Turning Data to Direction | France Hoang from boodleAI

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France Hoang 0:00
Look, we’re living in a data world, right. And so we want to help people leverage the power of deep. And in particular, we want to help people find more than people that are looking for.

Alexander Ferguson 0:17
Welcome to UpTech Report. This is our apply tech series UpTech Report is sponsored by TeraLeap. Learn how to leverage the power of video at teraleap.io. Today I am joined by my guest, France Hoang, who is based in Colorado, co founder and chief strategy officer at boodle.ai. Welcome, France. Good to have you on.

France Hoang 0:34
Hey, great to be here, Alexander.

Alexander Ferguson 0:35
So your product is a people focused, predictive analytics platform. So for those out there, if you’re maybe a nonprofit fundraiser, a VP of sales, head of marketing, or maybe political giving campaign, anyone really around raising money or increasing sales from people, this might be an intriguing platform, you might want to check out. Now friends on your site, you state, its data science, not rocket science, very catchy title. I like that. Tell me that. What was the problem you guys initially saw and set out to solve?

France Hoang 1:07
Yeah, so look, we’re living in a data world, right. And so we want to help people leverage the power of data. And in particular, we want to help people find more than people that are looking for. And so the problem we’re trying to solve, every organization that is trying to increase money, increase sales, raise money, there’s a problem they almost all have, which is where do I devote my advertising or sales or fundraising dollars, and most importantly, time. And so what we do is we help organizations make predictions about what people are going to do in the future, based on the data they already have. And so that’s why we built the platform. And that’s why we built a company,

Alexander Ferguson 1:48
this whole idea of applying data science, and then really, I guess, machine learning, or this concept of how do you look at a lot of a lot of data and come up with answers. It’s not necessarily new, but it’s applying it maybe a new way. So help me understand like, what what are people doing before they say, let’s use your mind that platform? That’s cool, what is that? But what are they doing before that?

France Hoang 2:10
Yeah, so this is obviously a problem a lot of people are trying to solve, and a lot of people are solving already. So you know, what makes us different. So to do good machine learning, Ai, data science, right? You need data, you need algorithms, you need computing power, and you need the code that pulls it all together. The big sticking point for a lot of people is the data, right? their data they have is either incomplete or inaccurate, or they just don’t have enough of it. And so one of the things that differentiates us is, we have built a very powerful engine that takes the data that people already have just names and emails, names, and phone names, and mailing addresses. And we’re able to identify who those people are in the real world, which allows us to bring in hundreds of data points about those individuals into our system. So we complete the data picture.

Alexander Ferguson 2:59
So basically, all someone needs to provide is an email address or, and a name, and then you provide all the other data points

France Hoang 3:06
correct into our system. And so we solve the data problem for organizations that don’t have the data necessary to do good predictive analytics. Because a

Alexander Ferguson 3:15
lot of there are a lot of great algorithms out there, you can use to run this data science, but you have to provide all the data itself. So that’s one of the nice simplicity parts of your platform. Yeah, so

France Hoang 3:25
we solved the data part. The other part is, you know, there are a lot of great tools out there. In fact, you can code it yourself, right? I mean, it’s not that difficult. But bringing it all together and creating a platform, that in four or five clicks, you can bring in your data, married up with this other data set in create and test the model and deploy it all in an easy, you know, platform. That’s the trick, right? So it’s not getting from point A to point B, it’s finally getting from point A to point B, you know, in style comfortably right and quickly. So that’s what we provide.

Alexander Ferguson 4:00
I like that in style. And quicker. Because I mean, the the folks that are, are using your platform and your targeting are the VP of sales, head of marketing, as we’ve talked about earlier. And those people aren’t data scientists necessarily. We haven’t trained in that skill set. So the idea of having to learn new algorithms and have to turn all this do they have to know that skill set to be able to use your platform?

France Hoang 4:24
Absolutely not. That’s that is part of what we’re trying to solve. So while we’re data geeks, we’ve loved data science, we love data, we love getting into it, right? Our customers, they may feel the same way about data, but really, at the end of the day, they just want to increase their sales, right? They want to increase their fundraising, they want to find more of the people that look like their best customers are donors. And so they want to get to their answer quickly. And so what we saw, we saw all the messy problems of bringing in the data, you know, tying it up together, creating and testing the models and so what they get back is actually information, they you know, they just want to know a question, they ask us a very simple question, Who should I ask for more money? Or who should I ask to be my next customer? And we provide an answer. And we, we take the data they have in salt, all the other problems, which would normally take a team of data scientists to do, frankly. And so, you know, I will tell you a quick story, Alexander, you know, we have had a number of customers that come to us that have their own data science teams. And it’s been quite validating, because when they say, look, this is what we do internally. And they talk to their process, it’s identical to what we do for our customers, the difference is, they have a team of four or five data scientists a half million dollar budget, the process takes a couple of weeks, they’re doing custom coding, instead, you’re right in a sass platform where you’re living the same output to our customers without them having to do that massive investment that would otherwise be required.

