How Spoonshot CEO Kishan Vasani grew Spoonshot to $480K revenue and 20 customers in 2019.

hello everyone my guest today is kashan vasani he has worked in the food tech industry since 2011 he's the ceo of techstar's back taste ai business spoon shot their food brain predicts consumer tastes and food trends he previously worked in two leadership roles over 3.5 years at just eat prior to this he founded a digital marketing agency okay shawn you ready to take it to the top sure all right so tell us about spoon shot uh what the company does and then what's your revenue model are you guys a pure place sassa company or no well we have a suite of products um across the food domain tackling different ear problems all of those problems go up to our kind of our vision what was the vision problem which is essentially around helping people have better experiences with food so everything we that's where we kind of have that passion we want people to enjoy everything they eat every experience they have with food so we've come up with a different number of solutions one is a recommendation engine for the food service industry hospitality industry and delivery platforms we have a front of house restaurant tech product which is called guest intelligence and that helps people understand or restaurants understand what's going on in real time in their restaurants and then there's our fmcg or cpg product which is helping large manufacturers around product innovation by understanding trends early okay so just because we don't have time to go down every single product kind of rabbit hole here are any of these a sas product do you bill any of these a monthly recurring um so two of them are but let's talk about let's talk about the um what we call it we're launching spoon shot genesis next month that's the one we should talk about which is the cpg product and that's a pure sas model okay which of the two right now though are sas uh the recommendation engine okay so you have taste intelligence personalized recommendations and guest intelligence you're saying the personalized recommendations engine that is a sas model yes okay so because the show obviously is focused on sas i want to double down on that here for a second and then talk more about what you have coming up which is genesis it sounds like um so let's start there personalized recommendations help us understand um again where where are restaurants using these recommendations are they actually in the restaurant or pre-restaurant where are they so there are anywhere that there's a in like online or digital interaction with a restaurant or a consumer right so it could be inside their mobile app it could be in their website it could be at a self-service kiosk any kind of digital interactive point of sale essentially okay but it's obviously mostly tailored for online and what are people restaurants i guess paying for this on average per month or per year would you say so it's probably around the two thousand dollar mark per month is is our sweet spot um it really depends on the size of the the brand they were working with and of course how much more value we're adding size based off size based upon the volume of for example transactions they might do etc okay so it's all about making sure that we always say the technology should pay for itself so they're really feeling the value and not it's a they're getting incremental gain but for us recommendations really about the trust factor yep okay good so it's based off number of transactions not number of staff or number of seats or number of locations correct so the difference here in our sas model is that we choose a number with the with the customer up front after because we have a 60-day pilot model where we test out the technology with them and show them value up front and then we pick a fixed monthly subscription amount based upon the 60 days that we work with them okay well if they use your software to grow their business say in three months you basically have killed your ability to drive expansion revenue because you have a locked in fixed fee it sounds like for some period of time why would you why would you agree to a fixed fee because of the nature of this particular product right we want people to not worry about exactly what how many api calls they're making and stuff like that so that's why we have the trial up front so we understand not just what they're doing because the pilot is or the trial is usually like a certain percentage of their traffic or users or whatever it is but then when we discuss with them the actual the annual deal we talk about where they're planning to use it and how much they're planning to use it so it's a combination of the value that we're providing and the volume that they're going to use sean isn't the number isn't the number one driver of value if you price your own number of transactions and they used your software to build their business and double transaction volume wouldn't you want them to be paying you double if that is your value metric well our value metric is not this is not our primary product as i mentioned earlier so what's valuable to us is that people use the technology and what in this world of data you know having ownership of the pipeline itself is valuable to us for our food brain to learn a lot more about what's happening in the world of food and trends okay so you have this core product it's a fixed price after the six day pilot and what you're saying is your upsell revenue and things like that can come from upselling your other products potentially exactly or it's or it's used in a different part of our business like for example we have as i said um taste intelligence and the restaurant technology products so it's it's not even about directly cross selling or upselling to the existing customers who are using personalized recommendations but it is about helping our food brain to understand more about what's going on in the world of food i got it okay put this on a timeline for me when'd you launch uh this product we launched the company about just over three years ago and this personalized recommendation product about two years ago okay so then 2016 company launched 2017 recommendations yeah okay and then you had to think about that for a second just want to make sure that's right yeah i'm just like what year are we okay got it good and then how many customers have you scaled to just on the recommendation engine today so i mean i i'm this is one where i'm not sure we should disclose that i'm not sure the nobody told me that i had to kind of disclose that information but i can tell you that we have more than 20 customers on it okay um by the way did you listen to an episode before you came on nope okay yeah we do one every day we've been about three thousand so it's we follow almost the same format every single time so you you wouldn't you would have known um but look let me i mean let me get in your head a little bit so um in terms of customer count you held back there a little bit why would you see it as an advantage or disadvantage to either share or not share that number by the way some people see it as an advantage if you have fewer customers because it's higher touch more care um i think it's more from a size uh perspective we have competitors as well and so i'm conscious of that so like that's one of the things it's not normal to kind of for private companies to reveal exact details around what they're doing um but yeah i mean that's just the way we've taken our position on this thing just more than 20. yeah i don't know i get it well look the show's about learning and so um the the other flip side is i let people come on and kind of spew out relatives all day long without getting details so the reason i asked the customer account number is because we have people listening uh that are looking to launch their companies and trying to figure out where do i go to get my first 10 customers how should i do it so tell us that story how'd you get your first five customers um the first five customers is for us was all about leveraging our my existing network because i had worked in in this sector for a few years now i was able to tap into some of the other members of my network who had also launched food startups and and and we saw this as an opportunity to kind of really show them the power of this technology because personalization has a leader in every uh vertical almost we like netflix amazon pandora but food there isn't a hard like well-known leader in personalization so it was it was when you paint that picture i think people really buy into the idea so leveraging the network was definitely valuable okay but but that's not helpful for my audience because leveraging they don't know that means leveraging the network so where where were you before this you're talking about the agency you had before you quit the agency and launched the software no late agency was before i worked there was agency where i had my marketing agency then i worked at just eat for three and a half years and then i launched this startup okay so your first five customers you're saying we're also customers of just eat and you saw this need and built it for them and then signed them up first no no my first five customers were in my personal network through having networked in this industry who had also started food businesses or food tech businesses or restaurants and they would like use our technology okay i can leverage that relationship and how did you do did you pre-sell did you pre-sell the idea and then spend money on developers or did you develop it and then sell it um it was kind of a little bit of both to be honest i mean we had this very very strong idea...