A Conversation with Adrien Navarre (TickerTrends)
From a teenage quant finance YouTube channel to building a leading consumer pre-purchase intent data platform
Adrien Navarre is the CEO and founder of TickerTrends, a financial alternative data business focused on consumer pre-purchase intent. The platform turns social discussion, research, clickstream, and other digital signals into KPI forecasts that lead transaction panels and reported metrics, applied across hundreds of public and increasingly private companies.
Before going full-time on Ticker Trends, Adrien studied computer science at George Washington University, transferred to SMU in Dallas, worked full-time as a software engineer in robotics and machine learning, and ran a quantitative finance YouTube channel he started at 14 or 15 that grew to tens of thousands of subscribers. The business has scaled from a solo side project to a team of over ten, almost entirely on a single pre-angel check and reinvested revenue.
We covered a lot of ground:
Adrien’s Background. The teenage YouTuber to startup founder story (GW, SMU, robotics SWE in Dallas, classes by morning, day job, Ticker Trends 6pm to 1am)
Building in Public Early. Why stealth mode is usually wrong, and how a small blog and YouTube audience attracted ML researchers and VCs that changed the trajectory
The Pre-Angel Check + Going Full-Time. The rolling stress curve at every stage of company growth, from “can I afford to do this” to “now I have to scale this into the implied valuation”
Finding Product-Market Fit. A year and a half of feedback loops before identifying the scalable problem worth putting all the eggs into
Team Today + Hiring Philosophy. 5 to 6 software engineers plus 2 to 3 commercial roles, biased toward recent grads with no industry baggage and clean-slate thinking
Management + Delegation. Setting structures and expectations clearly so others can execute, freeing the founder to spend more time on creativity and direction
What “Pre-Purchase Intent” Actually Means. Why social, research, and clickstream signals lead transaction panels, and where breadth advantages compound over panel-bound competitors
Case Studies. Duolingo (the ChatGPT brand erosion showing up in social long before DAUs and revenue) and American Eagle (the Sydney Sweeney call against foot traffic providers who got it wrong)
Data Strategy. A blend of proprietary collection and licensed third party data, with infrastructure built so every new dataset improves every company in coverage
Breadth Over Depth. Why they add data that answers questions for hundreds of companies, not bespoke signals for one name that may not matter in two quarters
Product Tiers. A low-cost retail offering using widely available data (think Google Trends, applied well) and the KPI Forecasting Suite for asset managers, funds, and corporates
LLM Integration, the Inverse Way. Never advertise it, deliver structured outputs to clients, keep determinism and accuracy at the user layer, run all the malleability behind the scenes
Bootstrapping vs Raising. One pre-angel check, never raised since, and how he thinks about strategic capital now that the business has hit an inflection point
Delegation as Replication. Hiring people who are better than you at the role, then influencing direction rather than execution
Hiring Process. Trial tasks with deliberately hard or near-impossible problems, plus paying attention to what candidates do in their free time
[Note: you can also listen on Spotify]
Bryan Wagman: All right, Adrien, thank you for coming on today. Really appreciate it. It would be great to kick things off if you could just give our listeners a bit of an overview about yourself, your story over the long term, who you are, what you do, and how you got to the spot that you’re in today.
Adrien Navarre: Sure. Thank you so much for having me on, Bryan.
A little bit of my background: I’m the CEO and founder of Ticker Trends. Ticker Trends is a financial alternative data business. We do KPI forecasting, primarily focused on pre-purchase intent data. So anticipating when people will make purchases based on social discussion, research, clickstream data, and different forms of digital alternative data that indicate some form of pre-purchase intent ahead of when they decide to sign that enterprise contract, or swipe their credit card, and make the purchase for different public and private products and brands.
My background before starting Ticker Trends: I studied computer science throughout university. I went to George Washington in Washington, D.C., then transferred to Dallas to Southern Methodist University, and was doing computer science pretty much the whole time. When I got to Dallas, I was working in robotics and machine learning full-time as a software engineer, and that gave me a lot of exposure to more complex computer science concepts.
