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San Francisco, California, United States
Mode is a UK-based fintech company that offers a digital banking app and other financial services. The company was founded in 2014 and is headquartered in London. Mode's app allows customers to buy, sell, and hold Bitcoin, as well as earn interest on their Bitcoin holdings. Additionally, the app provides a variety of traditional banking services, such as a Visa debit card, direct debits, and faster payments. Mode aims to simplify and modernize traditional banking by offering a range of financial services within one app, with a focus on transparency and low fees. The company is also committed to sustainable and ethical business practices and has implemented measures to reduce its carbon footprint.
Last updated
In 2023, Mode Analytics's revenue reached $89.6M. The company previously reported $40M in 2021. Since its launch in 2013, Mode Analytics has shown consistent revenue growth.
Mode Analytics reached a $300M valuation in 2020, set during its Series D round.
Mode Analytics has raised $79.9M in total funding across 7 rounds, most recently a $33M Series D round in 2020.
| Year | Round | Amount | Valuation | % Sold | Source |
|---|---|---|---|---|---|
| 2020 | Series D | $33M | $300M | 11% | |
| 2019 | Series C | $23M | $103M | 22% |
CEO
Derek Steer is the CEO of Mode, a new startup enabling data scientists to share and discover work. With Mode, analysts are more productive and connected, both publicly within the data community and privately within organizations.Before co-founding Mode in 2013, Derek was an early member of Yammer’s Analytics team. There, he built tools to identify strong leads and customers at risk of attrition within a pool of freemium users. Derek has also worked in antitrust economics and on the Monetization Analytics team at Facebook.
| Question | Answer |
|---|---|
| What's your age? | 38 |
| Favorite online tool? | - |
| Favorite book? | - |
| Favorite CEO? | - |
| Advice for 20 year old self | - |
Mode Analytics serves 750 customers.
Mode Analytics employs approximately 51 people as of 2026, down from 168 in 2021, including 32 sales reps that carry a quota. It serves 750 customers that rely on its solutions.
Mode Analytics generates $89.6M in revenue.
The CEO of Mode Analytics is Gaurav Rewari.
Mode Analytics raised $79.9M across 7 rounds.
Mode Analytics has 51 employees.
Mode Analytics is headquartered in San Francisco, California, United States.
all right uh let's see if this works okay cool uh so I'm Ben I'm gonna talk about finding products Market fit with data um a quick bit uh about myself so I uh if you want the sort of this talk is a lot of stuff drawn from a Blog this is kind of the vibe of that blog um but kind of more importantly for this talk I am one of the founders of a company called mode mode is a bi tool built for data teams it looks like this uh it also looks like this this is kind of the visualization draggy drop ebi stuff of our customers uh so uh these are some folks that use the product today but obviously this is not like what mode was like in the early days this has taken some time for us to build uh back in the early days this was actually Mode's first product it didn't look like all that fancy stuff it looked like this and instead of having any customers we had this these were the other two co-founders of mode one of whom is here and talking in like 40 minutes later so check them out um but anyway so the point here was generally like where mode is today obviously is not where we started uh and there were some like rough moments along the way that took us some time to to get there uh and so this is actually a message from the one of those other co-founders uh back when things weren't doing so great uh that said hey you know what I don't actually think things are looking so good uh I don't think that mode has product Market fit right now uh and so this was like this was like what the world was like uh back before some of the stuff that we built and some of these customers we had um and and this was like kind of that that world so this is kind of a very basic graph what product Market fit is like prior to product Market fit everything sucks you get message like this from Josh uh after product Market fit everything seems good um and so the question really to talk about here is how did things get better like what is it that that got us from the left side of that graph to the right side of that graph uh and how do we actually figure out where we were before uh to get to to where we are now um so let me talk about two main reasons uh and two kind of big things to think about as you're trying to do this uh one of those reasons is figuring out what people like this is kind of obvious but want to talk about like some ways that you can do this a little bit uh and second is figuring out when to actually start scaling um that finding product Market fit isn't just a matter of saying hey what do we need to build but it's also timing it right uh and so I want to talk about that a little bit too so uh to get things started we can talk about this Finding Traction bit uh and to talk about that actually I'm not going to talk about mode I'm going to talk about a different company that y'all are all probably familiar with but aren't necessarily aware that you're familiar with uh which is a company called the not um so they actually had an office I think in this building uh for some period of time um but what the knot is is it's a it's a website for building websites for your wedding so if you've had a friend get married uh and you go to websites like this like Danielle Love sharon.com or Whatever uh that are like your friends wedding websites uh the knot is the the vendor that hosts these uh and so when people want to be able to set up these websites they can go to the knot it helps them set up things like Registries it helps them set up you know here's how to rsap for all these things that kind of stuff uh and the nod is is the service that does that and so then that was one of Mode's early customers um when they first started using mode they actually recently just released a mobile app uh so they had this kind of main website builder but they also wanted to have a mobile app for people to be able to do the various things they they need to do to plan their wedding and that kind of stuff and that was that was this app the 133rd most popular app in the lifestyle section of the App Store um when they released it it didn't like do so hot uh it was something that was not terribly popular um it was a really important Initiative for them to figure out hey like this is what we think of as the future of our business we need to be able to figure out how to get people to to do this sort of stuff on mobile as well as on the website uh and so how do we build that and this app didn't did not have sort of product Market fit when they they initially launched it their first set of experiments were basically like build a bunch of features go look at how they're used on something like Google analytics see if they work if they don't try again and kind of like rinse and repeat that it was basically just iterative process of build features see what happens build features see what happens build features see what happens um it didn't really go anywhere that the app basically stayed stuck where it was and it never actually got popular and so what ended up happening was one of the the folks who was the the like head of their data team uh started poking around how people were actually using the existing app uh and he made this chart this was a chart that he made in mode uh the specifics here kind of don't matter that much but basically you can imagine each one of these kind of further out Rings is is people sort of progressing through different stages of of the website so the green bar might represent the home page the purple bar might represent the registry page whatever and so it sort of tracks the different patterns of how people are moving through through the site and so he made this chart just kind of see how this Behavior looked uh and the thing that he noticed was this down here uh which was this very kind of strange Brown Spike of like hey why are people continuing to come back to the same same page over and over and over again they're consistently doing it like what's the deal with what's going on here seems like there might be some pattern here to pay attention to and when it turns out that was this page it was a page that was like a countdown for your how long until your wedding uh that it was a single page that said hey you're going to get married in 86 days in 85 days in 84 days um and people kept coming back to it day in and day out like taking screenshots of it and then like sending it to their friends or posting social media on social media or whatever and this was like a kind of small thing that they built they didn't think it really mattered when they first built the app but and putting it together they realized like this was how people were actually using it this is the thing that people wanted to do and by looking at that that chart and kind of following the behavior of what users were doing they actually started leaning into this behavior and built out what is this which is where it looks better you can add these photos it was easy to share on social media and actually the app started to take off because of it they really leaned into the way that people were using it rather than trying to force them to do do the things they originally wanted to do another example of this is a company called bourbon um this may be an example some people are familiar with uh this was like Foursquare for bourbon you would go somewhere you would drink bourbon you would take a picture of it you would check in you say I had this bourbon I liked it um turns out nobody really wanted to do that uh the the people who like were doing this actually didn't really care about the check-in stuff they just like take pictures of their bourbon uh and so the app was like hey wait a minute maybe we'll just have more picture sharing stuff instead of bourbon chicken stuff so they changed their name from bourbon to Instagram uh and that's now like how Instagram actually got started was again this was not the point of the original product the point of the product was to I guess like have social networks around bourbon uh but they saw this other kind of latent behavior that they followed and so it ends up happening a lot of times in early stages of products and something like this uh we'll spend a whole bunch of time building these features and you'll have like some small gimmick in the corner and the small gimmick in the corner is the thing that people are actually excited about uh and so when you're like looking for product Market fit this is actually probably the best thing you can do is pay attention to like what are your girls in the red dress uh that everybody's actually distracted by when you're trying to get them to to build the thing or do the things you want them to do um the part of this though that's kind of important is that people have probably all seen this this is basically the same as this of like okay you can design a thing and people will do something else the thing is like in SAS apps there is no actual physical path that you can see you can't actually see like the worn down grass on the dirt um you have to be able to track this in some other way this is something that is only sort of materializes in your data it only shows up in stuff like this um and so really like the the job of us and as early stage Founders or early stage products folks is to pay a lot of attention to this to try to do the best we can to kind of recreate that that grass pattern uh in the data that we have and the behaviors that we see to be able to see what are those places that people are actually like cutting the shortcuts in in the path uh and the more that you do this the more you pay attention to this like the faster you can find these paths uh and that's a really big part of of what this job here is is kind of more than anything as Andrew Chen says who's a guy who talks a lot about product Market fit stuff um really the job here is to reduce the time to do this eventually you will see these things if you build the app long enough and build your products long enough some things will emerge that are the kinds of behaviors that people follow somebody will just tell you them but the more you look at the data and kind of the more you're looking for these anomalies the faster you'll be able to find this and the better off you'll be uh and there's a reason that speed matters which is there's actually sort of another line on this chart uh this isn't the only line that really matters uh there's also a line here of like how much money you have left as a business um and so like if this takes too long this money line basically goes to zero uh it also if you like start to try to scale the business too quickly this money line will go to zero a lot faster which kind of leads to the second problem of okay once you sort of find these interesting patterns once you find these