artificial intelligence - Bastiaan Janmaat

How a SaaS Company Uses Artificial Intelligence to Generate B2B Leads – with Bastiaan Janmaat [151]

How a SaaS Company Uses Artificial Intelligence to Generate B2B Leads

Bastiaan Janmaat 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 algorithms structure information on millions of businesses and deliver reliable data and machine-learned 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 funding.

The company's investors include Goldman Sachs and Google Ventures. And their customers include companies such as Twilio, Box, Google, Amazon & SalesForce.

This episode is a story about 4 co-founders who decided that they could use Artificial Intelligence (AI) to help sales & marketing people to make better decisions.

They saw firsthand how the explosion of information available to sales & 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 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 customer's problems however they could.

The first version of their product was sold for $49 per 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 (AI) and how they're using AI technologies to help real-world problems for businesses trying to prospect and generate leads.


Click to view transcript

00:12 Welcome to another episode of The SaaS Podcast.

00:18 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 business.

00:29 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 in 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 A.I. technology but also did a ton of work manually to process the data they collected. In other words, they focused on solving customer problems. However, they could. The first version of the product was sold for $49 a month. Today their customers pay them anywhere from $10,000 to $200,000 a year.

01:25 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 A.I. technologies to help solve real-world problems for businesses trying to prospect and generate leads. I hope you enjoy it. Before we get started. I'd love to send you my free productivity toolkit which will teach you the habits hacks and tools used by successful founders and entrepreneurs. If you'd like to get a copy just head over to Also I'm delighted to announce the launch of SaaS Club, a premium membership site for software entrepreneurs. It will cover more in-depth content for people who are interested in going beyond this podcast. Initially, it'll start as a monthly training program.

02:18 Each month we'll have a live master class or mastermind and Q&A will cover the topics that are most important to the members. From finding a winning business idea to learning how to hire a great developer or learning the essential growth hacks for acquiring new customers. SaaS club is a place to connect. Share ideas. Ask questions. Get help and celebrate our successes. Currently, registration is open for founding members people who joined the waitlist many months ago. But if you're interested in learning more about SaaS club or want to be notified when public registration opens then you can go to

02:59 that OK that's going on with the interview.

03:01 Today's guest is the co-founder and CEO of DataFox, an artificial intelligence and prospecting platform DataFox help sales and marketing teams prospects smarter and have thoughtful personalized conversations at exactly the right time.

03:21 DataFox's algorithm structure handles millions of businesses and delivers reliable data and machine learns 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 funding. 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 & SalesForce. So today I'd like to welcome Bastiaan Janmaat. Bastiaan welcome to the show.

03:21 Excited to be here. Great!

04:02 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 everyday.

04:08 What gets me out of bed. My alarm clock I get up early. I'm definitely a morning person. So I love starting the day early. I cherish kind of the first couple of hours of quiet in the office when there aren't any meetings yet and I get to actually get stuff done. The 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 I had the opportunity to explore some some beautiful parts of the world before settling here in San Francisco so I feel like I owe it to my parents take the experiences and education they've given me to make something out of it. So

05:02 you know time is ticking. Got to got to make work of it. Now, is it true you were born in Japan.

05:09 That's true. Yeah. All my folks, they're Dutch but they moved out there before I was born.

05:13 So I think I came across that you were born in Japan raised in Singapore Dutch background. You went to college in Hong Kong as well as Europe.

05:25 So you gave them a lot less. They know for sure.

05:31 I think I had a similar background where I think I probably had gone to about 15. I think is almost 15 schools by the time I was 16 years old. Wow. My my my parents is moving around so much my dad was an international banker and we 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 go to school and be like you know kind of make friends but I probably will be here for like you know in six months time. So

06:09 you kind of keep in mind that it can be jarring right. Or I guess it's hard to commit to new friends and new places when you're not sure how long you're going to stick around there. But I mean look at you know it's probably you've probably been given a lot of tools where this is how you're able to you know meet so many new people and hosts a podcast the way you do it. 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.

06:37 Yeah yeah I think you're right. So I gave the audience a kind of an overview of DataFox but can you tell us in your own words a little bit more about the product.

06:47 Yeah for sure. So we we are a business that provides what we call automated customer intelligence i.e. information about the customers or your future customers in a way that's seamless almost effortless to you as a as a user how the technology works is we have software. We write algorithms that goes and they go and digest all of the world's business information. Think of anything you can 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, 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.

07:52 OK I want to dig down into in more into that and we'll sort of talk a little bit about how somebody can use a product like DataFox for for the intelligence and the prospecting. But let's kind of go back to maybe before 2013 before you launched the business. Where did the idea come from.

