Selling to Engineers: Ben Solari on leading sales at DataRobot & Jellyfish

August 29, 2023

Listen Now

Never miss an episode. Subscribe now.
Thanks for subscribing! We'll email you when we publish an episode.
Oops! Something went wrong while submitting the form.

Episode Summary

Ben Solari started as an equities trader before jumping into B2B sales at InsightSquared. He later joined DataRobot—an AI data platform—to lead their inside sales team.

After 4 years in that role, he joined Jellyfish as VP of sales—where he’s been leading their Boston-based sales team for nearly 4 years. Jellyfish is an intelligence platform for engineering teams—think Salesforce, but for managing engineers.

If you’re not super technical, sales is a great way to get your foot in the door in the startup world. That’s how Alex (our host) got started at Yelp before eventually founding Dock.

But what happens when you have to sell a very technical product to a very technical audience?

Ben Solari joins us in this episode to talk about leading sales teams at DataRobot and Jellyfish—two products aimed at engineering leaders.

Alex and Ben discuss:

  • The benefits of being a seller with an equities trading background
  • How Ben learned to sell technical products
  • DataRobot and Jellyfish's early sales strategies
  • Scaling DataRobot's sales team to 30 account executives
  • Jellyfish's top-down sales pitch
  • Collaborating with marketing

Links and References


Breaking Into Software Sales

Alex Kracov: I'd love to start the conversation with how you got into sales. Because I think before your first sales job, you were actually in equity trading and sort of more on the finance side of things. Can you talk about how you got into the world of software sales?

Ben Solari: Yeah, it was by accident for sure. In college, I started writing fundamental analysis, papers on equities. I did equity research on the side. Seeking Alpha was the website. Facebook IPO 2012 was coming up, and I did a lot of IPO-related research with them. I found myself doing equity trading out of school. I did that for a few years. At the time, Startup Weekends and Lean Startups, and there was kind of some grassroots tech ecosystem that was happening in New York City. I'd always been very entrepreneurial. I start to find myself in these Startup Weekends and hackathons and stuff. Coinciding pretty much with the burnout of short-term equity trading was my interest in starting a company was ramping up. And so I had been looking to start a company, looking like Boston was a good place to do it. I was calling around my network to just find a job, basically. I want to start a company right off the bat. It turns out you have to pay rent. I didn't really have much credentials to raise around at the time. And so a friend of mine, Dave Miller, was interning at a company while he was at HBS. It was at InsightSquared. That was sales and marketing analytics. I don't know if you ran into them from your Lattice days.

Alex Kracov: Yeah, I'm definitely familiar with them.

Ben Solari: The BI analytics space. Basically, it's like if you want to get your foot in the door, sales is a good way to join one of these startups. They were around Series B at the time. That was 2014, kind of the BI. Tableau was one of the hottest companies, kind of up and coming at the time. Salesforce was early in establishing major dominance with sales and marketing and that whole ecosystem's upbringing. So yeah, I lucked into a foot in the door. I started as a BDR.

I think probably a few things from equity trading helped. One was certainly stress management, which there was no salary. Eat what you kill. At the end of the month, whatever you made, half went to the family office. Half went to you. So it was very culture of meritocracy. That was rooted in the way that I enjoyed working. It was a meritocratic environment and also just having strong, maybe first principles, perspective on asking why questions, digging in, I think.

Early in my tenure at InsightSquared, I came into a series B company. They're all kind of messy. There was a ton of process there. But I noticed that a lot of people were doing things very linearly. Like, oh, you get the lead list in the morning. They had a content engine that was pretty insane early in the B2B lifecycle. There were some really nifty marketers, and they built a pretty good lead and content engine there. People would wake up, come in, just dial the MQLs for the day. I think I was definitely asking questions around why we do certain things. I was surfing through the CRM trying to see what touchstones are unturned. I think they were very transparent and competitive there. It's the culture. Actually, the InsightSquared sales DNA, there's a lot of people that I've stayed very close with in tech. I hired quite a bit of folks over. At DataRobot, a lot of my early team was actually a lot of my peers there. I think that was a very good kind of sales MBA because you're selling to sales leaders there. So yeah, that brought me to the DataRobot. That's where we reset in terms of there was nothing. There was nothing there. No machine, no engine. DataRobot and Jellyfish were both around the same place as far as when I joined them. It was like the first 20 or so customers, those just exiting founder-led sales. DataRobot was 2016. Jellyfish was 2020. Both of those were kind of founder-led and then I came in as those companies were trying to establish actual go-to-market processes and engines.

