Looker Revenue, Valuation & Funding (2017)

hello everybody my guest today is frank bien and with over 20 years experience growing and leading technology companies he's built his career on nurturing strong corporate culture and highly efficient teams before looker he was SVP of strategy for storage vendor versatile which was acquired by VMware and VP of strategic alliances at big data pioneer greenplum leading their acquisition by EMC which is obviously not called pivotal he led product marketing and strategy at early scale-out data warehousing company census and was VP of solution sales at Open Text earlier in his career he had executive roles at Dell and the Federal Reserve Frank are you ready to take us to the top let's go all right so tell us about look or obviously there's people understand anything from the bio it's a data play but what are you doing how do you make money yeah you know I mean a lot of people are talking about data I think what lookers trying to do is finally deliver on this promise of creating data cultures and you know I mean that sounds you know big and and wild and everything but really it's about getting every person in an organization to use data to make better decisions it's what everybody's been talking about for 20 years and no one's really been super successful doing why because it's a hard technical problem you know I mean you know you know data you know comes from lots of different places that are very messy you have to clean it up you have to present it to people in the right ways you have to make sure that it's not siloed there were really big you know technical problems that had to be solved over the last 20 years that that even make it reasonable to put data into the hands of the average user and give me a sense of how people are paying for this is it kind of pay-as-you-go per API integration or is it SAS model what's your model yeah I mean we're a SAS company you know we're a subscription model so we pay or we collect you know based on a number of users plus a platform fee it's a pretty traditional SAS model and can even a general sense of the the size of customer people paying ten bucks a month 100 a month a million a month yeah I mean our average customers you know the low-end would be about thirty thousand dollars a year and the high end would be a million I mean so it really runs the gamut so you're very much in the kind of high-touch enterprise space yeah yeah I think we did really well and lots of you know 50-person in under tech companies and then that grew you know grew to companies like uber and Facebook and things like that but but we serve anyone who has a need to access a lot of data and give me the backstory here what year was year one so this we're about six years old the company was founded by a guy named Lloyd Tabb a genius and sort of the tech industry a lot of people know him and he was at a company called Borland way back in the day and he saw mosaic which was he on the first web browser and he quit and he formed a company called commerce tools and and really was one of the first people in Netscape and and helped build that stuff out and he connected with a guy named Mark Randolph he was obviously the founder of Netflix he was a with read at Netflix the original CEO and they got together you know really looking at this data problem and I joined a couple months later and off we went so you were I mean you were there right from beginning and in 2011 yeah I was probably about eight nine months in okay and you know obviously don't be offended here but why they need you oh you know I think you know Lloyd knew what he was good at and Mark knew what he was gonna be good at marks on the board still and you know they wanted someone to come in and help you know run it from a business side okay interesting so 2011 you launched this thing with these partners naughty bootstrap to raise capital yeah we definitely raise capital I think the first year was all about the original customers really capital efficient and once we had a repeatable business model and the ability to go out and sell repeatably to customers we raised money and we've raised about 180 million so far yep so okay 180 million bucks raised founded in 2011 what have you grown your customer base to today yeah so we're about 1200 customers maybe 1300 now somewhere in there but you know work we're growing really nicely yep what do you say really nice thing are you talking about 50 percent year-over-year 20 percent a hundred percent you know we don't go specific but more than 50 that's for sure that's pretty I mean I assume with a hundred eighty million raise you're you're not going from $1 to $2 of ARR yeah that's impressive growth it besides your company you're at yep yep no we're we're really seeing this this you know there hasn't been a breakout vendor in a long time in data I mean tableau might have been the last one and you know I think I think what we're seeing is we're seeing that kind of trajectory where we're a real SAS company on that trajectory in this data space yeah now I mean we can do a little bit of back in napkin math you just mentioned kind of minimum contract by using a 30 K annually range and if you have 12 on our customers it's fair to say you guys are well past the 36 million dollar err our mark at this point correct mm-hmm yeah absolutely but below 100 still yeah I would that's that's a good range okay good big enough where it doesn't hurt you competitively but still gives you enough credit to say listen to this guy who knows what the hell he's doing yeah I mean we're about 400 people now you know we have eight offices worldwide and you know we're really capital efficient whereas headquarters in Santa Cruz California oh I'm envious I'm envious you're not up being affected by the wildfires are you no no we have that you know the Sonoma stuff was what's up this way and there were a couple fires down here but no not too much okay so we under kind of understand teamwinter so know what you're doing tell me more about the economics of this space because it is competitive I mean there's a lot of people vying for kind of this data space how are you acquiring customers yeah you know it's interesting I think a lot of people have been talking about how you get data into the hands of users but if you really think about all the technologies they really grew up around the limitations of the database so you had lots of things to take load off of the database you had ETL you know data prep visualization had all these little pieces and what was ready and what was needed in the world of data was one platform that kind of swept all that stuff together and did it in a different way and that's what we do I mean architectural e we're very different we run on the big fast cloud databases and the Hadoop stuff and all those kind of things that people talk about but really you know we provide this full platform that allows people to kind of do everything but literally I mean literally how are you acquiring folks do you use some of your team there's an outbound sales strategy there's a product marketing what do you tribute acquisition - oh sure yeah I mean so you know we we believe really in focus so we originally