Omer (00:12.080)
Welcome to another episode of the SaaS Podcast.
I'm your host Omer Khan and this is the show where I interview proven founders and industry experts who share their strategies and insights to help you build, launch and grow your SaaS business.
This week's episode is a story about four co founders who decided they could use artificial intelligence to help sales and marketing people make better decisions.
They saw firsthand how the explosion of information available to sales and marketing people was overwhelming and making it harder for them to do their jobs.
They decided to use data science and machine learning to capture millions of data points about companies and people and then turn that data into actionable insights.
But they also knew that they needed to move fast.
So they started building the AI technology, but also did a ton of work manually to process the data they collected.
In other words, they focused on solving customers problems however they could.
The first version of their product was sold for $49 a month.
Today their customers pay them anywhere from 10,000 to $200,000 a year.
In this episode we talk about how they came up with the idea, how they got started, what they did to get customers, and how they've continued to grow the business.
We also talk about artificial intelligence and how they're using AI technologies to help solve real world problems for businesses trying to prospect and generate leads.
I hope you enjoy it.
Today's guest is the co founder and CEO of Datafox, an artificial intelligence and prospecting platform.
Datafox helps sales and marketing teams prospect smarter and have thoughtful personalized conversations at exactly the right time.
Datafox's algorithm structure handles millions of businesses and delivers reliable data and machine learning suggestions where and when they're needed.
Prior to launching Datafox, my guest was an investment analyst at Goldman Sachs.
He and his co founders launched Datafox in 2013 and to date have raised $9 million in fund.
The company's investors include Goldman Sachs and Google Ventures, to name a few.
And their customers include companies such as Twilio, Box, Google, Amazon and Salesforce.
So today I'd like to welcome Bastien Janmaat.
Bastien, welcome to the show.
Bastiaan Janmaat (02:45.289)
Excited to be here.
Omer (02:46.410)
Great.
Now I always like to ask my guest what gets them out of bed.
So tell me a little bit about what it is for you that drives and motivates you every day.
Bastiaan Janmaat (02:53.930)
What gets me out of bed?
My alarm clock.
I got up early.
I'm definitely a morning person, so I love starting the day early.
I cherish kind of the first couple hours of quiet in the office when there aren't any meetings yet and I get to actually get stuff done.
Bigger picture, I get a lot of motivation out of, you know, my folks having given me kind of an awesome childhood.
I might get into this later, but I.
Although I'm from the Netherlands, I grew up in Southeast Asia largely and had the opportunity to explore some beautiful parts of the world before settling here in San Francisco.
So I feel like I owe it to my parents to take the experiences and the education they've given me to make something out of it.
So, you know, time's ticking.
Gotta.
Gotta make work of it now.
Omer (03:52.850)
Is it true you were born in Japan?
Bastiaan Janmaat (03:55.050)
That's true.
Yeah.
My folks, they're Dutch, but they moved out there before I was born.
Omer (03:59.370)
So I think I came across it that you were born in Japan, raised in Singapore, Dutch background.
You went to college in Hong Kong as well as Europe, so.
Bastiaan Janmaat (04:11.090)
Yeah.
Yeah.
You've been worthless.
Yeah, no, for sure.
Yeah.
Omer (04:17.190)
I think I had a similar background where I. I think I probably had gone to about 15, I think it's almost 15 schools by the time I was 16 years old.
Bastiaan Janmaat (04:28.950)
Wow.
Omer (04:30.150)
My parents were just moving around so much.
My dad was an international banker and we kind of moved a lot.
And I kind of look at that and I'm sort of really jealous now when I see people who kind of like grew up in the same town and they know all the same people that they knew when they were at school and stuff like that.
Whereas I was like, I'd go to school and be like, yeah, you know, I kind of make friends, but I probably won't be here for like, you know, in six months time.
So you kind of have a different mindset.
Bastiaan Janmaat (04:56.660)
It can be jarring.
Right.
Or I guess it's hard to commit to new friends in new places when you're not sure how long you're going to stick around there.
But I mean, look at, you know, you've probably been given a lot of tools where this is how you're able to meet so many new people and host podcasts the way you do.
It takes something to be able to get energy and be energized around meeting so many new people.
So it's a double edged sword.
Omer (05:23.080)
Yeah, yeah, I think you're right.
So I gave the audience kind of an overview of Datafox, but can you tell us in your own words a little bit more about the product?
Bastiaan Janmaat (05:33.320)
Yeah, for sure.
So we are a business that provides what we call automated customer intelligence, that is information about the customers or your future customers in a way that's seamless, almost effortless to you as a user.