08:13 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 there 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 in your company. And what made this difficult was that there were just so many companies out there right 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 non-engineering background so I guess I didn't have a real problem solvers mind at the time I came.

09:14 I wound up going to a business school. That's what moved me from London to here to the Bay Area and it was in business school that I met met people who had who did have a real problem solvers mind about things you know 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 bunch of activities on campus and beers and barbecues and what we. Well the idea we came up with was to completely automate that that job that I had had but also the job of anybody who's who's looking for a needle in a haystack you know what we what we realized was that anybody in a sales job anybody in a marketing job people in in real estate had hunters and recruiters people working in banking people working and investing jobs.

10:07 There so many people whose day to day involves both a researcher hat and a sales hat. The sales hat is often you know as 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 and finished my MBA. But we started working on this prototype. Again these algorithms that we're go 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 very quickly. We had a working prototype that showed that it could it and mine through all this information at a much much faster rate and just make better suggestions and what any any one of us could have done manually.

10:57 So that was the genesis of it. And then 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 how they use data to drive growth.

11:14 We now count as our customers. Okay so kind of I want to kind of dig a little bit more into that. So

11:22 you've got this idea and wait and wait you start getting this data from do you start to think about talking to potential customers to sort of validate the idea what was some of the steps that you guys start take which helped to form a picture that there was a business opportunity here. Yeah

11:49 yeah. Those are the exact two right questions. You know part two big questions early on where OK what is what what data could we actually reliably generate that would be used to make these types of decisions so what and where would the data come from. And then number two validating the market need. Right. What 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. So two of us spent myself being one of them spend most of our time on that validation piece talking to a potential customers. And the other two were. So you know spend their time writing these algorithms and analyzing all the data out there in the public domain and what we can do with it on the I guess to start with the latter on the data sources side.

12:44 What we learned was that if you think about the average business it's you can kind of all the information out that business you can kind of think of as an iceberg where what's clearly visible above the sort 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 some some 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 that's what you would find out if I gave you an assignment to just and deeply research one business only one business.

13:30 You would 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 read blog posts. Other people have written about the business and that will give you think much more of a picture for you of what that company is 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.

14:16 So that's the that's some of the technology we've built. We knew we had to build early on. You know these algorithms would basically figure out what are the key milestones that these 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 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 ray of people was that kind of some of the big the jobs I rattled off there 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 one job is business information most essential, most critical those are 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.

15:18 So that's why our business has developed developed pretty early on into catering into a number of different industries amongst them anybody working and B2B sales and marketing and anybody working in a financial investment or advisory type job. The comment you made about the data is is really interesting and in terms of like the natural language processing can you get 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 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 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.

16:20 OK OK. You know I could break down the HTML and say OK here is going to. This is the URL this is the this is where the number of employees is going to 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 right. And so I think you kind of gave the example here about the idea of a human having to go through that. But but is it maybe just kind of in some kind of example that you can think of just where people are going to be at that point or people.

16:58 Yeah let me give you two quick examples So first of all let's imagine a cyber security company if you're a cybersecurity company and you're trying to sell your offering then and then filtering up a bunch of companies based on you know 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. What 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 a security breach recently or maybe businesses who are 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 sales people at at the cybersecurity company would probably be doing that probably set up a bunch of Google alerts and probably calls talk to a lot of people to kind of figure out what those businesses are that are experiencing breaches or hiring new CIOs and DataFox you could you could pull that list.

18:11 You could run that search and in ten seconds because we've we've 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 a very specific word pattern so there we we pick up on when companies have had breaches when they've hired CIOs. We could even help you figure out which companies are looked to be hiring a lot of people that have security backgrounds and that that's the kind of you know smart prospecting that will be very difficult to do without DataFox or are totally different.

18:49 Example one of our customers in 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 you're going to. That's that's exactly when you look for a new office cleaning services. So same thing. Data Fox keeps track of Twitter blog post 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 cleaning service and they've seen their business grow very strongly as a result of that.

19:28 OK. So

19:29 you 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.

19:41 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 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.

20:04 Let's see the first. You know honestly the first I guess the first prototype we've 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 need an offering that would cover a high number of companies but also do 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 we could start showing to people and that was the purpose it served initially that we could show it to people and different kinds of jobs and different kinds of industries and companies and get get early feedback on what part of this resonates what seems most useful. How would this fit into your workflow. And and we went from there I think we really started selling our first contracts a year or more in and that's probably one of my biggest lessons frankly actually that I think that came too late.

