SaaS Interviews with CEOs, Startups, Founders
Can Mode Beat Looker? 140% Net Dollar Retention and 600+ Customers Say Yes!
18 Oct 2021
Chapter 1: What is the main topic discussed in this episode?
I'd say 150 is best in class. Are you around there? Well, that'd be a little bit of a stretch. Okay. We won't push further. Between 110 and 150. All really good numbers. Yeah, we will be. We'll be closer on that to 150. You are listening to Conversations with Nathan Latka, where I sit down and interview the top SaaS founders, like Eric Wan from Zoom.
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My guest today is Derek Steer. He's the CEO and co-founder of Mode. Before joining Mode in 2013, he was a member of Yammer's analytics team where he led sales and marketing analytics, drawing upon his experience on the monetization analytics team at Facebook and his background in antitrust economics. Derek, are you ready to take us to the top? David, thanks for having me back. You bet.
We recorded exactly almost one year ago today. So I'm not going to repeat stuff there. But for those of you that have not heard of Mode before, give us the quick sort of 15, 20 second. What is Mode doing? Yeah. So we make data analysis software. We aimed it primarily at analysts and data scientists to make them much faster at delivering analysis to their business.
And in doing so, a big part of what they do is share with other people in their business. And so what we've ended up building is something that works for your whole company, a little different model than traditional business intelligence, but a lot of companies that are really forward-thinking in the way they use data, like
Lyft, Twitch, DoorDash, you name the folks who are really doing it well, use Mode to run their businesses. Now, you told me a year ago your team size is 120. And pre-call, we were talking about some changes you made to your AEs and BDRs and SDRs. So what's the total team size today? And then let's dive into the sales motion. What changes have you made? Yeah. It's hard to keep track of.
We held relatively steady last year because big year of uncertainty, a lot of shifts in customer base, that kind of thing. And We came out the other side really great and have been having an awesome year so far. We are at a high 160s now, team size, and really trying to get over 200 as fast as we can. Why is that the goal? Oh, lots to do.
I mean, the short answer is the goals get higher and higher in every department in the business. But I would say I put it in two general places. So there is a standard late-stage company go-to-market scaling thing that happens that just requires bodies. And we can talk about the BDR AE structure, but that's one of the places in particular where we found that we can win by having more people.
The other one is we've got a really technically complex product. And it used to be that engineers would join our team and they would kind of say like, wow, it seems like this team has done a lot of work. Like we've really produced a lot of stuff with a pretty lean technical team. How many are on the team?
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Chapter 2: What is Mode and how does it serve data analysts?
How many engineers today? Ooh, I think the EPD org is in the like 60 person range at the moment. Actually, I don't have the exact. You've grown that then. You've grown that about 20 then since we last spoke. So we had a pretty lean engineering team for size. And I think that's right. We've grown it all over like a little bit product. You know, we've grown like we have an EPD ops team now.
We added a whole management layer that I think was missing before. We really learned how we have a new engineering leader who he runs engineering and product and design and arrived in, I think, April of this year. And so we've been steadily growing it behind his leadership and have pretty aggressive plans to continue expanding it.
Just to get more product context here, but also get into your head a little bit about pricing and scale, you sort of have like three, I think, three sort of key products, right? Your SQL editor, notebooks, which touch on R and Python, and reports and dashboard. You then choose to sort of package those in unique ways across three separate types of pricing plans, studio, business, and enterprise.
But then you also have like the Helix data engine, for example. How do these things all work together to make up a pricing plan for mode? Yeah. I'll back up and talk about just some of the basics of how we think about it. But the first thing is, it's really one product. It has a bunch of features that are tied together, but the important thing, and this will always be the case, right?
I don't ever want to separate SQL from Python in mode because the point of the product is that you can move seamlessly between the two. If we break those into separate a la carte items, I don't want our customers to have to choose. And a big part of the reason is, Folks don't realize exactly what they're going to use before they start using it.
And a lot of what we've done with our interface enables people to level up into jobs that they didn't know they could do or hadn't done before. In the Python world, if you want to set up Jupyter, the standard tool people use for Python for data analysis, right? You set up this Python notebook on your desktop. The fastest way to do it is using a product called Anaconda. It's still hard to do.
