How Xeneta CEO Patrik Berglund grew Xeneta to $29M revenue and 200 customers in 2024.

hello everyone my guest today is patrick berglund he's the ceo and co-founder of oslo based zanetta the leading price comparison platform for containerized freight transforming the shipping and logistics industry with data analytics he's a logistics and tech enthusiast possesses a true passion for modernizing business processes related to logistics procurement and the supply chain patrick you ready to take it to the top yes beautiful all right so tell us about this company and again this is one of those things which i love because people might call it an unsexy area which means there's a lot of opportunity right so paint a picture for us what does this tool actually do well hey come on container shipping provides 70 of global trade it's a 200 billion dollar industry where they buy and sell the actual transportation not the cargo value inside right but these companies they they buy and sell something that looks like a commodity but is missing proper transparency of price development right so imagine you were buying something that you know prices shifted all the time up and down one time you paid 1 000 the other day three three and a half thousand so very volatile and you bought a whole lot you bought for let's say 10 million dollars or 100 million dollars and you were always questioning do i have a good rate do i have a good price is this the right spend this is what companies are facing whether it's automotive companies chemical companies fmcg whatever industry they all move their product in these lego boxes and it influences all of us and the big question these big spenders have is is this a justified price and the way we come at this is that hey let's just collect everybody's price and then we aggregate it and they can see how they perform yep so first off what's the ins and by the way are you pure place sas company do you is it a sas model yes okay so i want to get in that second but first how can you convince all the people that are doing all the shipping to give you all their data there must be some incentive structure you've set up yeah well you have the initial chicken and egg problem yeah because giving a proprietary contract away to a third party like us in the beginning it makes no sense but as soon as you know that this third party have all the other contracts that will make me smart in my negotiations with my suppliers then that's the incentive okay well so wha i don't understand that again if you told me nathan i have all your competitors data now i need your own you're basically saying you'll also give if i give you mine you'll also give me my competitors no i don't give you competitors what you're questioning is do your 50 million dollar is that 50 million spend competitive are they justified is it plus market 20 or is it minus market 20 you don't know i know because i've collected all the pricing from everybody else so that i've aggregated that and said here's the average okay you're giving averages you're not saying you're cheaper or less than company y or z precisely because that's proprietary confidential information so now you never know what adidas is paying nor that i'm saying anybody's using the platform right but i can show you the spread of the market and the average and then i can show your own yeah interesting okay i imagine my audience is very number driven educate us real quick i mean they see these shipping containers and we're in this trade war and so this is like top of people's minds right now like what does it cost i mean on average to take one of those shipping containers give me the size dimensions like go from like new york city to paris yeah that's not a very high density trade give me a high trade yeah let me give you an example so so a 40-foot container is like a semi trial trailer in terms of volume so it's a lot of cargo that goes into one of these boxes one vessel might carry anything from 10 000 to 20 000 of those boxes so they're massive right now for the u.s trans-pacific would be the biggest trade right so they would go from shanghai hong kong tianjin or whatever port in far east right into west coast and east coast of u.s so shanghai to long beach would be a very big trade right and due to the trade tariffs spot market rates going skyrocket high because companies want to rush the cargo in before the tariffs comes into play right so capacity gets scares and moving a box from from let's say china main ports has went from let's say fifteen hundred dollars to two and a half three thousand dollars right from one box all right and then you can there's there's several vessels every week going fully loaded on that trade right more or less could be at some space capacity and so forth right do you get volume discounts if i say okay i'm going to ship 10 000 on the thing can i get my cost down to like a thousand per container that's what makes this market so interesting from my friend of you right because let's say walmart moves a million boxes here just to give you some numbers right well target moves 250 000 who knows right they have different procurement power now the cool thing about this is that you can find a big volume company that expects to have the best rates in the market to have a mediocre price because they went to market at a point of time when the market was high and this smaller guy nailed it and went to market when the market was down right and this is so it's sort of non-comparable because of the volatility of the market and this is what makes it consistently relevant to have this data available for these businesses interesting this is almost like uber right but but you're getting fares updated real time based off supply demand well not fully because we're not this is this is really where it becomes different i'm not offering the actual booking or the actual trade just the data just the data from a procurement point of view because you got to understand the cargo doesn't go from port to port it goes from door to door yeah so you know putting that together is like a 16 stakeholder on average orchestration right in order to have the cargo moving i'm just saying there's a very volatile component on the deep sea here in the middle that we provide pricing transparency on got it okay let's dive into your model here i want to get more of your story patrick i get the product now i get the market it makes perfect sense to me um what do people pay you for this let's say again we won't say yes or no on in terms of if nike is a customer of yours but let's say as an example they are what would a company like nike pay you well anything from anything from 20 25 000 a year to almost say 400 000 a year and why would someone be on the cheap side of that versus the expensive side what do you price against so different products different market intel and amount of users amount of seats right okay is the amount so i'm really interested in the in the data-based upselling so number of seats is one of them is are there any other kind of number based things like like number of data points you're giving them or things like that yeah so we can provide more granular information and insights that relatively speaking are more valuable for some of the high value volume buyers right an example is let's say that you want to look at the market average development between sean and hambury that's fine but i could also potentially aggregate all the data per carrier saying that this this shipping line has this spread between a to b whereas this other shipping line has this spread in their long-term contracts and imagine if you buy for two hundred thousand dollars to 200 million dollars a year sitting with that intel while sitting negotiating on the other side of the table with your suppliers you have incredible leverage because you know how they perform relative to their competition right yeah and you know like the the season where you get better prices is the fall so you'll wait to negotiate until the fall when you know you have leverage or something like that yes for instance now there's also other things we do api deals and stuff like that where we can offer them more access to our data which you know they're basically buying our asset if you set up an api right so that comes at a higher price ticket as well okay put this on the timeline for me when'd you launch what year we launched in 2012 i'd say okay 2012 and then from 2012 to today how many customers have you scaled to a few hundred okay couple can we say north of 200 but south of five yeah okay good and how did i mean where was your head in 2012 i mean what what possesses a guy like you to go you know what i want to go try and get 16 people from door out to door or door in their door out to all degree and get the data and jump in what were you doing uh i used to work for a freight forwarding business called quinongle you're probably more familiar with ups or fedex sure sure ginongo has 70 000 people employed and they're the world's market leader when it comes to ocean freight shipping it's a massive company and one of the things that annoyed me is that i kept on selling to my clients at very different prices all the time and they always ask me is this a good price how do you justify your price and you know think about other commodities coal grain soya beans coffee beans uh gold silver electricity you'll find indices that that shows you the price development and you can then relatively say that you know here's my price and this is the market this industry had no transparency and that's why i'm so flabbergasted you know...