Jason Davis is passionate about the promise of data science and analytics. He went to school for it, earning a BS in computer science and a Ph.D. in machine learning, data, and statistics.
Jason loved getting his Ph.D., but he figured out fairly quickly that a life in academia was not for him. What excited him most about data was its incredible power to transform the real world.
He cites the example of self-driving cars. This is a technology powered by data, and one that would have been possible only in sci-fi films in the not-so-distant past. But today, the promise of self-driving cars is real, and it’s likely only a matter of time before they’re in mass production.
Since completing his Ph.D. program, Jason has founded several companies that help businesses harness the power of their data. His latest, Simon Data, is a customer data platform (CDP) focused on helping marketers strengthen and accelerate their conversations with customers.
Thinking specifically about marketing, Jason shares two concrete data applications. The first is focusing on the specifics of the customer journey and lifecycle. When you know your customer behaviors inside and out along the journey, you can identify the sweet spot for critical moments like the sales pitch or the upsell.
The next is using data to speed up the marketing pipeline. When you understand what your customer is doing at each stage of the journey, you can unlock opportunities ripe for optimization. Can you create a welcome email sequence that advances subscribers toward the first sale more quickly? Is there an opportunity to create a white paper that addresses common objections, so they’re out of the way before a sales call?
To maintain a steady flow of data for each team within your organization, integrate data collection and analytics into the UI. Utilize UTM codes in links to see where inbound leads come from. Leverage a CDP platform to understand known and unknown users’ behaviors across your various digital channels. Create an email triggered to send after a new product purchase—one that includes a link to review the product so that you can connect feedback to individual customers.
But where should data science sit in an organization? It’s a tricky question. When IT holds responsibility for the data, it can sometimes cause bottlenecks in data projects. At the same time, data science is a specialized function, and when you hand out data to everyone across the organization, you end up losing out on some of the expertise data scientists bring to the table.
Jason says that, for now, a centralized approach to data science is best. But as the technology continues to evolve, he sees a future where all employees are able to dig down into the data.
And just because data scientists are doing the heavy lifting doesn’t mean other teams can’t or shouldn’t develop an interest in data. When there’s transparency around data across the organization, individuals can begin to form hypotheses around ways to improve their specific business functions.
In the end, it’s about striking the right balance between letting the experts do their thing and empowering your other employees to leverage data when and where they can. Greater data literacy across an organization never hurts, and it’s exciting to think about what the future holds for data.
Rob Ristagno: When’s the last time you met someone who loved getting their Ph.D.? Today’s guest on the CEO Campfire Chat is so passionate about his area of expertise, which is data science, that he thoroughly enjoyed studying it in depth. Today, he uses that knowledge to help marketers build stronger relationships with their customers. Looking for simple steps to accelerate your marketing processes and grow your revenue? You won’t want to miss this episode.
Announcer: This is the CEO Campfire Chat with your host, Rob Ristagno. Taped in front of a live studio audience. Join us to hear successful growth stories for middle market companies, just like yours. Sponsored by the Sterling Woods Group.
Rob Ristagno: Welcome to the CEO Campfire Chat, recorded live in front of a studio audience of senior executives. I’m your host, Rob Ristagno, and I have the privilege of introducing you to Jason Davis, co-founder and CEO of Simon Data, a customer data platform (CDP). Jason previously founded the adtech company Adtuitive and actually sold it to Etsy in 2009. He is also an investor in and an advisor to several other tech startups. He has a Ph.D. in machine learning from UT Austin. Jason, welcome.
Jason Davis: Thank you, Rob.
Rob Ristagno: A Ph.D. in machine learning, super impressive there. Tell us a little bit about what got you interested in academia to begin with.
Jason Davis: I mean, look, I finished my undergraduate degree and I was really sort of interested in the world of statistics and applying this to large scale data processing and analysis and intelligence. I had a great opportunity to pursue that in an academic setting. I really enjoyed my time in grad school and getting my Ph.D., but even in hindsight, I always joke it took me five years to realize the value in data isn’t in the algorithms and machine learning. Really the value in data is how you use it and what the applications are. Sometimes that does require deep analysis and algorithmic work, and a lot of times it doesn’t require…
Jason Davis: Really, what got me excited about data almost 20 years ago in an academic setting was the promise of what data science can bring to the world. I think recently we’ve seen some real advancements around things like deep learning that, for certain applications around self-driving cars or natural language processing or machine translation, they really have been transformative in driving business processes and business outcomes. I imagine in five years, six years, 10 years, self-driving cars in the real world will be a thing, to some extent. That’s real disruption from machine learning.