Alexander Ferguson 5:53
It’s fascinating that effectively an AI tool playing the data scientist role, now where’s the data scientists gonna go, but I’m sure there’s still a need for them. It’s just really the small businesses that don’t necessarily have the data scientists in house or are ready for that scale. This is a good platform, tell me understand those some use cases, maybe we’ll walk through one of your clients a case study of how it’s helped someone in particular,

France Hoang 6:15
sure, we do work with both commercial and non commercial clients. So I’ll give you an example of both. So for example, we have a number of nonprofit partners that we work with, and, you know, they will have a huge data set of donors. And they will ask us, you know, I want to find more donors that look like my best donors now, and I don’t want to spam the entire universe of people on my list, you know, I would rather only ask those people for more money that are most likely to give and frankly, want to hear from me. And so they will write us a list of their of their donors, their best donors, we will use that list to build and then test and validate a model that can find who else looks like those best donors. And then we apply that model to maybe the rest of their donor database, or maybe their their email list, and we tell them, look out of these 10,000 other people, you could ask, here’s the 1000, that really you should spend your time and effort on. And what’s great about that is then their fundraisers can talk to 1000 people, not 10,000 people, right. And now we don’t have 10,000 people receive a message we have 1000. So I think it’s a win win for both sides. On the commercial side, the problem is oftentimes, is similar but a little different in the sense of verbal, commercial customers, they’re often having to deal with the problem of lead flow. Like I have 100,000 prospects a month, but my salespeople can only talk to 10,000.

Alexander Ferguson 7:40
Wait, there’s a way we can go through 100,000? How can I do it faster? Basically, how can I look at it?

France Hoang 7:45
Yeah, which 10,000 should I talk to, right? And so we help identify the best 10,000. And so we have one customer where they literally had 100,000 leads in a two month period, we identified the best 40%, the best 40,000. And you know, we kind of break down the leads into a B, C’s and DS, the A’s being the best G’s being the worst? Well, the A’s actually converted became customers at a rate five times better than the deeds. And so clearly, right, that has an effect on the

Alexander Ferguson 8:14
data proves a point that if you’re if your time is spent on the right people, then it you’re gonna get a lot better results was better time. I’m curious, though, like what point does like how many leads? What’s the numbers that make sense to have to use a platform like yours, where, like, if someone’s only getting 100 leads a month? I mean, they could just manually do it themselves. So where’s the tipping point that it makes sense to use a platform like this?

France Hoang 8:38
Yeah, so we are, you know, we pride ourselves on being able to serve a variety of customers. You know, it really depends on the organization, you know, you’d have a lead prioritization problem, or a customer segmentation problem, and one where solving that problem produces a return on investment that makes sense for you, right? He obviously don’t want to spend 10s of 1000s of dollars in a platform where you only are making, you know, a couple $1,000 back. So I think there is necessarily, you know, a point where it makes sense and when it doesn’t, but that’s why we also besides the platform, we have a dedicated customer success team. And so this is AI as a service. This is really cutting edge stuff. A lot of organizations don’t know how to deploy and implement these kind of data scientists

Alexander Ferguson 9:20
have handholding to go through it you’re not alone basically, you’re not

France Hoang 9:24
alone. And we certainly wouldn’t expect someone to buy a platform like this and just kind of go off to the races on their own right so we you know, we give you a beautiful car to get from point A to point B we also give you you know a copilot and a you know somebody sitting in the car service

Alexander Ferguson 9:38
as needed to help you along I like I like it’s I think we’re in an age of both SAS as a platform to be able to use but services alongside so that as you move forward you’re not doing it by yourself unless you can do it unless you got it. And

France Hoang 9:55
yeah, and I will upsell Xander, we do have customers that are pretty You know, very knowledgeable, in fact about data science and they could do everything the platform does on their own. They choose to use the platform anyways. Because, again, right, even though you can build your own car, if somebody has already built a beautiful car, you might as well write it. And so our platform internally to do the same type of modeling they would otherwise do manually, but we do it faster, we do it cheaper, right? And in some cases, we get better results in nothing else, because you can iterate fast. Right, right. You know, we had a, we had a customer that there was a data set they’ve worked on for a couple of weeks to provide a useful model internally, we took that same data set, and we provided a model back in 45 minutes, right? So they were they were like, Look, even, you know, the model is great, but really the turnaround, right? The ability to like iterate quickly, is incredibly valuable.