21:03 Even though building a data company is hard and building that first kind yes minimum sellable product takes a while. I still say that I still think back wishing we had we we priced it sooner and higher frankly me, I guess you 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 wrong. They know where the data's weak. But as I think back we should adjunct that price up five to ten times much much sooner than we did.

21:38 So how much were you charging when you started out at some point. I

21:42 think we had like $49 a month tier. I think you know it was it was a double edged sword. The cost of having the downside of having that$49 tier was it was so cheap that any literally anybody would just come in and check it out for a month. And so it led to you know suddenly in a very diverse and broad customer base that we had to 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 you know people who checking 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 you know noise and false positives us thinking oh great this person you know really wants to use our product where then they cancel a month later because it just wasn't that essential to them.

22:38 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 you know 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.

23:07 So the pricing inside 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 will kind of better align 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 maybe can go and check it out but they're probably not your ideal long term customer.

23:55 And I guess one hard thing about charging more is the fear that it's going to be harder to sell.

24:04 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 it kind of thinking about if you will going back like what would you have done differently. I'm

24:15 wondering if there's some advice that you can share with people who are maybe in a similar situation maybe you 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 going to 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. You know they don't they don't they know the skeletons in the cupboard.

24:49 Yeah. My advice really I would start by saying look it really depends on your market right. There's some really great businesses out there that have for for home freemium works really well where there's a there's a free-tier. There's a cheap-tier then there are slightly more expensive teirs. But pricing is listed on the website. It's very transparent. The company may not even need sales reps. That was not that was not where we thought we'd be 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 are we ultimately don't list pricing on our website because it's steep and we have you know we don't have a bunch of free users and we have fewer customers but they are high paying customers if you,

25:41 So for those folks who have B2B SaaS products where they think they'll end up charging you know, enterprise type pricing. My first advice would be take your price down from your website if its up there because with your price up you can do any experimentation. If your price is on the website then at least you can test different pricing and different positioning with the you know with the prospects you're talking to. The second thing I'd say we learned is move move off of the 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 know you're not going to you're not going to be fully up and running on day one. So if you see for example. If you sell a CRM product or a workflow product or like ours one where you see great ROI from DataFox a couple of months after you've started using it because you'll start seeing all the deals that are closing as a result of the date and the insights that we're providing So we move to annual annual contracts only it was jarring at first that we just drew a hard line that's been a very helpful development for us.

26:50 It also just means that your new customers because it's a it's a bigger commitment. They also put in a lot of time upfront to really get on board and get trained whereas it's as 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 like that I'm not sure about this. I'm moving on. And then I guess the third thing I'd say is once you close enough deals and know that you have product market fit. I would say hire sales reps because it's easy.

27:22 But again it's for a product like ours where your product costs five five to six figures a year and you're selling annual contracts. You just you know it's really hard to do that part time. As

27:35 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. Well one of the biggest mistakes I think I made early on was because I was you know one of the founders and I'm so emotionally kind of connected to the product in 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 right.

28:01 So and we weren't using Salesforce or CRM at the time so I had my spreadsheet of maybe a hundred 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 realize that not everyone is going to be a buyer and certainly not everyone's going to be a buyer immediately. So you know you just got to work it like a funnel and you've 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 you know we closed them all and obviously that was never going to happen because there are all kinds of reasons why someone on the long desire. So when we when we brought on our first sales reps that really changed and the business started growing much faster obviously as a result.

28:53 So we talked about some of the the sort of the skeletons in the cupboard and sort of knowing all the things that sort of the ugly and undesirable things about you know your your own product as a founder. What was some of those things about DataFox in the early days that that you knew about.

29:12 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. It's you know I give you that example earlier of any time there is 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. Now 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 are those signals actually mattered to our customers.

30:11 It's how we learned where we'd find them. It's how we learned what the recognizable patterns are and how we'd how we could teach an algorithm to do it automatically. So I guess it's maybe a little bit related to that thing. Program 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 continue to do it manually but it taught us how to automate and ultimately scale so it's critical for us to do it that way.

30:48 And how were you selling the product in the early days when you said you know you had sort of a sort of a plan starting from forty nine dollars a month where you 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 going out and having sales conversations and then bring people on board and it was kind of an onboarding process.

31:13 Yeah in terms even in terms of where where our customers came from. Yeah I mean yes in the early days. So it was it was outbound sales first of 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 Christ. You know like again 5 to 6 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 we invested a lot in SEO where 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.

