Daisy Intelligence Revenue, Valuation & Funding (2024)

hello everyone my guest today is gary saranva he's the former head of ibm canada's data mining and data warehousing practices and is passionate about ai and its ability to transform how retailers grow their businesses and establish an edge in an increasingly challenging and competitive environment under gary's leadership daisy intel it's daisy intelligence.com has established a track record for delivering verifiable financial outcomes for a rapidly growing list of global clients gary ready to take us to the top absolutely so obvious question when we talk about anything retail is is kovac good or bad for you i think in the short term it was bad we had some you know uh you know big pause on the sales but you know so enterprise sales big acv you know our acvs like half a million and up and so you know sales slowed a few retailers had trouble and paused services so our kind of revenue this year was flat slightly down compared to last year but i think overall the market the va our value proposition will be stronger post covet as uh you know companies will look for more automation it's going to be more competitive than ever so having kind of you know pricing better pricing better promotions uh and being able to automate more of the work that human beings do retail merchants are overloaded with work so i think given that e-commerce has gone up dramatically so i think for us i don't well i wouldn't wish it on anybody i think in the long run it's a good thing for us so how many enterprise accounts are you working with now today i know last time you came on in april you said you're working with about 25. yeah we're still the same number of banners like this year as i said has been flat i think sales is now starting to come back you know we expect to close you know some counts for the balance of this in q4 here and then get back to the plan was to double in size this year we didn't do that just because of that covet i think really pop it a bit it was like a big pause button well so what was monthly recurring revenue last month now last month recurring revenue was uh 500k a month 500 okay yeah so you're i mean flat to like a slight decline when you came back on in earlier this year you said you're doing about 600 000 a month yeah a slight decline with a few losses uh customers that went out of business like a couple customers really struggled so well so walking through you just told me pre-show that you went out and raised another round how were you able to raise another round where you weren't getting crazy diluted considering the company had shrunk a bit um because uh you know our existing investors believe in the company and canada there's government matching programs to help uh companies you know we're in a hot space ai we're one of the for the fat one of the fastest growing companies in canada even though this year was been a slight decline we're still in the top 100 fastest growing companies so between the space the belief in our value proposition um our existing investors put some money in it and the canadian uh kind of government-backed vc funds put a couple so we raised like five million a couple weeks ago so that's what it only extends our runway you know for another 18 months five million total that includes the government and the vc stuff yeah okay so that means what total raise today is about 20 million yeah we've raised yeah about 20 million in total yeah interesting and is the is the money that the canadian government puts in are we talking like shred financing or no they actually have a program that's equity the program yeah matching it's like so so this round was convertible debt so it's like so it's nice it's very good because it won't it'll convert into the b round we have three years to convert into it's it's a three year term and convertible into a b round so they've been doing it with a lot of tech companies uh you know who've been burning cash vc backed really extends our runway given that sales is kind of slow for the year you know and just picking up now and what did you use as the cap on that convertible that there's no cap on it so it'll just be a discount to the next round so i see and is that you know typically unsafe so we'll see like a 20 discount is that similar to what you got yeah yeah i'll be about a 20 discount to the next round and we expect you know like we expect to do a b round within 12 to 15 months so the goal is you know to cross the 10 million arr threshold this year or next year you know i think that that's the plan we build we feel the market coming back a lot more sales activity in the last month i think companies are realizing that the new normal is going to be you know the old normal is never coming back and everyone's come to grips with they got to get back to business so feeling the momentum picking up again actually significantly in the last 30 days so did you change anything gary about your burn i mean again back in april i think you told me you're burning about five hundred thousand dollars a month and net burn uh 65 people on the team did you have to trim anywhere to get more runoff yeah we trimmed i mean we lowered burn we let some people go i think it was you know an opportunity to you know make sure we have the right people on the bus you know i'll throw us a bit of that and then just you know cut back burn to extend the runway and uh you know even though we raised we're still gonna make we have 18 months of runway that's that's the that's the worst case scenario and so that's that's that's the plan we have in place so we're really being very very diligent on making