REM Episode 1: James Underhill from MongoDB

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‍As of 2024, the AI cat is essentially out of the bag, and Sales Ops and RevOps leaders at SaaS companies of all types will need to find ways to utilize this new technological advantage to stay ahead of competitors. 

And that’s why Scalestack’s Head of GTM and host of Revenue Engine Masters Adam Ferris invited James Underhill, Senior Director of Sales Operations and Strategy at MongoDB, for this inaugural episode. 

MongoDB is the leading modern general-purpose database platform, designed to unleash the power of software and data for developers and the applications they build. Its database platform has been downloaded over 210 million times and there have been more than 1.5 million registrations for MongoDB University courses.

Additionally, MongoDB happens to be an early adopter of Scalestack’s platform, and has achieved impressive results by utilizing the company’s proprietary AI solutions, including:  

• $1,000,000’s in New Sales Revenue
• 100,000's of Accurately Enriched Accounts per Month
• A 53x ROI from Scalestack Platform

In this episode, Adam spoke with James to learn why he believes Sales & RevOps leaders who master AI as a part of their go-to-market strategy will beat their competition in the coming years. 

This wide-ranging conversation covered a variety of topics, including:

• Why a Customer-Centric Approach Is Important To Data Collection
• How To Enhance SDR Productivity With AI Copilots
• Why Accurate Sales Data & RevOps Data is Necessary 
• The Strategic Application of AI in Sales & Marketing

Below, you will find summaries and excerpts of their conversation broken down by topic and edited for clarity. 

But for the full effect, we highly recommend you give this kickoff episode of the Revenue Engine Masters podcast a listen, like and subscribe. 

Introduction

Adam Ferris: Thank you for joining our Revenue Engine Masters podcast! A monthly podcast where I speak to Go-to-Market and RevOps leaders who will unveil actionable insights and strategies for today and tomorrow's market. 

Every month I'll be speaking with Revenue, Marketing, Sales Ops and RevOps rock stars.

We'll learn about how they're building future-proof, go-to-market strategies, by optimizing their systems, go-to-market data and processes, to drive consistent revenue growth. 

Specifically, we’ll focus on how they're investing in, adopting or thinking about using AI to achieve their goals. And today, I couldn't think of a better guest to kick off our inaugural episode, than our friend and Scalestack customer — a trailblazer — James Underhill, Senior Director of Sales Ops strategy and intelligence at Mongodb, which has achieved some amazing growth, and I think we can all learn a lot from James. 

So, James, thank you for spending time with us today.

James Underhill: Thanks, Adam. I'm pumped to be here.

(00:23:59) Why a Customer-Centric Approach Is Important To Data Collection

During this episode, James emphasized the importance of focusing on a customer's perspective, especially regarding their decision-making process in technology adoption, application development, and funding rounds. He also mentions how he was once a customer of MongoDB and centers his experience when thinking through his role at the company. ‍

James Underhill: Before Mongo I was at Twitter kind of working on big data systems and that brought me into contact with MongoDB. 

So that's a little about me, which I think is important, because it informs the way I think about our customers and the data we need to aggregate and rely on. The way that I think about it is, think about the perspective of the customer.

What's going on? Think about it? We got problems. We got solutions. We're trying to map them together. Right? So think about the evolution and the journey of a potential customer, as they're going through their journey. They're fundraising. They're starting to build products. They're making decisions about their techstacks. In this case, I'm talking about a MongoDB customer. Obviously, it can be very different across different landscapes as they start to build technology. ‍

‍And as the decisions are made they become more difficult to reverse. So we need to take a customer-centric approach and try to think about when and why they are making decisions.‍

‍And if we can have a data model and a system that is able to identify the why and when — if you can answer those questions — you’ll find success. And AI helps make this possible. 

(00:29:28) Why Accurate Sales Data & RevOps Data Is Necessary 

Additionally, James highlighted the necessity of having accurate and relevant sales data for GTM teams. He cautioned against the assumption that existing data can be repurposed for new use cases without considering its suitability and relevance to the specific needs of sales operations, and noted that— thanks to Scalestack’s platform — MongoDB has access to fresh data from all of their trusted sources on demand. 

‍James Underhill: In my role, you're ultimately accountable for making things happen for sales. We're the very bottom of that funnel, right? Once, you get to the bottom where sales needs to be like working good leads, and they need to be converting those leads on the customers. That's the area where I live and accurate data is our oxygen. 

(00:24:55) How To Enhance SDR Productivity With AI Copilots

Adam and James also discussed how to enhance the productivity of Sales Development Representatives (SDRs) by using an AI copilot and other tools that help prioritize daily tasks, manage leads, and answer open questions. This approach aims to make SDRs more efficient and focused on executing their core responsibilities.

‍Adam Ferris: You mentioned earlier that AEs, SDRs, BDRs, whatever your acronym is at your company, have a tough role. They have to do account mapping. They have to do account planning research. Oftentimes, we're requiring them to do account, contact or lead enrichment. And they have to bounce around from various systems to search for the right insights at the right time.

‍Given where AI has brought us, what is that going to look like for these folks to execute on their job now and in the future, say 2 or 3 years down the road? And are AI copilot helping the average SDR do better or worse? 

James Underhill: I think ”augment” is one word that you could use for sure to describe how AI is affecting sales reps. It’s certainly augmenting functions of their current roles and I believe they can be used to make sales reps as effective as possible in executing their daily tasks. 

AI copilots enable us to eliminate a lot of manual work and research, so SDRs can spend more time engaging with prospects. And thanks to Scalestack, each of our sales reps receive specific instructions on who to target, what to emphasize in their outreach and what form that outreach should take. 