Jason Davis: But really, when I look at other types of business processes where you can’t measure the whole world, you look at something like a self-driving car and you have LIDAR, you have your cameras, your audio, you can measure everything. It’s even better than a human can. Then the machine can actually process it as well as humans can. At least, that’s where the technology is headed.
Jason Davis: You look at a lot of other business applications. You can measure a business. You can measure a few interactions with a customer and you measure some aspect of your production process. But there’s no way you can measure the entire thing. It’s too complicated. Then you sell someone a widget that they consume in the comforts of their own home, like who knows what kind of experiences they’re having because it’s not how the world works.
Jason Davis: So ultimately, when I look at a lot of the business applications, and my experience academia is the promise of data and machine learning and how it’s applied, really it’s still a huge, huge open question and how to really, I was in breakout groups a few minutes ago, really how to merge thinking about your business and intuition and creative process, your gut, and overlaying that with your data, the analysis, the algorithm, the machine learning. It’s still something that so many businesses struggle with today. It was really one of the gating factors that led me to say, “Hey, I’m going to move away from academia. I see a world of opportunity around bringing data into applications to a broader set of businesses.” And that’s the direction I took.
Rob Ristagno: It sounds like there’s really a lot of untapped potential there. Everyone in the audience I’m sure is waiting to hear you tell us exactly how that happens. I do want to comment, I think you were the first Ph.D. graduate that has ever said they enjoyed the Ph.D. process. A lot of people tell me how-
Jason Davis: I really-
Rob Ristagno: … how grueling it is. But it sounds to me like if you hadn’t done that five years there, you wouldn’t have seen the light, so to speak. You wouldn’t have been able to really understand the magnitude and the potential of how data can have on the world. Everyday applications like driving or businesses or whatnot.
Jason Davis: Yeah. I mean, some of the mechanics of the process were a little bit grueling. But my peers, my thesis advisor, my thesis committee, these are the super smart people who… I actually didn’t learn a ton that’s directly relevant to anything, but the way that it teaches you to think is directly relevant to everything.
Rob Ristagno: Excellent. Excellent. All right. So let’s talk then a little bit–so your academic experience opened your eyes to this private market opportunity. Well, you started with Adtuitive, but we’ll come back to that in a minute because I want to hear a little bit more about what you’re up to now. For people unfamiliar with Simon Data, give us the 30 second elevator pitch. What is Simon Data? What do you do?
Jason Davis: Right. So Simon is a data platform that enables direct to consumer brands to fully transform their customer experiences by fully leveraging their investments in data. When I look at the world today, you really have this bifurcation of data. Businesses with these grandiose data strategies, huge investments in their data teams and their Snowflake and their data infrastructure. Then the other hand, you have huge aspirations around transforming the customer experience. But at the end of the day, the technologies, the marketing technologies and the data technologies that exist in these two separate worlds really are just worlds apart. And-
Rob Ristagno: Gotcha. So this is the problem that you’re out to solve.
Jason Davis: Exactly. And that’s the problem that we’re solving.
Rob Ristagno: Gotcha. Say a little bit more about this problem for people who aren’t in that space. Tell us really what the conflict is and how you’re going to go about solving it.
Jason Davis: Yeah. I mean, in some sense, when you look at the richness of data today, for anyone who’s been tracking Snowflake’s IPO, I think perhaps the biggest SaaS IPO, at least in recent times, if not ever, and still find it… In some sense, it was a business that I never even expected would exist, but they are, in some senses, last-mover, that has enabled every single business, from the time you hit a $1 million in revenue to Fortune 50 companies, to fully warehouse and centralize all of the data. Snowflake isn’t the only player. There are offerings from Microsoft and Google and Amazon and their Cloud services. But there’s really been a disruption around data technology that’s facilitated data centralization.
Jason Davis: Today in the enterprise in particular, there’s just been a huge transformation in their ability to warehouse this very, very rich dataset that just describes the entire business from supply chain to customers to more. The fundamental challenge there is how do you use it? How do you access… Just because you have the data within the four walls of your business doesn’t mean that marketing can fully use it to identify customer segments or cohorts or think about the customer journey or life cycle holistically, and overlay that with this incredible complexity that exists within these systems.
