In this episode of Mastering CS: Candid Leader Insights, Irina Cismas sits down with Andrey Tirel, Senior Customer Success Manager at Jellyfish, a platform that helps engineering leaders see how their teams spend their time. Andrey has been in customer success for 10 years and has seen the function from every angle: early-stage startups, acquisitions, giant companies like VMware, and Snowflake before most people had heard of it.
He shares what CS looks like when your customers are CTOs and VPs of Engineering, how to handle the build versus buy objection with a technical audience, what good adoption actually looks like beyond login data, how he uses Glean and AI to build collateral and analyze data that used to require a data science team, and why relationships are still the sticky sauce that no AI can replace.
What You’ll Learn
- What Jellyfish does and what the Senior CSM role looks like across a book of 20 to 30 enterprise accounts
- How Andrey handles the build versus buy objection with engineering teams
- What good onboarding looks like at Jellyfish and how it has evolved over time
- How Jellyfish measures adoption beyond logins and what broad versus narrow usage signals
Key Insights & Takeaways
The build versus buy objection is really a maintenance cost conversation. Anyone can build. The question is whether you want your engineering team spending resources on internal tooling instead of the product they were hired to build.
Good onboarding has an asterisk. Getting the right stakeholders in the room at kickoff, configuring to real use cases, and following adoption data to know who needs more enablement is what actually constitutes a successful onboarding.
ROI is subjective, like Netflix. Everyone defines value differently. The job is to make sure the right people across the organization are having that conversation and that you have data to back it up.
Page views tell you more than logins. How deep someone goes into the product and which parts of the app they use regularly is a much more meaningful signal than whether they logged in.
Podcast Transcript
Intro
Irina (0:05 – 0:27)
Welcome to Mastering CS Candid Leader Insights, the podcast where we dive into the world of customer success with industry leaders. I’m your host, Irina Cismas, and today I’m joined by Andrey Tirel, Senior Customer Success Manager at Jellyfish, a platform that helps engineering leaders see how their teams spend their time. Andrey, I’m really happy to have you here.
Thanks for joining!
Andrey (0:28 – 0:29)
Yeah, of course. Pleasure to be here!
What the Role Looks Like and Who the Customers Are
Irina (0:30 – 0:39)
Before we dive in, give us a picture of your day-to-day, who are your customers, and what does the work actually look like day-to-day?
Andrey (0:39 – 1:35)
Great question. Jellyfish is an engineering management platform, software intelligence platform, AI observability platform, depending on how you want to look at it. So being a CSM here means you are in the middle of it all.
So you are dealing with your existing book of business, with CTOs, VPs, down to even individual engineers at times. So your week looks pretty crazy at times, but at the end of the day, you’re dealing with your champions, your sponsors, the buyers of your platform. Our typical segment, depending on the segment, typical book of business is somewhere between 20 to 30 customers.
So you’re dealing a lot of this with your cadence calls and with the risk management internally, with executive teams on both sides. So you are kind of at the heart of it. And I will say what makes Jellyfish unique is that you don’t have salespeople that are pressuring you to do something.
That’s something you see in CSs where sales need something, and you need something back. Here, it’s really a true cohesive partnership.
Irina (1:36 – 1:40)
And right now, what’s the one thing you are trying to move the most?
Andrey (1:41 – 2:44)
Honestly, it’s always adoption, right? Adoption of any product. We’re always pushing adoption.
But also the thing about Jellyfish, maybe specifically, while the company has been around since 2017, we’ve been a series of startups since I’ve been here since 2012. So we have not had to raise additional capital, but we’ve also had to not only go from being the leader in the space, which we still are, right? We created the space back in 2017, but now the problems are different in engineering.
So as the market has shifted, I’m sure you’ve heard of all the wonderful AI tools like Cloud and Cursor and everything else. And now it’s easier than ever to build software, right? And now how do we measure that, right?
And that’s something that we’ve been solving for the past two years and continue to go deeper in because at the end of the day, engineering is transforming. As we know it, we’re going through a little bit of a renaissance moment. So now we’re at the heart of coaching executives and giving them industry best practices and sharing our research that we publish publicly.
I can certainly share that information after this podcast. But the idea there is we’re helping engineering make better decisions with data they never thought would be a topic when they started their engineering careers.