Adam Ferris: I love it. It's what I would like to call the next best action. It’s when you have a rep bouncing around between various systems. And you can create play books for various channels

(00:52:08) The Strategic Application of AI in Sales & Marketing

Finally, James explained that AI in sales and marketing has an incredible amount of potential, but its effectiveness depends on how it's used. AI should be applied with a clear understanding of the goals and objectives it's meant to achieve when added into a company’s technology stack. He stressed the importance of focusing on the right areas and problems where AI can provide real value, rather than indiscriminately integrating AI into operations.

James Underhill: We're using AI, but I believe that how we think about using AI is probably more important than the tool itself. 

I am accountable to my own success or failure, and if I don't trust that the information you're telling me is good information, I’m not going to listen to it. That’s how sales reps feel. 

And they shouldn't, right? Like, if you're gonna build a system that you believe is going to make your team more able to do their jobs, they should be entirely bought in and there should be understanding up and down the org as to why and where you are adding AI capabilities like Scalestack. 

Conclusion

Throughout their conversation, James and Adam made clear that AI is destined to become a standard tool for RevOps, Sales Ops and other go-to-market professionals. At this point, the race is essentially on to see which companies will be the first to effectively adopt these technologies and use them to leap ahead of their competitors. Right now, AI tools, like the ones provided by Scalestack, can help accomplish tasks, such as: 

• Enriching and understanding all of your inbound leads 
• Pulling relevant key attributes from all of your data sources 
• Utilizing AI to score & prioritize accounts & leads 
• Maximizing sales team productivity with an AI research & pipeline generation copilot

Please give the episode a listen on Apple Podcasts, Spotify or wherever you get your podcasts and be sure to like and subscribe. 

And to learn more about how Scalestack helps sales and RevOps teams bring clarity out of go-to-market data chaos, simply book a short demo with our team.

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Transcript

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ADAM: Thank you for joining our Revenue Engine Masters podcast of the month, where I speak to go-to-market and RevOps leaders who will unveil actionable insights and strategies for today and tomorrow's market. Every month, I'll be speaking to revenue marketing, sales ops, and RevOps rock stars. We'll learn about how they're building future-proof go-to-market strategies, optimizing their systems, their go-to-market data and processes to drive consistent revenue growth, and specifically how they're investing in and adopting or thinking about adopting AI to achieve their goals. Our marketing ops, our rev ops, our sales ops teammates are the nucleus of our orgs. They're the glue that holds our integrated go-to-market strategy and motion together to drive predictable revenue and scale. And today I couldn't think about a better guest to kick off our inaugural series than our friend and Scalestack customer and a trailblazer, James Underhill, Senior Director of Sales Ops, Strategy and Intelligence at MongoDB, which has achieved some amazing growth. And I think we can all learn a lot from James. So James, thank you for spending time with us today.

JAMES: Thanks, Adam. I'm pumped to be here.

ADAM: Yeah, me too. Right on. Let's jump into it. I'd like our audience to get to know you a little bit better. How you started your career in sales ops and where you're from, the path that led you down to a successful career in sales ops, especially at MongoDB. And feel free to start even further behind if you'd like.

JAMES: Yeah, for sure. No, thanks for the intro. And yeah, I've had a really interesting career that I'm very thankful for. My background is more in data analytics. And I think that background and curiosity around dated information has helped to kind of progress me in the direction that I've gone with my career into sales ops. And so something about me that might not be the typical sales ops profile is that I come from out of university doing data analytics roles and then eventually over time getting more into data engineering. Because in order to do really strong analytics, you have to have an understanding of pipelines and data transformation and really deeply understand databases and how they work. And I think it was through that time Before Mongo, I was at Twitter working on big data systems. We used Hadoop and Apache Spark. Understanding those big data systems brought me into contact with MongoDB. So the way that I started to understand distributed data and scalable systems was through more understanding how databases work. And I just purely started doing a lot of research about MongoDB myself, understanding how NoSQL worked. And then funnily enough, I think that I... The way that I got kind of introduced to Mongo was through like the LinkedIn advertising. I think there must have been like a cookie in my browser or something. I started just getting ads for like jobs at MongoDB. And so I truly like didn't, I wasn't super familiar with what sales ops was, but I like, no joke, like I got a targeted ad for MongoDB. You know, one of those things I was like, you could be a, and it literally was like senior analyst MongoDB. And I was like, oh, that's that database. Like that's super cool. Like they're in New York. Wow. Like I remember clicking on the job description. There was a couple of things that really resonated with me. Number one is it was a clear opportunity to use data to have a business impact where the impact was well known. It said, you're gonna work with executives, you're gonna be able to work on initiatives that deal directly with sales. And I remember, you know, in college, I had a professor that had said at one point, you know, you want to be more important in your career, get closer to sales. That's what people really care about. And that was something that like in my head, you know, I hadn't been super... I worked... a little bit with sales at Twitter, but I wanted to get more deeply integrated. And so I applied for this job, and I think there was a lot of things in my resume that really resonated with them, just about my ability to create insights. But when I got on board and started working at SalesOps, I really didn't know what I was going to be doing. And my first time kind of running like a comp planning cycle was, you know, a learning experience to say. It wasn't what necessarily... I had to learn a lot fast. But... everything that I've encountered at MongoDB has been an opportunity to get closer to the business. And I find that's just where my interests like deeply are. Like, I feel like my personality, I have a, there's a part of me that just like wants to go into a office by myself and just like write SQL all day and like write Python and like just analyze numbers. But there's a part of me that like conflicts with that and is like, no, I want to be like in the rooms where decisions are making, I want to inform them. And so I kind of have a clash between these two personalities where I really am interested in the actual data analysis. But the latter side of me tends to win, that I want to be helping to make those decisions and really seeing where the impact happens. That's a little about Mount Me. And I think it's important because as a reason in my career at MongoDB, like the novel ways that we use data to help sales, I think are not necessarily traditional from the sales ops function. And particularly now as we're, you know, we're going to continue this conversation about how we're using AI. Like, the way that we think about how you use AI is probably more important than the tool itself. And I think that's where that background comes from. How should we be using this thing? Not just are we using this thing? So we can definitely get in to talk more about that.