Rob Ristagno: So centralizing and cleaning your data, that’s, I don’t know, table stakes. You need it to happen, but by itself, it doesn’t really drive any business value.
Jason Davis: Yeah. The first problem is, look, data’s hard. If you look at the problems that the marketing teams face today, it’s minimally their workflows. You have some instincts around… Valentine’s Day is coming and you want to target anyone who meets certain criteria as a customer. You think about the mechanics today that are required to affect a lot of these marketing campaigns and applications, they require tremendous IT resourcing. The workflows and the data and the pipelines to get them done are just really slow.
Jason Davis: So really our value proposition starts at saying, “Hey, how do we put data in the hands of the end user? How do we empower the end user so that they can move, not 50% past it with IT, but 10 or 20 or 100 times faster on their own?” You think about the disruption that Excel and the spreadsheet brought to basic data processing on your laptop to do some financial analysis that we all do today. You asked what does that look like to actually put the power of your petabyte scale data in the hands of the marketing function tomorrow and what does that technology look like? That’s an analog to the types of problems that we’re solving.
Rob Ristagno: Gotcha. So marketers, when you first meet with them, when you’re first talking to them, are they intimidated by this? Or are they are excited and they’re like, “Finally someone understands what we’re looking for”?
Jason Davis: I mean, the pain is sharp. When you’re fundamentally limited by your IT resources and your engineering tickets to even get a most basic task done, then the conversation is very straightforward. It really comes down to, “Hey, if you can help me make this easy, then let’s have another conversation on how you can make this easy.”
Rob Ristagno: For people who kind of dabbling, you’re kind of like, “Oh, maybe it makes sense. It sort of sounds good. I hear people talking about data and customer data platforms and single view of the customer.” What are the two or three use cases that you’re seeing your clients use, your customers use, just to get started? What exactly are they doing with the data? I think people kind of get, yeah, there’s some potential there, but bring it down to earth for us here. Just what are two or three applications that any business person would understand?
Jason Davis: Yeah. I mean, look, we think about this in two worlds. The first world is really when you’re looking at your marketing program today and what the highest leverage activities are. We’re just hyper-focused around the customer journey and lifecycle marketing. Thinking about whether you’re an eCommerce brand or a travel hospitality brand or a financial services brand, if a customer interacts with your website, interacts with your company, with your brand, with a product in any way, shape or form, what sort of marketing responses should be taken on the back of it? Or if you’re a financial services institution and people will have an investment account, but not a checking account, at what point in that customer journey should you be having a conversation with the customer to ask them to open up a checking account? That’s really the primary use case that we focus on across our customer base today. It’s really just [crosstalk 00:10:51]-
Rob Ristagno: So if you ask too soon, it’s going to be overwhelming maybe. If you wait too long, you’re just leaving money on the table?
Jason Davis: 100%. 100%. These are just foundational challenges that exist within certainly all the core verticals that we focus on today. That’s the one. The second thing is, look… The second thing is actually more simpler. Today, you have a marketing operations pipeline and you’re doing a whole bunch of stuff. You have an aggressive set of goals. But the problem is it’s too slow, so let’s look at what you’re doing, whether you’re creating an audience or whether you’re triggering a message or whether you’re using data to do some personalization to give someone a year end review or whatever this might be. The processes that you have in place today, they’re just slow and we can make them faster.
Rob Ristagno: Gotcha. Any questions from the audience about how Jason sees people that… Yeah, Brent?
Brent: Yeah, Jason. I’m just curious and trying to understand Simon Data a little bit better. Do you see yourself competing with the likes of an Acxiom, more of Redpoint Global or an Epsilon or Oracle, those types of businesses? Or are you something different than they are?
Jason Davis: It’s a great question. When we break down the category, and Oracle, I will probably put in a bit of a different category. Acxiom as well. Really what we are is a data application. So there are aspects of Oracle systems that have some more functionality, but our response, the same with Acxiom and same with Redpoint, in essence is more adjacent. Redpoint would be closer and Oracle is wider, right? But really the difference is every single year in the enterprise, the buying data grows by 42%. That compounds year over year, over year, over year. If you look at these technologies that were built 10, 15, 20 years ago, they were built for scale of data, which is like 2% of what it is today. That doesn’t even account for the complexity of the data and health modeling and whatnot. So that’s really the big difference between us and the systems.