things that that might be places of sort of markers or product Market fit how do you decide actually when to scale uh and for some people actually this is the bigger problem than finding the interesting kind of elements of what people like uh so this is a one of the kind of the famous growth VPS from Facebook who basically said actually the bigger problem that startups have isn't necessarily finding these things but it's identifying when they have it rather than like hey we had an early signs of something we think that's good enough uh we're ready to scale when you're actually not not Where You Think You Are uh and sort of use another another kind of mode example uh this is an actual graph of our marketing spend sort of anonymized by month uh and with some numbers wiped out um but you can all kind of see the problem here which is this giant Green Mountain of wasted money uh this was also the time that Josh was like Hey things don't look so good right now um and basically this was us finding thinking we'd found product Market fit and we hadn't that we thought we had found something that worked we thought it was time to scale to spend more money on ads and all that kind of stuff uh and it turns out kind of obviously we didn't um and so then our like money left line if we draw that over this would look like this uh the zero is not the same like zeros down here but still uh that's not the chart that you want to see so uh the question then is like okay how do you know when you're ready like how do you actually know that it's time to start spending more on marketing or scaling the product as opposed to to you know kind of continuing to develop and iterate my initial answer to this like probably around the time that we started spending all that money on on uh marketing was like it's probably just retention if people keep using your product it's probably good like that's good enough right that's what other people say um and so people have probably seen charts like this this is from mixpanel uh basically you know like the number of people who come back after a certain number of days uh if this number looks good then great like you're ready to scale if it doesn't don't simple enough right uh so not really so the example I want to call out like why this doesn't actually work I'm going to draw a couple examples from from two companies that you all may have heard of uh one is greenhouse and the other is front so Greenhouse is an applicant tracking system basically if you like have job Recs on your website Greenhouse will power those they will then help recruiters sort of Usher candidates through the job process so they'll move people from One Stage to the other candidate goes from an interview to an on-site to an offer things like that and so it's a management system for for that and for recruiters uh front is a shared inbox uh it's basically for support teams to have a single inbox where people can email and multiple people in the for the company can then respond to that email and see like the conversations that everybody's having uh I'm going to I'm going to put these things using a graph that looks like this which looks like a huge mess right now but I can explain what it means in a much simpler version which is this um so basically these this is like looking at users in the days they used a product uh and so if you look at this first user for instance what this is saying is the user used the product on day one on day two and on day four or on day two you can say that users one three four five all use the product but number two didn't um or like if you look at individual day you can say hey this block represents user one using the product on on day two and what you can like pretty obviously kind of calculate from this is retention rates um so on day one all five of them used it so it's a hundred percent four out of five use it on day two so it's eighty percent kind of and so on out of where it's like 80 60 60 40. um so basically like if you then Zoom back out of this you could get a chart like this that shows the different patterns of how people are actually using a product um and so this is a hypothetical it's like me making it up of what Greenhouse May well look like I don't actually know um but but you could imagine a pattern that looks like this for for a greenhouse uh where people use it sporadically like recruiters aren't logging in every day people aren't progressing necessarily through uh recruiting pipelines every day some of these people might be hiring managers that are only coming in when they actually have interviews things like that um so they have this kind of like sporadic pattern of of returning but on this chart if you look like 90 days after like three months into this actually 97 of these users still come back so in the time between 90 and 120 days there's actually 90 percent of these 200 users uh are all still engaging with Greenhouse which seems seems pretty good um so this is front this is another version of this chart could represent the same thing you can imagine these blocks mean the same thing uh in the front's case it's very much intended to be a daily usage app it's something that's supposed to be like you use it every day it's like your email um if you use your email every day or most people probably use their email every day they aren't using it once a week or something like that uh so in this case though if you draw the same line because of this usage pattern after three months only 12 percent of these people are actually still using from um so it's a very different sort of retention pattern a very different group of people are using it uh and in this case you'd probably look at this and say like that doesn't look so good so looking at these two charts uh on this top one from Greenhouse you'd be like okay great now it's time to expand it's time to scale let's go hire more sales people let's like Jack up that marketing spin chart all that stuff like we have product Market fit it's all great um for this chart be like no we're not there at all we have some few people who like it we've sort of found maybe the the like path in the sand or the path in the uh in the grass that people are following that a handful of these people really really gravitate towards but clearly this is a product that does not seem terribly sticky yet uh and does not have product Market fit the problem here is if you just look at Daily retention rates for these two things they will look exactly the same uh that the way this math works out is these two things can actually show the exact same retention rates from on like a day by day basis and really what that means is like that metric even if that metric seems like seems like a really good one it turns that may not actually show up in simple metrics like retention and so the point here is uh what Steve blank says Steve blank is the author of uh forceps The Epiphany which is like the kind of one of the sort of Canon of Silicon Valley kind of stuff basically like these sort of one size fits-all approaches like just use retention or just use certain metrics do not work for all startups you have to really think about what it is that that your startup needs um and so what do you do instead of that basically Define like what fit looks like for your very specific product what does it mean for someone to use your product in the way that you intend them to use it or the way they should be using it uh and how would that actually show up in these sorts of retention metrics so for instance for Greenhouse uh that may not look like just this three-month retention or daily retention um it may actually be things like how many hires do they make um this is actually so both going to have the reason I use Greenhouse in front for these two things is they actually published a study a bit back with with one of these VCS saying hey these are the metrics they use to decide like when to scale and this is actually what they said so it was like okay if we have a number of hires made if that's staying steady that's product Market fit for us that's a sign that we're ready to go for front um they want user to be engaged early and often again it's not a sporadically used app it's an app you're supposed to use every day uh the point here is to to use it early and often and so their definition of product Market fit they're things that they looked at to see when it was time to scale was how often are people sending messages in the first 30 days that gets them past like the tire kicking phase of them logging on and sending a few messages in the first couple days shows that you kind of wanting to stick around but it doesn't have to wait for months or years to see if they actually use it so this was the metric they used and so if you're thinking about these things I was trying to say okay like what's the metric for my business how do I how do I actually do this um that kind of help brainstorm a handful of ideas from other businesses or people who thought about this one if you are a product that needs to be used every day something like what front is makes sense um things like daus over Maus which effectively measures how many days in a month people are using your product uh it's really important a high number there matters um so 50 they're using it say 15 days a week or 15 days a month is a really important kind of threshold similarly if you're like a SAS business that's selling B2B obviously in that case it's usually follows much more like a work week kind of pattern and so people are using your product uh more than four days a week like how many percentage of your users are using it four days a week shows hey These Are People For Whom the product is really sticky and it's something that's really valuable if you if you want competition but then something like a high MPS is a pretty good sign of product Market fit because people have a lot of choices you need to know that people like actually like your product it's a thing that they want to use it's a thing they will choose over the other Alternatives they have NPS can actually be a pretty decent measure of that if your product is a vitamin and not a painkiller so if your product is something that's kind of nice to have you want to actually see that people will be upset if they take it away so this is actually a metric that I think superhuman used the email client um where obviously people have email clients that's very much a vitamin not a painkiller to have that so they wanted to check and see if people be disappointed once they started using it if you take it away like will I be upset about that and if they're not then people could probably walk away from pretty easily and the last one if you're a product of sort of an unknown demand so you're trying something new you have something that you don't actually know that that uh people will pay money for in this case you actually should see them paying money you want to see things like Revenue growth um you want to see things like LTD do you ever cack stuff like that that demonstrates people actually put money where their mouth is they're not just going to say yeah it looks great we'll kind of keep using it we'll actually pay for this and find real value in it so the last point I want to make on this uh before kind of ending things with questions is this chart I've shared this chart a number of times uh I will say this chart is actually a lie this is not what product Market fit looks like at all um it's a nice little graphic or whatever but in reality as you're building businesses product Market fit looks much more like this where it's this kind of iterative thing you find it and then you move to a different Market you try to Market to a new segment um you expand to selling to different types of customers and you no longer have product Market fit with those folks and you have to find it again um and so it's a it's like a sort of ongoing Journey uh to do this this is not something where it's like you are pre and post product Market fit forever um you are pre and post product Market fit for particular types of buyers for particular products for particular segments and all those sorts of things so uh these two things it's not actually like these are the two things you have to figure out is what people like and what when to scale it's actually you have to constantly be figuring out uh what it is that people will like next uh the people that you're trying to sell to what's important to them next and figure out when to scale further so like how do you keep moving up that chart um when you're ready to move to a new market to a new product to a new segment all those sorts of things it's an ongoing part of of continuing to build a startup so uh that's real stop uh if you want anything more from me this is the Twitter and then some stack and you could also just search for my name on LinkedIn it'll show up or you can go to bend.work um that's it that's not that doesn't say questions uh cool I'll stop there and if there are questions I can answer questions all right thank you
All figures on this page are taken directly from interviews or are estimates from public sources and proprietary models. Not financial advice. Read full disclaimer.