It'll take you half an hour with someone who's done it before sitting next to you pointing out how to do it. Whereas if you want to use Python to do a very simple... The example I always use is Median. So SQL is a really bad tool for calculating a median. In Python, it's trivially easy. It's just one line of code.
So if you want to do that, you can just go look up the line of code on Stack Overflow or wherever, click on the word notebook in the mode interface, and we will save you a half hour of setup and just take it directly to the notebook so you can get a medium. So that leveling up is really important. And we've always thought about that with respect to price.
Most of our industry prices like editors versus viewers. And we get some pressure from our customer base to do that because we have customers that have 500 editors, 6,000 viewers, right? Is that your biggest account, 500 editors, 6,000 viewers? I don't actually know the stats on the biggest one lately, but the last time I checked, that was about right. That company is probably bigger now.
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Chapter 3: What changes have been made to Mode's sales structure?
And I think today we might charge even more. So we've gone through a bunch of models. I think the big thing, so you asked about Helix and what that does to pricing. And think about Helix. So Helix, for folks who have listened to my previous appearance on this show. Thanks for calling back, by the way. We always appreciate return guests. So I guess you enjoyed that.
Hopefully you enjoyed the first one. I did. That's why I'm here. I love that. Thank you. I like the directness of the show. Yeah. So the thing about Helix is it's an in-memory data store. And what it allows for is I'm an analyst. I do some analysis. I pass it off to whoever it may be. Let's call it our head of revenue ops. She can slice and dice.
She can do any kind of quick visual analysis operation without writing code. after I've shared this thing with her. And part of what allows that to be fast and performant is that we've got this in-memory data engine that can take up to like 10 gigs of data. So way, way, way beyond what Excel can handle or like a desktop Tableau or something like that.
So I can put like a really, really big data set in there, excuse me, for her to go analyze. And Helix is what's going to enable that. But the challenge for most of the company is that Helix adds a new cost for us. So that cost is really big for some customers, smaller for others. And we try to limit it in a couple of ways, right?
The way that aligns to our cost, because it's possible for someone to just put us underwater on our contract with them pretty easily. Mm-hmm. So the way that we manage it is we have two axes. The first one is throughput. So we say, okay, how much data do you even pull into Helix over the course of a month? We're going to give you a range for what you're allowed and then... Measured by what?
Like gigs or what? Yeah, exactly. Just storage amount. Yeah. right? It's all transitory, right? So it's coming in and out of memory. But the question is, how much do you put into memory over a given period of time? So that's one. But it turns out to be really hard to reason about when you're just doing analysis day to day. And it's tough. We have to do something like that.
But it's just, I think people don't have that in their minds, like, okay, day to day, I'm doing this. And that's, I mean, it's bad for them because it's hard for them to reason about how much they're using. And it's kind of bad for us too, because when we're selling to a customer and we say, Hey, we think you're going to have high throughput. Their answer is almost always like, well, let's see.
Yeah. Let us start off on a small plan first. So what's the second throughput. And the second one is what the second one is the size of an individual data set, right? So how big of a, of a data set can I, can I pull back? Um, and we soft limit it at different tiers of service. So, you know, the lowest one, um, I forget exactly what the lowest one is. Wait, Derek, I don't think I understand this.
So the first one's throughput. I don't understand the difference between that and how much you want to pull back. Isn't it saying you're measuring gigs through the system? We are, but we're measuring how many gigs in one shot versus how many gigs aggregate over a month. So it's like per project-based pricing versus monthly pricing.
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Chapter 4: How does Mode's pricing structure work?
Because it is the natural part of the workflow. And I guess anyone... Can you share that? Can you share how many editors are on the platform today just across all the customers? That's not stuff that we share publicly. Okay. So... Is there a range you can share, Derek? Like above a million or above 10,000? It's above 10,000. It's not in the huge millions.
I mean, it'd be tough to get a million people with... Well, you could have viewers. Couldn't you have viewers? Like editors plus viewers be well above 100,000? I think the thing that's probably interesting about this, or at least kind of sheds light on the way that the product works, is the ratio of editors to viewers.
And it varies from company to company because some companies have bigger data teams and so forth. But in generalā¦ you're looking at for most companies, right? Even just a potential set of people, you're looking at between 15 to 20 viewers per editor at like a mature mode organization. And it's just based on team size and what's happening. Right.