Outside of work and school, I was always very personally interested in financial markets. That mixture of experiences with machine learning and software engineering, and then my extreme personal interest in financial markets, is what led me to start Ticker Trends. I initially built out a lot of the infrastructure and software to have a tool to manage my own portfolio with some form of edge that I believed I had at the time in interpreting different forms of alternative data.
Eventually, I started making some blog posts and YouTube videos about what I was working on. This was while I was a part-time student and also working a full-time robotics job. Fortunately, at that time, a venture capital firm reached out to me and gave me some pre-angel seed capital to work on Ticker Trends full-time.
That’s kind of been the story since then. I’ve been working on it for just over two years now, almost two and a half years. We’ve grown from just myself to now a team of almost over 10 people, and we’re continuously expanding, which has been a very exciting journey.
Bryan Wagman: Awesome. I’d love to hear more about those very early days of Ticker Trends, particularly, you mentioned the extreme interest in the financial markets, and then eventually it kind of became Ticker Trends, which had a blog and a YouTube. I’m wondering, what was maybe a typical day when you were working on this stuff in the weeks leading up to actually deciding, “Hey, I should try to make this a business”?
Adrien Navarre: Yes. Well, I guess a little bit of context as to why I was interested in financial markets. When I was much younger, I had been very excited about producing content on YouTube. What I found when I was looking at different topics to make videos about is that it was extremely under-covered on YouTube and different public social channels to talk about quantitative finance and automated trading strategies.
So when I was very young, maybe 14 or 15 years old, I started a YouTube channel around different quantitative finance strategies and would produce content even with my limited knowledge. I was able to attract an audience just because no one was talking about it publicly. That channel had a little bit of a following, tens of thousands of subscribers.
That’s what really seeded a lot of my early interest in financial markets. It was kind of a snowball effect, where I started to have some success in making content on that YouTube channel, and it motivated me to keep going deeper and deeper into financial markets and quantitative finance. Since having that YouTube channel, it’s stuck with me the rest of my life, and I’ve always loved financial markets.
Leading up to when I went full-time on Ticker Trends, a little bit of what my schedule looked like at that time is I would wake up very early, and I had some morning classes at university, since I was a part-time student and was taking two or three college classes. Then I would leave my college classes and go to a full-time job working at the robotics company as a software engineer.
After working at the robotics company, around 6 p.m., I would then drive to my own office for Ticker Trends and work from 6 or 7 p.m. until midnight or 1 a.m., and then go home and do my homework for university. So I had a very extreme life setup at the time.
I knew that what I was working on with my own project, Ticker Trends, was my passion, but I also knew that what I was doing was unsustainable in terms of lifestyle. I think I always knew that something would change eventually. Either something would take off with Ticker Trends, or I would decide to go a different direction. But I definitely was doing a little bit of a Hail Mary to see if something could turn up.
At the time, I was just maximizing my exposure to potential new opportunities. Once I started producing content about it, even though it wasn’t attracting a very large number of viewers or listeners to the content I was making about Ticker Trends very early on, the people who did watch my content were very high quality. It would be machine learning researchers, or people deep in finance or venture capital. So I would meet all these interesting people, even though I was only getting a couple hundred views or reads on my blog posts or YouTube videos about Ticker Trends very early on.
I think it definitely taught me that this idea of going in stealth mode early on for a startup is not always right. It’s really important to be very vocal about your long-term ideas and what you’re working on, because when you’re working on something interesting, it will attract very unique, intelligent people, even if it’s not a large quantity of people at the very beginning.
Bryan Wagman: Yeah, that’s really interesting. I’d like to hear more about that period where you were deciding, or waiting to see, if Ticker Trends was going to take off, or if you were going to have to do something else. How long was that period? How long had you been actually trying to make it work as a business, or generate revenue, or enough to be able to go full-time on it? And what were either the positive signs or some of the negatives that you were watching for and considering?