Jason Davis: A lot of our strategy in the enterprise is you have a system that you’ve deployed with Oracle now. It’s been there for 10 years, for 15 years, and it works, but there’s some limitations. What we can actually do is integrate very deeply into those platforms and overcome some of those limitations, accelerate your workflows, and really just help you get much better ROI around your technology investments.
Brent: So is it easy for a customer to migrate off one of those platforms onto your platform? Or do you coexist with that platform?
Jason Davis: Yeah, so our strategy is really mostly focused around coexistence. You look at marketing Clouds and marketing systems today, there’s a tremendous amount of infrastructure. I think part of the broader disruption that I think we’re seeing in the category is really this question of can a single platform do everything? We’re certainly taking it where we’re being as focused just as possible on this one problem.
Brent: Great. Thank you.
Rob Ristagno: Focus, actually, it’s something we talk about a lot here. One thing in particular we like to talk about is how do you focus on your best customers, your whales, your super fans, your most profitable segments. Tell us about your ideal customers. What segments do the best with your solution, and more importantly, tell us a story about how you figured that out. How did you figure out this product market fit?
Jason Davis: Yeah, no, it’s a great question. So what was funny is we service two segments. One is a mid-market segment, which tends to be faster growing, more disruptive brands. Then we also service the enterprise sector. What’s interesting is you have two sides of the continuum. They’re not always split along enterprise versus mid-market, but you have folks who have their data infrastructure in place, but really you’re just challenging these… Then you have folks who are fairly data proficient, but they just want to do a lot more. So really, I’ve been speaking about our value proposition across the speed and the workflow axis. Data’s too hard. It’s too slow and you can’t do what you want to do. That’s the first half of the vision of what we’re doing.
Jason Davis: The other half now is asking how do I actually really apply some science, some optimizations, some experimentation to this? How do I really understand the lift of my channels? How do I really think about deep personalization in a highly one-on-one fashion? So what’s interesting is we have this barbell of sorts where we have charges of customers that really just focused on the workflow, the speed aspect. Then there’s this inflection point where once you can master that, then you can graduate to phase two once you solve your speed issue and your workforce issues, and now you can think about some of these more disruptive and next generation opportunities.
Rob Ristagno: Gotcha. Was there some trial and error and trying to figure out who your ideal customers were? I see-
Jason Davis: It’s funny. I’ll give a simple anecdote. Maybe two anecdotes actually. So the first anecdote I got is I remember talking with our CTO when we first started building the technology and I was like, “Matt,” We’ve been on the map for, you know, 18 years now, “we’re just going to build this piece of infrastructure.” I’m the data science guy and Matt is the data architect and the data engineer. I was like, “We’re going to build this platform in 18 months. Okay? The platform will be done. Then we’ll be a data science shop there on out.”
Jason Davis: Really what we learned was two things. One is there so much demand just within the platform. So much pain around workflow. We just look at buying decisions and business cases. Like that can support a full business. That was the first thing we learned. The second thing we learned is building this data platform was way harder than we thought it was going to be.
Rob Ristagno: [crosstalk 00:16:56]. Yeah.
Jason Davis: So it took almost four years just to get the full set of functionality we wanted to get done completed. Now we’re still making huge investments on that as well. Yeah. So I think while I went through the two phases and the two halves of value of our vision, really that first component of just thinking about the workflow and making data easy really is… I characterize that as our biggest learning in marketing.
Jason Davis: It’s a funny story. To that, one of our first customers was a business based in New York called BarkBox and the initial use case we worked with them was around some of their reactivations, if you cancel your BarkBox. They had a woman who was running these reactivations. They go out whenever, like the last Friday of every month. She’d start off on the Tuesday before that. It would be this huge spreadsheet. When you took everyone who canceled, and then you removed everyone had been a customer for less than six months. You removed anyone who wrote into support and said, “My dog passed away.” Yeah. There were all these exclusions and it was like a 10 tab spreadsheet that applied all this business logic. She got to the last tab. She’d dump it out and put it into their email platform and send out the reactivation asking you to go and resubscribe to BarkBox.
Jason Davis: We pitched these guys and they loved the vision. We got into brass tacks. Her boss came to me and was like, “Look, Jason, if you guys can just get us out of this spreadsheet for this process and automate it, you will convert to a [inaudible 00:18:40] contract.” That was really our first point of focus and our first point of success.