So, so, you know, what, what I think about our customers. That's more aggressive though, than the one you told me earlier with your big customer, we had 500 editors and 6,000 viewers where I get 10 to one ratio. This is a 21 to ratio. Yeah.
So, so 10 to one ratio is like kind of what we see at the, of well, 10 to one ratio is editors, but not necessarily people who are analysts and data scientists. Right. right um so so that's going to be like tech forward companies if you were to look at you know someone who is not a super techie company it's going to be much higher like we have um
We have some media companies, for example, that operate differently where it's like a lot more viewers. And in fact, like the viewership is so large that they don't even do the viewing in mode. They build a separate web portal and like the data team will slot stuff into the web portal. It's like, you know, a totally separate thing dedicated just to viewing.
Like there are no editor features even in that portal. But you're still charging for those? We do. Yeah. Because those are people who get value. And because the price is low enough, I mean... It's not $3K a year for viewers, is it? Sorry? It's not 3K a year for viewers, is it? That's just the editor price. Well, the thing is that by charging for viewers, we're able to lower the editor price too.
So we charge one price across everyone, which is 300. It's a 10th of what I described to you, right? So $25 per user per month is the list rate. And then especially as we get into these big, like many thousands of employee companies, we negotiate contracts that have stair-step seat pricing, as I think most SaaS companies do this. Totally standard. Really standard. Yeah.
But you mentioned 10,000 editors. I mean, if you have that 20 to 1 ratio, I mean, you're talking then you've got maybe, I don't know, 200,000 viewers. So an ecosystem is pretty healthy here. I mean, yeah, there's real usage of our product. It's
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Chapter 5: What impact does Helix have on Mode's data analysis capabilities?
Yeah. Yeah. The, um, the actual numbers are slightly lower than that. I think a lot of that is because we're, well, we're, we're like leveling people up from our BDR program. Right. So like a lot of our sales reps are entry level folks when they start. Yeah. Got it. So that might be like a one 50 K on target earnings against the $750,000 quota or something like that. Yeah.
But I mean, we also have a guy who's like a few years out of BDR territory who did a 600 kick quarter. Wow. Very cool. Okay. Very cool. That system makes a whole lot of sense. I'm trying to think if there's anything else here that I'm missing and we can learn from you. I mean, look, one thing I will say is you've decided this is going to definitely be a VC-backed company.
And once you're on that path, you got to always be on that path, which means you have a funding announcement about every 12 months. We're 12 months away from your last funding announcement. Is there anything you want to share with me? No, not now. But what I'll say is, look, the market's nuts.
And there's just a dance between how long do we think it will be nuts versus how much further can we get to raise our valuation. That's the calculus that I'm doing. And I think the further we can distance ourselves from COVID, like I just told you, we have a high churn here. I think a lot of people did. This year is great, right? Yeah.
you know what i want to do is say hey we've got continued really awesome growth both from new and existing business like the year we're doing awesome like let's write this out for a couple more quarters before we raise because we're just going to look that much better. So that's kind of the math that's going on in my head.
But what I'll say is, we talked last time about bootstrapping versus raising and that kind of thing. And what I told you is that I believe in the austerity a little bit in the early days. I think that the hunger from having less cash makes you more focused and productive. And there becomes a point where that is no longer true.
And the two things for us that point to like, actually, I'm very excited to go raise more money. And I think that the business needs to be highly capitalized. Just tell me that amount real quick. If you do go raise amount, like what will the next amount be? It'll be like $50 million, $60 million, but break off $100 million. I can't. I mean, it just depends on what's out there.
Like, honestly, I think the swings are so wide. I mean, look, so Fivetran is in our general space and just raised a behemoth 500 something million dollar round at a $5 billion valuation. Are they at your same revenue level? They're a little bit ahead of us, but not much. Okay. Interesting. So you think your next round could be at north of a billion-dollar valuation? I don't know.
I would love that, certainly. I don't think it'll be $5 billion. I also didn't think my chance would be either. I was going to say, the press speculated back in 2019, your Series D, $23 million, was about a $100 million valuation, a little north of a $100 million valuation. Your Series D, $33 million. Was that at around a $300 million valuation?
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