How the technology works is we have software, we write algorithms that go and digest all of the world's business information.
Think of anything you could find out there about companies.
Our algorithms go out and find that information, make sense of it all, structure it, and then find exactly the pieces of information that our customers need in order to be effective in their jobs.
Our customers, they range from people working in sales to marketing, business development, bankers, investors.
Anybody who's trying to find either the right customer or the right investment opportunity.
We harness all of that information to help power those decisions and grow their businesses.
Omer (06:37.700)
Okay, yeah, I want to dig down in more into that and we'll sort of talk a little bit about how somebody can use a product like Datafox for the intelligence and the prospecting.
But let's kind of go back to maybe before 2013, before you launch the business.
Where did the idea come from?
Bastiaan Janmaat (06:58.460)
Yeah, the idea came I had worked as an investment analyst in London for four years.
My job had been to find high growth companies to try and invest in.
The job was basically part researcher, part sales rep, where with my research hat on, I had to go find indications or data that showed that a business was taking off.
And then I'd put on my sales hat and cold call the company and say, hey, it looks like things are going really well.
We can help you grow even faster if you want to work with us and let us invest in your company.
And what made this difficult was that there were just so many companies out there, literally millions of businesses.
And so it was kind of like finding a needle in a haystack.
And I always found that strange, but I had a business background, not an engineering background.
So I guess I didn't have a real problem solver's mind at the time.
I wound up going to business school.
That's what moved me from London here to the Bay Area.
And it was in business school that I met people who did have a real problem solvers mind about things.
Some brilliant computer scientists in the Bay Area here, and a couple of us, four of us actually, we had really gotten to know each other well over a bunch of activities on campus and beers and barbecues.
And what we, the idea we came up with was to completely automate that job that I had had.
But also the job of anybody who's looking for a needle in a haystack, you know, what we realized was that anybody in a sales job, anybody in a marketing job, people in real estate, headhunters and recruiters people working in banking, people working in investing jobs.
There's so many people whose day to day involves both the researcher hat and a sales hat.
The sales hat is often, you know, has a big component to it that requires a human touch and in person interaction.
But the research piece was something that we thought we could largely automate for, for a lot of people.
So I hadn't graduated yet, I hadn't finished my MBA yet, but we started working on this prototype again, these algorithms that would go and find indications that businesses were taking off.
And very quickly.
It took us a while to get to the point where we could actually commercialize and sell the product.
But very quickly we had a working prototype that showed that it could mine through all this information at a much, much faster rate and just make better suggestions than what any one of us could have done manually.
So that was the genesis of it.
And then year four years later, now a bunch of very large finance institutions, very large corporations, but also young companies who are just trying to be more efficient in how they grow their business and how they use data to drive growth.
We now count as our customers.
Omer (10:03.000)
Okay, so kind of, I want to kind of dig a little bit more into that.
So you've got this idea and where do you start getting this data from?
Do you start to think about talking to potential customers to sort of validate the idea?
What were some of the steps that you guys started to take which helped you form a, a picture that there was a business opportunity here?
Bastiaan Janmaat (10:34.590)
Yeah, yeah, those are the exact two.
Right.
Questions.
Our two big questions early on were, okay, what data could we actually reliably generate that would be used to make these types of decisions?
So where would the data come from?
And then number two, validating the market need.
Right.
Would people actually use it?
Would it actually help them get their job done?
We're lucky to have a big co founding team.
Pretty unusual, I guess.
Four of us.
I know that's a lot.
So two of us spent, myself being one of them, spent most of our time on the validation piece talking to potential customers, and the other two spent their time writing these algorithms and analyzing all the data out there in the public domain and what we could do with it on the, I guess to start with the latter on the data sources side, what we learned was that if you think about the average business, all the information about that business you can kind of think of as an iceberg, where what's clearly visible above the surface is pretty basic information about a company, Right.
What's its name?
What's Its URL, where is it located?
Approximately how many people work there?
Has it raised funding?
Some information like that?
You can get that from frankly some free databases out there or a quick Google search.
But what's really interesting and telling about a business is what is the part of the iceberg that's below the surface.
That's what you would find out.
If I gave you an assignment to just deeply research one business, only one business, you would, you would dig much deeper into the materials you find online.
You would read every news article that's been written about that company to get a sense of the milestones they've reached over time.
You would figure out how they're spending their marketing dollars.
Are they sponsoring conferences or are they investing in webinars?
What's their marketing strategy?
You would read blog posts other people have written about the business and that would give you paint much more of a picture for you of, of what that company's up to.
And so we realized early on we'd have to leverage and build some really great technology that harnesses natural language processing to interpret all of that largely text based content.