How to Handle the Build Versus Buy Objection with a Technical Audience
Irina (2:45 – 3:07)
I’m super curious because you mentioned something that I also see in the CS space, particularly the build versus buy. How do you mitigate this? What’s the objection handling part?
Or how do you convince the technical side that in some cases, buying is still better than building it on its own?
Andrey (3:08 – 4:40)
Yeah, you hit a nail on the head, right? Anybody can build, right? You and I can build software in some way.
The question is, can it scale? How reliable is your data? End of the day, anything you build has to have the best data available.
So that’s part of the problem. The maintenance costs, the objection handling would be something along the lines of you have to maintain it, you have to have requirements gathering, and is your team, you can call it platform engineering, you can call it a DevOps team, you can call it an internal tooling team, but is that what your team engineering team should be doing? Do you want to waste your resources or use your resources on building internal software?
And that’s a philosophical question in some ways. And the answer to that is, in some ways, yeah, you think it’s a one-time investment, right, one to build, but people don’t realize there’s an AWS build behind it, there’s maintenance, there’s the changes you’re going to undertake the next year or two as a company. So how do you keep that updated, right?
You can build agents that can kind of do that a little bit, but then you’re spending money on agents and you’re now getting the expertise, you’re now getting the proprietary technologies. So you’re building for now, you’re not building for the future when you have a platform like Jellyfish, I think we actually have a visual library, how our UI has changed over the years. And I think I actually use this as part of training for newer CSMs, because again, people would look at our platform and say, hey, look, the product is hard to use here.
It’s like, do you remember what it looked like before? So again, it’s eye-opening for some folks. So that’s kind of innovation you get with a platform like Jellyfish, because we’re a platform, we’re going to innovate, we’re going to listen to our customer feedback, and we’re going to go faster because we have AI as well in our engineering teams.
What Good Onboarding Looks Like at Jellyfish
Irina (4:42 – 4:48)
Tell me, why does good onboarding look like at Jellyfish? What are you aiming for in the first few months, and how do you know when you’ve got there?
Andrey (4:50 – 6:43)
In this, I’ll say like, there is always an asterisk to all of this. Good onboarding means the executives are on board, so we had a good kickoff, we had alignment, we got the right stakeholders in the room initially. We’re configuring to their requirements; we’re prioritizing use cases.
Our product, especially as we’ve matured our platform, it’s pretty quick onboarding. We can get it set up quicker with less change management than ever before. Just to give you a perspective, when I first started here, it could take six to months to get somebody onboarded, and that was hard, right?
Because on the one year contract, then you have half the time left to prove value, and you try to prove value as the ship is being built, right? So it is hard in some ways. So we’ve drastically improved that part of the experience.
A good onboarding means for us is we got the right rollout adoption guide. We know who’s going to use a product, we understand the cadence that’s expected, we understand who the platform is for, right? And then we back it up with data.
So are those people using it? Are they enabled, right? Do they need more enablement?
Is the executive buyer, sponsor getting their results? So again, this is that continuous cycle. So they’re able to say we’re getting ROI in so many different words than us saying we see ROI this way, because I think, and this might take us to another question, ROI is subjective.
I think that’s what every company will say. Because if I think about ROI personally, outside of work, what’s the ROI of Netflix, right? We all have Netflix, but what is the ROI of that, right?
I get streaming movies, but how many friends and family members do we have that pay for a subscription like a Netflix, like a Prime, but don’t take any use of it or some advantage? So yeah, come renewal, you pay every month thinking you’re going to use it next month, next year, right? But it isn’t subjective that ROI, and there’s going to be people say, hey, I watched 10,000 hours on Netflix, I get value every month, right?
And again, that’s similar to a thing to be self-software. So we want to make sure the right people have those opinions and we’re having a forum across the organization.
How Jellyfish Measures Adoption and What Moves the Needle
Irina (6:45 – 7:01)
You mentioned working on adoption and being the things that it’s on your desk as we speak. I assume that this happens past onboarding. How do you measure that part of the adoption and what are the things that are moving the needle on this area?
Andrey (7:02 – 10:31)
Yeah. And so there’s a lot of things we can do to move the needle and adoption has always been, I’ve been in customer success for 10 years, but you always want somebody to adopt. And for Jellyfish, we don’t charge for seats.
So people logging into the platform, there’s no upselling involved, right? We want everybody to take advantage of that platform as much as they can. And again, the power is in the data, right?