Rob Ristagno: I think there are tons of people in the listening audience right now cringing when they heard that story because a lot of us can relate to the managing your business through 20 tabs of Excel. That’s a great story. That’s a great story.
Rob Ristagno: When your organization is facing revenue shortfalls, you feel lost in the wilderness. Data science provides the breadcrumbs you need to find your way out of the thicket, and you also need a guy to lead you down the right trail. Scout X, a proprietary approach by Sterling Woods, empowers you to quickly unearth winning sales and marketing strategies. It’s founded in our fundamental belief that the answer always lies with your best customers. Are you ready to find your way back onto a winning path? Head to SterlingWoods.com to begin your journey.
Rob Ristagno: You told us a little bit–you alluded to it briefly, just a more scientific way to bring marketing sales and marketing ideas to life. A lot of people just, “Hey, we have this idea. Let’s do it. This sounds like a great product. It sounds like an amazing campaign. This seems like a good sales pitch.” You have a more scientific approach. Tell us a little bit about that.
Jason Davis: Oh, I’ll give an example on both sides, the workflow and then the optimization side. On the workflow side, the way to think about what we’re building… I think a spreadsheet is actually a good example. You look at how data is transformed today at petabyte scale, and it requires writing code, it requires writing SQL. Even within that, once you write the code, your code has to run somewhere. So it really requires a lot of infrastructure that, for so many applications, need to be owned and run by IT.
Jason Davis: Really a lot of our strategy in terms of accelerating those workflows is change the ownership, reduce or eliminate a lot of the coding that gets written, and build in into a platform. Really put that power of deep data analysis and operations in UI, in the product, so that someone, who has some data instincts, but is not an engineer and doesn’t know how to write code, can be successful and work much faster. So that’s a lot of what we do, is taking a category which is traditionally focused on data transformations for IT and with code, and bring that upstream into a UI that’s easy to manage, that doesn’t do a 100% of things, but does 80% with 2% of the effort. It really just changes ownership around data. So that’s the first aspect.
Jason Davis: The second thing I’ll get hyper-specific because I think a lot of the optimization challenges are super nuanced, but one challenge we see just with 99% of our customers in the market is you have marketing on the one side of the house and then you explicitly have data science on the other side of the house.
Rob Ristagno: I see.
Jason Davis: I’ve talked with so many of our CMOs. You ask a question, like what are your data scientists doing? The answer is always two things. One is they’re the smartest people in business. The second thing is but I can’t quite tell you exactly what they’re doing.
Rob Ristagno: Gotcha.
Jason Davis: I think one of the challenges here is really just how they orient to the world. Data science has a pile of data that represents everything that happened in the past and they’re analyzing this pile of data to make estimates on who’s likely to churn or what customers are at risk as an example. Marketing is really looking forward. They’re saying, “What can I tell my customer tomorrow? How can I educate my customer? How can I incentivize my customer to really change their behaviors?” It’s really just a different way of thinking across these two disciplines. For us, success is how do you take some of the analysis the data science is running, customers who are at risk of churning, but then overlay the different reasons why you might be churning and more critically the different responses you can take to avoid churn.
Rob Ristagno: Gotcha.
Jason Davis: How do you connect those two from workflow perspective? One. Two is how do you think about you actually attributing business outcomes to the data science work? So value of data science comes in taking the model, overlaying it with marketing context, and then looking at the whole pipeline and saying, “Hey, you put two and two together and now you have 20.”
Rob Ristagno: Gotcha. I mean, this is a question I hear all the time, curious to get your thoughts on anyone else in the audience set. Where should the data science department sit? Because traditionally it was more of business intelligence. You saw it coming to finance or something like that. But now data science has benefited in forward looking things. Does it go to marketing? Does it go into sales? Does it stand alone? What are you seeing out there in the marketplace?
Jason Davis: I mean, most of the time it sits independently, and I think it has to with that today because I think the field is just too immature. The technology, the tools, the algorithms just don’t have a state of maturity for them to live outside of the dedicated function. I think it’s just too complicated. So it’s sort of like catch-22. If you centralize it, they’re divorced from this deprecation. If you decentralize it, then they’re actually not data scientists, they’re just like your analysts. Then invariably, they also just get pulled into doing like my gate is broken type of thing and help me understand why your analytics doesn’t match this report. So it’s just hard to execute it there generally as well.