ADAM: That's great. It goes back to my philosophy, which is rev ops, sales ops. That function is the nucleus of the company. You talk about writing SQL and Python and being in a room, but also interfacing with the executives across the org that the ELT and supporting the sales team, you really are interfacing or liaising between product and product marketing and marketing and the ABM strategy and the go-to-market motion and the SDRs and the AEs and the leaders. And it's such a critical function nowadays. Before we get into the topic of AI and how you're thinking about investing in and adopting not just a tool, but AI as a strategy, tell our viewers a little bit more about your stack, how your go-to-market team is organized, because it's wildly complex. And a little bit more about Mongo solution, your ideal customer profiles and your ideal customer profile and your buyer personas.

JAMES: Sure. So within Ops at MongoDB, we've got... More or less three pillars. One is a strategy and planning team, which is aligned at the theater level, you might say, with each of the VPs in the different areas or regions. They're kind of like the chief of staff of that VP. And they have people that are more focused on analytics, more focused on planning, forecasting. But they're kind of like the go-to people for the VPs in each theater. We also have a team that's exclusively focused on compensation. compensation incentives drive behavior. We believe that very strongly and we invest a lot in understanding how we can create the right incentives. That team's super important. And the last team is the organization I'm part of, which is kind of like our core technology, sales ops team. So that's, you know, working with Salesforce ecosystem of, of tools that plug into Salesforce that enable reps to do their jobs, the data that they use. And then, you know, you were saying just a little bit earlier, like the whole ecosystem of, you know, ABM and marketing and everything flowing to sales. Like one thing I think is really important about like my role, but just in general is we are, we're kind of like the last, we're the ultimate like accountability for making things happen for sales. We're the very bottom of that funnel, right? You have all these things at the top of the funnel where it's like, you know, in theory, we're trying to convert from, you know, into MQLs, like down the pipeline, right? But like once you get to the bottom where sales need to be like working good leads and they need to be converting those leads into customers, it's like, that's the area where I live. So if anything up the funnel is a problem, like I'm the one who's gonna be knocking on their door. And so understanding how all the gears fit together is really important and making sure that we're partnering really closely with the rest of the organization because the organization needs to have alignment of their goals and needs to be clear about what we're being held accountable for.

‍ADAM: That's great. I love the analogy of how all the gears fit together. I think Sam Jacobs, CEO of Pavilion, said this, revenue is a team sport. I believe in that. And you have a role that really embodies the revenue as a team sport and how you have to liaise between the different departments to make those gears work. So your reps at the end of the day are focusing on the highest propensity leads and accounts at the right time every day. Before we get into AI adoption, every org has tons of fires to put out. These are challenges and they're every org, no matter what stage in your evolution you're at. These are prioritized. We just got out of 2024 planning. Many of us did. And we're entering into fiscal year 24, some 25. What can you reveal to us today about the top three business challenges your team and that you prioritized to solve with AI systems processes across RevOps and SalesOps that long ago?

JAMES: Yeah, sure. And just a quick aside, something you just mentioned, Sam Jacobs. I sat next to him at a dinner a couple of years ago. We started chatting. We both went to the same elementary school, high school, and college. And we were both class president at the same high school. No way. And you're based out of New York, right? And I'm based out of New York, which is why we're at this networking dinner. So maybe there's something in the water over there that drives people when they go to market. But that's just funny. But you were asking about our top three priorities. So this is so important. Before I talk about the actual priorities, I think one of the pitfalls that people in an ops organization get into is they're too quick to go into solutioning. They perceive what is the most immediate solution. And solve that. It's like the thing, you know, the difference between important and urgent, right? Like there's always urgent things that are happening all the time. And I can see sometimes where like people are biased towards, you know, what they're getting slacks about in that day and they're focusing their attention there. But as a leader in the organization, it has to come from a concept of what are the biggest rocks? And our time and our attention has got to be way overly focused on the big rocks and not the things that are just happening day to day. So establishing your priorities are super important. So for us, there's a couple of things. And we've had a whole offsite to talk about these. But for me, one of the biggest ones is I've seen MongoDB grow from... a couple hundred million at the time, NACV to now 1.5 plus billion. We're trending towards a $2 billion ARR organization. if we even, like churn should be preventable. If churn goes up a percentage, a percentage and a half at 2 billion ARR, that's a lot. And we want to be able to prevent that as much as possible. So how can we, and we also don't want to keep just throwing more and more and more spend into our GDM, like human capital functions. So the question is, how can we efficiently scale our go-to-market organization and be proactive about making sure customers have a really good experience. So kind of talking about the customer success function right now. That's one thing. And then in general, it's just another thing is also, so that's kind of retention. And then on the acquisition side is like, how do you supercharge? We want it like the acquisition is obviously like that top of the funnel for everything that goes through and really, you know, extracting a lifetime value from customers. So how can we get really efficient at that as well? And in particular, like in a lot of organizations, including MongoDB, people who are focused on acquisition are still early in their careers. And as they mature in the hierarchy or seniority of reps, they go into more like strategic accounts and that type of thing where they're focused on acquiring new workloads and accounts. But people who are exclusively focused on acquisition tend to be more junior in their careers. So how do you really augment those people? And then just the remaining one is like operational. I don't know if the word is operational, but just like excellence in the sales process. How do we make sure that at scale, that it's clear what everyone needs to be doing, that they have the playbook to be able to do it and they can execute against it? Because at a couple thousand reps, It needs to be super clear how everyone, we have all these different types of reps, all these different types of ecosystems. How does everything align together? Like that master plan's all got to line up. It's got to be a well-oiled machine.