Early Days of DataRobot

Alex Kracov: It's a story that really resonates with me, because I feel like most business people who are trying to break into tech — I started as an SDR too essentially at Yelp. That was my way in. I didn't go super deep into sales, but I went into marketing. And so yeah, I think just smart people who want to break into tech and configure things out and disrupt the status quo at the company is the best way to break in, as your story says. It's like you get your feet in the door as an SDR, and then start trying to make things happen as you go. You must have been pretty successful, because I think you're only at InsightSquared for a little under two years. Then you went to DataRobot, and you were working in machine learning and AI long before the current hype cycle that we're in. You started to touch on the story a little bit. But what were those early days of DataRobot like, and what made you transition away from InsightSquared? Because it seemed like you had a good thing going over there, too.

Ben Solari: Yeah, I mean, the DataRobot story is interesting because it came in at a time when — I was only in Boston for two years, so I hadn't really laid up much of a network yet. They were actually one of my customers in InsightSquared. They bought InsightSquared from me. They had come onto my radar right around their Series A, and I just thought that that was a very unique space. And so I had stopped by their office in person. I dropped off a few books that I had read, that I liked, with the sales and marketing leaders over there. So I planted some seeds, maybe six months to a year, before I ended up joining them.

I think a lot of candidates that I talked to that are in the startup space that are looking for their next company, my main advice for establishing criteria that's important to you when you're thinking about what company to join is having that kind of investor's mindset. Even though you are not a venture capitalist, if you're joining a company with your time in an area, often in your career where those are very finite invaluable years, you're certainly investing in that company. And so there are some facets about that company that should excite you, that you should have some conviction in even early on in selecting that company. I think, definitely, there's always luck involved. But DataRobot, for sure, was one where it felt like it would be a good investment if I had money to invest early on looking at the potential breadth and depth of use cases that you could solve with that. I kind of had that investor's mindset and mentality.

Alex Kracov: Then I've spent some time trying to figure out the product that DataRobot actually sells. The words I kept seeing on the website: automated machine learning. That's how you described the category. And so from my understanding, it's like a system that helps companies run different datasets and, I guess, train their machine learning data. Did I get that right? Is that actually what the product does? What did it look like in those early days?

Ben Solari: Yeah, so DataRobot came out of what was called Kaggle competitions. Kaggle was a global, open-data science competition network that private companies can sponsor problems for community of folks to solve. Think of it almost like a spelling bee where you don't know what words to ask how to spell, but you've got some ideas of a bunch of different people that can take a crack at it. It's sponsored by a company who's looking to solve a certain problem.

The founders came from that space where the premise of the product was partially them stringing together a bunch of computers to try parallel attempts of automating what a data scientist would do by hand. They were, very early on, pretty much the pioneers of bringing automation to data science. Now you see with LLMs and the advancements now where you have like algorithms that are tuning algorithms or giving a feedback loop that's automated. DataRobot was very early, if not the pioneer, in thinking in that direction. But they were doing it for what's called supervised machine learning problems.

The easiest way to describe that is, you have a series of observations in the past. Let's say, it's loans that you made or leads that came in. Lead scoring and lead conversion was actually a very prominent use case. You've got a bunch of observations, and you have attributes about those observations, like, where this person lives, how much money they made. Or for a lead, it's like what their title was. Then you have an outcome: that lead converted, that loan was either successful or defaulted. And so what DataRobot did as a product is, it ingested these static spreadsheets that had observations and outcomes, and it basically competed hundreds or thousands of algorithms or variations of algorithms, all building models on that same data set. Then it basically came up with a leaderboard of the top models in terms of their performance. And so it was, in nature, a code-free way to develop these high-performing algorithms that otherwise would have taken days or weeks or months for advanced data scientists to build by hand.

It's basically automating. There's a lot of variety of AI problems. This problem space that it originated in is called supervised machine learning where you know the outcome. LLMs and self-driving cars, that's other areas of the spectrum. But if you think about use cases that could be addressed just with that area, I mean, it was limitless. Like lead scoring, fraud detection. We had sports teams looking for player performance. Who's likely to get injured? Who's likely to move up levels in a minor league baseball? Fan behavior, hedge funds, doing trading models. It was a lot. It's a double-edged sword because, on one hand, you're enamored by all the possible use cases, and the TAM feels limitless. On the other hand, it's very hard when you have a lack of specificity to train and enable, first off, a team of people to be aware of all those use cases and then to bring a degree of specificity or differentiation to your messaging. Because it's just very hard to train a team of people, to build up a marketing department that is so use-case-driven.