And data-driven decisions. So the value is your engineering engineers having better one-on-ones. You’re actually improving your team morale, which is, again, how do you measure that?
It’s a separate topic. We can give you some clues to that and some data, but improved metrics, end of the day, are we moving quicker? Are we getting better at running our teams?
And we have the data to tell you how your engineering teams was before and after. So we can show you our line of products. So if you’re adopting it and using metrics and data-driven decisions, we can see that in your adoption data.
Today, we don’t qualify people to power users versus casual users, so things of that nature. We break it down into, hey, do we see continuous repeat cycle, right? So seeing people, we don’t care about logging data either, because that’s not good enough.
We look at something called page view. So not necessarily time on page, but we’re looking at how deep are they going into the product. Are they opening the Jellyfish on a regular basis?
What kind of parts of the app are they using? So we have some pretty good granular data that we can see. And so what good looks like is consistent usage across weeks and months, looking at a good broad data.
So example B, if somebody is only looking at one piece of the product and they’re only using it for that one thing, that’s narrow, right? We want broad adoption, right? So we need to get in front of that person or represent some collateral, find a way to get in front of that persona and see what exactly they’re using it for and ensure they are aware of the rest of the product can do.
So those are examples of how we can drive it. I’m one person though, right? So one of the challenges we all have, and the AI is making this more easier, right?
I have 20 to 25 customers right now. I’ve had as many as 40, right? So let’s imagine I have 25 champions, I have 30 champions, but then that’s usually multiple that you have executive sponsors, so managing all of that all at once.
That’s just at a high level, let’s say double it to 50. So 50 users I’m managing in some way, shape or form. They all need different things.
They have executives, right? They have different pain points, but this is not even touching the end users, which I think has been honestly a topic I’ve been thinking a lot about and AI is helping us get there. So how do I reach that end user?
I’m not in a scaled or a low touch team, right? I’m managing enterprise accounts. So how do I get in front of those people?
And we can build some automated campaigns and emails. But, you know, Irina, I don’t know how much you work with engineering leaders yourself today, but they don’t like email very much. That’s what we’re always finding out is they don’t read email as much as more than they have to.
We have Slack channels, right? We can take advantage of Slack, but then there’s customers who are on teams, which we can’t get access to, right? So there’s always like, how do we reach our audience about our way?
Slack, I think is the greatest invention in engineering we’ve ever seen. I personally love using Slack with my customers because it cuts the noise, reach it down to next to zero. So now I can get in front of those people and we can encourage almost a 3D feel like, hey, we have a joint Slack channel.
I monitor it. We have other people monitoring it. We’re not going to use this as a support channel.
That’s not the intent. But this is our strategic channel where we talk about product releases. We talk about new features.
And I can DM you, you can DM me if you have something specific that we want to cover. So in that adoption pinpoint. So if I identify a persona and they are in Slack, I’m going to go after them one way, shape, or form and go connect with those folks, understand what I want them to do.
And maybe I find a champion along the way and things like that.
The Operational Infrastructure Behind CS at Jellyfish
Irina (10:32 – 10:39)
What is the operational infrastructure behind all of this? What platforms are central to how a CS actually runs day to day at Jellyfish?
Andrey (10:40 – 11:29)
Yeah, and it’s lots. We have a CS Ops team. Yeah, it’s not very large, but we have CS Ops team.
So our main platform is our CRM. I have my opinions about it, but only that’s part of the problem, right? Because you need proper data for any CRM.
On top of that, in the last year, we’ve added Glean. So again, that Glean is our AI tool to harness customer data. We have Tableau.
We have our obviously our own product. And then we have Slack. Those are the main thing, email, right?
I think that’s table stakes these days. So I would say the primary tool that I personally benefit from the most is probably Glean. My big thing is it doesn’t have access to all the data because we have customer data.
It’s a little bit sensitive. Our data science team has to do a little bit of cleanup before that data is available to a platform like Glean. So again, it’s something that we’re working on.
But Glean has been a huge value addition as the world of AI changed, especially in the last year and a half or two.
How Andrey Uses AI as a CSM
Irina (11:31 – 11:44)
Where has AI landed in your wake? No, I’m not referring into how your product, how Jellyfish uses it. I’m more into what are your specific AI use cases as a CSO?