Interpret it as a human, but do it for millions of companies at the same time.
So that's some of the technology we knew we had to build.
Early on these algorithms would basically figure out what are the key milestones that millions of businesses have achieved.
On the market validation side, I think this was really eye opening for us because my co founder and I, we had both worked in finance, so we knew there was an enormous opportunity to help financial institutions make more efficient decisions around businesses.
But what we learned very quickly from speaking to a very diverse array of people was that kind of the, some of the jobs I rattled off earlier, people working in real estate, people working as recruiters, and then especially people working in sales and marketing.
I mean you think about in what job is business information most essential, most critical, does it really move the needle for you?
It's in sales and marketing.
It's where every minute spent on the wrong potential customers is very costly.
Right.
It's a minute spent in the wrong, the wrong place.
So that's why our business has developed, developed pretty early on into catering into a number of different industries.
Amongst them, anybody working in B2B sales and marketing and anybody working in a financial, investment or advisory type job.
Omer (14:21.420)
The comment you made about the data is really interesting.
And in terms of the natural language processing, can you maybe kind of give an example of that just for people who aren't maybe familiar with that to kind of understand why that's important.
And I'm trying to kind of, kind of help people sort of see the difference between, okay, you have, like, structured data, which is like, very easy to kind of go out and crawl.
You know, if I wanted to go out and say, I don't know, something simple like go to go, go and find the company name and the URL and the number of employees or something like that, well, I could probably go and crawl company pages on LinkedIn.
And I kind of know, okay, okay, you know, I could break down the HTML and say, okay, here's gonna, this is the URL.
This is where the number of employees is gonna be, blah, blah, blah, whatever.
And I can just run that on a hundred thousands of whatever pages and get that data back.
Job done.
But when you have to read an article, so let's say you have an article about a Fortune 500 company that shows up in the Wall Street Journal.
Nothing in there is going to be structured.
And so I think you kind of gave the example here about it's a human having to go through that.
But is there maybe just kind of in some kind of example that you can think of just for people?
Just kind of hit that point home for people?
Bastiaan Janmaat (15:43.980)
Yeah.
Let me give you two quick examples.
So, first of all, let's imagine you're a cybersecurity company.
If you're a cybersecurity company and you're trying to sell your offering, then then filtering a bunch of companies based on how big they are or where they are, that's not going to get you very far.
Right.
That's not going to tell you much about whether those are the right types of businesses to reach out to.
If you work for a cybersecurity company, what you're really interested in is to reach out, is in reaching out to businesses that have either had a breach, security breach recently, or maybe businesses who have very recently hired a new cio, because that CIO might be looking for new ways to shore up their cybersecurity practices.
So that's what a lot of, you know, a lot of the most successful salespeople at the cybersecurity company would probably be doing.
They've probably set up a bunch of Google alerts and probably talk to a lot of people to kind of figure out what those businesses are that are experiencing breaches or hiring new CIOs in DataFox.
You could pull that list, you could run that search in 10 seconds.
Because we're monitoring all the content that's out there about pretty much every business you would want to reach out to.
And we're looking for very specific word patterns.
So we pick up on when companies have had breaches, when they've hired CIOs.
We could even help you figure out which companies look to be hiring a lot of people that have security backgrounds.
And that's the kind of smart prospecting that would be very difficult to do without Datafox.
Or a totally different example, one of our customers in New York, they offer office cleaning services.
And so for them, the absolute golden nugget for them is if they find a company that's just leased a new office.
Because obviously, that's exactly when you look for new office cleaning services.
So, same thing.
Datafox keeps track of Twitter, blog posts, press releases, any mention of a business opening a new office, we get it in real time.
We send that opportunity straight through to that office clean service.
And they've seen their business grow very strongly as a result of that.
Omer (18:13.940)
Okay, so you kind of looked at the data sources.
You gathered a bunch of insights about the type of information that was out there and what you could get hold of and what you needed to do.
And at the same time, you went out and you started talking to potential customers to get some better sense of what the business opportunity there was.
So what was kind of the path that sort of you guys then took?
Like, how did you go about building the first version of that product, and how long did that take you?
Bastiaan Janmaat (18:49.930)
Let's see, the first, you know, honestly, the first, I guess the first prototype we built very quickly, but, you know, it wasn't something we could sell yet.
As you can imagine, people who need this service, they need something that they needed.
An offering that would cover a high number of companies, but also do so with great depth of information, very high quality bar.
So the way I remember it, we basically had a prototype within about 30 days that we could start showing to people.
And that was the purpose it served initially, that we could show it to people in different kinds of jobs and different kinds of industries, Industries and companies, and get early feedback on, hey, what part of this resonates?