Alexander Ferguson 10:52
So you say you could simply upload a set of data, a list of people and within 45 minutes, you could have back here’s the people you should be paying attention to. Absolutely. Okay. Now, this leads, though, to another point, you said like even people who know what they’re doing, as far as integrations or expandability, if someone has a data scientist, or someone who really is into this, know the knowledge of how to make it work. Is there like a back end? Or is there a wave to integrate? If they want to do more? Or is it still a closed system, and if they need to go beyond they need to go outside?

France Hoang 11:24
Yeah, so one of the great things about being a startup, and being agile, literally, is we’re always on the lookout for how to grow the platform in a way that adds value to our customers. And so all the customer, all the developments on our platform have been driven by customer requests and needs. And so you know, we we do a new release every two weeks. And so we’re built on microservices architecture, we have customers ask for things that didn’t exist, the platform, were able to quickly iterate and within release them, and this idea of like integrating more closely with, you know, customer systems is certainly something we’re open to

Alexander Ferguson 12:03
got, it’s, it’s nice to have that type of mentality and approach that, hey, if I want an idea, I know that you guys are listening in you, you can can add to it, taking a slight pivot here for four, whether it’s the VP of sales, or marketing or fundraising, in the role that they are in their job. Aside from your platform, if you were to give any advice to what they have to do in their daily work and activities, what kind of advice or tips or insight would would you share with them?

France Hoang 12:36
Yeah, so I think it would be the same advice that I tell my own team, right, which is, at the end of the day, listen to your customers. Like there’s the ground truth. You know, I think one of the, you know, I, I’ve been on multiple founding teams, been with multiple companies. I think one of the reasons why the team I’ve been a part of had had success is we’ve stayed very humble, and we don’t fall in love with our own ideas. And we don’t fall in love with what we think is supposed to happen. We look at what’s really happening. And so I may come up with the best marketing campaign, I may come up with the best product idea. But if the customers don’t like it, then it doesn’t matter. Right. And so I think, whether you’re raising money for a nonprofit, or you’re creating a camp, marketing campaigns for your direct to consumer business, you know, always going back to what does the customer want? What does the customer need? And most importantly, what does the customer telling me?

Alexander Ferguson 13:29
That’s, that’s powerful. Always good to listen to the customer? Yeah, so it was good, good idea for boodle looking forward here. What can you share of your roadmaps of what you’re excited about upcoming and where you guys headed?

France Hoang 13:41
Yeah. So our next thing that we’re kind of the next iteration of the platform, while we built this very powerful people focused predictive analytics engine that answers the question of who should I talk to next? We’ve come to realize that that’s important, but not complete part of the equation. And so the next set of questions we’re answering is, where, when, and what? And so this idea of what we call transaction analytics, which is not just analyzing people’s customer databases, or donor databases, but looking at the entire set of order histories, all the transactions and be able to identify where our sales trending or where is fundraising trending, and who and what is trending, and maybe answer why, and then tie that back into the who. And I think that idea of transaction analytics combined, when people focus predictive analytics will unlock even more value for our customers.

Alexander Ferguson 14:36
While I’m excited to then see as you guys move in that direction, want to do another interview to hear more about that. And you guys are in 2018 is when you started so it’s like are several years in the play. For those who want to hear more about the journey though, stick around for part two of our discussion, where we’re going to dig a little bit more deeper into the journey that you have been on the insights you’ve gathered, so stay tuned for part two of our discussion for those who want Learn more though, go to boodle.ai that’s BOODLE.AI. And you’ll be able to request a demo. Right? They can check it out. Absolutely. And take it for a spin. Thanks again for joining us on today’s apptech episode. We’ll see you next time. That concludes the audio version of this episode. To see the original and more visit our UpTech Report YouTube channel. If you know a tech company, we should interview you can nominate them at UpTech report.com. Or if you just prefer to listen, make sure you subscribe to this series on Apple podcasts, Spotify or your favorite podcasting app.