Andrey (11:45 – 13:15)
There are endless use cases for AI. I think we have. So from creating a collateral to pulling data that I need, right?
So I can go into Glean and it can build me a good, maybe not perfect presentation, but it can build me a PDF. I can ask it to analyze a bunch of adoption data, right? Say, hey, here’s all the data that we have.
It’s hard to get it in, but I can load that. Tell me what I’m missing. Here’s what I think is happening.
So I can treat AI as an industry expert, right? In our own technology, what we think that adoption looks like. I can ask it to do mini benchmarking sessions.
Say, hey, this is an enterprise company. Maybe throw the logo at it. How does their adoption look compared?
Then build me a chart that shows you how our best customers are adopting Jellyfish versus how they have adopted Jellyfish, right? So I can build collateral on data that has been historically impossible to build without pulling data science. So that’s one use case.
Of course, you have the stable stakes one. I think we have a lot of new features coming out. Great, how do I present this?
Give me a talk tracks, right? So things that would be CS enablement in a historical context. Still important, but guess what?
I can do my own. I’ve been in the theater long enough. I understand the product probably better than most other people who are newer to Jellyfish.
But even I myself, okay, how would an engineering leader use this? What would be the positioning of this kind of data, right? Sometimes that’s missing, right?
Because products, hey, we built the features. Here’s the thing. Who’s the audience, right?
How are they going to take it? How are they going to use it? What ceremonies can these people do?
So again, I can use AI to shape some of those narratives and talk tracks, especially around some of our newer features.
The Habit That Has Carried Andrey Through Every Role
Irina (13:16 – 13:27)
Before Jellyfish, you moved through a lot of different technical companies. Is there a bit a way of thinking or something you learned the hard way early on that still shows up in how you run accounts today?
Andrey (13:29 – 15:47)
There’s absolutely a learning in every company. So I think this Jellyfish would have a… So my career has been two bookends right now.
So I’ve been at Jellyfish four and a half years. I spent three and a half years at a company called Intralinks. But in between, I had a small startup that got acquired by FIS.
I did Snowflake. Before anybody knew what Snowflake was, I did Lacework, which was, again, another acquisition. And then I did VMware, which is a giant company.
And I did about a year in all those other companies. So you see different working models, right? I happen to see firsthand, especially in the early days of CS, for me, what the CS means in different companies is a very different thing, right?
So you have to learn by the fire a little bit, right? And I’ve seen the good, the bad, and the ugly. But also that’s the reason I think I’m here at Jellyfish for four and a half years, because I can certainly go do this again.
But for me, I look for culture, I look for people. End of the day, in terms of how I run my book of business, the product has to be exciting enough for me. So this is an exciting space.
I love that where we’ve been a foundational kind of new technology company, right? That’s something that wasn’t there before. Solved a problem, actually, that I ran into when I was doing one of the smaller startups, where I could use Jellyfish as a CSL, which is crazy, because it’s an engineering platform.
But that’s what we had when I was at Amount. So end of the day, I think what grounds CS is relationships. AI can replace a lot, right?
And it can do a lot, right? There’s always this topic of AI is going to replace humans, right? And sure, there’s these pieces AI can be good at, but I don’t see AI replacing customer success, right?
We are the sticky sauce on top of a good sticky product, right? We make customers see value. AI can help us and enrich us in how we have those conversations, but we’re the humans behind it all, right?
So no CTO wants to talk to AI all day, make the decisions. It’s not a very comforting world to think about. I just talk to machines all day and sit in front of my laptop and don’t interact with anyone, just run agents all day.
So I think we’re that human relationship connection. That’s my special sauce. I love building relationships from CTOs down.
Easier said than done, but it’s been at the heart of everything I’ve done across all these companies. You can’t solve all the problems, right? Relationship cannot solve a product issue, cannot solve a support ticket, but I know how to deescalate and I know how to build those right connections.
And end of the day, that’s a cornerstone of what we do in customer success.
Irina (15:49 – 15:56)
Andrei, this was a really interesting conversation. Thanks again for taking the time to talk to me today.
Andrey (15:56 – 15:58)
Okay, my pleasure, Irina. Thanks for having me.
Irina (15:58 – 16:04)
To everyone listening, thanks for tuning in. Until next time, stay curious, keep learning and mastering customer success.