32:15 There's a bunch of information that we expose that people can stumble across if they're searching for certain things and that that was a great source of leads for us initially. So what were you doing it was just it was content marketing blogs that kind of thing but it was automated so you know we have we cover a couple of million businesses for those millions of companies. We would have a simplified version of that data set that we would just yeah expose for free. So let's say let's say someone searched for you know like 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 automatically generated page and would bring us because it was noble content. It would bring us quite a lot of traffic. Got

33:15 it. Are those pages still up there on the site.

33:17 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 DataFox the three DataFox page about Dropbox or you search DataFox cybersecurity or you know largely different things.

33:39 I was I was kind of looking at a 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 and ranking wise it's like top you know 50000 sites which is huge. I think it's all those pages. 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 post a day and you know it's like well how are you doing.

34:05 Here you go. Awesome. All right. Let's talk a little bit about, machine learning and I think it would be helpful.

34:18 I mean people being here I mean everyone has 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.

34:39 Yeah sure.

34:40 So first of all I was you know the term A.I. Artificial Intelligence gets tossed around pretty pretty loosely but it's kind 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 you remember if you would you would scan a piece of paper and then your computer would be called OCR optical character recognition and recognize the characters and put it in a word doc. Yeah that was A.I. at one point and then now it's not it's just scanning. So the goal posts do move a little bit. And machine learning is it is a type of artificial intelligence where you know machines are taught or teach themselves to to make certain interpretations or to 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.

35:53 Machine learning is critical to us in how we actually do that. And some of it involves I guess I can you know to 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've 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 constantly finding instances like that because we taught the machine to identify certain word patterns which really cool when when the algorithms get smarter and smarter over time.

36:57 So another way we use it is a lot of people who use DataFox they use DataFox to find competitors to a business. Yeah, as a sales rep for example if you close a deal with one company then the next thing you should do is find the other 10 companies just like it and use the same pitch or the same sales approach with them. Now when in covering millions of companies we could never manually keep keep updating lists of which companies compete with whom. And so we use our algorithms to look at things like what are the what are the unique keywords that describe the 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. That's another indication that those companies might be competitors where machine learning comes in is within the DataFox platform our users.

37:50 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 they submit that as a suggestion or converse say they might see it competitor list where they like how that's weird I don't view that as a competitor. And so they'll down vote that and say that's not the that's not I don't think that's a competitor. And so again the algorithm takes that into account and get 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.

38:26 So you start off with kind of this training data and we kind of I like the example sentences and you use that to kind of feed the algorithm and from that it starts to learn in terms of the example of data breaches. What kind of sentence and what kind of structure of a sentence will will kind of infer that there was some kind of data breach. And it starts to look for those but then it it sort of starts to kind of goes through the 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 OK well I think this might also be a data breach or whatever and it just keeps kind of building up from that. Right

39:07 exactly. And that's just you know one example in terms of how we use it on in how we procured data. Our customers experience the benefits of the A.I. that we've integrated into our product as well. For example if you're if you're a sales rep using DataFox then you are 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 a couple other businesses you should take a look at they look really similar.

39:40 Oh and by the way this company you met with six months ago you know they were too small at the time but they just hired this new CIO or or whomever might be a great might be great timing and a great opportunity to reach out. So that's where you know our our solution and our use of A..I really starts to, I don't know. Kind of ultimately make our customers jobs a lot easier in the nitty gritty of their day. It's like hey I'm a 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.

40:13 I think one interesting thing I came across when I was recision DataFox was that you actually I don't know if this still is the case but I had 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 helping them to develop smarter insights from that. Can you can you talk a little bit about that.

40:43 Yeah I mean it's often the first step we take in on board the new customer is we help clean up all the enrich all of the information they have about their existing prospects and their customer base. Also a lot of our customers they use Salesforce or you know even if they use other CRM as we come in and we enrich all of their information will help them identify the businesses that they perhaps thought were that that were below the radar. They thought those companies were small but in fact they've been growing very quickly. And so we are updated information on those businesses helps surface them or sometimes they'll have a bunch of just issues. But they're CRM they'll have companies that don't have actually closed down or companies that have merged and should be one or simply typos and very very quickly or are we called the diagnostics tool.

41:39 It comes in and it cleans up their whole CRM. The benefit to us is obviously in addition to just getting them onboard it 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.

41:39 Datafox

42:02 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 could I just explain to us of the like what is account-based marketing.

42:18 Yes sure. Account-based marketing is another one.

42:18 Another funny kind of term that I think if you hear the definition in layman's terms I think people will be like what how is that new. What it really means is focused marketing efforts into specific companies or specific accounts. There's a there's an how you'd say, narrative that, until recently marketing was kind of especially especially digital marketing, tt 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 a place and non-digital as well of course and you wouldn't you wouldn't be able to focus 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 so.