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Valuation
$300M
2023 Revenue
$89.6M
Customers
750
Funding
$79.9M
Avg ACV
$119.5K
Team
51
Founded
2013
| Year | Milestone | Source |
|---|---|---|
| 2023 | Mode Analytics Hit $89.6m revenue in December 2023 | |
| 2021 | Mode Analytics Hit $40m revenue in September 2021 | |
| 2020 | Mode Analytics Hit $19m revenue in October 2020 | |
| 2017 | Mode Analytics Hit $5m revenue in December 2017 | |
| 2015 | Mode Analytics Hit $500k revenue in December 2015 | |
| 2014 | Mode Analytics Hit $50k revenue in December 2014 | |
| 2013 | Launched with $0 revenue |
| 2017 | Series B | $13M | - | - |
| 2015 | Series A | $7.5M | - | - |
| 2014 | Funding round | $2.3M | - | - |
| 2013 | Funding round | $500K | $5M | 10% |
| 2013 | Funding round | $550K | - | - |
| Year | Milestone | Source |
|---|---|---|
| 2023 | Reached 51 employees (July 2023) | |
| 2021 | Reached 168 employees (September 2021) | |
| 2020 | Reached 120 employees (October 2020) |
okay hey everyone uh so my name is Derek steer I was the founder and CEO of mode um I'm going to be talking about really one thing which is uh the thing I think you need to focus on above all of the problems that you may see out there in your businesses um but you know I had the baby title so so you're all here uh my background really fast so I started my career in economic Consulting I studied economics in school I went on to work in Tech at Facebook first and then at Yammer both before and after the acquisition and in those jobs you know I was doing analytical jobs with those companies and uh I noticed something which is that both of them were building internal tools they kind of were fed up with the stuff that was like available out on the mass Market wanted to work in a different way and so they built stuff on their own and if you looked around at like all the other companies uh you know Google LinkedIn the ones that were like really good in the way that they work with data they were doing the same thing they were building their own tools uh and so my co-founders and I you know after having built these tools at emmer kind of scratched our heads and thought like okay that's weird maybe we should just go start something where we build these exact tools because we think everyone's going to want to work like Facebook so let's go build those tools and give them out to our audience right let's be the people that enable the rest of the world to be as analytically competent as the best companies so in 2013 we started mode to do that I did it with two other guys one of them is sitting in this room somewhere he just gave a talk on another stage uh on the on the CTO stage like 40 minutes ago so if you were lucky enough to have been in that room you're getting a lot of mode content today um and then another one was an engineer um so so Ben is my my co-founder my fellow Economist neither one of us know how to code and then we had one guy who did which is going to be kind of a theme of this conversation so about four years ago oh and last thing I should probably mention is the reason it says former CEO is I left mode fairly recently so I did a nine year run as a CEO got the company to Mid eight figures in revenue and then uh you know step down to to head off and work on a new thing um but I want to talk about about mode because it's where I learned a lot and about four years ago I started keeping this dock for myself and this is a screenshot of the actual doc this is the actual title uh lessons learned the hard way um kind of tells you a little bit about the place I was in when I made this document uh at least when I started um and I don't know if you can make any sense out of these notes that's not really what I want to talk about today I really just wanted to bring this up to say there's a lot of stuff that came to my mind like once I started thinking about like all the different times I had some like problem that felt existential that I really needed to go solve you know I would go like write it down or refer to something on this list and kind of use it as my mental check of like okay am I thinking about this the right way um and so you know as I was thinking about putting together this talk for today uh it occurred to me I could talk about all of these things you know like and these things probably many of them seem obvious like solve a real problem yeah okay of course right solve a real problem yeah we gotta solve real problems right vitamins painkillers all that stuff you want to be solving something that actually matters to people okay great we know that uh you're always selling good culture versus winning culture I actually think the the previous talk about um about you know remote not remote to me it really is about like what is the flavor of culture that you are creating remote not remote is it good is it winning is it both um those are different things in my mind there's all these sorts of things to keep track of and I got to feeling like being a CEO was this really difficult Balancing Act where I was trying to keep 25 plates spinning there's about 25 items in this document now lessons that I learned the hard way in reality there are more lessons than that but I I wanted to you know kind of keep this list for myself partially so that I could go reference it just to make sure I wasn't missing a plate that had to be spinning and the more I thought about it the more I thought like that doesn't matter like this is actually an impossible task you can't keep all of these things in your head all the time you're destined to fail and instead there is one thing right I thought about all these things it's like okay if I keep all these plates spinning what's the impact that that has and for me as as an analyst by trade my job for a long time was solving problems you know like and and my boss you know the CEO previous companies that would put me on the hard problems and I got to go crank on them and that was what I got really into and so that's where I naturally gravitated but it turns out that this is actually what I should have been doing as CEO right you can screw up a lot of things as long as you figure out what you're doing right and do more of it and if you take one thing away from my talk today this should be it and so the rest of my talk what I'm going to do is I'm going to tell you an example of how I got this stuff wrong and hopefully through that some of you will be able to identify oh I'm making some of those mistakes I should probably refocus my efforts on what's going right and go do more of that and then before I get into this example um just as sort of like an internal test I want you to think about you know big successful companies you've worked at or your friends who've worked in some of these hyper growth companies and you know what you hear from them about what it's like internally right because in the companies I've been in where I look around and two-thirds of the people arrived at the company in the last year it's an operational Horror Show it always is right the fastest growing companies seem to always be in the news like you know now there's a we work show and there's an Uber show like these companies were such horrific disasters internally that they had TV shows it's like that good and and yet they're so successful really success these are like the most successful companies so why well is that they found something that was really really really valuable and they just kept doing that thing at the expense of a lot of other things um the example I want to talk about today is blogging and actually I'm going to talk about uh my my co-founder Ben who is in this room just coincidentally um these are two blog posts that he wrote one on the left is the first post on the mode blog ever and you can see it was in September 2013 which is just after we started the company so the founding date of the company was August 23rd 2013 and then you know within like a couple of weeks Ben was cranking out blog posts and do a lot with like the roles of the founding crew you know we um we didn't really understand how valuable would this would be but we had this hunch since Josh was building the product Ben and I were trying to find ways into the market I worked on a SQL tutorial to teach people data analysis skills and Ben started blogging um and this was back when people liked Nate silver and they were reading for economics and stuff like that uh and and so that's what he was producing was content that felt like that it was high quality analytical content and the goal was to get people to think mode is a company that is associated with great analysis these people understand what great data analysis looks like and I could probably learn something from them and maybe someday I'll buy some software from them that was the thinking right so we were a long way from having a product in the market and we were starting to build that up and the way we did it is you know Buzzy posts about the MTV Video Music Awards um this image on the right is uh is from Ben's uh it's from Ben's now blog which he has rebooted I'll get to that in a minute uh you can see the real through line here is pop stars I don't know how he I don't know how he ended up with that but um but I'll get to the old and the new blog and how we ended up with this so um an interesting thing happened so we got this really great feedback early on from analysts and data scientists that this blog was really valuable to them right um in a more of a fun way it wasn't teaching them how to do their jobs it was just like really great and they would say we we want to read it like we're looking forward to it when is the next mode blog post coming out I can't wait to see what crazy subject Ben is going to tackle next on this blog um and so we had him do more posts until mid-2014. when we launched our product and you know what happens when you have a product in the market is you need people to use it and so you start thinking about that as one of your primary goals right all of this stuff around hey the analysts really like us we're doing a great job with our brand it's kind of fuzzy so how do we turn that into something that makes money you know for us right it takes a lot of money to build data analysis software it's really technically complex we're raising money from VCS right from the beginning and so we needed to show some appreciable growth in order to go and do that next round so that's the pressure and the pressure led us to start making different decisions and you can see on this chart that Ben's blog production kind of leveled up so this just shows cumulative posts right been kind of leveled off and other people wrote blogs I wrote blogs we had our marketing team write blogs we had some guest people write blogs and they were different kinds of things right there was stuff like this you know 12 python data visualization libraries it's popular so these are the four in order from top to bottom of the slide the four most popular mode blog posts of all time in terms of just the traffic to the posts um shouldn't be too surprising right it's for the same reason that the conference organizers asked me to stick a number in the title of my talk here uh it's that people attend that people view that it gets them excited right the other key feature of these posts is that they rank on Search terms that