Because at the end of the day, that was the wedge that we took. Data scientists weren't necessarily lining up at the door to automate and work, that they felt very proud of. It's also democratizing something. What was instilled is like a very cherished and finite set of skills that we were starting to broaden and open up the door on. We were taking an approach where we were going to department heads or heads of functions with use-case-based messaging that automated machine learning or AI, was essentially powering the back-end or opening up the potential for those use cases to be addressed either more accurately or at scale.

DataRobot Sales Personas

Alex Kracov: Got you. It sounds like you had multiple personas you were dealing with, right? I assume the data scientist more directionally understood the problem that you're actually trying to solve. But maybe the functional leaders, the marketing leader or the sports coach or whatever was the one who might benefit from the output of the models themselves. How did you think about your entry point into organizations? Were you always working through the data scientist? Would you work through the functional leaders? What did that sort of experimentation look like as you figured out the sales motion?

Ben Solari: I'll get into the personas in a second. Think about the time I joined DataRobot. There's only 20 customers. Oftentimes, it's challenging because the founder — there's the grand vision of what your product is trying to do, and you want to communicate that grand vision to rally employees, to get investors excited about it. So you have, on one hand, this grand vision. InsightSquared is like we are going to power SMBs. Every SMB is going to become data-driven, and we're going to be a platform to do it. There's 2 million SMBs. I'm just making up a number. So that's like this broad vision. Then I started to look at the CRM of who our actual customers were, and it was all B2B SaaS companies. We're not selling to SMBs. I know what SMBs are. I have barbershops and landscaping companies working outside right now. They're not buying our product. It's B2B SaaS companies’ high growth.

So I started to refine your outreach. It's like, okay, if that's the motion, it's a little bit easier to narrow in and start to create some more efficient messaging and more efficient activities. Then when I go to DataRobot and I start to just take a look at the first 20 customers, it turns out three of them were fintechs that were using us for this underwriting use case. I wasn't there in the first year or two when the founder-led approach acquired that two-dozen base. But I was like, okay, if fintechs, that at least gives me a base to start with. I can build a list of fintechs and lenders and start really honing in. Even though we can solve all the world's problems here, I can find 250 lending companies. Start to hit them with a really tight messaging around improving their underwriting models, which just has a very clear and distinct ROI for them.

If you start to create little pockets of that type of approach, it takes this very broad potential. You start to become very use case and market segment-driven and narrow the scope, which is helpful. Then there's the personas. But I think that has been — sometimes I'll meet with founders or I think about the founder-led sale. Founders are often wired to talk about their company from the fundraising lens and the fundraising mentality. Then you usually hire a salesperson and your first couple of salespeople. I think someone's got to point them in a direction that's a little bit narrower than maybe the grand vision of where eventually you want to go. Then it starts to snowball from there.

Alex Kracov: Yeah, we definitely — I even had that problem at Dock. Dock is a really flexible platform. I think of it as, like, any B2B company in the world, any companies working together in theory, you could do things within Dock, even recruiting use cases or finance use cases. But then, as I started going into founder-led sales and really getting into, okay, where's the most value we're providing, it was really sales and customer success teams, specifically onboarding and then enabling champions as part of the sale. We've niched down over the years to go attack that problem set. Maybe in the future, we brought in a backup. But that was a good learning for me. Because it just was hard figuring out the marketing, messaging on the website. It got all muddy. What do we say in sales conversations? It made things a lot more complicated when we tried to be horizontal as opposed to focusing. Then traction was a lot harder. So that story resonates quite a bit.

Ben Solari: Then you have the personas, right? That's obviously a horizontal layer on top of a more narrow segment-based messaging or use case-based messaging. Both DataRobot and Jellyfish have been top-down sales. That means a few things. One is, you have to level up your communication. I think whether it's myself or a lot of the reps that I've hired, having very strong business acumen — some of them have it naturally. Others have had the aptitude and coach ability to just pick it up. But being very business-centric around zooming out.

Let's just take the underwriting example. I'm not targeting an executive at a lending company with automated machine learning messaging. I'm saying, "Hey, lenders like you typically do 50 to 100 million in underwriting volume a year. We've seen case studies of our automated approach to model building have a 2% to 4% uplift on their incumbent underwriting model. And so for you, that would mean a $1 to $4 million lift. Are you interested in taking a look at the challenge model or taking a look at what we could build?" It's very business-centric, and you will have plenty of opportunities to peel back that.

Smart business executives, for the most part, are where they are because they're often all very smart people. So they're going to quickly get into the brass tacks of like, how does this work? I've been burned by vaporware, blah, blah, blah. They're going to have you quickly get into the tech. But it's such a noisy environment out there right now. The automation of sales and marketing has been a double-edged sword. Orders of magnitude are more noisy in terms of inbox noise. So you have to be very to the point on your messaging. Getting the first at-bat with that level of seniority starts to open up the door in terms of, how does this play for your team of leaders or frontline team?