ADAM: Wow, there's a lot to unpack there. You talked about churn reduction, you talked about increasing selling opportunities, rep effectiveness and efficiency, and there's this conflict between rep effectiveness and efficiency, but these are many times... junior non-tenured folks that are stepping into this really critical role and you're there to support them. That's a difficult... Cedric, I believe your CEO posted something or was on your careers page the other month that said, hey, at MongoDB, we have a huge TAM. It's not going to be a problem of finding more TAM. The challenge will be where to focus. When you think about solving these challenges, you mentioned something earlier, AI is not just something you just buy. It really takes a lot of investment and thought. How do you think about, before we get into that, what is your approach for driving consensus in the org, at the offsite, in MongoDB, we're saying, here are the three priorities we're going to go solve in 2024, 2025 fiscal year. And this is the approach we're going to take to really reduce churn, increase selling opportunities, and drive rep effectiveness.

JAMES: Yeah, that's a really good question. And it comes from this like... So first of all, at Mongo, within our leadership, we're very Socratic in terms of decision-making and feedback gathering. That's one thing. Secondly, we really like... And as I've gotten it more... As I've gotten deeper into my career, more mature, I feel in some ways more like a product manager than like an ops manager. And I mean that as like we're developing... some cases products and in some cases process but the process of doing diligence and quantifying impact and prioritizing just becomes more and more and more important which is what product managers do and so you know you're asking like how do we get consensus like you know how to give an off site or just amongst the order about what we need to do and it's that exact same process i'm not like I would be not doing my job if I was to say, show up and be like, you know what, guys? I put together this intellectual memo where I haven't talked to anyone, but this is kind of me postulating these things are the right things to do. That's not how it works. We interview deeply. It was a couple of ways, but number one, we're interviewing deeply the people who are doing the actual work. What's preventing you from getting your job done? What do you worry about? All the way up the different levels of management. And then when we talk about it, we're both reviewing the information that we have from the people who are doing the job and getting the feedback from people who own those functions. And it's a combination of those things. As leaders, we decide, hey, it looks like something we talk about is the average CSM is really effective at doing their job when they have the time to focus with a particular customer. But the problem is they don't have the time to focus on N customers, 100 or whatever. They can focus on 10. So the question isn't how to make them learn how to do their job better. The question is, how do they scale what they're doing to 10x the customers? And so then those types of insights determine what our order priorities are going to be. So it comes from the field. It comes from the people doing the job. And it's just my job to help organize those thoughts and then get us to a final conclusion. And then we execute.

ADAM: Yeah. I love that you called your role like a product. I think about my role as a product, that really should be everybody's role. It's like, how do I drive operational excellence within my given function and collaborate across the org to make sure we're all rowing the boat in the same direction? That's really great. You said something like, How do I focus, how do I drive efficiency gains and focus my CSMs and or my reps on N accounts within their patch or to reduce churn or drive expansion or renewals or drive new acquisition? How is... How is AI going to help with that? And how are you thinking about AI and ML to help you make sense of all the data that you have? Leads coming in, named accounts, territories, equitable account distribution. That's so complex at an org like MongoDB is exiting at 2 billion ARR

JAMES: Right. So the way I think about the use cases for AI, they fall into two buckets. One of them is obvious. And so I'll talk about that first. But I think it's actually less valuable than the second one. So the first one is obvious is like time saving. Right. If there's a defined process that you're clear, like, you know, I just need to be able to take this information, digest it and come up with like an outcome. AI can be really helpful in that. One of the things that we've done really successfully at MongoDB is to build a question and answer tool. But it transcends just question and answer. It helps people really understand the meaning of technical concepts so that they can get into deeper conversations with their customers. But the idea in general, we talk about AI of like, hey, like, you know, if somebody is doing this thing, it can kind of maybe like be reduced or augmented with AI is very real and time savings is very real. So that's like the first, and there's a lot of use cases I can go into there. That's the first one that I think most people is pretty obvious what AI can do. However, the more valuable use case that I see is using AI as a mechanism to point us To help us make micro decisions, point us in the right direction. So an example of that is if I'm an account executive and I could be working any one of 100 accounts or 1,000 accounts, the opportunity cost of me working the wrong account is huge. Because if there's another account in that 100 or 1,000 accounts, that could be a unicorn. I want to go after that account. And so you could think of AI as like helping me just purely to automate my workflow so I could touch more of those accounts. Or I could think about AI as actually helping me make the right decision so that I just focus my effort on that account, even without any automation. The focus, that's where the value is. And the way that, I mean, we can talk about how Skillstack is helping us, but I mean, we're focused on both of those problems, the automation and like the insight. I think what people tend to miss more is the value of the insight and how to actually do that. And how to actually do that goes into some of the other projects that we've been working on together and some of the stuff that I've been working on with territory management at Longboat AP.

ADAM: You mentioned earlier AEs, STRs, BDRs, whatever your acronym is at your company. These hunters, these farmers, they have a tough role. They have to do account mapping. They have to do account planning. They have to do research. Oftentimes, we're requiring them to do account and contact or lead enrichment. They have to bounce around from various systems to search for the right insights, the announcements at the right time. And so they can connect the dots with pattern recognition and multi-thread effectively and reach out with authority and with personalized research and relevant content across multiple channels. It's super difficult. To accelerate deals. And oftentimes, we've created this system for them. I've done that at every company I've been at. I've created the proverbial squirrel. You mentioned focusing. You mentioned the word focus. Focusing on the right insights at the right time because the opportunity cost is too great for them to focus on the wrong account. The wrong... Considering AI, what's required of a tool and or a data structure to be able to enable the role of these early stage folks, SDRs, BDRs, AEs, CSMs, to execute on their job now?

JAMES: Yeah, for sure.

ADAM: And in the future, how is that going to evolve two or three years down the road?