we knew our audience was looking for so there's a deliberate strategy here very different from what the blog was originally doing and it worked we got that kind of Evergreen content but there were two issues with it the first one is that the people who used to be excited to come to the mode blog who would show up and read every single new article you know they would go one week two weeks maybe three weeks reading like product announcements or other stuff that just wasn't what they wanted right they were there for the entertainment and then they would just stop you know you take people away from the thing that they really want they're going to stop coming and so that utility diminished pretty substantially and quickly the other problem was it turned out that even though these things were driving traffic it was kind of a drop in the bucket because we had this SQL tutorial that you know now does hundreds of thousands of unique visitors a month these blog posts are like a drop in the bucket compared to that and don't convert particularly well right so if anything you could say okay the blog is helping us to get you know um information about new products out to our existing customers to some new customers but we certainly lost that feel that we had early on meanwhile this is what Ben did okay this is this is Ben's this is what Ben's LinkedIn should look like um but LinkedIn doesn't let you do it this way so he started off as a blogger um which I think sort of betrays you the real value but he started as a blogger uh then he ran support and why would why would someone like Ben run support um for the same reason that he did all of these jobs which is we needed someone to run support Ben is the hardest working person alive and he's also one of the smartest he's basically the smartest person in every room and so what you know when you take those things into account a lot of problems look like Ben shaped problems even though he doesn't necessarily have experience running a support team my faith in Ben to succeed at like a really like a really high level doing that job I had 100 faith that for every single one of these jobs that I put him in he was going to absolutely crush it so he did that we put him in analytics which was sort of natural fit because that's what he had been doing his whole career uh we made him the head of product because as we grew our engineering team we had this need for someone to run product we had after we hired someone to do that he became a product manager on one of the teams um and then in 2020 when I was raising money we didn't made him president and he ran all internal operations at the company and then after that marketing and then eventually he made his way back to blogger and then back to marketing and then back to blogger and you know he's just been doing all these things um these look like the kinds of problems that don't need to get solved like the way to to look at this slide for all of you each one of these titles represents a place where Ben performed well and didn't have a big impact not on the scale of the blogging and being out there in the community and this is the point that I really want to get across we felt like we just needed all of these things felt like crises at the time we have to have someone to run support and Ben is the only person at the company who could possibly do that job I don't know if that was really true I don't know if other than maybe president I don't know if you've been had to do any of these jobs and so we have to weigh that against what would it have been like if he had just been doing the blog the whole time so we get some sense of that actually because he started doing it again right in that first slide I showed you the two side-by-side posts old and new well this yellow slice is new we did an interesting thing so Ben said you know what I finally I think the company has grown you know we're 250 people we think we can uh we think we can afford from me to go and just do blog community and that kind of stuff and that's going to be really good for us we're going to get that benefit that we got way back in the early days and so that's what we did and he said you know I think the corporate blog is kind of gone right people don't go there for you know analytical content anymore they go there for the product announcements and that kind of thing and so in order to do this right I'm going to start a new brand I'm going to do this on substack instead and it's going to be brand new um and I supported him in that and I think it was the right decision because what it did you know data scientists we were sort of cursed with a very difficult Market um analysts and data scientists don't have phones so you can't call them don't do external work they spend all their time on slack not email so it's really hard to get in touch with them selling to them is hard and it turns out that the way that you sell to them and probably the reason we felt like the blog was working so much in the early days is that Community is the the one way in which they kind of organize externally so we started doing that again and the results were really good so what you're looking at here is a mention in the analytics engineering Roundup this is a publication if any of you spend your time on data Twitter uh then you know this right this is probably the most popular newsletter among our Market of data team leaders analyst data scientists and once Ben started writing again what you saw was was newsletters like this referencing Ben quoting him you know and talking about like how brilliant and often controversial his writing is over and over and over right it wasn't just that he was building a new audience which he was and very fast it's that every Friday when he posts a new post data Twitter is lighting up with conversation about that specific thing and it's getting included in every other Roundup then speaks at every conference he wants to speak at he's on every podcast that he wants to do and the result what what comes back to us is in sales conversations are reps tell us well I wasn't sure how we were doing in this deal and then I heard from their VP of data that she reads Ben's blog and thinks he thinks about the world in exactly the right way and thinks oh well if these people make a product that reflects these views on the data world then it's a product that I'm going to want to buy and that started happening pretty frequently the chat the challenge with this stuff is it's not as measurable as those you know listicles and clickbait blog posts right there we had a real clear sense of what the impact was here it's a little fuzzier right and so the question I think that everyone often has when they think about this particular thing right like figure out what you're doing right and do more of it the question that I hear people ask is how do you know how do you know if you have product Market fit how do you know if this thing's good how do you know if that thing's good and there are a lot of ways in which you can use metrics and in fact the talk Ben gave 40 minutes ago was about exactly how to do this with metrics but what I'm going to tell you is in addition to that you feel it every single person at mode could have told you in the early days that the blog post that Ben was writing were extremely valuable and every single person today can tell you that the blog post that he is now writing are extremely valuable and so when I look back on this and I think about what mistake we made as a company in that's on me I was the CEO what mistake I made was instead of letting Ben do this thing that was so clearly valuable I was running around trying to keep blade spinning solve all of these other problems and when I think about where the company would be today had he just continued that the entire time because it really approves right we lost a lot of the benefit and had to regain it we would be in a totally different place we'd probably be at the center of the data conversation at a much larger scale than we are today so that's my talk thank you very much uh again Derek steer you can find me on Twitter Derek steer or just email me directly if you have questions but appreciate this and good luck on your Journeys finding the thing that you do well and doing more of it [Applause] thanks man
Introduction hey folks 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 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 uh we make data analysis software uh we aimed it primarily at analysts and data scientists to make them much faster at delivering analysis to their business and in doing so you know 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 uh 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 lift twitch door dash kind of you name the folks who are really doing it well uh use mode to run their businesses now you told me a year ago your team sizes 120 and pre-call we were talking about some changes you made to your aes and bdrs and sdr so what's the total team size today and then let's dive into the sales motion what changes have you made yeah oh it's hard to keep track of um we held relatively steady last year because like you know kind of big year of uncertainty a lot of shifts in customer base that kind of thing uh we came out the other side really great and have been having an awesome year so far uh we are at a 160 high 160s now team size and like really trying to get over 200 as fast as we can um why is that the goal oh uh lots to do uh i mean the the short answer is the goals get higher and higher um in every department in the business but i would say i put it in two general places so there's a standard like late stage company go to market scaling thing that happens that just requires bodies and and we can talk about the bdr ae structure but that's one of the places in particular where we found that like we can we can win by having more people um 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 you it seems like 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 how many engineers today ooh uh i think the epd org is in the like 60 person range at the moment actually i don't have the right you've grown that then you've grown that about 20 then since we last spoke so 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 um you know we've grown like we have an epd ops team now um we had a whole management layer that i think was missing before we really learned how we have a we have a new engineering leader who uh 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 uh buying his leadership and 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 you know in a you know the helix data engine for example how do these things all work together to make up a pricing plan for mode yeah um i'll back up and talk about just some of the the basics of how we think about it but the first thing is um it's it's really one product uh 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 you know 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 you know 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 right in the python world if you want to set up jupiter right this is 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 uh it's still hard to do like 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 like a very simple the example i always use is median right so sql is a really bad