Alex Kracov: Yeah, it's a great lesson. You just need to tie things to the business outcomes. Yeah, I've been in this trap, too, where it's like, "Hey, look at the cool stuff my product does." But no one actually cares. That's just a means to an end. You got to talk about the end, which is, okay, what is that outcome that you're driving in the business? I imagine your background as an equities researcher also helped you do that, too, because you just really understood how businesses work and understood where the value is created.

Moving Upmarket with DataRobot

Alex Kracov: Before we get into Jellyfish, I want to just say for a second, just finishing up the DataRobot story, I noticed on the website DataRobot works with 40% of the Fortune 50, which is super impressive. Did the founders and did you sort of focus on super enterprise from day one? Was that more of a gradual move up market? How did you think about the segmentation from a business size perspective?

Ben Solari: I was hired as the first, what they called, inside sales rep, which was basically now everyone's an inside sales rep. I don't care who you are. No one's doing steak dinners for the most part anymore. We're all over Zoom. But at that time, there was segmentation. There was the inside and the field. Field was dispersed in different regions. I was hired probably as an experiment to see — maybe there were two different visions. I know my CEO, Jeremy, he had the vision that we could actually sell this thing over Zoom. He was a data scientist. He wanted to be able to measure, optimize. He was very oriented to scale. I felt like he loved the idea that there was people that would have good Salesforce hygiene, the number of at-bats and iterations was much higher. I know that that was his vision. Then contrast that with the field where hard to measure, you still got some of that relationship centricity and art-not-a-science type approach.

Needless to say, I think I was hired as an experiment to see if they can sell it. My initial job was to just figure it out and do that in parallel of what the majority of the motion was built around — from 1 to 80, 1 to 100 the four years that I was there. So I kind of had a little segment of the business that was focused on outside the Fortune 2000. We were doing 25, 50, 100k, 250k deals over Zoom, which even five, six years ago, seven years ago was somewhat doubted if that was possible. I think we did a very good job of value selling. Again, remote or not in nature. But definitely, the focus, when you think about moving the needle for big companies and being able to tie your value to it, it was probably about 80% enterprise. Then I built up a team in Boston, in London and Singapore that was focused on mid-market, typically outside the Fortune 2000.

Alex Kracov: Very cool. What was that experience like? Because you grew, I think, the sales team to 20, 30 reps. From my understanding, this is your first time at least managing a team of that size. What was that personal journey for you like, and how did you learn how to manage a sales team that big?

Ben Solari: I'm still learning. But I'd say a few things. One is, DataRobot had a culture of hard work. I know that that's kind of cliche and somewhat of a trope but it was definitely — starting at the top, Jeremy is just an absolute grinder. The guy was always working. That permeated down in the culture. And so I think having that as a basis of expectation setting. DataRobot was very good. They raised a lot of money, so there was no shortage of resources to throw at things. I'll contrast that to probably a more traditional scale of experience with Jellyfish.

But getting back to the question of what it was like, I was basically just in the trenches with the team in Boston for the most part, or going out and visiting London or Singapore a few weeks at a time. I felt like because I had walked in their shoes not too long ago, there was not an element of ivory tower. This guy doesn't know what he's talking about. For better or worse, I'm starting to grow out of this now as a sales leader. But co-selling, I was on the phones with them, strategizing with calls, helping them close deals. Very much like in the trenches with them, which, at the very least, I believe, earned a lot of trust and respect.

When I asked the team to try things or to do things, there was at least the belief that I was doing it from the front with them versus not necessarily walking in their shoes. A lot of experimentation, a lot of trust with the team and in the culture. There wasn't a ton of structure to support much a ton of oversight. But it was a lot of osmosis and a lot of just being in there with them that at least helps, I think, weather the storm. Because there's a lot of bumps and ups and downs. That's what we're here talking about. And so I think having a team that trusts you is a superpower when it comes to them riding out the storm and believing that there's going to be a positive outcome on the other side. That's a lot of the reason why you're doing it.

Alex Kracov: Yeah, I think one of the best attributes that you described of startup leaders is being a servant leader: being on the front lines with your employees, showing them how it's done, making mistakes alongside them. It's great to hear that story from your perspective at DataRobot.

Ben Solari: When I look back on my one on ones with COO Chris or CRO Rich, mentors to me, Sean Gardner in BisDev, when I look back at my one on ones with them, the value that I got that I'm now bringing with me to Jellyfish, it's like they had all been through a high growth run before. A lot of them came from Netezza. Sean was at Alteryx. It was almost like therapy sessions. Actually, Jeremy did this as well. It's almost like I liken it to war where everyone comes in and they're just like, "We're getting killed out there." People are dying left and right. It's bloody. We're getting it from all angles.

