day, how they work through their process and how they serve prospects and customers, like really having that understanding and then being able— that makes you such a better solutions partner for them.

Makes sense. What's the hardest lesson you've had to learn in your career?

There's a lot of those. Um, you know, I, I, I think like credibility, I think credibility through data, through insights, um, and through delivering results and outcomes. I think you, you can't build that credibility without being an expert in your craft, and you can't be an expert in your craft if you can't, you know, follow the data that shows you that you're doing a great job. And, um, you know, I, I worked really hard to put up a revenue effectiveness dashboard for my team this year with very clear OKRs that are directly tied to our outcomes. And, um, I think that, that credibility is such a huge piece, and I think you really gain a lot of trust throughout not only revenue leadership, but, but peers in the organization if you have that credibility.

What's the best thing you've read in the last like 12 months?

Oh, I'm such a podcast girl. My commute's 2 hours and 45 minutes, and I always drive—

Okay. Best podcast.

Drive for 40.

I love this.

Yeah. I mean, um, you know, definitely the Revenue Leadership Podcast. podcast has been amazing for me. I've learned so much from you and your team. I gotta tell you, like, that's, it's my, it's been my go-to every week. Yeah.

Love it. Uh, great plug to end, end the episode on.

Yes. But one that's well deserved. I would not say that, like, ask anyone, uh, that works with me. I'm a pretty ruthless person and I would not say it if I didn't mean it.

Okay, good. Well, uh, this was awesome. We, I'm, I'm really excited that we could get like so deep into some of these details. It seems like you've just made so such a, uh, such a cool impact on that business. So, uh, appreciate you sharing so openly and, uh, congrats on a, on a great run.

Awesome. Thank you.

Thank you for listening to the Revenue Leadership Podcast. If you enjoyed it, don't forget to subscribe and you can find a link in the show notes and be sure to leave a 5-star review, share it with your network, and please join me next Wednesday for another great conversation.