tool for calculating a median in python it's like trivially easy yeah 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 you directly to the notebook so you can get immediate so 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 like uh there we have customers that have 500 editors 6 000 viewers you know like is that your biggest account 500 or 6000 viewers who i don't actually know the stats on the biggest one completely but but the last time i checked uh that was that was about right um that that company is probably bigger now yeah yeah yeah did you just you're charging for editors viewers are free well that's what our customers want um no but that's not how we do it we charge the same price for every single person and oh okay look in our overlords history we've tried both methods um and when we when we split out editors and viewers the feedback we got was well like i've got this product manager who's like not really an editor they're not going to spend all their time in mode so i don't want like a full i want to pay like half editor price for them and give them access sometimes it's like well it doesn't work like that you know you can't have like a half-time license yep um the important thing is there's a total cost of ownership that has to line up generally with the market like this is a competitive market so we are beholden at least somewhat to what our competitors charge which would be what look or tableau these kind of companies or someone yeah those are the two that we see most frequently right so so like for you know if customers evaluating all three products they're gonna come in in roughly the same range and the question is how you get there and how it scales um the the challenge with editor viewer is the editor seats needs to be very very expensive and people don't like that like what the expense give me what's expensive well we did this we charged 3000 a year per editor okay yeah and i think today we might charge even more so we've gone through we've gone through a bunch of models i think the big thing so we asked about helix and what that does to pricing um and think about helix so so helix for for folks who have listened to my previous appearance on this show uh thanks for coming back by the way we always appreciate your return yes so i guess you enjoyed the hopefully you enjoyed the first one i did this why i'm here i love that i like you i like the directness of the show yeah so so the thing about helix is it's an in-memory data store and what it allows for is i'm an 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 right she can slice and dice she can do any kind of like 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 we 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 mode the company is that helix adds a new cost for us so uh that cost is really big for some customers uh smaller for others and and you know we we try to eliminate it in a couple ways right the the way that aligns to our cost because because if so it's possible for someone to just make put us under water on on our contract with them pretty easily so the way that we manage it is we have two axes the first one is throughput so we say okay you 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 just uh storage amount yeah right um it's all transitory right so it's coming in and out of memory but but the question is how much do you put into memory over a given period of time yeah 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 it's tough we have to do something like that but it's just i think people don't don't have that in their their minds like okay day to day i'm doing this yep um and that's i mean it's bad for them because it's hard 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 gonna 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 exactly it's throughput and the second one is what the second one is the size of an individual data set right so how big 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 i don't wait dirk i don't think i understand this so the first one's throughput what's 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 uh how many gigs in one shot versus how many gigs aggregate over a month i so it's like per project based pricing versus monthly pricing uh the the throughput one is we just look so like there's a lot of ways that people put data through mode or the the the primary two are kind of ad hoc analysis like kind of one-off things you write a sql query some data comes back that contributes to your gigabyte limit throughput right um but also i write a sql query some data comes back if that data set is more than 10 gigs in size we won't load it for you all right right and and so we have we have what a pricing mechanism that is like how big is that data set right can you bring back one that is 10 gigs can you bring back one that's five gigs one gig and so forth so derek just to put all this in a beautiful little package for my audience tell me if this following statement is correct you are upselling your product based upsell is based off like sql editor notebooks reports and dashboards your usage your utility-based upsell is based off throughput or you know one project going through and how big that is and then you also have seat based upsell which are editors and viewers are those the three main pricing axes it's really just the two ladder ones um because because the the sql python r et cetera that's just the product right we're not we're not seeing that included in your free plan um the exception to that so actually why maybe the correction that i would make is from a feature perspective the way that we upsell is on standard enterprise stuff like octa skim yeah you know like sso right yeah yeah exactly right i mean real stuff which like you know i i i it is probably the best differentiator or the best uh segmenting feature right because like bigger companies needed smaller companies don't and it's actually funny i'm i i curse this a little bit right now because we are going through so we bought mode.com mo we were motivated i was going to say i was going to congratulate you on that congrats what about more or less than a hundred thousand dollars more more than a million less a lot less between there you guys have between 100 and we'll call it 500 thousand dollars there you go yeah that's that's the range um it's i actually feel pretty good about the price we paid that's not a bad i mean that's not a bad price for a four-letter domain name four-letter real word i feel like you know the real business in the big house on the corner now yeah uh now did you do that after the 33 million dollars here was a series d we did oh man well we did it after the round before announcing the round i see okay perfect yeah um which is the right time because like you don't want someone to be able to go look up the f the financing amount and be like these people can pay more yep the story of the domain is a really funny and interesting story um but to to put the to put the bow on the pricing thing um we mowed overwhelmingly grows with seats 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 uh that that's not stuff that we share publicly okay okay um so can is there a range you can share derek like above a million or above ten thousand it's is about ten thousand it's it's not in the huge millions i mean like it'd be tough to get a million people with you know well you could have viewers couldn't you have viewers like editors plus viewers be well over a hundred thousand let me uh i think that the thing that's that's probably interesting about this or at least kind of sheds light on the way that the product works is um 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 general [Music] 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 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 we got 10 to 1 ratio this is a 21 to ratio yeah so so 10 to 1 ratio is like kind of what we see at the of well 10 one ratio is editors but not necessarily people who are analysts and data scientists 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 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 still charge you're still charging for those we do yeah um because those are people who get value and and because the price is low enough i mean you know it's not three people charging for users is it sorry it's not 3k a year for viewers 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 tenth of of what i i subscribe to you right so so 25 dollars per user per month is the list rate and then especially as we get into these big like many thousands of company uh of employee companies we negotiate contracts that have uh stair-stepped seat pricing as i think most sas companies do this totally standard really standard yeah so but but you mentioned ten thousand editors i mean if you have that twenty to one ratio i mean you're talking then you've got maybe maybe i don't know 200 000 viewers so an ecosystem is pretty healthy here mode mode is i mean yeah there's real usage of our product um it's i think the important thing like if i were you know teaching a course on this or whatever my advice to other folks is we this happened all through the natural workflow of the product right like we attached ourselves to something that was inherently viral and and that enabled us frankly to trip all over ourselves like we introduced customer success as a department very late in mode's lifetime um and in fact the first true customer success leader who actually had customer success experience has been at the company for two years that makes a lot of sense yeah it's and this is an eight-year-old company all right so like we went for a long time without it because we just saw such incredible growth naturally and so what just um so obviously just talked about like number of editors but across how many logos now do you have using you i think you're at 600 last time we we Currently serving 750 customers spoke are you about a thousand number we have on our website that's still the number i'm going to give you interesting why have you stopped sharing that it's not that we stopped sharing it it's that we just do it at milestones usually i think sometimes the milestone is we made a new website sometimes the milestone is like about uh you know a big number so is the next milestone you want to celebrate a thousand customers we'll wait for that i think that's i think that's a very good sensible one we'll probably probably go out there you've got to be flirting you've got to be flirting with that right now yeah i mean you've got to be flirting you've got to be very close to that by christmas i bet well you know i will say like we had kind of an interesting year last year where we had higher than normal churn um and we we had a lot of companies so something about mode that is a blessing and a curse is that we come in alongside analysts and data scientists or alongside traditional bi rather right so so we serve the analysts and data scientists who a lot of times is using a looker or a tableau and like pulling their hair out they just hate it for their own workflow and so for us you know that's an opportunity and we say hey we serve you we can come in alongside but then we grow we grow to all of the other non-analyst non-data scientists just through this natural sharing mechanism that's like part of the analytics