focused on a lot of e-commerce kinds of companies really saturated that market moved out from there and it's sort of the bigger you know the bigger you know fortune 2000 kinds of things we're about 80% inside sales so it's an inside sales SAS motion and then you know we're doing more and more enterprise sales as well and what do you like to keep I can as you're scaling of us you've raised capital so you have crater an ATM that works you should you know how to put money and get money out what are you trying to keep your kind of money velocity at when you want to get your CAC back how quickly they're due back yeah I can hear me you know my question about about about payback period how quickly do you like to eat your money back yeah I mean so you know we look at you know sort of those SAS models we would look a little bit like a Marketo looked originally or something like that but if it's more than 18 months you know we start to worry got it fair to say you got you're kind of between 12 and 18 yeah yeah that would be and how do you decide when to get more aggressive there or more conservative you know what I think if you look at a lot of SAS companies you know growing too fast can be a problem the wheels come flying off the bus it's not just that simple unit Union economics but there's a lot of other things and how fast you can scale and grow a business we're a data company we look at this you know a hundred ways from Sunday I mean maintain a lot of metrics not just about ourselves but about other companies that we really look at and and would like to be and and you know that's that's how we drive it so so you know we grow as fast as we need to grow that we can do that successfully without jeopardizing the success of the customer and can you tell me a story since you are a data company I bet you have some kind of unique data points you look at the maybe other SAS platforms don't actually look at tell me about one of those unique metrics you know though so if you think about SAS the the big thing is really that it's subscription and the customer can quit if it's not successful so the whole motion you know I think about when I used to sell perpetual and they used to call it drive-by selling and all that kind of underground right now it's not like that anymore the customer has to be successful so what you can build around metrics in terms of understanding how people are using the software where adoption could be better how you can actually improve features in the product you know based on how people are using it that's what's really interesting so not only do we what do we do that obviously internally we you know we look from a customer success perspective it where there might be problems and when we need to jump in and when we need to help a little bit more but a lot of our customers now are doing that too you know big software companies like Autodesk and things like that are really using this kind of data technology to understand how they can better serve and support customers so what I mean you because you have such a large sample size and you have historical data you probably know what you have to get a customer to do in the first seven days for them to like drastically increase their potential lifetime value with you what is two of those things that you have to get them to do you know we have to kind of follow the dollar if we're not showing something in their core business model that they weren't able to see with some other tool and that is for a lot of like tech companies that's like some cohort of users over time you know I had people join at the last holiday season you know where they say are they worth more than the people who joined in summer a year later or you know why how does that look so so it a lot to do with like retention cohorts or cohorts of user performance over time and they're having a lot of difficulty doing that and other data tools because so much data is involved and we can usually show stuff like that really quickly so so it's really fine something core to the business model not there's so much like goofy science experiments and data like everybody everyone's trying to put yeah exactly and I think what we want to do is just go back to the basics like let's show you why you know lifetime value of a customer's X and how it can be why ya talking about your own metrics I mean what do you see in terms of annual retention yeah so I mean we run and it's you know what you look at SAS called negative churn so does the existing set of revenue that you have grow or shrink you know irregardless how many new customers you add and you always want to be in that negative churn thing so it's that notion of land and expand and that's really what we want so what we want and what we look at so we look at cohorts of users over time how much they're worth you know a day one and how much they're worth it at year one and year two and what is I mean how negative are you right now oh so you'll see like you know negative 25 percent on a cohort you know in the bigger customer so you look at you know when we go into a signal or some big you know fortune 2,000 customer you know we definitely want to see it grow substantially over you know the first year in two years so I just want to verbalize this in two senses to make sure I understand completely clearly when you look at the cohort that signed up 12 months ago today so December 2016 and you look at all those signups what you're saying is if you lost call it 10% of your revenue you gained 35% from that growth so net ign��c ative 25 yeah exactly that net retention numbers the thing what is what do you I mean what are you losing gross is its to give a significant amount to make up before you get back to even we're looking no no definitely not I mean you want to you know as a SAS company your plane in interspace enterprise space if you're more than 10 percent losing more than 10 percent or below 90 percent overall you know from a gross retention you have a problem so you're above that oh yeah that's great that's good now what levers are you pulling from year one to year two to increase that contract value is a number of seats or is it some utility metric what is it you know what its value I think everybody knows they want to do stuff with data and they're having difficulty doing it and we want to go and actually show real value to to the the users that are you know looking at customer success or looking at how they might use data to improve like developing a product or something like that when see success there you do that though anchor that don't like her you have a dash for that says we saved you ten million last year this year SAV 40 million so we're for Xing your price point yeah you know so what we we have definitely those stories where you know someone comes in and says hey we were able to do this and that but but date is full of that stuff you know what we want to see is we want to see blanket usage so it's always about usage it's about what is our you know sort of ability to penetrate not just sort of the data team or the CFO or the finance team or things like that but how do you get this used like I think of like companies like thread up that we're using it in the warehouse