JAMES: So I think the way that a lot of people normally think about it, which is the wrong way, is to think about it from my perspective as a seller. From my perspective, how does this thing need to work in order for me to do this? The way that I think about it, the way that we are now thinking about it, is think about the perspective of the customer. What's going on? Think about it. We got problems. We got solutions. We're trying to map them together, right? So think about the evolution and the journey of a potential customer. It's called, say, prospect. As they're going through their journey, they're fundraising, they're starting to build product, they're making decisions about their tech stack. In this case, I'm talking about MongoDB to a MongoDB customer, obviously. It could be very different across different landscapes. As they start to build technology, now they're making decisions about their tech stack. And as the decisions are made, they become more and more difficult to reverse. And so what we want to understand is from their perspective, when and why are they making decisions? And if we can have a data model and a system that is able to identify the why and the when, well, it's really the three whys, why anything, why MongoDB, why now? But if you can answer those questions, thinking about it from the perspective of the customer, not for me, like if you design it for me, it's like, well, I want to be able to send as many emails as I can. I want to be able to, maybe I want to know like, you know, who the VPs of engineering are. It's like, those are helpful, but like, focus on the perspective of the customer And that's when you're going to see their best results. Because then you start to think like, okay, well, when are people making decisions about their tech stack? When they're doing new application development, when they're getting new funding rounds, like those all derive the pieces of information that you want to understand. And then the information derives the data that you want to be able to collect. And that's how like together we've built this kind of spotlight tool.

ADAM: Really cool. Let's take... a scenario right now. The average SDR. Are the copilot tools out there today, are they augmenting the average SDR to do better versus the old way or what they're doing now? Will it augment their role?

JAMES: Yeah, the way I think of it, augment is one word that you could use for sure. I think Maybe another word you can use is focus. There's going to be, if you think about the SDR role, you can break it down into the first thing is I show up to work. Well, what am I going to do today? Where am I going to focus? And then maybe it's like, well, in order for me to know that I want to talk to these accounts, I've got all these leads. I've got to sort through them. And then maybe there's some open questions I have. So I've got to maybe do some research and figure out those questions. Okay, now I've got an idea of where I want to execute. So the idea of using co-pilots to be able to make SDRs more productive is to be able to eliminate as much as possible the concept of what should I be doing right now and have them executing and make them as effective as possible in executing. And so it's can we eliminate a lot of that manual work, a lot of manual research? And by the time that we get on a call, what if they could easily just have an understanding of kind of, the type of business problems that we expect that they might have? How do we augment them with information about, you know, we sell a technical product. So I'm an SDR, you know, I'm doing qualification with you. You're asking me something about networking capabilities, AWS. Like, how do I know the answer to that if I'm brand new? You know, like there's a lot of ways that AI can augment what they're doing. But I think it's a combination of like focusing on the areas where they actually provide the most value. and then augmenting in their ability to do that.

ADAM: I love it. It's what I would call like the next best action. When you have a rep bouncing around between various systems and you can create playbooks for various channels, various types of leads or accounts and those plays, but when you require them to do the pattern recognition and connect the dots, without surfacing the right insights at the right time and saying, here's your next best action. Let's go execute. The tool will augment their ability and their learning to say, oh, the tool is helping me with this pattern recognition. So I know why I'm focusing on this next best action, why this is the right insight, the right analysis. Yes.

JAMES: You got to know why. You got to know why. And this is the lesson that I learned quickly and have ran with, but I see other people making, is that you can't just tell a salesperson, do this. It's like, okay, well, as a sales, somebody in sales, I am accountable to my own success or failure. And if I don't trust that what you're telling me is good information, I'm not going to listen to it. And they shouldn't, right? If you're going to build a system that you believe is going to make them more able to do their job or be more helpful, it has to explain why. They have got to be bought in and committed and have conviction That, hey, these are the good leads. Like, okay, well, I have to call them and I have to have conviction that those are going to convert. So what's the why? And I think you see in like ops organizations, like too many people are throwing like scores, like black box numbers that people will be like, this is the 99 lead. But sales rep calls 1099 leads and they all are kaput. They lose trust. So the why is important. And people don't always understand that. Especially when you're talking to people that are a little bit more detached from sales. If you're working with an analytics organization or a consultant. I've had tons of consultants say they can build a magic model that's going to tell us all the best leads. Maybe, but in order to get the people who are going to be accountable to creating sales to work on, you have to explain why.

ADAM: Yeah, LLMs are going to be, I believe they're going to be commoditized very, very soon. So if you have the right data set, though, the right targeted data set with the right context, and you're focusing on workflow optimization for your team, given the right data set, you're going to provide them with focus so they can execute. That sounds like a simple concept on the surface, but for your sales ops colleagues, maybe at a company that's earlier stage, or maybe at a company that is at the stage of MongoDB and has to solve for this wildly complex go-to-market motion, what are the top three things your colleagues could invest in now to be able to execute on what we're talking about today?