job so imagine it's a pandemic you got tightening budgets you're trying to figure out what you can cut and you look at your data stack and you say we've got two like bi dashboarding kind of products one that is optimized for everyone at the company one that's optimized for the analysts and data scientists and look there's a longer conversation here i i think mode actually delivers for the rest of the company in a way that's uh it's different and very effective and we've we've shown that we've been we've been tremendously successful in empowering entire companies over and over and over again but if the person who makes this decision is like ignorant to the process like if it's if it's like a cfo for example who who cares purely about the numbers and is like this one costs a lot it's optimized for a small team i don't understand their website because it's not aimed at me so i think we should get rid of it yeah that happened to us a fair bit like we had certainly our worst churn year ever last year as i think a lot what is that you turn more than 20 of revenue uh on a on a i think about this in terms of customers so not more than 20 of revenue we turn more than 20 of customers though logos uh yeah which which we by the way are we have gone way the other direction like we are 12 to 13 percentage points better on retention this year than we were here would you will your ndr will your net dollar return this year be well of like 110 percent way beyond okay wow that's great even even churning so even churning you know more than 20 of logos last year we still had net dollar retention above 100 because that's incredible so there's just because of the way that the product works right like we've never had net retention under 100 that's incredible well look if you've got i mean i'm seeing right now these public the valuations you're seeing in private public markets and sas even more than revenue growth the the ones with higher net dollar retention but even lower revenue growth are getting higher valuations than faster growing revenue companies so like ndrs like i'm seeing at least just the most important thing if you're raising right now would you agree well i mean i i don't i don't know about that i i don't know what's important raising right i mean feels like just hype honestly as anything that looks like it could be a breakup company's getting a mega round um and that's all your attention i think is an important thing that contributes to that yeah mode has just always been good like again it's not something that we had to engineer it was just an inherent part of the process and so you know it's been frankly quite easy like we'll do yeah i guess i'll just say we'll end the year way way way north of 110 like in a true best-in-class net dollar retention i'd say 150 best in class are you around there uh well that'd be a little bit of a stretch okay we can say between we can be in between we won't push further between 110 and 150 all really good numbers yeah yeah we will be we'll be closer we'll be closer on that to 150. that's great and what is um obviously you probably can't share like actual hard numbers in terms of the Monthly recurring revenue revenue but when you look at just your growth rate over the past 12 months on a percent basis what does that look like uh i guess what i'll say is this year's growth rate will be double last year's years uh so so if last year's growth rate was a hundred percent this year's growth rate will be 200 uh i'm hypothetically i'm not going to give you the exact you know but i'm i'm i'm just trying to translate the ratio you just gave like yeah that's that's the type of thing that i am saying i see got it got it got it okay cool um my best guess in terms of your revenue is like you've got to be you know in in 2020 you close somewhere around 20 million bucks in ar i mean can you guys break 40 million bucks in ar this year uh again i'm i'm gonna i'm gonna plead the fifth on that one but the i would what i would say is like we are i think the most important thing is and we didn't haven't talked about the bdr piece yet the most important thing is we kind of by accident discovered the thing that is going to carry us through the next several stages of growth um and it has been an interesting journey because like you're like okay no one no one can learn anything from where we are in revenue though it is interesting but like the thing that was really fascinating that happened last year um that has totally changed the trajectory of the business is uh we have we had a small bdr team that you know it was made even smaller by like personal life circumstances so people were sick they had family stuff whatever it may be and we in like you know october november of last year so almost a year ago we found ourselves in like a pretty bad situation with respect to incoming leads and my philosophy on this has historically been you know the inbound leads are going to be high quality leads we're like you know at the time we were a 95 plus percent inbound business we've added some outbound into that since but but not a ton right it's primarily an inbound business still and my thought was okay this this inbound business is basically going to close itself like people if they want to try mode they're going to try mode and and so we don't need to have a zillion bdrs to qualify them we we need enough but you know a couple is enough um it's very very wrong so going down to one bdr active at a given time was a big part of the cause of slowing that down and our cmo so correctly identified this and made the correction um our bdr's report into marketing so she said hey we're not going to make this mistake again we're going to over hire from what we think we need so we you know we had three or four but we're gonna go up to six seven uh and we did that in january we hired a bunch uh on board of them and then in q1 which for us starts in february uh oh my goodness things went the opposite way and we kind of realized like oh actually the leads we're getting are proportional to the number of people that we have who are even taking phone calls doing qualification now sending outbound emails that kind of thing so everything started to go the other direction and then we just have kept growing bdrs and it works like we just keep adding more we keep generating more leads like everything about it seems to be scalable and it's great not just because it's adding revenue to the business and feels like a thing that we can continue to invest in it's also great because our bdrs turn out to be the most successful sales people at mode quant derek let's try and this is great like like qualitative can we quantify how many bdrs right now on the team full time uh i want to say that our bdr team is 18 people and how many sdrs uh that's the same thing for us but we split it into we split it into like studio right so there are people who are like trying to upsell on our studio audience that we have corporate we have uh enterprise which is like a little bit more you know high touch for a given customer so we've essentially tracked it um and the the folks who are running that team you know this is a well hold on derek so try and keep quantifying for me so there's 18 there how many account executives do you have oh we have eight right now and one and how many not even doing new business really she's just doing uh kind of strategic upselling and how many account managers do you have you know working post sale um we do so so there's one for strategic we we've kind of split this interestingly between csn um and uh we'll add those together how many cs how many cs folks oh i don't know the answer to this this team has changed a lot it's been growing a lot recently like we just hired four new managers for this team oh wow more than more than what definitely more than how many i think that the cs team overall is 25 people which includes customer support and sales engineering the sales interview is pretty small let me repeat that back to you so you have 18 bdrs or sdrs you have eight account executives and then you have 25 more than 25 customer success folks i yeah look again customer success is inclusive of like support right we want support and solutions engineering and customer success all together do your cs reps have an expansion a dollar expansion quota they have to hit yes well they do they do for smaller accounts so basically we say like for for our smaller accounts there is a clean handoff between new business ae and csm um because it turns out that you can manage the relationship very directly there uh smaller companies tend to not need quite as much hand holding right like it becomes more complicated when we're like managing big training sessions and doing other things to drive engagement across like a a business of many thousands of people so in those cases right the enterprise csms are on the hook for retention but not growth necessarily and then we have sales people whose job it is to drive growth um and that's especially relevant in a in a company where like as i mentioned you've a big part of what drives our strong net dollar retention is just the natural like virality of the workflow right it's not even our product it's the workflow that we are attached to it makes sense on your aes the eight folks there do they all carry this standard sort of million dollar quota uh they're all at like five times ote to i mean that's like the benchmark that we use yeah that's totally extreme so your standard there it's usually like 200 000 on target earnings against a million dollar quota yeah yeah the um the actual numbers are slightly lower than that i think a lot of those because we're well 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 150k on target earnings against a 750 000 quote or something like that yeah but i mean we also have a guy who's like a few years out of you know bdr territory who did a 600k quarter wow very cool okay very cool that that system makes a whole lot of sense um let me i'm trying to think there's anything else here that i'm missing we can learn from you i mean look one thing i will say is like you Raised have you've decided this is going to definitely be a vc backed company and sort of once you're on that path you've got to always be on that path which means you have a funny announcement about every 12 months uh we're 12 months away from your last funding announcement is there anything you want to share with me uh no not now but um what i'll say is like look the market's nuts um and there's just a dance between like how long do we think it will be nuts versus how much further can we get to raise our evaluation that's that's the that's the calculus that i'm doing and uh i think the for the further we can distance ourselves from covet right like i just told you you know like we have a high churn here i think a lot of people did um this year is great right like 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 that's we're just going to look that much better so so that's kind of the math that's going on in my head but what i'll say is like you know we talked last time about bootstrapping versus raising and that kind of thing and what i told you is that like i kind of i believe in the uh austerity a little bit you know in the early days i think that the uh the hunger from having less cash makes you more focused...