What seems most useful?
How would this fit into your workflow?
And we went from there.
I think we really started selling our first contract a year or more in.
And that's probably one of my biggest lessons, frankly, actually, that I think that came too late.
Even though building a data company is hard, and building that first, I guess, minimum sellable product takes a while, I still think back, wishing we priced it sooner and higher, frankly.
I guess you hear this advice a lot, right, that founders tend to be a little cautious in how they price their product because they know about all the skeletons, they know where the data is strong, they know where the data is weak.
But as I think back, we should have jacked that price up, up five to 10 times much, much sooner than we did.
Omer (20:24.150)
So how much were you charging when you started out?
Bastiaan Janmaat (20:26.950)
At some point I think we had like a $49 a month tier.
I think, you know, it was, it was a double edged sword.
The cost of having, or the downside of having that $49 tier was it was so cheap that any, literally anybody would just come and check it out for a month.
And so it led to suddenly a very diverse and broad customer base that we wanted to support, obviously, but with requests that weren't necessarily aligned with where we wanted to take the product long term.
It also just led to a lot of tourists, people who check it out for a month or two, but it's $49, so it might not be essential to their workflow.
So it also led to some kind of noise and false positive, us thinking, oh great, this person really wants to use our product where then they'd cancel a month later because it just wasn't that essential to them.
I say it's a double edged sword though, because the positive outcome was that tons of people would just come and try it out.
So it gave us access to a very wide array of folks who we could then learn more about.
And I guess that was ultimately a helpful step in us learning that there were certain jobs where we fulfill an absolutely mission critical role like sales, marketing and investing and others where we'd be more of a nice to have.
Omer (21:52.770)
So the pricing insight is really interesting and I've heard several founders say something similar where they wished they had charged more for the product earlier.
And also not just in terms of kind of like the economics and sort of matching the value of what they were bringing to the market, but also because they felt that they would have attracted better quality customers that were kind of better aligned with the long term vision of what they wanted to do with the company.
And I think you kind of alluded to that where you kind of get some of these tourists and people who are maybe you're kind of going to check it out, but they're probably not your ideal long term customer.
And I guess one hard thing about charging more is the fear that it's going to be harder to sell or no one's going to buy it or people are going to kind of balk at you and say, why would I pay this much?
And so kind of thinking about if you were going back, like, what would you have done differently?
I'm wondering if there's some advice that you can share with people who are maybe in a similar situation, maybe would want to sort of intuitively feel like they should be charging more for their product, but are maybe a little reluctant to do that because they're looking to get more customers on board, more feedback, and maybe thinking they're just gonna get too much pushback if they charge too much for the product too early on.
Especially when they know all the things that don't work as well.
Right.
They know the skeletons in the cupboard.
Bastiaan Janmaat (23:35.050)
Yeah.
My advice would really, I would start by saying, look, it really depends on your market.
Right.
There are some really great businesses out there that have a for whom freemium works really well.
Where there's a free tier, there's a cheap tier, then there are slightly more expensive tiers, but pricing is listed on the website.
It's very transparent.
The company may not even need sales reps. That was not where we thought we were headed and it's not how our business has turned out.
Our business has turned out with more of an enterprise or higher priced approach where we ultimately, we don't list pricing on our website because it's steep and we don't have a bunch of free users and we have fewer customers, but they are high paying customers.
For those folks who have a B2B SaaS product where they think they'll end up charging enterprise type pricing, my first advice would be take your price down from your website if it's up there, because with your price up, you can't do any experimentation.
Whereas if your price isn't on the website, then at least you can test different pricing and different positioning with the prospects you're talking to.
The second thing I'd say we learned is move off of monthly pricing to annual pricing and annual commitments.
Especially if you have a product that requires a little bit of onboarding, some training where you're not going to be fully up and running on day one.
For example, if you sell a CRM product or, or a workflow product or like ours, one where you see great ROI from Datafox a couple months after you've started using it, because you'll start seeing all the deals that are closing as a result of the data and the insights that we're providing.
So we moved to annual contracts only.
It was jarring at first, but we just drew a hard line and that's been a very helpful development for us.
It also just means that your new customers, because it's a bigger commitment, they also put in a lot of time up front to really get onboarded and get trained.
Whereas if it's a small price or only a one month commitment, then it might not be the top priority for them that month.
And then at the end of the month they're like, I'm not sure about this, I'm moving on.
And then I guess the third thing I'd say is once you've closed enough deals to know that you have product market fit, I would say hire sales reps because again, for a product like ours, where your product costs five to six figures a year and you're selling annual contracts, it's really hard to do that part time as a founder.