Adrien Navarre: I think throughout the whole journey, the way to describe it is that I would still consider us, relatively speaking, a smaller-sized business compared to some of the other data companies. What happens in an early startup journey is that each time you think there might be no more stress, or, “If I just get to this specific goal, then things will get easier,” you find that at each goal there will be a new thing that can start stressing you out at that stage of the company.
I’ve gone through a couple of those now. Initially, like you described, the thing that was stressing me out was, “Can I have enough capital to go full-time on this?” Eventually I got that capital, and then it created a new stress, which was, “Now that I have this capital, I need to turn this into something real. I need to scale this up.” I had some money up front, but the revenue hadn’t caught up to the implied valuation of what that investor gave me the capital at.
At each level, and even later on once we were making enough revenue to start growing and scaling the business, and even now as we’re scaling and gaining new clients quite quickly, there are still different stresses and interesting dynamics. If your goal is to keep growing a company, then even when you have success or when you’re scaling, it can still be a very difficult challenge. You want to allocate resources effectively, where you’re reinvesting or directing your effort toward the right segments of the business to keep it growing.
That initial period after I got the capital to go full-time on Ticker Trends, it still took maybe a year, year and a half, before we found a direction for the business that would allow us to scale to some of our aspirations of what we wanted the business to be.
It’s one thing to have an idea of what you want to go to market with. But once you go to market, going through that period of feedback and iteration took us a while. The more we listened to feedback, the more we had people using our software and our platform, the more we explored new ideas, the better things got. Eventually we hit the inflection point of having our products set up in a way that was very scalable and solved problems that a larger set of higher-quality clients would have.
Once we hit that problem and identified it, we started to put all our eggs in that one basket and scale the business based on the success and demand we had for the problem we were solving most effectively.
Bryan Wagman: And what does the team look like today, and how has that evolved since your first hire?
Adrien Navarre: The team today is primarily software engineers. We have about five or six software engineers, and then the remaining two or three would be non-technical, either sales or marketing or growth or financial analyst roles, and a little bit mixed between all of those.
Early on, I was focusing a lot on recent grads and college students. With myself being a solo founder and being so young, I worked best with people my age and people in a similar situation to myself. I think that brought a similar excitement to the business and willingness to go all in on the idea.
As we’ve scaled, we’ve scaled in all directions, and we’ve needed different team members for different reasons. As much as college students are really great for having the adoption of new technology, and you can work with a lot of really smart people, there are also reasons to have people outside of just college students. So we’ve gone in both directions as we’ve grown.
I’ve always looked at it as one of the most important things we can do is curate and develop talent. Many of the software engineers we hire are people with little to no prior financial background who then get into financial markets and financial concepts through working at Ticker Trends. That’s been very exciting for me, the development of talent. Hiring is so important.
That’s always what we’ve biased toward, rather than hiring people who have worked in the industry for a long time and might have biases toward how things should work, versus coming from a clean slate.
Bryan Wagman: Yeah. Do you feel like there have been any significant challenges from a management and team-building perspective over the years, or any big ways in which you’ve changed how you approach managing people and building the team?
Adrien Navarre: I think in my approach to managing people, what I’ve come to realize is that a lot of the best thing I can do as the CEO and founder of a company is set up the structures and expectations to be as clearly defined as possible. Then that can set other people up for success.
I’ve tried, increasingly over time, to dedicate more of my time toward creativity and general direction, and then leave more and more of the execution to other team members. That’s allowed me to increasingly delegate more responsibility to other people.
Bryan Wagman: Yep. That’s interesting. Maybe we can start to move into the platform itself then. You mentioned earlier that what you’re trying to measure is consumer purchase intent. Could you elaborate on that a little bit, and how that differentiates you from what some of the other large platforms are evaluating?
Adrien Navarre: Yes. Like you described, we focus on consumer pre-purchase intent data. This is anything where a consumer is researching, discussing, or hearing about different products, brands, or concepts that might lead to a future purchase.