Introduction hello everyone my guest today is derek steer he's the ceo and co-founder of mode prior to co-founding mode in 2013 he was an early member of yammer's analytics team there he led sales and marketing analytics drawing upon his experience on the monetization analytics team at facebook and his background in antitrust economics derek you ready to take us to the top let's do it all right so i want to jump right in with yammer how many people when you were building out these tools at yammer how many data scientists was yammer employing to work on this problem so we had when i arrived the company was probably like 140 150 people we had a team of maybe five analysts and then there was i forget whether it was right when i started but um my mode co-founder josh eventually split out to start a data engineering team that was responsible both for building out data pipelines and managing the quality of our data and also for building internal software that looked a lot like mode for us to do our jobs and that was something that a lot of folks were doing at the time is like they were hiring up teams of developers to work on these sorts of internal tools so what i want to get to now is i imagine you probably had a cushy salary at yammer when did you build up a conference say you know what i'm taking a risk we're going out we're going to build this and we're going to build it for everybody well uh the the cushy salary only came after the acquisition by microsoft okay um you know i the the the decision to go and start a company really had to do with the fact that i was waking up every morning thinking about this particular problem and going to bed every night and and when i talk to other would-be founders the advice that i give them is make sure you're obsessed because it's pretty hard and you know i think that obsession with making the lives of analysts and data scientists better is like what has gotten me through some of the challenges of just starting and running a business and so let's so you can actually can you share i always like to measure opportunity costs and those sorts of things i mean can you share what salary you gave up when you left uh well it's i'd have to go do some stock math because uh the my microsoft shares at that time uh were worth a fraction of what they would they're better now yeah yeah yeah if you look at the price now it was a seven figure annual type type of amount like but but i had no way of knowing that it was it was less than that i was actually getting paid really well at microsoft now you leave and some of your early investors are your yammer bosses so yeah so so walk me through i think you raised 500 000 from them how much of the company did you sell uh boy i don't remember the so so i guess it was about 10 right the valuation was like in the range of 5 million which we you know we were just triangulating around like kind of standard yc evaluations at the time um it was a pretty straightforward negotiation in fact um the you know we wanted to do everything really above board and so we we told our boss that we were gonna leave and we you know declared our intention to leave and then after leaving went back to david sacks and said hey we got this thing that we're gonna go do here's what it is we brought him a deck and on the second slide he was like okay yeah i'm in let's do it like like the it's fine the second slide was um it was github for data which which was like you know in in 2013 that was like an awesome pitch um but more importantly he had seen our work and and understood the value of what we did at yammer and that we could go and do it on a bigger scale and so he so so he was in and then it was just a very quick like maybe quicker than i was even prepared for a conversation around like like okay how are we going to do the deal like what's the what are the terms etcetera yep branching off a totally different direction some of your earlier customers you talked about were folks like twitch and lyft and you said in other interviews you start off as sort of a three thousand dollar per seat license but then quickly realized you're gonna have teams signing up for massive amounts of users and three grand a seat one scale what are customers paying you today on average per seat oh uh the way that we do it is is based on scale and we have folks who are buying you know four five six thousand seats we also have a piece of technology now uh called helix where we do some data processing on our end and and you know that costs us money and so we do we we have a cost that's associated with that so the way that we do it now is we have a platform fee and then a per seat fee um the list price on the seat fee is 25 per person per month okay and it you know it works like any kind of sas product where like we we go we we batch it so like you know the first 500 might be a certain price in the next 500 another one yep yep that's great and so walk me through how you learned from sort of like you know when you discount your pricing did you let twitch come off a 3000 per month seat down to your new cheaper pricing how do you handle grandfather accounts and pricing experiments oh boy grandfather accounts so so i think we've been through probably like seven different models that that vary a little bit i mean we we introduced freemium not initially but later and that threw a wrench into something so we had some people who decided they wanted to go freemium and some people we kind of grandfathered into pricing on a business tier um in general we tried to do right by our customers um that's really it and if you made an early commitment to mode and you want to keep doing it then we're we're going to try and and accommodate you um and i think i'm really proud of how we've supported some of our early customers who grow to be to be very big and i think that they're trying to do right by them has been a part of that Currently serving 600 customers and how many customers are you serving now today just logos not seats yeah uh it's around 600 this is okay the most recent thing that we published yep that's great and you know one of the things you have on your website is 50 52 of fortune 500 folks are using you guys i mean obviously there's probably significant expansion opportunity there to add more value to that cohort but eventually you've got to open top of funnel and move back downstream to get more customers and add more products and drive more arpu how do you think about that transition has it already started um i think the mode is atlanta expand business and and the the important thing to understand about mode is that we started off as a very analyst centric company that's my background and my co-founders backgrounds and that's the thing that we knew how to solve and maybe most importantly that was the hole in the bi space right bi generally as a category decides that the technical person's job is to specify the system right they build out a menu of metrics and then the actual analysis or dashboard building or whatever is going to happen by people who aren't professional analysts or like aren't necessarily super technical mode is about supporting the people who are technical like that's that's where we started at day one the where we've evolved and i think you know pricing is tied into this right so the notion of paying 3000 per seat per year is focused on delivering a certain value to analysts only right like that's a price you can command as a very critical workflow tool for one person but when the value is actually distributed across many people like in mode's case the value is not in providing some groundbreaking new way to do analysis what we provide is great collaboration around that analysis so the value is is uh experienced by lots and lots of people so a lower price spread across an entire audience makes more sense so when we think about the way that we expand you know that was a part of it was shifting from that pricing model to one that again is now like 25 percent per month as opposed to 250 per seat per month when i look at the way that we expand in the future it is about going wall to wall like you were saying okay well you know what's with 50 of the fortune 500 how do we how do we allocate resources between existing and new customers the the answer is we've always got to be bringing people in the top of the funnel but price isn't necessarily the most important thing for us there the most important thing is landing because we have a product that sort of naturally like just through the natural analytical workflow starts to expand the thing that we're the sort of path that we're on now and what we've talked about with our customers and a little bit publicly is you know once we expand once as a as an analyst or data scientist once you've shared your work with lots of other people at the company those people need to be served well too right like as a as an analyst your reputation lies in the thing that you produce and that you hand off to them right you are doing a good job only if you produce a good product and mode has to do that part of a good product is something that they can expand upon work further from and so what you're going to see from mode in the coming year is a lot more that's geared toward making the analyst look like a superhero by empowering their own customers previously like when we started all about empowering the analyst now empowering the analysts to empower their customers analyst is a superhero i want to come back to that in a second but first with this approach where you're sort of trying to democratize this sort of technology means a lower price point more people you know you started doing this i think i think more than 12 months ago what does your company growth rate look like over the past 12 months on a percent basis with this sort of strategy well you picked an interesting year to ask that question yep um you know i i think i we we tend to keep the like high level revenue and and the growth stuff generally within the company um but it continues to grow pretty fast and and be an exciting business to be a part of like we are we are in the tens of millions revenue range i know that's i never say derek you know you know i listened to the show so you know i'm going to push you on this i won't pin you down exactly but i know the show i read the show can i i mean can we put you in sort of a 100 year over year growth club i mean would you is that a fair statement yeah yeah i think so uh it's it's been a little bit lower than that this year i think in some in some very interesting ways um but it is yeah i think that's like the right range to put us okay well i mean look at the hot space right i mean we had frank bien on uh you know seven months before before you know the acquisition happened and i straight looked at him on that call and said are you an acquisition talks with google right now for billions of dollars and he said no and then smirked at the same time something's happening here right so you're in a very very hot space and and one you know on the flip side you know folks like you get all the attention where people don't spend a lot of attention are the folks that try to do what you do but then they fail for some reason and i talk to a lot of those folks and usually the reason they fail is they fail to identify a utility value for the data they're surfing like they don't enable their customers to actually deliver and become a superhero internally so let's now go to that sort of the business right you know a lot of sas companies that are in your space at your scale uh are building communities to drive stickiness and expansion do you have sort of a analyst superhero community you're building somehow so so this is the company that does this really well is fish town uh the makers of dbt i've never heard of them oh man company to watch for sure they did a series a with andreessen pretty recently um you know they live between five tran and mode so five tran brings all your data into a database you need to then transform it to make it you know really effective for analysis dbt is kind of the product right that's that's gonna be the next thing and if we were gonna build it we would do it the way that they did it um and the name of the company is is fishtown so they've built a really excellent community i think they're really good at that um we've been talking a lot about like what the future of this is going to look like and and we've done some things in the past that have been really effective so you know the founding team for modis three people um myself ben stancil and josh ferguson of the three of us josh is the only engineer right ben and i can do analysis we can write some code but like it's different from building production software so in the early days what we did is we very quickly hired some other extremely capable engineers and then they worked with josh on actually making the product and what ben and i did you know we did some amount of customer development and understanding what the product should become though we also had an intuitive sense for that because we had done it ourselves internally at mode already or sorry at yammer um so so we knew kind of what it should look like so we spent a lot of time writing blog posts building content and essentially just making sure that the mode brand was associated with good analysis and i think that's the thing we're really good at like like we're not our our dna is not to have like the the super thriving slack channel that that fishtown has they're going to do that better than we are and they are doing an awesome job of it right now what we can do really well is make the best analyst and data scientists focus content uh the type of thing that helps businesses understand the value of good analysis um one of those things that has been honestly i on essentially a whim in early 2014 wrote this sql tutorial and you know i had taught sql basically every business i've been at the big problem with sql as it was taught like if you were to go buy a sql book at that time and maybe even still today uh it's focused on software development which means that the first chapters are going to be about setting up your database getting data into it and doing all this other stuff that doesn't matter at all if you're an analyst like if you get hired as an analyst you're going to come into a situation where that already exists and what you need to do is