JAMES: So I think you have to make sure that you have the right data. I think people... So that's number one. I think people operate a lot under the assumption that they can take the data they have and use it for some new arbitrary use case. And, you know... For the people who are listening to this who I'm assuming are working in sales, I'm sure it's no unfamiliar conversation where you say, oh, hey, guys, the data is no good. The data is no good. The data is no good. I'm sure, Adam, you've been in organizations where the data is no good. These are problems I think about deeply. Having managed the data at MongoDB and thought about it a lot, Data is good for a use case. If you want to understand who the buying personas are at a particular company, you need specific contact level data to understand that. And maybe that data is stale. You might say, oh, we have title. Well, where's the title coming from? How often is it updated? Is it reflecting their current role in organization? Skillstack helps to update that information, right? And so just having that role at an arbitrary snapshot point in time Well, now for this use case, it's bad. But maybe just to like bucket them into like, okay, they're like a VP or up, maybe that's perfectly suitable. And so I think like an investment in making sure that you have data that fits the use case of the thing you're trying to do, and this goes back to like that style of product thinking, is super important. People just take generic data sets and like, yeah, we have data, let's use it for this. Inevitably it comes back, data's no good. So one is having the right data. Two is having the right process. Obviously, process is kind of the recipe to making it all work. And I think what's important about that, you know, I could speak about that for a long time. But what's really important is just going back to the why and making it simple. Like at Mongo, I've gone through various stages of orders of complexity. When I first showed up at Mongo, you know, I was a data analyst. I put together a score. That score wasn't received super well. Then we hired a consultant. They put together a score. That score didn't go over super well. Eventually, we just landed on basically profiling accounts by the number of software developers that they have and the number of people that mentioned MongoDB on their LinkedIns. There's literally no math. It's just, hey, this company's got 10,000 people, 5,000 of them are software engineers, and 300 have MongoDB skills versus a company that's 100 people, has 60 software engineers with three people. You can look at it and understand, okay, this one might be more complex, but this one seems more tech-forward. that is clear what you're being shown. And so the process and how you use that data, it should be like Occam's razor, right? It's like simple is better. And it also helps to determine the why and be able to articulate the why. And then it's the execution. So the execution is like, well, how do you actually put this information into the hands of the sales team and productionalize it? We've built custom tooling to be able to allow people to do that. It starts at the territory management level, leaders allocating accounts to their people based on how strong or the profile of those accounts via this data that we have. And then the reps being able to see and understand, okay, just because I see this thing, well, what do I need to do about it? And having a playbook of saying, okay, I see this. That means that these are the probable things that we could talk about and be able to put together a PG strategy. So it's like the right data for the right use case, the process should be simple, and then being able to actually productionalize it.

ADAM: I love it. Data integrity creates focus and trust between the teams. Simplifying your ideal customer profile and those signals or triggers that matter to you, like the number of developers and the number of mentioned MongoDB skills, that simplification is amazing. can be really difficult for orgs. Sometimes they overthink about signals and firmographics and technographics of their ICP. And I love that you've simplified that. So your reps trust that data and then they can go focus and they go, cool, my James and his team has set me up for success to focus. That's great. I wanna talk about your role, sales ops in particular. How will your role evolve in one, two and three years?

JAMES: Yeah. So let's start at the top. Like, what are the goals that we're trying to accomplish? So sales ops should be driving, the way I think about it, should be driving sales productivity. That's the goal, right? And as I've gone through my career at MongoDB, the last, you know, let's call it the last eight to 10 years have been a period of hyper growth for a lot of companies. And we've been very hands-on. Sales ops have been deep in the weeds of everything. We will continue to be very deep in the weeds in territory planning and quota management and forecasting. All the seminal activities that feed into making sales be able to do what they do. The way sales ops is going to change is the way that I think a lot of... At the end of the day, we drive productivity, but we're a cost center too. If you hire more incremental heads in sales ops, is that going to make more money for the company? To a point, no. There's declining marginal return. And so sales ops is going to have to really understand how to create scale. And the way that I've always theorized scale happens is through technology. Technology scales. There's a reason why organizations are much more willing to invest in technology than they are in human capital. Human capital doesn't scale. They don't work on weekends. They get sick. And those are humans, and humans are great. But this is where AI starts to come into play. But I think without talking about AI specifically to your question, Sales ops is going to have to focus more on how are we productizing what we do. And so like right, you know, in my journey in MongoDB, if there's a question about forecasting, typically what happens is somebody sits down with a sales leader, they put on a spreadsheet, they talk through, what does this deal? What does this deal? What does this deal? And they've got to figure it all out ad hoc. And then if that same question comes up next week, they have to do that exact process again. So it's like, how do you put together a playbook of like, hey, these are the types of questions that happen. How do we build the tools where we can easily be able to solve those, create the right level of visibility, create the right level of access, just like here, give them a shovel, be able to quickly and easily see, okay, this is how these things affect your forecast. So it doesn't create more work for people having to spend actual time, our most valuable asset, figuring out. So sales ops needs to become more invested in product and understanding how to build process and tools that allow us to scale better. Because you can't just grow at the same proportion that sales does forever. And particularly if you want to be a profitable company, you can't do that.

ADAM: Yeah, Salesforce just launched a retrieval augmented generation for their CRM. Really cool, Einstein. Give us some insights into the way AI is going to positively impact your role in sales ops in the near future.

JAMES: Yeah, I mean, one example... It can productize your function, right? Yeah, yeah, totally. So one example is this concept of this... Q and a question question and response function. I mean, so we built this tool, we'll call it coach GDM or coach go to market. And, um, It started as a technical answer, like, hey, I don't understand how this thing on MongoDB works, and it'll give you an answer from all of our public-facing documentation. That's really cool. But what it has become now is something much more powerful that reflects how sales ops will change in the future. So imagine you've been in sales. Imagine you're putting yourself in the shoes of a rep, and they're like, I have a question about... Just like my customer wants a SOC 2 report. I'm not really sure who to go to. Somebody asks if this is on the product roadmap. I don't even know who the PM is. I'm curious about my comp plan. Dude, the level of complexity traditionally to be able to just route, organize information, and then just be able to get information back in the hands of the rep who should be selling and not worrying about all this is crazy. And so what we've now been able to construct using AI is a single entry point for all questions. I can ask the question, hey, when's payday? And it will understand this is a question about compensation. These are the verified sources, and it's a RAG, retrieval augmented generation. It pulls information and gives you that answer immediately. You want to know, hey, when is my comp plan coming out? Okay, this is when that's happening. How does PPC peering work on Azure with MongoDB Atlas? This is how this works. And I don't have to think about where do I go. And the best part is like, you think about like 80, 20, like the bot doesn't know the answer to every question, but what it does know and is good about knowing is using LLMs to categorize. So it's like, Hey, I understand that you're asking a comp question. I don't, and it can, it gets a sense of like relevancy based on the, is on the vector similarity. But it can say, yeah, I don't have the answer to that. But I can tell you the person. You don't have to start ask 16 people, is James Underhill the person who answered this question? Hey. Think of all the slacks you send there. Be like, hey, don't want to be a bother. But would you happen to know? Or are you the person who knows? that's over. The tool is like, hey, this is a comp, you should talk to this person. Or if it's like, I own an organization, a sales support team, and if there's a question that goes to them, it could just create a ticket for them. They're like, all right, I'm creating a ticket for you. Here's a link, you can follow it. Because I don't know the answer myself. Myself isn't the bot. So it's like, there's so many ways just to think about And the use case there is both it's valuable just to get them access information, but just focus them on selling. Give them information quickly, focus them on selling. And then from an ops perspective, there's so much wasted time in people being like, especially in a big organization, does anyone know how the AR is calculated on a global channel? And 16 people are like, well, you should talk to this person. It's like, whoa. Like the tool understands like what you're asking and can either get you the answer or tell you who you need to talk to and you just do it.