What makes this unique is that we have a lead time to many of the purchase datasets that are so heavily relied upon by many industries. We also have a lot of contextual and supplementary information that gives us information on the sustainability of different trends. Is something going to be a temporary lift in sales, or a meaningful multi-quarter, multi-year trend that can impact sales for a very long period of time? These are things that we can track and forecast and apply to markets very well.
There have also been other advantages to our approach, such as breadth of coverage. When we look at many of our competitors, when they have a transaction panel or any panel-based data, you start to become limited by the size of your panel when trying to cover more niche subjects, topics, brands, products, or ideas.
What’s so amazing about social data is that your panel is effectively 10 or 100 times larger, or a thousand or million times larger, than any credit card transaction panel dataset. So even for things that are very niche or small, we can cover and track them, whereas many of our competitors or other people in the industry don’t even have sufficient data to track or collect.
There are advantages both to tracking and understanding large businesses, and also advantages to us having a far wider breadth of coverage. We’ve now even expanded into private markets and are tracking small CPG brands all the way to the largest companies, like Pepsi, Coca-Cola, Cava, or big software companies. This data is equally applicable to almost any industry that has some consumer-facing product.
Bryan Wagman: Yeah. Could you maybe give an example of a recent investment thesis, or something from the blog, where there was something that your data uncovered that the market did not see?
Adrien Navarre: Yes. I think two examples we tend to give, which showcase some of the flexibility of our insights, would be first around Duolingo.
Toward last year, in 2025, around the proliferation of ChatGPT and a lot of the language models, there started to be changing consumer dynamics around how consumers viewed language models versus language-learning apps like Duolingo. It started to show up first in social discussion, and then very, very slowly over time trickled into company metrics like the DAU number, paid subscriber number, and revenue growth number of Duolingo many quarters later.
At that time, we had written about some of the early controversies that Duolingo management was causing, and that was causing pretty severe impacts to the sentiment and perception of Duolingo early on, when many consumers were making that consideration of whether they move to language models or keep using Duolingo. That eventually caused a deterioration of their brand, in addition to some key brand or social media managers leaving the company at the time. That showed up in social data far before it showed up in reported metrics or other datasets, and then later caused Duolingo to reprice quite significantly.
Another one was our coverage of American Eagle. At the time, the first Sydney Sweeney campaign was very controversial, and there was a lot of discussion around whether it would have a positive or negative impact on brand performance. There were some data providers – and I think this kind of connects to my earlier comments about sample panel bias – there were some other providers, specifically foot traffic, making headlines with very large news agencies that this campaign was having a negative impact on American Eagle.
We had very different data and information based on our insights, and we came out at the time and said that this was having a severe positive impact on American Eagle sales. When the company came out and released results, there was a big upside surprise in performance because Sydney Sweeney did have a net positive effect on the brand.
Those are two examples of where our data was applicable, but it even extends all the way to things like software companies or B2B service providers, where we can pick up on professionals’ discussion around whether they’re going to switch providers, what enterprise AI software they’re going to use, etc., and apply a similar methodology.
What’s so interesting about social data is that it’s interpretive. Like I described before with American Eagle, two people could have been looking at the same signals and coming to different conclusions about their impact on the brand. Part of what’s made Ticker Trends so successful is our ability to consistently translate the impact of those events, both directionally and magnitudinally, to how it will impact brands both short-term and long-term in public and private markets.
Bryan Wagman: Yeah, that makes sense. I’m interested in better understanding the operational flow of the company from the perspective of how you identify the data or dataset that you want to use. Are you purchasing it? How do you think about buying the data or collecting it through scraping or something like that? Then once you do have the data, how does that flow through your operational infrastructure? Are the engineers the people finding it, or are you just buying big bulk datasets and then seeing what’s in there? I’d love to understand more about how all that works.