Jason Davis: But I think, look, five, 10 years from now, that will change. I think we’ll be more de-centralized as the function matures. But generally today, I think most organizations do adopt a centralized approach and I’d argue that most that have a decentralized approach, they’re really not generally doing data science. They’re just hiring data scientists and having them be data analysts, and then maybe over-paying more.
Rob Ristagno: Gotcha. Okay. Just a related question, and we have Kurt, one of our former all-star CEOs on the line here, just talent-wise, what are you looking for when you’re looking to hire someone? Not necessarily a scientist, but someone who’s going to be using data to grow the business. What attributes do they have? How do you screen for them?
Jason Davis: It’s a great question because really, I mean, the skill sets are wide. A lot of our engineering focus is on data infrastructure. I’ve been talking about workflows, but really the hardest part of workflows from a data perspective is getting them to actually work. Broken data can result from so many different reasons. Data engineering is such a different discipline than data science. You don’t really have to have a deep understanding of statistics to architect large scale data pipes that can do large scale data processing. You need a very different skillset.
Jason Davis: You know what? In some sense, when I look at our engineering organization and our CTO in particular, the biggest strength that we bring is really just this discipline around [inaudible 00:25:58] operations. How do you set up the systems to be reliable, to be resilient, and also the processes so that when they do break, and by the way, they will break, that you’re going to recover from them in a way that’s fast and minimizes downstream impact. So I think one of the biggest disciplines around data when it comes to operations and data engineering architecture is this operation component. It’s as much of a mindset and an orientation as it is sort of a hard skills.
Jason Davis: Because on the other side of the spectrum, you have your data scientists, and I’ll just paint a bit of a caricature, but a data scientists who is really into the math and statistics and their head is in the clouds… Like they could be working on an algorithm for two weeks and the system could be broken with huge negative business impact and they can’t wake up. So not only are they different skill sets, but they’re very different approaches to action to work. They’re very different personalities in some sense as well.
Jason Davis: So I think on the other side of the spectrum, again, you need to look at where you are on your maturity curve. I think many businesses want to hire data scientists and they actually just need data analysts. If you don’t have a data analyst and you’re hiring data scientists, I think you’re making a mistake. If you’re on the earlier part of the maturity curve, making sure you bring on data scientists and data analysts who understand the business. That actually needs to be the first thing to come. Can they be a partner? Can they help you think through problems? Because ultimately, you need to be as good about reasoning about data you don’t have, is that if you do have, if you’re going to really understand how data can really bottle processes.
Rob Ristagno: Kurt, do you have anything to add? I know you run HireBetter. Anything to add for people screening for… Say you’re a CEO and you’re screening a technical hire. What do you see work out well? What attributes should you be looking for?
Kurt: Well, we don’t do tech, so I’ll give you my best answer. Jason actually, I think, hit it on towards the end there where he said that really make sure you understand what you truly need and don’t go chasing the data analysts if what you really need is a BI or whatever. So really understand what you need. Scope it very well on the front end before you go looking. Then what I would say is get somebody much smarter than yourself in the tech space to really do the interviewing to make sure that they know what they’re doing. As a tech guy, I have to rely on smarter people than me to do that.
Rob Ristagno: Makes sense. Makes sense. Thanks. All right. Any other questions for Jason? I was going to shift gears to talk about his other startup, but any other questions about Simon Data? How he’s grown the business? Yeah, Dave?
Dave: I do. Thanks, Rob. Thanks, Jason. Really interesting so far. So two questions. One, would just love to hear a little bit more about Simon Data as a business. Like where you guys are in your evolution, your business model, your go to market. Number two, I’m personally more of a B2B guy. So I’m curious, do you have any B2B clients? Is that even something that is relevant for your business? Do you apply it to your own business? Tell me a little bit about that.
Jason Davis: Yeah. I’ll answer that first because it’s more straight forward. We’re not focused on B2B. We do have some clients that have B2B applications, but they also have to have B2C applications. Sort of we’re the TripAdvisor and we work on both sides of the marketplaces. The reason for that is, look, you need think about how the data is modeled and a customer is very different from an account. An account has all sorts of details. If it’s the CEO, if we’re the stakeholders. It’s just a level of data modeling that we’ll get to at some point, but it’s not our core focus. I think one of our strengths in the technology is scale and B2C tends to be bigger than B2B. So those are a couple of the reasons why it’s not it’s not something that we really do today. So that’s number two for you.