It's important as a founder to prove the product market fit, I think.
But then at some point you need sales reps. One of the biggest mistakes I think I made early on was because I was one of the founders and I'm so emotionally connected to the product and the business.
The way I worked our sales funnel was kind of like every single prospect I reach out to, I think should become a buyer.
And we weren't using Salesforce or CRM at the time.
So I had my spreadsheet of maybe 100 companies or something like that and I would just keep reaching back out to them instead of widening the funnel and just going after a much higher volume of prospects, which is, I think the right thing to do.
The right thing to do is to realize that not everyone's going to be a buyer and certainly not everyone's going to be a buyer immediately.
So you just got to work it like a funnel and you got to make sure you're putting new prospects into the top of the funnel.
I, on the other hand, was just, I had my list of 100 and I wouldn't sleep until we closed them all.
And obviously that was never going to happen, but because there are all kinds of reasons why someone won't be a buyer.
So when we brought on our first sales reps, that really changed and the business started growing much faster, obviously as a result.
Omer (27:38.610)
So we talked about some of the sort of the skeletons in the cupboard and sort of knowing all the things that sort of the ugly and undesirable things about your own product.
As a founder, what were some of those things?
Things about Datafox in the early days that you knew about?
Bastiaan Janmaat (27:58.290)
Yeah, I think the main thing was that early on we did Quite a bit of manual work on our end to help deliver the data quality that our customers deserved.
But I think back to that very positively because it's how we made our technology better.
I gave you that example earlier of anytime there's a security breach at a business out there amongst millions of businesses, Datafox picks that up.
That's pretty automated now, but way, way in the beginning, it was us or people we hired would find news mentions or blog posts or government filings that referred to these events.
And we'd highlight the sentence and we'd say, this sentence is about this company and it's about a security breach.
And over time, that's become more and more automated.
It's how we learned which of those, we call them signals, which of those signals actually mattered to our customers.
It's how we learned where we'd find them.
It's how we learned what the recognizable patterns are and how we could teach an algorithm to do it automatically.
So I guess it's maybe a little bit related to the thing Paul Graham of Y Combinator always says, do things that don't scale.
This was definitely as a data company, these were the key things that we did that didn't scale, but were.
That wouldn't have scaled had we always had.
We continued to do it manually, but it taught us how to automate and ultimately scale.
So it was critical for us to do it that way.
Omer (29:33.790)
And how were you selling the product in the early days when you said you had sort of a plan starting from $49 a month, were you sort of driving traffic to the site and people could sign up?
Or was it still a kind of a sales process where you were going out and having sales conversations and then bringing people on board?
And there was kind of an onboarding process.
Bastiaan Janmaat (29:59.330)
Yeah.
You mean in terms of where our customers came from?
Omer (30:03.890)
Yeah, yeah.
Bastiaan Janmaat (30:04.530)
In the early days.
In the early days.
So it was outbound sales.
First the founders and then later our sales team.
And that has worked really well for us.
So we now have a pretty sizable sales team and it's a specialized team.
So some folks do the prospecting and others do the closing.
And given our product is priced at again, five to six figures now, anywhere between 10 and $200,000 a year.
That makes a lot of sense for us.
Earlier on, when we weren't priced that way yet, we invested a lot in SEO.
Again, this might be specific to a data company, but we've got all this great information.
Some of it we expose to Google's crawlers.
And so you can find all kinds of information about businesses, about conferences, about trade shows, about industries, competitive analyses.
There's a bunch of information that we expose that people can stumble across if they're searching for certain things.
And that was a great source of leads for us initially.
Omer (31:12.560)
So what were you doing?
It was content marketing, blogs, that kind of thing.
Bastiaan Janmaat (31:17.280)
But it was automated, so we cover a couple million businesses.
For those millions of companies, we would have a simplified version of that data set that we would just expose for free.
So let's say someone searched for, let's see, cybersecurity companies.
Then they might stumble across one of our pages that was an overview of the cybersecurity industry, which was an automatically generated page and would bring us.
Because it was novel content, it would bring us quite a lot of traffic.
Omer (32:00.280)
Got it.
Are those pages still up there on the site?
Bastiaan Janmaat (32:03.160)
They are, yeah.
You won't find it from our website, but if you search, I guess an easy way to do it is if you.
If you Google like Datafox Dropbox info or something like that, then you'd find the data fox the free Datafox page about Dropbox.
Or if you search Datafox cybersecurity.
I don't know.
You'd have to try a couple different things.
Omer (32:25.300)
I was kind of looking at the site and I was like, trying to make that connection between what you were doing with the blog because the site is ranked really high.