from day one right they were taking better pictures of merchandise based on how it was performing on the website looking at data that's what we want to see we want to see a real pervasive data culture and then the rest flows behind that interesting so with net negative to kind of 25% churn you could start to lie to yourself pretty easily in terms of unrealistic lifetime value numbers which could then throw your CAC all over the place how do you keep yourself anchored and realistic you know because those performers can lie to you very easily in terms of your lifetime value yeah I mean like when you look at lifetime value and that kind of CAC ratio in a SAS company it's almost meaningless I mean we start to look at magic number and stuff like that so you can look at sort of housing line you don't get the opportunity to talk about magic number very oh yeah so I mean so I mean it's defined in a number of ways but basically you're looking at what it costs from a marketing acquisition perspective and what how much your M RR is growing over those those points so it includes the upsell and things like that where the original CAC ratio doesn't include the upsell you're including gross margin in that as well or no yeah absolutely absolutely yeah so just can you actually say that equation real quick in case folks want to calculate them and I couldn't even tell you that equation you guys can look it up I'll put in the show notes it's a sales efficiency ratio it is a good metric one of them bessemer writes a lot of stuff like buying dieter and those go they read a lot on this stuff and those are great articles to read yeah a lot of companies that I talked to that I have that are doing kind of more than 50 million in ARR they start talking they change their terminology they start they stop thinking about lifetime value and CAC and what the number is they give me the is they say Nathan you know when we spend a dollar on CAC we expect it to drive you know you know 90 cents and new ARR right that exactly where can you share where you're at in terms of that net ratio I mean so we don't go through the specific numbers but it's it's really healthy we're very kasev cash efficient there's a lot of companies and sort of this this you know data space that have blown up and spent a lot of money and things like that and we knew we did not want to go that way so it's very efficient okay I mean with a CAC again with a payback period that you mentioned earlier I mean I assume it's actually somewhere around that right you put in at 90 cents yeah and then we see upsell and stuff like that so yeah it looks great and they stick with you over a long period of time absolutely yeah yeah interesting do you assume a minimum that you assume like a minimum four years minimum five years or no that you don't even do that we don't even look at that I mean so I mean if you if you look it would be seven and that you know that seems like a lot yeah yeah I know can lie to you interesting what other things are because of your size in your scale what other things are you looking at besides kind of how quickly you get paid back your growth things like that it's always customer success it has to be customer success I mean if you know because we're doing well now doesn't mean we're gonna be doing well in three years and some customer unless we're really paying attention to it so we just are constantly looking at how much how much feature usage you know how much are people actually committing into our code base you know there's code behind look or for data people how much are they doing that are they using new features you know so we really look at feature usage a lot what's your current target year in terms of breaking the magical hundred million dollar mark it's pretty soon okay you think you think next year maybe yeah potentially yep and is the way you're gonna do that going to be driving more wallet share from current base or offering some down market options at a lower price point to increase kind of market share yeah we probably don't chase down market very much I think you know if you were to look at us you'd say hey you know the you know let's be probably a little bit smaller but let's go after something like ServiceNow interesting okay very good this is super valuable Franklin's wrap up here with the famous five number one with your what was the last business book that's you read you know we're doing a lot with Pat Lindsey oh nice books you know five dysfunctions of a team and the advantages and stuff like that but when I think of business books I go back even further and I think like what I was reading college like Herman sa and like journey to the east and all that stuff right that is the real business book journey to the east number two is there a CEO you're following or studying right now I would say Frank Sluman man that guy's just you know I mean what he did it and then service now is just you know insane number three what's your besides your on what's your favorite online tool you know what I'm like I love these note-taking things in Google you know they kind of come along and just do it a little better and they they they keep making this thing called keep better and better and I just I love that kind of stuff number four how many hours I sleep to get every night yeah I'm a good sleeper so yeah I mean you know I can I can function I had kids that were little of a while ago so you know I know how to do it with less but I can do it yeah that's good and how many kids you have I got two two kiddos marry a married not but they don't have a great woman in my life that's good so number eight so the kids are out of the they're out of the home at this point empty-nester they're just now applying to college we're really citing very exciting and Howard are you Frank forty nine alright last question take us back between think about that because I see the wine behind you that's because you're already on wine this morning alright just kidding last question take us back twenty nine years what he was your 20 year old self knew you know what like pick your battles you know man I was fighting everything you know I just I was always picking everything and being the guy charging around I think you know one thing I learned one thing I tell people OFF and it's you know pick your battles there you guys have it from Frank pick your battles joined the earliest kind of start up team in 2011 behind look or a great co-founding team they've now scaled to helping over 1200 enterprise customers paying on average at a minimum twenty five hundred bucks a month actually much more than that because that just puts them at about thirty six million in ARR but Frank feels good potentially next year about breaking that beautiful one hundred million dollar ARR mark growing around over fifty percent year-over-year which is impressive at these large numbers one hundred eighty million dollars raised economics really health healthy negative twenty-five percent churn net negative churn which is great team of four hundred folks based out there in Santa Cruz California and other remote locations again helping you make more sense of your data Frank thanks for taking us to the top great cioud Anton