ADAM: So cool. What's the, you just said a lot there. What's one skill you're personally investing in as a sales ops leader to future-proof your career, productize your role, considering how quickly AI is evolving in the go-to-market landscape?

JAMES: The high level answer to that question is it's product thinking. And what I mean by that is figuring out the right thing to do. As a leader, I have to figure out, I have to know what direction to sail the ship. And then the people have to know how to sail. But if you're sailing the wrong direction, like you're not going to get to the destination, right? So prioritizing, figuring out, like I was kind of saying earlier, goals across the industry are changing. There's more focus on the bottom line and scale. But so still to me, it's understanding, well, what are the things that are going to influence that goal? And how do I make sure that our ship is sailing in the right direction? I think that like, As a leader, I don't need to be an AI expert. I need to understand enough about how it works that we can utilize it and then have the right people around me to be able to do the how and the what. But in terms of like the why, I need to understand, be able to answer the why questions and figure out like where we really need to be investing and make sure that those people get the right resources. And I think it can be very tempting for leaders and certainly it's very tempting for me to get deeper into the technology because I'm so curious and interested in it. But really like that's not my job. My job is to figure out what we need to work on and make sure those people are doing those things and resource appropriately.

ADAM: For your colleagues that are listening to this now and getting inspired by that, becoming a better product thinker and model thinker to focus yourself on that, which is important now. What are a couple of resources that you're using or the way you're educating yourself or evolving in your role that they could take advantage of to also do that?

JAMES: I think it's like, in terms of resources, I wouldn't say like, hey, you need to go off and read some manual. You need to get deeper with your teams. And I mean, obviously, the teams you manage. But what I really mean is the sales teams, the teams that you support. That's where the answers are. Because I think it can be really tempting to think that you know. Like for me, I met a MongoDB seven years last week. It's really tempting for me to be like, yeah, I know how they're doing this. I know how they use the system. The better way to do is this. That's not the answer. They have the answers. Process is evolving. The industry is evolving. Understanding what our customers are thinking and why it's hard to penetrate or get in with prospects. You've got to be deeply connected with the people who you're serving, your stakeholders. and then you so first it's like a deep connection to them and then second it's understanding that they all have different incentives across like a big organization they all have different incentives and priorities and so you need to be able to distill what is like uh inconvenience to them and what is like a critical thing that they're missing because people aren't always asking for what they need so you need to understand the actual required capability or the pain and then you should you should be an architect of the solution. And I think like where a lot of people, maybe there's a pitfall is like people just tell you, if I just had this thing like in outreach or whatever, I'd be able to go faster. And maybe it is, that is the answer, but maybe it isn't. And like, it's my job to come up with solutions that scale across the entire company. And so maybe the a hundred percent solution for like one team is like a zero percent solution for the other team. But maybe there's an implementation that I can put in place that's like 75% for everyone. And that's what I would rather do. And the product level thinking is being able to quantify, okay, if I do this, how does it affect this team, this team, this team? They're all similar, but have different incentives. They work a little bit differently. And how can I create the most value for the lowest cost? And so that's the level of thinking that you've got to be at. And I think a lot of people have those critical thinking skills, but you need to be deep. You can't be wrong about what the impact is going to be. And in order to know what the impact is, you have to know your stakeholders and you have to know what they're doing.

ADAM: Many of your stakeholders are earlier on in their career. They're not thinking like a product manager. They're thinking more tactically, like I need outreach to help me send more emails faster or something like that. How do you peel back the onion to get to first principles and help empower them to think more like a product manager to get the context you need to understand how you serve the 80% versus the 20%?

JAMES: Yeah. I mean, honestly, I feel like you kind of answered the question in your question. Like you just peel back and it's like, people will start off the first order of like, I need to be able to do this. And it's like, it's just like, you just ask why like five times, right? You have to be good at it too. Just like as a tactical note, you have to be good at asking why without making somebody defensive or like you have to be clear. That's like, I'm not challenging you. I'm trying to understand. And you have to like, come on. It's like, Hey, look, dude, I don't have a quota. Like, Help me understand. Why do you need to be able to send more emails? Like, I'm just truly like, and they'll be like, well, so the more emails I send, the more that I'm going to get responses. And that gives me more pipelines. Like, oh, then it's like, well, why do you think you need to send so many? I'm like, well, the message only, you know, it really resonates with like a small percentage of that. Oh, okay. Yeah. And then you can start asking other questions. Do you think if you had better data about who to outbound to, per se, would you still need to send more emails? Well, maybe not. Or maybe they still would. I don't know the answer. But it's just a line of questioning. Like you said, they get the first principles. And then once you understand the real problem, it's like, got it. So the problem that you really have It's all about getting in the bottom line of the problem. The problem you really have is you just don't know who to call. And so your solution is to call as many people as possible, which actually might work, but maybe there's a better way to do it, which is to be more focused.

ADAM: Love it. In a couple minutes, can you describe how MongoDB is working with ScaleStack and the impact that you get out of our relationship?