I mean, Alexa, ranking wise, it's like top 50,000 sites, which is huge.
Bastiaan Janmaat (32:40.100)
Yeah, I think it's all those pages.
Omer (32:41.780)
Yeah.
Now it completely makes sense because I was like looking at and I was like, it's not like you guys were like publishing blog posts every day and, you know, it's like, well, how are you doing this?
Bastiaan Janmaat (32:50.180)
Yeah, yeah, yeah, that's pretty smart.
There you go.
Omer (32:52.580)
Awesome.
All right, let's talk a little bit about machine learning.
And I think it would be helpful.
I mean, people have been here.
I mean, everyone hears the terms these days about artificial intelligence and machine learning and so on.
I think it would be good maybe if you could just give sort of a layman's explanation for people who aren't familiar in terms of how does machine learning work in terms of how you use it with your business?
Bastiaan Janmaat (33:24.520)
Yeah, sure.
So first of all, the term AI artificial intelligence gets tossed around pretty loosely.
But it's kind of funny.
The definition or our interpretation of what it means has also changed over time.
At its core, it means the automation of what would other be human processes.
But if you think back, maybe 10 years or something.
Then remember, if you would scan a piece of paper and then your computer would.
It's called ocr Optical Character Recognition.
And recognize the characters and put it in a word doc that was AI at one point, and then now it's not.
It's just scanning.
So the goalposts do move a little bit.
And machine learning is a type of artificial intelligence where machines are taught or teach themselves to make certain interpretations or do certain work.
As it pertains to Datafox because we're capturing so much information and having to find again, the needle in a haystack those key insights that really matter.
Machine learning is critical to us in how we actually do that.
And some of it involves, I guess I can just stick with some of the common themes and examples here.
I described how early on in our days we had to manually identify a lot of those key insights about businesses.
We manually find a sentence in an article that talks about a company having a security breach.
We built up a lot of what's called training data.
So many, many instances of security breaches in news articles or publications.
And then at some point we had enough of these examples that we could then allow our algorithms to basically do that automatically.
So now today, largely automatically, we're constantly finding instances like that because we taught the machine to identify certain word patterns.
What's really cool is when the algorithms get smarter and smarter over time.
So another way we use it is a lot of people who use Datafox, they use Datafox to find competitors to a business.
As a sales rep, for example, if you close a deal with one company, then next thing you should do is find the other 10 companies just like it and, and use the same pitch or the same sales approach with them.
Now, in covering millions of companies, we could never manually keep updating lists of which companies compete with whom.
And so we use our algorithms to look at things like what are the unique keywords that describe a business and what are the other businesses that look very similar to that?
Or what are the companies that keep getting mentioned in news together, let's another indication that those companies might be competitors.
Where machine learning comes in is within the Datafox platform.
Our users, sometimes they find a company missing.
So they'll see a business and then whoever they thought was the most obvious competitor isn't listed there and they submit that as a suggestion.
Or conversely, they might see a competitor listed where they're like, huh, that's weird.
I don't view that as a competitor.
And.
And so they'll downvote that and say, I don't think that's a competitor.
And so again, the algorithm takes that into account and gets smarter and smarter over time.
So it's what allows us to just cover so much underlying data and then surface the exact insights that are actually useful to our customer base.
Omer (37:11.790)
So you start off with kind of this training data and we kind of talked about like the example sentences, and you use that to, to kind of feed the algorithm.
And from that it starts to learn, in terms of the example with data breaches, what kind of sentence and what kind of structure of a sentence will kind of infer that there was some kind of data breach.
And it starts to look for those, but then it sort of starts to kind of go through this self learning mode where it's kind of coming back with maybe sentences that you guys didn't maybe feed in the training data and saying, okay, well I think this might also be a data breach or whatever.
And it just keeps kind of building up from that.
Right?
Bastiaan Janmaat (37:53.310)
Exactly, yeah.
And that's just one example in terms of how we use it in how we procure data.
But our customers experience the benefits of the AI that we've integrated into our product as well.
For example, if you're a sales rep using Datafox, then on your way into work in the morning, you can get an alert or an email from Datafox that says, hey, Omer.
Based on the customers you've had success with recently, here are a couple other businesses you should take a look at.
They look really similar.
Oh, and by the way, this company you met with six months ago, they were too small at the time, but they just hired this new CIO or whomever and might be great timing and a great opportunity to reach back out.
So that's where, you know, our solution and our use of AI really starts to, I don't know, ultimately make our customers jobs a lot easier.
In the nitty gritty of their day, it's like, hey, I sit down at my desk in the morning, what should I do first?