sure whatever we spend generates roi so we're focused on spending on things that are sales marketing customer success so new customers and keeping existing customers that's the that's the focus of the investment capital and so what's the total team size today the total team size today is like 55 okay so down about 10 from pre-covet or during coveted times and do you still have all 22 engineers or did you let any engineers go uh the engineering team is still 22. got it and quota carrying reps is that where you trimmed we trimmed a little bit on i mean there's something some reps we trimmed a few sales reps we trimmed that because we changed we also took this time to re-trench like a six-month pause we kind of you know updated our sales process and our sales team and changing the way we go to market a little bit so so there's some changes and that's with a couple of direct sales positions that we eliminated getting different kind of people involved in sales some more of the senior leadership team being involved in sales and trimmed a few client client people and a few kind of uh general and administrative staff really you know kind of you know trimming the access really but didn't uh take away any staff that's directly contributing to customers or bringing new sales so how many quota carrying reps do you still have today then so today we have i'd say we have like uh you know three quota carrying wraps but we but the process now but we've added with the leadership team being more of mold sales i'd say our before our sales team was about uh eight in total now it's like 11 in total with a different mix of skills right so i think we sell enterprise software the realization was that we're selling a partnership wrapped around a product not just a product and so getting the people who deliver to our customers more involved in selling that's been the big change and so that mix of quota carrying rep versus subject matter expert that's where we really brought in more subject matter experts and gave them you know a role to participate in sales more so let's dive into the product here because what you're doing is interesting now from what i understand your theory of retail product which is for rio again retail solution you're actually managing and looking at transaction data and sorting it in a way that these retailers can use ai to make better sort of product you know and urgent decisions do you consider yourself sort of fintech platform i mean are you like second measure you're reading statement descriptors yeah i mean we deliver the goal is net income growth and we do have an insurance fraud solution which is more traditional fintech so in the same way that we help retailers make smarter merchandising decisions we help insurance companies with fraud detection and underwriting and claims automation so our system it says what are the best operating decisions you can make to grow net income that's really the deliverable and although retail and insurance sound wildly different at the core it's if i make this decision what's going to happen and these are the core decisions in retail merchandise planning is the core kind of weekly promotions prices inventory allocations and also you know you know what to promote what not to promote that important mix and then in insurance it's underwriting claims processing you know fraud detection and gary you saw your audio sort of trailing off just make sure your microphone's plugged in nice and strong for me um in terms of the retail side of things what what data is the retailer feeding you so they can you can help them make better decisions around what to promote yeah we get their transaction log receipt so literally every item and every transaction bricks and mortar and e-commerce so the glory glorious detail i know every single item in every transaction for several years and then around that is all what promotions they did what were the prices they charged you know all of the kind of operational details so obviously once you swipe your consumer swipes or credit card in a daisy intelligence retailer outlet that's sort of after the fact data you then have to sort of use after the fact data to make to enable the retailer to make decisions before they have any real data to base that on right so so how does analyzing data after the fact help you make the retailers make smarter decisions well because we have a mathematical theory that's independent of the data it's like the laws of physics you know if you had like i think of an autonomous race car you know you wouldn't there's no historical data how to drive a lap around a race track you're not building a predictive model so you have the laws of physics and you say okay i can simulate the laws of physics and so and then the historical data just kind of configures the simulation like the gravity wind resistance friction of rubber on the road so the same way we use the historical data to calculate cannibalization halo sales elasticity and then we plug it into our laws of physics which is this kind of theory of retail and similarly we have a theory of risk so we're simulating the future and you don't necessarily have to have ever done it in the past it doesn't need a labeled example so the historical data just lets us calculate seasonality and you know elasticity cannibalization halo pull forward and we plug those into our laws of physics and then simulate the future to say what's the optimal decision set i can execute even though you've never done it before gravity is a law you can't arbitrage gravity no matter what it's going things are coming back to the ground so if what you're saying really