JAMES: Totally. So it starts with what I was talking about, about the data model, right? We progressed in our evolution of account profiling from, you know, a smoke score or a propensity score to this concept of just looking at the number of developers within a specific market and globally. We put together a metric that we call TAM, Total Addressable MongoDB Market. where we look at the number of developers and we have an estimated number of applications per developer, an average spend per application. So we're able to extrapolate that to an expected spend or a potential spend in an account. But really, it's linear with the number of developers. So what we use to profile accounts is the number of developers. And we use ScaleStack as an automation platform to be able to get that cut in a number of ways that are really valuable for us. And what I think is a really good example of good data modeling is because We worked with you guys to get a very specific data point for a very specific use case. And when I talk to other sales leaders and they say, or sales ops leaders or sales leaders, like, yeah, we have Zoom info. We have inside view. So, you know, we have a number of employees and stuff. I'm like, you just have a generalized data set. And then you just apply that to like any use case you have. It might not be the right, it might not answer that question. You got to start with the questions. We started with, how do we identify customers? companies that have a propensity that fit our ideal customer profile. And one of the first things that's part of that is they're building software. So we got to know, hey, if you don't have any software engineers, you're probably not building software. Number one. Number two, does MongoDB actually solve their problems? Okay, well, if people are listing that they have MongoDB skills in LinkedIn, or if there are jobs that mention that they want to hire people with MongoDB skills, that's a really important signal for us. So there's still other pieces of the data model that we use for territory profiling, but those key pieces And getting those programmatically is super valuable. And then we look, as I said, at the regional level and the global level to understand where the real buying center is, where technology is being developed. You might have a ton of employees in New York, but the developers are in Tel Aviv. That's pretty common. So like, where are you actually selling to? So it helps us to answer a bunch of those questions. So that's kind of the first piece of our engagement with you guys. And then second is now how do we, so it's like, it follows basically directly the three steps. So it's the right data model, answer the right question. And then it's the right process. So it's how do we take that data? How do we package it and deliver it to the sales team? And we do that using our own, we've got a custom platform. We call Argos, we build in-house as a territory management tool. And the third piece is productionalizing. And that's what we're using. We've built together this tool called Spotlight. What that effectively does is it looks at similar pieces of information across news, jobs, and consumes the other territory profiling information that we have. And what's really cool about this Spotlight tool is it addresses that kind of second value area of AI that I was talking about. Like imagine if I could spend infinity time reading all the news in the world about accounts that are building technology, I would probably have some pretty good leads. But as a rep, I don't have infinity time. But we can send tons and tons and tons and tons of information to these LLMs and categorize. For the viewers, we have a number of use cases that we've trained these LLMs to identify. People are building new technology. They are replacing technology. There's like new executives in place. There's fundraising events. There's things that we know that are clearly correlated. These are heuristics that we use to identify when a selling event can happen. And those are super important because we're able to take the data on like, this is a good profile for a company. And this is a good timing. And so that all comes together into the automation where the tool spotlight will create a custom email template and we can integrate it where we're going to be using sales loft. A lot of people will probably use outreach, but it integrates into the stack that you already use. to be able to supercharge using SDR as an AE or whatever your function is. It's like, you don't have eyes and ears all over the place, but AI can. And so you point it in the right direction and it's able to glean information that you might have missed. And I think that is a really valuable piece. It's not just writing an email for you, like ChatGBT might be like writing a college assignment for you. It's actually like, you wouldn't have known these things. And now you do, and it's flowing through your workflow very organically.

ADAM: So cool. Let's rapid fire this last part. Who in the RevOps community, sales ops, RevOps, do you admire? A team or an individual that you'd like to give a gratitude shout out for to inspire you in what?

JAMES: Yeah, I mean, there's a lot of awesome teams out there. I mean, one that I would just highlight is the Datadog team. Datadog and MongoDB are, you know, we're kind of like sister brother companies. We're both in New York. Dave, our CEO, is on the board of Datadog. But I would talk to TJ Boscully and Mika Grodin. They're great dudes over there. And I know they're keen on investing in AI as well.

ADAM: Cool. Who are you going to nominate to be on this podcast next? Give them a shout out. Let's go with them. Why not? Sounds good. Great. Cool, James. Wow. I've learned a lot from you today about the challenges you're solving, how you're investing in yourself, and who you admire, your sister company, brother company, Datadogs. In two sentences, two to three sentences, recap your actionable insights for our community of listeners around our theme today, like building AI into the go-to-market strategy to generate high-performing revenue engines now, to focus your team now and tomorrow.

JAMES: Two or three sentences is tough. I'll do my best. I think AI is a really powerful tool, but it is a tool just like many other things. And so I think people get caught up too much and they're like, we're going to throw this thing, just throw it into our tech stack and it's going to be great. You got to really think about how you're using it. Just as important as a tool, more important is understanding the direction that you're trying to go. This, we need to reduce churn. The ways to reduce churn are X, Y, and Z. Of X, Y, and Z, these are things that AI can help with. That's where we start. Unlike other tools, AI is something that you need to get. There's a learning curve of understanding how it works, what it's not good for, and where to improve over time. And that's where working with valuable partners like ScaleStack can really help accelerate your time to value. But just to reiterate, you got to be focused on the right areas. If you just throw AI at your stack, I get so many emails from outbound or inbound from vendors that like, we're an AI power. It's like, what does that mean? Like, where are you focused? Because there's going to be a hierarchy of problems with different levels of value that you need to make sure you focus on the highest ones and make sure that AI can actually solve that thing.

ADAM: Well, thank you to our viewers today and especially to you, James, for spending time with us and giving us a peek into your world, your function, how you productize your thinking and your role to execute in your strategy at MongoDB. For everyone that joined, we'll see you on the flip side next month. And James, thank you. Take care.

JAMES: Thanks, Adam. Appreciate it.