How Habu CEO Matthew Kilmartin grew Habu to $21.6M revenue and 50 customers in 2024.

hey folks my guest today is matt kilmartin he's the ceo and co-founder of habu the global innovator and data clean room software he's passionate about entrepreneurship and developing technology to help brands and their digital transformation he's got 20 years of experience in his career working for innovative software and data companies such as salesforce crux and akamai matt you ready to take it to the top let's do it all right so what does a clean room software yeah so what it basically means is uh two companies that have data um who have a common business interest to collaborate around each other's data but they don't necessarily want to give each other their data right so meaning it shot cleanroom software is neutral infrastructure where two parties can effectively do analytics across distributed data sets this is cross beam space not really i would say okay because that's the closest like i try and i try and pattern what's interesting is a lot of the um cloud data warehouses do this are doing this now so aws gcp um microsoft are trying to do this and that's where data sharing now happens at the cloud data layer and then snowflake and data bricks have capabilities around this as well and so maybe for my audience give some use case so the use case i have is you potentially are going to partner with a company you want to share email lists cross beam is like sort of how you do this anonymously without like giving up data what's the use case people use youtube for use case addresses we work with a lot of so so i know cross beam because we use it um uh a use case for us is um disney disney's a client disney owns hulu espn and they have all the tv viewership data basically um l'oreal is who is a big advertiser and they want to know what are the all the disney and hulu what do you know about my customers basically right so disney doesn't want to give all of their data to l'oreal loyola doesn't want to give all their data to disney they use our software to do data collaboration so i see big media companies like roku disney are examples of clients um and then also another big one is with retailers and manufacturers so like grocers and people in cpg companies interesting are those are two big use cases disney and hulu and grocers and cpg those are yeah and we have other verticals as well that we're successful in um but those are meat entertainment retail cpg um advertising those are a lot of the use cases of which people are using our software for today and are disney and hulu both paying you or is it one side of the marketplace uh today it's one side of the marketplace um disney or hulu well disney who's part of disney actually um i go with the comparison here grocery store or cpg jane uh it can it can be either actually uh it depends who the actual licensee is right so so it could be the grocer if they're the ones saying hey we're standing up an environment for all of our cpgs to collaborate with it can be the the grocer but also it could be like um l'oreal is a client and l'oreal is basically saying hey i want to go work with all the beauties i want to work with beauty people i want to work with sponsorship people and they can basically influence it as well but our our our our um we want to make it friction free for data collaboration so we are not charging both sides i see i see okay so you're charging one side and what is one side paying your current customers one side paying on average per month to use the tech yeah uh well per month we look at a sort of a annualized basis so our average acv is um uh by mid 300k range and how do you get someone paying 600k what's how do you upsell consumption so when someone buys our um software they get a certain number of data clean rooms or slots for collaboration and as they uh use them and as they use more they uh they grow and and there's an upsell opportunity okay so let me try and give an example um let's say heb the grocer is paying for your software they have x number of slots a slot might be filled by procter and gamble because they sell dove shampoo and dial okay that's how it all works yes yep you got it your quick study okay interesting interesting interesting oh what's going on there youtube good to see you guys now imagine this you love watching these interviews with sas founders but imagine if we took all of the valuation data out from over 2807 interviews i've done manually saves you a lot of time well we've done this we've built it into the beautiful interface inside of founder path check this out i'll show you how you can access this in a second but you log in you connect your stripe account you see your valuation real time you can see what it changed over the past 88 days and even set goals for valuation this year now the secret evaluation is there's many different ways to value a sas business so the reason you're going to see three or four different valuations inside of your frowner path dashboard this is all free by the way is because 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seat model right now it's more based on as people are are consuming more slots and collaboration okay interesting then also um um we're starting to look at some different modules as well right so sort of version one of the software was around um you know let's just let's just stick with the l'oreal example l'oreal has their beauty customers right that's a data set and then there's a data set with you know heb or whoever else we're starting to do now things more on machine learning where they might have machine learning models built around skin types or propensity to buy other products and and how do you now start to do machine learning on other data sets so that would be a more advanced module um but out of the gate it was sort of more dated to data and now it's more advanced modules are around ml and more model data and when was out of the gate when did you launch uh we've been building we've been at it for three years built technology for a year launched commercially two weeks before the shutdown so march of 2020 is when we launched commercially mm-hmm and uh what was that like how'd you get your first customer uh beg burrowed steel um yeah listen it was a weird time right because as you can see by our price points um it's kind of an enterprise sale and we're selling to big brands there wasn't a lot of appetite for companies to to enterprise buying at a time during covid in the beginning and so we had to get super flexible and so we actually did some uh shorter term deals we call them first value deals where instead of letting someone sign up for a year we'd have more flexible terms and start using our software and and you know we probably did um well actually interestingly enough of all those all those deals we did sort of six-month deals with to do sort of pocs or first value engagements every single one of them has continued on as a customer how many how many pocs have you done to date uh well now we're done with the pocs that was like two years ago um okay like that was two years ago we first launched now we're doing proper property but take me back because there's so many listeners that are in that state right they're launching pocs to get going how many do you have to launch and how many like converted to paid and what did you learn oh yeah great question um so for us because it's a little bit because it's a like enterprise it was less around um volume and in quantity and more around quality right like one of...