is a mathematical rule like a law and you make it something that everyone starts using you well it can't work for everybody because at some point consumers don't spend money across all the retailers using daisy intelligence how do you account for you know we're optimizing the market given a given entire market we're looking at all the competitors in the market as well so it's optimizing a total company and we've modeled the retail dynamics and insurance dynamics to say if you do these things and obviously you know there's some assumptions like you know the market is fixed you know there's a 700 billion dollar grocery market and you know for the grocery industry and you know given that the money is fixed yes it will shift between companies you know you know every dollar you grow is taken from somewhere you know it's either customers not spending that at another competitor or a restaurant or something else so so that that's the dynamics we figured out and you know it's not like i wouldn't say it's like einstein's theory of relativity that's a very absolute provable laws ours are you know they work to drive massive sales growth and we've grown our client total company revenue by three to five percent which you know on a base of uh you know our largest customers it's more than a billion dollars in annual revenue so it works good enough to generate that value i'd say it's quite a it's uh it's not an absolute law but it's it's pretty close because it works we use exact same math at every single customer i mean if you're taking a company that's processing a billion dollars in gmv and you're giving a three to five percent improvement that's a 30 million to 50 million dollar more revenue for them they're only paying you 100 grand a year 200 grand a year i mean if they're if you're truly having that kind of impact why aren't you making more money from them that's what we're struggling you know it's the human change management and you know getting them to say yes i agree with your analysis you know they do 20 other things too and retailers say well what about the things i did did they have an impact you know and so you know it's this human change and you know isn't that always the fight though every vendor to that retailer is is trying to have direct attribution so that you can charge more that's the whole battle yeah and we had no retail will ever admit to us that you did this right like because if they do that it's negotiating leverage so i mean we we have a very solid mathematical calculation that's pretty pretty strong and uh you know then we're asking say hey we'd like to measure all the other things you did too so we can do attribution we're you know we're happy you know we're not saying that they're not doing nothing you know and so that's we're figuring out how to tell the value attribution story i think once we then we're on the cusp of that i think that will drive our our growth more you know we've been more of a challenger sales process in the past and so we're stopping the challenger process and being more hey what you do is great you know the world has changed you know there's risk of change you know our software works in the new world and you know you can keep doing what you're doing and so you know retailers got to say is the risk of staying the same more or less than the risk of changing so now as opposed to saying we're really great and you know what you did was crappy you know that was totally not a valid approach on the 11 quota carrying reps that you currently have how do you decide what quarter to give them as folks scout at 10 20 million in error this is a constant battle so what do you say you're quote at yeah we said i mean we set it at a number like you know you know two or three deals per person right so that's that's the number we cover the total you know the total the business plan by you know double right figuring that the average what's just the dollar though gary i mean what's the dollar so it's a million dollar a year quota two million that's like a million and a half quota for the and then the team leaders got a team quota you know that goes above that right so the plan next year is to you know grow revenue by more than 5 million in arr right so right we're going to grow it from you know 6 to 12 right you know so you know we have like three quota caring reps who and then our leadership team is like they're comped on the total company it's not like they're carrying a sale so you don't have 11 quarter carrying reps you have three quarter carrying three carrying reps and that and the leadership guys who are the four of us who get involved or there's like six of us who get involved they're where they were more comped on the total company profitability you know our from our shareholders right yeah it's a different comp model you're still burning though to invest in growth right how much are you burning per month absolutely yeah we're burning today you know about 300k a month you know so we dialed it back from the 500 yeah five million we raised that gives us like and we have some debt financing you know from espresso capital and we have a commercial bank oh tell me about that how did that work um we've had an espresso capital facility for several years so we've got a five million dollar facility that's shared between a commercial bank and espresso capital so the sum of the two is five million based on multiples of uh mrr and so we have access to you know another three and a half million in that facility on top of the five we have as long as we keep growing so that gives us enough runway to burn on average you know 300k for the next 18 months how