43:25 And we do this ourselves. You know we have a list of a couple of thousand companies that we're most focused on selling to right now and we can we can spool up LinkedIn ads or Google ads that are only shown to people who work at those companies. So it allows us A.) to 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 we're trying and whose attention we're trying to get now an integral piece to being able to do that is the underlying data. Like you actually have to know which companies to focus on. And you have to know enough about them to figure out what which which marketing channels will actually help. Reach those accounts so that's account based marketing. And in a nutshell and so a lot of people say what that seems kind of obvious like of course you're going to market to the the customers that you're most good prospects you're most interested in capturing.

44:26 It's just that you know it's been difficult to do that until recently. You know just what you mentioned 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 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 the company with something like you know does your CIO know blah blah blah so you know he gets bombarded or he or she gets to.

45:03 Go OK.

45:04 And in terms of like the size of the data folks and you guys don't talk about revenue but can you share any other kind of metrics to kind of help folks get a sense of the size of the business.

45:18 Yeah sure. Just a rough rough sense I guess so we've been around almost four years. We've raised about $9,000,000. You mentioned some of your backers Goldman Sachs, Google Ventures, Green Visor, 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. You know 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.

46:03 Awesome. All right let's get to the lightning round I'm just going to ask you seven questions just trying to answer them as quickly as you can. Sure. All right what's the best piece of business advice that you've ever received?

46:16 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 within from your team. What book would you recommend to our audience and why? 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.

46:48 I think one of the first business books I ever read was like I think it was like the only the paranoid survive or is another and you know but yeah I remember that. What's one attribute or characteristic in your mind of a successful entrepreneur? I think EQ is emotional intelligence is critical?

47:08 My co-founder and I often pass each others past each other in the hallways and we're like man it's all about the people. All of our biggest successes and but also our most stressful moments are all about human motivations and our employees or our customers or our partners.

47:23 So yeah and that's funny coming from a guy running A.I. business. It's very interesting.

47:31 What's your favorite personal productivity tool or habit?

47:42 I I love Calendly and aText. Calendly lets me very easy 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 and words phrases or paragraphs that I use often is that an app on your phone?

48:04 No it's all it's it's own the way I'll use on my laptop.

48:10 It's either chrome integration or just a plugin.

48:14 I'm going to check that out what's new business or a crazy business idea you'd love to pursue if you had the extra time?

48:24 I'm not sure about a specific business idea but I would I would think I'd pick something in a very boring industry where if you just focus deeply on customer's problems and you can go in in an industry like ours where the problem is no secret. Sometimes we have to build on what I consider equalizing features. Kind of like a feature that some other products offer and so we got to make sure we ticked that box too. I think if you if you sell into an extremely boring industry then you don't have to build any equalizing features you can only focus all your effort on innovative features.

49:02 That's an interesting one what's an interesting fun fact about you that most people don't know?

49:09 You mentioned 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.

49:19 This was in the 80s so wow I don't have a birth certificate. So how does. I didn't think you could do anything without a birth certificate.

49:27 Yeah. Basically my parents created their own documents that they signed and got notarized.

49:35 Wow. And finally what is one of your most important passions outside of your work? Also related to your previous question that it's family for me.

49:46 My brother lives in Brussels. My sister and her and her husband live and live in Singapore. My parents still live in Singapore so my family is very spread out so.

49:58 The more time I can come get to see them the better. Do you go back to the Netherlands much. Probably once or twice a year. Extended family there. I guess that's going to be a bit of a challenge like trying to catch up with family. You can't just go like take one plane right. No it does make it easy. Lots of Skype. Awesome. That's

50:18 you know 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. You know it's kind of interesting to go to get a kind of 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 That's right. And you know if they want to get a demo 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.

51:04 Twitter is probably just @bastiaanjanmaat very difficult handle.

51:11 I will I will include that in the shownotes. So it's easy for people to get out there. Awesome question. I really appreciate the time. Thank you very much and I wish you all the best with DataFox.

51:11 Likewise Omer, thanks!

51:26 All right thanks for listening. I really hope you enjoy this interview. You can get to the show notes by going to where you'll find a summary of this episode and a link to all the resources we discussed today. If you enjoyed this episode then head over to iTunes and subscribe to the podcast. And consider leaving a rating and review to show your support for the show. If you're in a good mood if you're not already on iTunes just head over to and click the itunes. But.

51:57 Thanks for listening. Until next time take care.

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