write the world's best select statement like you need to be able to get data from a database and transform and do stuff with it you don't need to do the other stuff so i wrote this tutorial that we call sql school um and lives on mode's website you can go to it and there's a little resources tab mode.com resources it is one of the most popular tutorials on the internet and i think the reason for that is it approaches the user as though their only knowledge is excel right it's like we're gonna assume you're not a programmer you know some concepts from excel uh you know like what a spreadsheet looks like but maybe you haven't worked with databases before and we're going to teach you all the basics of that stuff without forcing you to create a database and do all this other heavy lifting and that's been tremendously i mean this thing gets hundreds of thousands of uniques a month yeah i'm saying that for a while so so so like that you know that's the place when you think about community of of analysts i think the place where would be really great in the future is around uh continued education making great content that kind of thing Raised so let me just ask you something i mean you just raised 33 million bucks i believe right i mean would you do you ever say you know what let's let's pull off five million of this and go buy a community like codewars.com where developers are competing on quizzes i mean you're already doing a bunch of these quiz sort of things on this resources page you get a list of a million engineers by doing that i mean do you ever would you ever go by community like that oh i think it's hard i mean so for our audience authenticity is critically important um i think that's true for for technical audiences in general um people just smell sales and that kind of thing and nothing feels worse to me than buying a community right like it just well i don't i i i just think i i think that our our customers in the market would would view that as uh too corporate maybe yep yeah no it makes good sense makes sense so content's a strategy you know maybe not build your own community just put out great content that allows these analysts to become superheroes internally love that all right look community forms around that too right like it's not a deliberate effort for us right now to like have a conversation going in a forum or on our slack channel or anything like that like that's not an explicit effort that we have um but but it but it probably will be at some point yeah talk to me about your team today you mentioned the founding team how many folks are on the team today total about 120 one okay how many engineers 40 ish plus some more for product design okay and how many quota carrying reps do you have do you know seven eights for new business okay yeah when did you hire your first out of curiosity what revenue were you doing when you had your first quota carrying rep okay uh so so the first rep um so we were a little more than a year old as a company we had been in public open beta we had some folks who are using the product um this is october 2014. okay okay um were you at half a million run rate already or no not even close not in close okay no we were at like um we were uh somewhere north of zero and south of 50k so oh wow an arr yeah oh wow okay look and so the composition of this founding team is a bunch of people who are like pretty analytically sound and have done analysis on larger businesses in plenty of different areas like there's an understanding of how products should work and how go to market should work from like running the numbers at bigger companies but it's very different and like i certainly had to learn this the hard way it's very different to sell than to analyze sales and had i known that at yammer i probably would have liked done some parts of my job differently but anyway we realized pretty quickly that we wanted someone who was going to be a more of a sales expert and then in the process of interviewing people found someone who coincidentally worked with it worked at yammer but but we didn't know him super well his name is chris davis he was our first sales hire um he was like kind of fresh into ae land he he had come out of being a a very successful bdr at yammer um had done a couple of deals but but didn't really have a ton of skill closing yet um eventually we ended up hiring an advisor to help him so someone to come and do deal review she came to our office every monday went through our crm sales manager sorry who is she uh her name's emily's husband she also she was at yammer uh briefly as well i mean we were hiring the people we knew who were good you know um and she had gone to admiral so while she was at adroll uh she would after her you know day job at ed roll she would show up at our office on monday we our office was like three blocks away at the time so she would walk over from adroll do our deal review monday afternoons um and then eventually we hired her as our as our first sales leader the company from like you know ballpark Monthly recurring revenue you know 500k to 10 million yep and that was that 10 million i think you hit what about october or last year 2019 yeah i think i think that's right somewhere in there yeah that's great hey last question i want to ask there's a lot of folks earlier stage stats coming able to access debt these days probably on much better terms than what you raised the 375 i think you raised 375 back in 2015. is that number accurate and if so why'd you do it and do you regret it or was it powerful um i heard you like bootstrapping i love bootstrapping oh you love bootstrap i love it um here's what i'd say so i got a really great piece of advice in the early days um that i didn't actually realize was advice at the time from yammer's then cfo mark wilway who said uh because a different gamer group had split off and raised immediately like day one at like a 20 million dollar valuation on a business that you know [Music] mode has surpassed um his take was that 20 million valuation is not good for anybody and what i saw was that they made hires right off the bat that probably didn't make sense they weren't like the necessary people to find product market fit now i don't want to be too critical because like startups are hard i don't know what's going on there yeah but what i can say is that um a certain amount of hunger you know sharpens the senses and and really focuses you in and i think look we had it easier than most like we raised basically on day one from from david saxon from some other yammer execs and then we went out to people that they knew and raised another round that i think 375 i think is right yep um or or actually i think it was more like one 1.2 million then maybe we added 375 to that or something like i don't remember the exact order okay but we raised a bunch more money from uh formation eight and from some other folks at that time and we just had money like we had money to spend even from the earliest days and if i were to do it differently i would bootstrap longer the resources are also different today like you can just go get 100k and aws or google credits and you can you can you can run your business pretty much for free for a pretty long time um i wish we had done that i wish we had i wish we had gone super super lean in the early days because i think we would have found the market faster we've been focused i mean you can hear me on other podcasts talk about some of the experiments that we ran with like like you know github like a pub like an open source community all on github with data um you know there are other places where i've explored that more but uh i think we would have just gotten much faster to like the core elements of value with less money that's great well hey you're on a tear sounds like almost number sent you over your growth in a really tough year for everybody you know if you're you know in the tens of millions of 10 million and 25 bucks a seat and 600 customers you've got to be getting close to a million seats here soon we'll hopefully maybe you'll hit that next year we're we're all rooting for you man uh any any not any acquisition you're not any acquisition talks with google are you mean frank uh uh no there are not an acquisition dogs in google fair enough and did you guys talk about you just raised a 33 million i mean usually at that stage you're probably selling somewhere between five and ten percent of the business where you're sort of in that range uh again those are the sorts of things you know we don't talk about the valuation publicly so uh i'm sorry maybe maybe years down the road we can go back and talk about that okay very good i will i will hold you to that you'll get an email from me exactly every year from this point forward and we'll keep following up derek let's let's wrap up here with the famous five number one what's your favorite business book uh hard thing about hard things there are some chapters that i still go back to on a regular basis number two is there a ceo you're following or studying you know uh i i have a guy that i connect with on a regular basis and we met just through kind of silicon valley stuff a guy named jim benson who is now the ceo of chorus and and it's i i think he's just like clearly excellent at what he does and uh always has something really insightful to share with me and because he's running a business of similar scale uh it's super super helpful i think that's important to find mentors who are you know the types of people you aspire to be and and who are running businesses you know or have run business that look kind of like yours number three derek besides mode what's your favorite online tool for building the company uh besides memo was going to be my answer i use it a lot i bet i think coda honestly like the the ability to to structure to structure documents can they be a notion can they beat notion i just think they went through very different paths you know he he raised 60 million bucks based on day one pre-revenue notion was basically bootstrapped and did a massive round they both basically do the same thing you like coda though i like both products um coda happens to be a thing code is a customer of ours uh so is notion um i like working with with both teams that i use notion personally uh we just happened to use code mode uh that's just the way it evolved like someone on a product team i think started using it and that's the way it went when you have you can you can embed you can embed mode into coda but not notion that is one critical thing is today mode embeds working code very good that is the most diplomatic answer i've gotten from a ceo who has cus who has two customers and has to give an answer that caters to both of them but also i felt like it was very honest there's reasons you love those number four derek how many hours of sleep you get every night uh depends on the night i i have a um i have a toddler uh so that's been decreasing so uh it's yeah in the range of like six to seven okay and just one kid or multiple just one yeah i have a one and a half year old married with a kiddo and how old are you how old am i uh i am depending on when you air this 35 or 36. when's your birthday uh january 7th oh happy happy early birthday that's great thank you okay take us back 15 years what's something wishing you when you were 20. i i this is gonna this is such a cop-out answer i know but like i i am very happy to have generally done the things that i have been passionate about and i have found great success in that i don't think i knew it explicitly but it's just what i happened to do like i've been the type of person my whole life who gets extremely motivated about certain things and i and i really succeed where i am motivated and where i'm not motivated i i don't succeed quite to the same degree uh there's my other diplomatic answer for you um i i think this is really important and and as i reflect you know it's not something that i really thought about but it but it has been probably the most important lesson of my life is to always make sure that i'm doing stuff that i'm passionate about and when you asked me earlier about opportunity cost and salary like it doesn't even it doesn't even factor in for me really like it's i just was so passionate about starting this particular business with those two co-founders that like there was nothing that was going to get in the way of that guysmode.com making analysts superheroes internally externally everywhere launched in 2013 with again three co-founders all together raised 500k at a 5 million valuation right on day one have scaled since then finally up past 600 customers hundreds of thousands of seats on the platform passed a 10 million dollar runway last year and almost up to that 100 year over year mark obviously in a tough year for everyone but hot space we'll see what they do next derek thanks for taking us to the top all right thanks so much for having me one more thing before you go we have a brand new show every thursday at 1 pm central it's called shark tank for sas we call it deal or bust one founder comes on three hungry buyers they try and do a deal live and the founder shares backend dashboards their expenses their revenue arpu cac ltv you name it they share it and the buyers try and make a deal live it is fun to watch every thursday 1 pm central additionally remember these recorded founder interviews go live we release them here on youtube every day at 2 p.m central to make sure you don't miss any of that make sure you click the subscribe button below here on youtube the big red button and then click the little bell notification to make sure you get notifications when we do go live i wouldn't want you to miss breaking news in the sas world whether it's an acquisition a big fundraise a big sale a big profitability statement or something else i don't want you to miss it additionally if you want to take this conversation deeper and further we have by far the largest private slack community for b2b sas founders you want to get in there we've probably talked about your tool if you're running a company or your firm if you're investing you can go in there and quickly search and see what people are saying sign up for that at nathanlacka.com forward slash slack in the meantime i'm hanging out with you here on youtube i'll be in the comments for the next 30 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