Having that sense of calmness and groundedness to be like, "We are in no different shape. This is part of the ride." It's so strengthening and grounding to take the chaos that's happening around you and turn it from like "I need to control this chaos, or things are falling apart here," to knowing that that's just part of the rebuilding and rebirth that you have to do to scale. Then pause and re-jigger and get ready for the next run. That's all part of the journey. And so them instilling that in me and seeing that run, I do feel like I've brought that perspective now to Jellyfish. We're pretty much the same time period. Probably, a little bit earlier in many ways. But it's even helpful for me to tell myself it's part of the ride. And if you have that mentality, you don't let as much affect you. You can just focus on building and getting better.

Joining Jellyfish

Alex Kracov: Let's get into Jellyfish. You joined as the VP of sales. I'd love to know why you joined. What was the state of the company and the sales team when you joined?

Ben Solari: I'll go into just a quick origin story. I was in Singapore on a hike with one of the intern engineers from the US, Mohawk, actually. I was on a hike with him, and I very distinctly remember asking him as an engineer. I'm like, how do you know what everyone's working on? Who is good? I could list my team from 1 to 30 on five different metrics. I can tell you our conversion rate, sales cycle by rep. I'm like, how do you know who's good? How do you guys know who's working on what? The answer was so hand wavy to me at the time that I'm just generally skeptical. I'm just like a natural skeptic.

And so when I met with Phil, who's one of the co-founders of Jellyfish, I was really, really — I had lived this pain at DataRobot, where I saw us go from 30 engineers when I started to 400 or 500. I don't even remember. I was there around 80 employees. I left at like 1,200. So the company had gotten so big. I had seen this pain of scaling an engineering team in this chaos but not having the same kind of infrastructure that sales or marketing has had just through legacy and time. Amidst that chaos, you can still hold yourself accountable and measure these certain metrics and know where to press, where to double down on. Every company is unique. There's unique challenges. But at least, the way that you're measuring and communicating progresses industry-wise is fairly consistent. I saw that engineering was really nascent and having much data to defend or to make these decisions, especially at scale.

2020 came around, and when I met with Phil, I was very intrigued by the problem that they were solving. They built the product. No, Phil, Andrew, and Dave were all ex-Endeca. They worked together in the past, often leading product and engineering teams there. They had built this out of the belief that engineering was the last black box within an enterprise as it related to analytical rigor and data-driven decision making. I kind of liken it to that field sales mentality of, let's say, pre-Salesforce. Somehow, deals were closed before Salesforce. It's not like Salesforce helped people close deals. It's not like Jellyfish is helping people build a product that they weren't building before. But there's definitely, prior to this space emerging, there's definitely a lot of anecdotes and instinct that I think is still going to be there and is always going to be there. Just like a great seller, sometimes you can't put your finger on it. It's just like that person's got it, and you can tell. But when you're trying to manage a team at scale and operate in a more efficient manner, I think I was very bought into the belief that Jellyfish or a company was going to emerge and help software development catch up to the rest of the business.

Alex Kracov: So you joined the company as a sales leader. Where do you start? Were you focused on refining the pitch on how you explain Jellyfish? I saw one article that explained it as Salesforce for engineering. Are you more focused on process or hiring? Take us back to how you thought about building out the sales team. Where do you focus?

Ben Solari: Where it all begins, Alex, BDR. I was messaging. I was trying out things on my own about messaging. It's a blessing and a curse that I didn't come from the dev space before. If I came from the dev space, I would have had a warm network to call on, to give myself some at-bats. I didn't really have that. Data science leaders didn't really interact. I didn't have a deep network of CTOs or anyone to call on. But it went back to, time and time again, it's like first principles. I just want to start with understanding it myself, whether that's what a cold email looks like, or a cold call, or a cold LinkedIn message, and then sitting on the reps listening to hear how they were pitching it and then trying to iterate and experiment. The only way that you can really do that early on is at-bats. You got to get yourself at-bats to see what works and to iterate.

You asked me what I did. Obviously, what was new with Jellyfish was — at DataRobot, I grew the team organically. I was the first hire. I think maybe similar to how you grew marketing at Lattice, where everyone that came after me was like interviewed by me. After me, I kind of inherited a team of five, three AEs and two BDRs. That was very new for me in terms of coming in cold. And so there was a lot of just relationship-building, empathy and understanding of what the last, whether it was one or two months or if people have been there for like a year, kind of in the trenches with the founders. There was a lot of just understanding and listening. Then there was just a lot of experimentation from first principles of trying to get myself at-bats or join calls with the reps. Thankfully, the two BDRs were there to get some new at-bats teed up. I was just seeing what worked. A lot of that was realizing, honing in specifically on a few key themes that you didn't necessarily need the whole pitch to know, like visibility, board reporting.