What should I do next?
Help me out here.
And that's the role we can play.
Omer (38:57.190)
Yeah, I think one interesting thing I came across when I was researching Datafox was that you actually, I don't know if this still is the case, but I read that you were kind of going through and bringing on new customers and they were actually, instead of them just directly using your data, they were giving you data about their customers and datafox was helping them to develop smarter insights from that.
Can you Talk a little bit about that.
Bastiaan Janmaat (39:28.990)
Yeah, I mean, it's often the first step we take in onboarding a new customer is we help clean up all the, enrich all of the information they have about their existing prospects and their customer base.
So a lot of our customers, they use Salesforce or.
But you know, even if they use other CRMs, we come in and we enrich all of their information.
We'll help them identify the businesses that they perhaps thought were, that were below the radar.
They thought those companies were small, but in fact they've been growing very quickly.
And so our updated information on those businesses helps surface them.
Or sometimes they'll have a bunch of just issues with their CRM.
They'll have companies that have actually closed down or companies that have merged and should be won or simply typos and very, very quickly.
We call it a diagnostics tool.
It comes in and it cleans up their whole CRM.
The benefit to us is obviously, in addition to just getting them onboarded is that it helps us grow our database too, because this is how we constantly uncover new businesses that perhaps we just weren't quite aware of yet, but our customers are asking us to go find information on now.
Omer (40:46.230)
Datafox, I came across this term on your website that it's the only platform built for account based prospecting or account based marketing.
Can you just explain to us a little bit like what is account based marketing?
Bastiaan Janmaat (41:01.670)
Yeah, sure.
Account based marketing, that's another one.
Another funny kind of term that I think if you hear the definition in layman's terms, I think people will be like, how is that new?
What it really means is focused marketing efforts into specific companies or specific accounts.
There's a, there's a, I guess a narrative that until recently marketing was kind of especially, especially digital marketing.
It was hard to track exactly which, it was hard to focus it on the businesses that you were really trying to market to.
You know, you'd buy a banner ad or you'd sponsor a conference.
I guess it applies to non digital as well, of course.
And you wouldn't be able to focus it specifically on the companies that you most want to be marketing to.
And now because everything is moved online and it's much easier to track things now you can actually focus your marketing efforts.
And we do this ourselves.
You know, we have a list of the couple thousand companies that we're most focused on selling to right now and we can spool up LinkedIn ads or Google Ads that are only shown to people who work at those companies.
So it allows us to A, spend our marketing dollars more wisely, but B, it allows us to really build a lot of attention and attention from an engagement with the exact accounts that, that we're trying, whose attention we're trying to get.
Now, an integral piece to being able to do that is the underlying data.
You actually have to know which companies to focus on and you have to know enough about them to figure out which marketing channels will actually help you reach those accounts.
So that's account based marketing in a nutshell.
And so a lot of people say that seems kind of obvious.
Like of course you're going to market to the, the customers that you're most, the prospects you're most interested in capturing.
It's just that, you know, it's been difficult to do that until recently.
Omer (43:15.410)
Yeah, just what you mentioned there about the LinkedIn ads and what we're talking about, the CIOs, it kind of reminded me of a Gary Vaynerchuk talk, I think I came across where he talked about, hey, running, you know, Facebook ads or something and targeting the, let's say if you were trying to reach the CIO of a company and kind of targeting everybody in that company with something like, you know, does your CIO know?
Blah, blah, blah.
So you know, he gets bombarded or he or she gets bombarded.
Bastiaan Janmaat (43:48.190)
Yeah.
Omer (43:48.910)
Cool.
Okay.
In terms of like the size of Datafox and you guys don't talk about revenue, but can you share any other kind of metrics to kind of help co founder folks get a sense of the size of the business?
Bastiaan Janmaat (44:04.440)
Yeah, sure.
Just a rough sense, I guess.
So we've been around almost four years.
We've raised about $9 million.
You mentioned some of our backers, Goldman Sachs, Google Ventures, Greenvisor, Slack, actually invested in us because we launched a very successful integration with them.
And then I guess the other metric that we focus on a lot is just our coverage.
Right.
So we currently cover 2 million businesses and we work just as hard to reduce that number as we do to increase it kind of in a funny way in the sense that we don't want it to be a vanity metric.
We don't want to cover 100 million businesses if there's a lot of junk in there.
So we're focused on really covering real operating, growing businesses.
And yeah, I guess in terms of company size, we're at about 40 employees at the moment.
Omer (44:49.600)
Awesome.
All right, let's get on to the lightning round.
I'm just going to ask you seven questions.
Just try to answer them as quickly as you Can.