much is in the bank right now though oh we just did the five million so we're like we're pretty close to the five so you actually pulled the full five million that that's five million in your bank equity on the espresso line though you haven't pulled the full five million line you only pulled two million yeah we've only pulled what we what we've used and what we needed so we've been kind of flat on that line for quite some time and for anyone else thinking about working with espresso or just venture debt in general what's the cost of that capital how's it structured it's pretty expensive capital i mean it's like 15 and a quarter percent like interest and there's no there's no dilution it's no equity the espresso guys have been they're fantastic i've worked with them for several years i mean al karim and his team are great it's expensive but it serves a purpose right and if you want you know capital that's non-dilutive you know you pay for what you need they also do the shred that's mrr plus shred kind of lending in canada so we so there's a that goes to combined effects gives you your monthly multiple and yeah they're they're great they do they take warrants no they don't take any warrants so it's so it's been great from that perspective and what about covenants obviously with any term loans there's typically covenants what's the worst one uh the covenants are like 40 year-over-year growth right so that one's tough in this code environment uh and then there's like uh you know total uh you know now liquidity covenant which we satisfy with since we raise capital but there's like a total networking capital coming in which is a formula based on cash in the bank plus receivables and all this kind of thing well so gary how do you deal with uh like how was the conversation with espresso you didn't grow forty percent year over year which means you would have broken that covenant do they call the two million dollar line back immediately and you have to come up with the cash it was just for drawing new new funds you need to show like so they wouldn't you know but they've been flexible so if we wanted to draw funds they work with their they're very flexible they're not an aggressive lender so they know that the only way they're going to get their money back is to make sure we succeed so you know when we needed to draw more money if there's a covenant that we didn't meet they took it to their credit committee and kind of case-by-case basis decided so smart they've been are super patient and super supportive lenders and using storage strategic debt to preserve your own equity what's that enabled you to do in terms of preserving your equity position in the company how much do you still own today yeah i own the 20 27 to the company which is still great i mean it'll be diluted in the next round with like uh you know so i mean it's so it's it's helped me not dilute by do you have co-founders or do investors on the other 70 sole founder yeah so investors own 70 interesting very cool all right gary let's wrap up here with the famous five number one favorite business book favorite business book these days are the five dysfunctions of uh ceo by patrick lencioni i've been reading that one that's a good that's good he's got a whole series of books i like those number two is there a ceo you're following or studying i can't say not really i mean i you know look at the whole world i mean i've read elon musk i've been more impressed by him after reading his book than i was before i thought he was a bit of a nut case but after reading his book he's still a bit of a nut case but he's quite quite the nutty genius i would say so number three what's your favorite online tool for building daisy favorite online tool now these days it's like sas metrics so we've been like building financial sas metrics uh i found this tool from a vc that was free it was like a spreadsheet tool that you put all your monthly recurring revenue in for for five years and uh spits out every sas metric known to mankind so i love the fact that it was free it was just and what's it it's sassmetrics.com yes yeah sas metrics calculator yeah i'll send you then i don't remember remember send you the link for number four how many hours of sleep getting every night i'm getting more of these days i sleep like you know seven hours a night my average that's not bad in a situation married single kids um i am married uh have three kids busy guy gary how old are you me i'm 55 i just turned 55 on friday oh congrats hey we have almost the same birthday i'm october 3rd all right on all right gary let's wrap up here with the last question what is something you wish you knew when you were 20 yeah i wish i knew about business i would have started the company a decade earlier and being a decade further along right i would wish i'd known more about business finance guys daisy intelligence playing in the retail and insurance ai space helping those brands make smarter decisions 25 enterprise customers right now about 6 million dollars in run rate they're about flat year over year due to cobit but they are burning less they're only burning 300 net burn per month right now they also did a raise of additional 5 million bringing total capital raised to about 20 million bucks team is still about the same caught 55 60 people 22 engineers three quarter carrying sales reps as they look to continue to scale in both retail and insurance gary thank you for taking us to the top thanks jason 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 back end 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 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