Mind you, my first week at Jellyfish was the first week of COVID. So there was also just like everyone just went immediately remote. There was definitely a stroke of luck, where that first year, pretty much every company was fully remote. 90% of them had no experience in doing this. And so we were tying ourselves on to some of the topical themes like remote versus hybrid or ensuring that your managers are equipped with tools that can help them manage and coach their teams, because they had COVID. Specifically, remote work was definitely a forcing function that pulled the entire engineering management space forward probably by four or five years. There was definitely a stroke of luck there. It was my job. It was our job to take advantage of that window with testing messaging and helping people solve these problems that was going on in real-time.

Selling Highly Technical Products

Alex Kracov: I know selling developer tools is often really hard. It's hard for salespeople to get the attention of the engineers and developers. But it sounds like it might be a different sale for Jellyfish because you're going after engineering leaders. How do you think about catching their attention? Am I right that it's kind of a little bit different than your maybe traditional developer tool sale?

Ben Solari: It is. It's a lot different. It's a blessing and a curse. We are heavily dependent on outbound. When I started, it was like 100% outbound. Last year, 80% of our pipeline was outbound. This year, it's 70%. So we've got a marketing engine. Kyle, he's heating things up for us. But when you think about the personas that we're reaching out to — VPs of engineering, CTOs, CFOs — they're busy running teams. They're literally in Zoom calls all day. They're not necessarily exploring the space, especially a new space. And so we need to insert ourselves into their purview somehow. That's, again, with that executive centric messaging that I was talking about with DataRobot.

The blessing is that a lot of people that are vying for the attention of these executives are not able to say that they directly solve them and their team of leaders' problems. Whether it's a log monitoring solution, whatever cloud consumption monitoring or optimization, whatever it is, for many of them, the developer space is solving the developer's problem. And so that's where you get the freemium motion or the PLG motion. You'll swipe on a credit card, consumption. One person or one team can do this. So I think we had an advantage where we could look a CTO in the eyes or his inbox and be like, "You probably just got out of your board meeting and got beat up for having stoplights and emojis instead of hard numbers." It's like we could actually talk to them empathetically about the problems that they were either facing directly and personally, or that they were shielding from their teams.

In 2021 and 2022, there's a ton of pressure on engineering leaders. It's like, what is your team doing? The CEO used to be able to walk the halls. At least, you see them in there. Now the engineering leaders were facing pressure about return-to-office policies. They're not necessarily reading everyone in on that. They're trying to fight that battle at the level that they're at. And here we are with a case study that shows we can justify that their team is — we did a whole report on engineering remote productivity. We actually found that there was very little impact or sometimes more impact of more productive teams that were remote. So we're inserting ourselves into a problem space that only usually a small amount of people in these organizations might be aware of. But we still had to fight through the inbox noise. Then a lot of our job turns to buyer enablement, which is organizations or leaders are not used to buying top-down solution. They're used to swiping their credit card or consumption model. So there's a lot of enablement that has to go along with the sales process around the business justification, the business case. Here's how you talk to your CFO around R&D spend and what it means to move the needle here. So that's been a long journey for us as we're refining and have refined that down. But outbound heavy and, I think, a double-edged sword, we've been able to have really solved these pain points and problems that the leaders and leadership teams are facing. But they're not used to buying a lot of software in this manner. And so when we do get them to the table and excited about the potential, it's our jobs to help them make the case and bring us in both from a business justification perspective, as well as team and culture messaging and justification as well.

Buyer Enablement

Alex Kracov: I'd love to know the tactics of how you approached buyer enablement, because it's something I think a lot about with Dock. Actually, it was the origin story with Dock, too, where we were selling it to HR folks. Then we needed the HR person to convince the C-suite and the managers and all the other folks that, okay, they needed to invest into a performance management platform. What does buyer enablement actually look like? Are you just creating case studies for them to forward along? Are there spreadsheet calculators you're sending? Are you joining internal meetings, co-selling? Can you take us by the scenes?

Ben Solari: I don't know how relevant this would be for Dock, but I'll tell you. As we've moved up market, we're now selling into the publicly-traded company space. Basically, the team is going into a publicly-traded software company's investor relations page. We're pulling down the 10k. We're feeding that 10k into ChatGPT. We're asking ChatGPT to identify what their annual R&D spend is, what the top three initiatives for their R&D department or technology teams are. We're utilizing the annual R&D spend as our core. Then you can take this premise, and it scales all the way down to a 50-person startup. It's super powerful when you have external factors or data that validates it.

