SaaS Interviews with CEOs, Startups, Founders
832: SaaS: Machine Learning and AI for Re-Engaging Customers, $250k ACV and $1.5m Raised
03 Nov 2017
Chapter 1: What is Yeti Data and how does it generate revenue?
victor look he's seen it all at sap mckinsey he's now launched a yeti data back in 2014 on a 1.5 million dollar convertible note four percent interest ten percent discount core team of three built for three years with his core team and some consulting folks he brought in he's now got about you know a little under a half dozen paying customers that pay between 250 grand and 500 gray per grand per year in terms of annual contract value looking to get up to the million dollar ar mark before going out and raising their next round ideally on a 15 ish
million pre-money valuation. This is The Top, where I interview entrepreneurs who are number one or number two in their industry in terms of revenue or customer base. You'll learn how much revenue they're making, what their marketing funnel looks like, and how many customers they have.
Chapter 2: How does Yeti Data utilize AI and machine learning?
I'm now at $20,000 per top.
Five and six million.
He is hell-bent on global domination. We just broke our $100,000 unit sold mark. And I'm your host, Nathan Latka. Many of you listening right now don't have time to listen to every B2B SaaS CEO that I've interviewed.
Chapter 3: What is the average annual contract value for Yeti Data's customers?
If you want to get access to the database I've created with year-over-year growth rates, customer accounts, margins, and many, many other data metrics and data points, you can go to getlatka.com. Here's the thing, though. This database... I keep it to myself. It's so freaking valuable.
Chapter 4: How has Yeti Data's customer acquisition strategy evolved?
And to preserve the quality of the data and make sure that the people that have access to it have a true advantage, I'm only letting 10 companies on each month. So we're full this month, but you can go to getlatka.com to get on the waiting list for next month. And look, there's big people on the waiting list. I mean, the biggest VCs you've ever heard of. You've probably heard of them.
Chapter 5: What challenges does Yeti Data face in a competitive market?
They're big, private equity, billions and billions under management. So it's an impressive waiting list. Go get on now at getlatka.com. Hello, everyone. My guest today is Victor Sherba.
Chapter 6: How has Yeti Data funded its operations and growth?
He's the co-founder and CEO of a company called Yeti Data, solving big hair problems for customers. Prior experience includes running product strategy in the data division at SAP. He was a McKinsey consultant and sales VP for Tadpole, Computer, and Udipy. I think I said that right. Victor, are you ready to take us to the top?
Chapter 7: What metrics are important for Yeti Data's growth?
I am. All right, good. How are you? I'm doing good. So loop us in here.
Chapter 8: What advice does Victor Szczerba have for aspiring entrepreneurs?
What does Yeti Data do, and what's your revenue model? How do you make money?
So Yeti Data makes what is called a virtual data warehouse that unifies customer touchpoints and make them actionable using AI. And our revenue model is a classic enterprise model using a SaaS go-to-market.
Okay, got it. And when you say enterprise, some people, when they say that, they mean $10,000 ACBs on average, others mean a million dollar ACBs on average. What is the average customer paying you per year?
Average customer is paying us around $250,000 to $500,000 a year.
Okay, so $250,000 to $500,000. Good. And what are they getting? Be specific. What are they getting for that? Is that typically a number of seats that drives the price up? Is it an additional feature set?
No, not at all. In fact, we love universal usage of our data with inside of a customer, right? The more they use, the more valuable we are to them. And so we don't want to put in any kind of artificial barriers and saying, oh, my God, there's only this many people get these reports, et cetera. Right. What what they get is they get an all you can eat model.
They point us at all the data that they have about their customers. We unify it for them and we run it through our models and we say, hey, listen, this is what you want to do about these customers to maximize your revenue and maximize your ROI.
Got it. So they are, that's helpful, that example. You're getting access basically to their customers and you're able to pick out things like, hey, this customer, this is very simple. This customer hasn't logged on to your tool in two months. They're very likely to churn, re-engage them by doing X. Exactly. Interesting.
And so what we do is we use all kinds of crazy machine learning, all kinds of crazy AI to kind of tease out things, patterns that are in the data that a lot of humans won't find.
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