Jason Davis: Number one around the business model, we’re a SaaS platform we price based off the data volume and then also message volume on the way out. Again, the focal point of the platform isn’t just insights and the analytics, it’s how to use the data to actually drive the market sale and how do you find segments, how do you trigger messages, and how do you use that to actually integrate the data and operationalize the data into your Oracle or whatever you might be using to drive some of your marketing processes today. Your marketing systems today rather. We focus on a handful of verticals. Some goods retail and commerce, travel and hospitality, financial services, grocery. So yeah.
Jason Davis: The business, we’re over 100 people today. We have 125. We raised $63 million today. We have a fantastic set of customers that I’m super, super happy about.
Dave: You mentioned helping your clients get it all set up. Do you have a services component? Do you have a whole team that goes in and bills for that, or doesn’t bill for that?
Jason Davis: So we have a support team, a client success team. It’s a big part of what we do. Really, for me, it’s about investing the right level of services. The money that we do put towards services is really designed towards making sure our customers will be successful in line with our core value proposition. So we offer technical services to make sure that the data can be integrated properly. Then we also have strategic services to make sure that you’re actually doing right things more broadly. Those are the two dimensions. We invest heavily. The vast majority of our revenue is the technology and the software as a service, but certainly double digit percent of our revenue is services.
Jason Davis: Yeah.
Rob Ristagno: What other questions do we have from the audience?
Brent: One more from my side. How long does it typical typically take to migrate off of one platform onto your platform, Jason?
Jason Davis: Yeah, so the thing is a lot of our deployment capabilities and strategies don’t really require to migrate often. A lot of the opportunity is, especially in the enterprise, you made huge investments in sales with Marketing Cloud, Adobe, Oracle, or any of these huge marketing systems, and they’ve been there for three years, five years, 10 years. But you have limitations on how you’re actually able to get data into them and to leverage the data. So a lot of our deployments are really just sitting as a new piece of infrastructure in front of itself. So in a sense, your current systems. The question there is how long does that take? With large enterprises, we always target getting that initial use case, improved point out the door within three months. For smaller brands or mid-market customers, we’re always focusing on less than a month. Certainly from a technology perspective, we’ve had savvy mid-market brands get up and running with a high degree of proficiency in like two days. But yeah, not all of our customers can [inaudible 00:33:18] that.
Brent: Yeah. Got it. Thank you.
Rob Ristagno: By the way, where does the name Simon Data come from?
Jason Davis: Data’s hard. Give a simple name.
Rob Ristagno: Okay. There you go. Simple Simon. It all comes clear now. Okay. Let’s talk a little bit about leadership. Some advice you may have for other CEOs or aspiring CEOs. This is not your first experience. You were the CEO of Adtuitive for a while. Tell us what makes you a better CEO because it’s your second time running a business.
Jason Davis: I can tell you how I think about running business. What did I learn from the last business? I think my last business made a fantastic product. It was super cool and it drove disruption, but it was in a limited market. It was really focused on smaller retailers who really struggled with AdWords and some ad buying experiences 10 plus years ago. Etsy acquired this business and it was a really a great fit for them. It was a great outcome for everyone. Learned a ton. Our investors were happy. I was certainly happy on all fronts. So that was a great experience. But this time around, it’s really around looking at the broader marketplace, really taking the more central theses that I have in my career and looking at the broader market. My last business, the algorithm I hacked together when I was finishing my dissertation. This business was much more of an evolution to build up, to really develop the core thesis as it exists today.
Rob Ristagno: All right. Well, thank you. I think this has been super helpful for us. What can we do to help you out?
Jason Davis: That’s a good question. I mean, we’re always looking to talk with folks who might be interested in learning more about our software. That’s always welcomed.
Rob Ristagno: What website should they go to?
Jason Davis: You can go to Simondata.com.
Rob Ristagno: Excellent. Well, thanks a lot. And thanks to our audience. This has been the CEO Campfire Chat with your host, Rob Ristagno. To listen to more episodes, sign up for bonus content, or take a two minute business growth assessment, visit CEOcampfirechat.com. See you next time around the fire.
Rob Ristagno: Like trees in a forest, your organization’s revenue problems can keep you from seeing a way out, even when the way out is right in front of you. Our data-driven solutions delivered the shortest, fastest route to increase sales and lower marketing costs. The trick is in knowing how to find and use the data you already have to reveal, test, and implement remedies in a matter of months, not years. When you can see the forest and the trees, everything improves. To learn more about our approach, head to SterlingWoods.com.