Our core premise is, if you have a team of 100 — let's just use a small example. If you have a team of 100 developers making $100,000 a year, you're spending $10 million a year to build your product, to build features that make your customers successful, to widen the moat from your competition, to move your business forward. That is the backbone of a technology-centric enterprise. It's investing in your people to build these amazing products. Our message to these executive teams is that, right now, that $10 million spend is often being managed in a very anecdote-driven, non-scientific way. It's people raising their hands saying, "This team is underwater. This team is spread too thin." And so without revealing too much, we've created some assessments and some collaborative exercises with our customers that help the CTO, the head of engineering, the CFO, all get on the same page around that core message, which is Jellyfish is going to be a force multiplier to what's often a very, very large spin relative to almost any organization. We'll do proof of concepts and technology validation that just helps clear a very low bar as it relates to moving the needle 1%, 2%, 3%, 4% on that annual number. It's still pretty significant.

Enabling Internal CS Teams

Alex Kracov: It's a very cool story and a very smart way to do the ROI analysis with the ChatGPT there. I love that. I'll think about how'd I do that for Dock ourselves. So the Jellyfish sale, they're not getting their hands in the product before they buy at the top-down sale. You're doing the pitch deck and buyer enablement thing. But then, I imagine the handoff between sales and customer success is really important. Because then, it's like, okay, sales team sets up the outcomes. Then you're handing it off to the customer success team to actually go help them achieve those outcomes and get it all set up. How do you think about enabling the CS team internally to make sure the customer hits their goals?

Ben Solari: We still do quite a bit of POCs. To your point though, they're mainly focused on de-risking the investment. It's not like they're fully configured or trained. So it's not like a developer tool where this is already used, and it's like a consumption model in terms of how you scale it or when it gets handed off to CS. But we are often plugging in the systems and using that to support the business case. But as it relates to the handoff, it kind of goes back to that challenge I mentioned with DataRobot, which is the breadth of use cases is wide. That's quite exciting. It's exciting to the company, because you're like we can sell to everyone. Or in Jellyfish's case, we can solve all of these different use cases. It's exciting to the customer because they don't want to buy a point solution for every problem. You don't want to buy one thing for your forecasting and scenario planning. You don't want to buy another thing for your cost capitalization reporting. So they like the idea of a platform that can help them solve all the problems.

I'd say, what we're working on now and what is vital to the success from a customer experience perspective, as well as scalability and efficiency perspective for Jellyfish is to work with the customer in light of all that excitement, and hone in on a less is more focus about where they want to start. And so I really think you're still speaking in outcomes, but it takes discipline from the seller and a lot of trust that you build with your champion and the customer to almost slow things down or pull back on the expectation setting and say, "Listen. I know we can do all these things for you, guys. That's why you're buying Jellyfish. It's that you eventually want to do all these things. But for you and I to get off on a good foot here, for us to have a quick win in the next 30, 60, 90 days, we've really got to hone in on one thing that is going to be important to you, important to the business. Give us the cultural momentum, organizational momentum to build on top of that with these other expanded use cases. But we've got to just pick one thing to start."

That can be challenging, because there's multiple personas involved in a sale. Frontline managers want one thing; executive wants the other. But definitely, I'd say, establishing that focus and then that continuity from the handoff. I think we're still trying to slow ourselves down and slow the customer down, because it's easy to get enamored by the potential there. And so we're coming up with some plays, coming up with some focused implementations that narrow the scope and trade off kind of breadth versus depth initially.

Partnering with Marketing

Alex Kracov: Then moving back up towards the top of the funnel — you touched on that a little bit that jellyfish was like 100% outbound, and now it's like 70%. Shout out Kyle. He's doing a good job — I'd love to know what's your relationship like with Kyle Lacy and the rest of the marketing team. How do you work with a marketing leader, and how do you two partner together?

Ben Solari: What marketing has done really well for us in light of what might on paper they call it the 70-30 split, they have prioritized and done a really good job of providing my team with collateral that we are using to both bring people into our funnel and to accelerate through the funnel. So I'll give you an example, five board slides for CTO. We built a template for engineering leaders to start to build around in terms of what they show up to a board meeting with. And so we made a PowerPoint. They can edit it, put their logo on it. It's probably been one of our most highest performing pieces of content organically. But it's not like a CTO, like I mentioned, is necessarily going out there searching for that. My team will use that piece of collateral in their outbound messaging. Hey, do you mind if I send you some board slides that other CTOs are using to communicate their teams to their board and their C-suite? On paper, that's an outbound meeting. But that's driven by some very high-value, high-touch collateral that marketing is producing.