Bastiaan Janmaat (44:56.440)
Sure.
All right.
Omer (44:57.560)
What's the best piece of business advice that you've ever received?
Bastiaan Janmaat (45:01.560)
It's to be to go beyond your comfort zone in being transparent with your company.
It's the best way to build trust and commitment with and from your team.
Omer (45:14.520)
What book would you recommend to our audience and why?
Bastiaan Janmaat (45:17.880)
Andy Grove wrote High Output Management.
It's a phenomenal book about motivating and leading teams.
Some great details on how to run one on one meetings and how to motivate your employees.
Omer (45:34.100)
I think one of the first business books I ever read was like.
I think was it like the only the paranoid survive or something?
It was another Andy Grove book.
But yeah, I always remember that.
What's one attribute or characteristic in your mind of a successful entrepreneur?
Bastiaan Janmaat (45:48.750)
I think EQ emotional intelligence is critical.
My co founder and I often pass each other in the hallways and we're like man, it's all about the people.
All of our biggest successes, but also most stressful moments are all about just human motivations in our employees, our customers, our partners.
Omer (46:08.910)
Yeah, and that's funny coming from a guy running an AI machine business, but it's very interesting.
What's your favorite personal productivity tool or habit?
Bastiaan Janmaat (46:23.410)
I love Calendly and a text.
Calendly lets me very easily schedule meetings with people.
They can just see my calendar and pick a time and Atext is a text replacement tool so I type in a little shortcut and it automatically pops in words, phrases or paragraphs that I use often.
Omer (46:47.530)
Is that an app on your phone?
Bastiaan Janmaat (46:49.690)
No, it's only on.
I'll use it on my laptop.
I think it's a.
It's either a Chrome integration or just a plugin.
Omer (47:00.370)
I'm going to check that out.
What's a new business or a crazy business idea you'd love to pursue if you had the extra time?
Bastiaan Janmaat (47:10.570)
I'm not sure about a specific business idea, but I would think I'd pick something in a very boring industry where if you just focus deeply on customers problems, you can, you know, in an industry like ours where the problem is no secret, sometimes we have to build what I would consider equalizing features.
Kind of like a feature that some other products offer.
And so we got to make sure we tick that box too.
I think if you sell into an extremely boring industry, then you don't have to build an equalizer features.
You can only focus all your effort on innovative features.
Omer (47:47.520)
That's an interesting one.
What's an interesting or fun fact about you that most people don't know you mentioned?
Bastiaan Janmaat (47:55.880)
I was born in Japan.
I guess the side effect of that is that I don't have an official birth certificate because as a Dutch baby there, they didn't really know what to do with me.
This was in the 80s, so I don't have a birth certificate.
Omer (48:08.970)
So I didn't think you could do anything without a birth certificate.
Bastiaan Janmaat (48:13.250)
Yeah, basically my parents created their own document that they signed and got notarized.
Omer (48:20.730)
Wow.
And finally, what is one of your most important passions outside of your work?
Bastiaan Janmaat (48:28.330)
Also related to your previous question, it's family.
For me, my brother lives in Brussels.
My sister and her and her husband live in Singapore.
My parents still live in Singapore.
So my family is very spread out.
So the more time I can, I can get to see them, the better.
Omer (48:47.780)
Do you go back to the Netherlands much?
Bastiaan Janmaat (48:50.020)
Probably once or twice a year.
Extended family there.
Omer (48:53.540)
I guess that's got to be a bit of a challenge, like trying to catch up with family.
You can't just go like, take one plane, right?
Bastiaan Janmaat (48:58.500)
No, it doesn't make it easy.
Lots of Skype, right?
Omer (49:03.430)
Awesome.
Bastian, I want to thank you for joining me today.
I really enjoyed this conversation.
Thank you for sharing the story of Datafox and kind of how you guys kind of went from idea to where you are today.
It's kind of interesting to get a behind the scenes look at what you're doing with the data and how you've gone out and acquired customers and built the company.
Now, if folks want to find out more about Datafox, they can go to datafox.com that's right.
And, you know, if they want to get a demo or whatever, they can kind of, you know, reach out to you guys there.
And if people want to get in touch with you, what's the best way for them to do that?
Bastiaan Janmaat (49:50.080)
Twitter would probably be best.
Bastian.
Yanmart is my very difficult handle.
Omer (49:58.060)
I will include that in the show notes so it's easy for people to get out there.
Awesome.
Bastian, I really appreciate the time.
Thank you very much.
And I wish you all the best with Datafox.
Bastiaan Janmaat (50:07.260)
Likewise.
Thanks, Omar.
Cheers.