And so I think this executive-centric sale has changed a little bit when you think about attribution. It's certainly like Kyle's goals are pipe goals. He doesn't care who puts their name on it. Same with me. Kyle and I have a shared pipe goal. We know what our revenue targets are. We have a good understanding of what pipe coverage we need going into a quarter, and how much pipe that we have to generate this quarter to give us that coverage. So if pipe coverage is low or pipe gen is low, there's really very little finger pointing or stepping away from the problem as it relates to. Because I know that there's more to the story than that attribution, and he knows that too. So we're both trying to be held accountable to certain targets within that split, but it's very much like a shared problem. Or when we're hitting our numbers — which, knock on wood, things are going well right now — marketing is sharing in our successes as well.

Alex Kracov: I think that's the exact right mentality. I know at Lattice, we always said one team one dream. It was sort of how we thought about marketing and sales. Because the buying journey is just not linear. It doesn't actually look like that marketing funnel that everyone looks at. There might be a marketing touchpoint, and it goes to outbound. Then it goes back to marketing. It's all over the place. And so it's a mess.

Ben Solari: It's so hard. As a marketing leader, it's so hard. We've got these outbound to inbounds where the team is making a bunch of noise within an account. That CTO is not necessarily going to want to take a demo with a sales rep off the bat, so they'll send someone. It's like, was that an inbound meeting or outbound? So I think the lines are definitely getting more blurred when you think about it, especially as you move up market. But I'm thankful that Kyle and team, Kyle came in with that approach. I think I'm embracing it and also bearing the fruits of their labor with a lot of the sales success here. Then it's a snowball effect, where we sign up customers. We get to then do case studies with them. Those case studies fuel more marketing content and community. So yeah, it's been good. Though it's easy to say when things are good. It's when the going gets tough, that's when the one team, one dream gets tested.

Alex Kracov: That's when the reporting starts.

Ben Solari: I know. Yeah, we've been fortunate. Always being tested but just focused on scaling at this point and scaling up that function. He's only been there for a little bit less than a year, I think.

Advice for Selling Technical Products

Alex Kracov: I'd love to end today's conversation with just a couple of questions around career advice. I think where I'd want to start is, like, you've spent your career selling into technical personas. First, data science. You're selling AI and machine learning stuff at DataRobot and now selling into engineering leaders at Jellyfish. What advice would you give to other AEs who are selling technical products that are a little over their head? How do you think about preparing yourself to be able to sell to those personas and those types of products?

Ben Solari: I'd say two things. One is, you don't necessarily need to know the tech as much as you need to know the problems that your tech is solving for. If you can't wrap your head around the problems that your tech is solving for, then it might be a red flag that the problem itself is not robust enough. If you can't get it, there's often a possibility that it's not just you who doesn't get it. And so you don't necessarily need to know the inner workings of the tech or be an expert in machine learning or dev tools. But when you're interviewing with a company or when you started a company, the best reps that I have seen are digging into the case studies. They're regurgitating customer stories and getting customer stories down.

Then just through osmosis, if you get yourself enough at-bats, that's the other thing. Even through learning through osmosis on the tech side, whether it's through your SC or a more seasoned rep, you still need to have these conversations to learn. And so it's probably like set yourself up with strong prospecting and outbound skills. Trade notes with people who are doing it well. Make sure that that's a skill that you always have that you're sharpening and doing well with. Because conversations and the volume of conversations can open up so much more development, let alone hitting your number. Then the second thing is, focus just as much or more on the customer stories and the basic problems that you're unlocking or solutions that you're enabling as you are the tech. Those are probably the two main ones.

Alex Kracov: Well, thank you so much for such an awesome conversation today, Ben. I think there are so many good nuggets in here when it comes to outcome-based selling around technical products. I think I have a lot to learn from i t. I'm excited to listen back on this episode. If people want to reach out and find you, or interested in even buying Jellyfish, where can they find you?

Ben Solari: Yeah, is the email. Shout out to Kyle. We just got a brand-new website.

Alex Kracov: That's great.

Ben Solari: Check out the website too, Yeah, Alex, I appreciate you having me on. I'm excited too. I think Dock is a great product. Offline, we should talk about that handoff process that you slipped in there.

Alex Kracov: I'll give you a demo, yeah.

Ben Solari: Thank you.

Never miss an episode.
Subscribe now.

Thanks for subscribing! We'll email you when we publish an episode.
Oops! Something went wrong while submitting the form.