Adrien Navarre: At Ticker Trends, we have a combination of approaches in terms of datasets. We have a lot of our own infrastructure around collecting our own proprietary datasets, some of which we’re one of the only, if not the only, provider to have. This is particularly around many of the social tracking datasets that we collect.
But there are also some datasets that we have to license from third parties. The way we view that is that Ticker Trends is not just the datasets we collect, but the infrastructure we’ve built around the utilization of those datasets. Early on, we proved that through some of the most easily accessible datasets. We could take data that’s widely available and use it more effectively than incumbents or other people in markets. Later, that translated to taking more premium datasets and applying the same methodology.
In terms of how our infrastructure operates, it’s actually very easy for us to add and maintain datasets, and take any time series or textual data, or different formats of data, and integrate it into our systems. But a bulk of our infrastructure is more around the effective utilization of those datasets in the context of financial public and private markets.
That’s what’s allowed us to be so successful. We can take any of these signals, plug them into our systems, use those datasets more effectively than anyone else, and it benefits all of our coverage, both of public and private companies.
That is very different from how some other people in the industry operate, where there’s a lot of work around each incremental dataset or a lot of work around each incremental company of coverage that they add. The system we’ve built from the ground up is very scalable in terms of the breadth of coverage we can have, and every incremental improvement we make benefits all of our coverage.
When we add a new dataset, it benefits all of the companies we track. When we make an improvement to our algorithms, it benefits all of the forecasts we’re making for public reported metrics. We’ve built a system where it can be very easily maintained and improved, and benefit all of the coverage we have across public and private brands.
Bryan Wagman: What are some of the datasets that your clients have found most valuable, and what do you find is common across those? Are there any themes that have popped up for you about what makes a dataset particularly compelling for people?
Adrien Navarre: I think what can make a dataset most compelling is a little bit along the lines of what I mentioned earlier. Many of our clients will have unique ideas and different topics that are important to them over time.
While, of course, there can always be value in very specific depth on a certain topic, like maybe having the best AI data center dataset is very valuable, for us, we think instead about having as many of our datasets cover and answer, or have an input to, as many different questions as possible.
When we’re adding a dataset, very rarely would it be to only improve one or two companies, or because it would only be applicable to one or two companies. Instead, we look at it as: will this incremental investment in our infrastructure benefit a couple hundred companies in our coverage, or ideally a couple thousand companies? Can this answer questions that might be relevant now and down the line, across many different market environments and investor focuses?
That’s come to pay off a lot. When new clients come to our platform, they look at the tools and infrastructure we’ve built, and they might have a lot of bespoke ideas and different applications for the information we’re providing for their own use case. That’s been very exciting for us, and it’s allowed us to grow so much, because since we can answer so many different questions, the way people want to leverage that data can get very creative.
That’s come from always focusing on being able to answer such a wide breadth of questions, rather than only being the best at answering one specific question that might no longer be relevant in a couple quarters, or one or two years.
Bryan Wagman: Could you share more about the different levels of the offering? There may or may not be some element of a customer journey perspective, where they move on from one to the next, but it sounds like there’s some lower-priced offering with the blog, I think, and then what you were just describing sounds like a higher-end offering where you can see all the tools and infrastructure in the background. We’d love to hear how you think about each of those.
Adrien Navarre: Yes. We initially started with a very low-cost tier early on. That was quite critical for us, because in order to iterate, we needed a large user base to get fast feedback on whether things were working and what people wanted.
Over time, as we polished that product and built new offerings within the software, we were able to move more to a professional offering, which is now our primary product.
We have one portion of the offering that is more simple alternative data tools. We’ve always wanted to keep that offering available because we believe we’re one of the only, if not the only, alternative data provider that has something for a more average market participant or investor. We believe that will always be an expanding segment as more people manage their own capital. So we have one offering for those types of investors.
Bryan Wagman: If I could ask one thing before we move on to the next one, what exactly does that look like from the client’s perspective? Is the analysis already integrated with the stock for that lower tier, or is there some sort of data-only provided offering as well?
Adrien Navarre: In the lower-priced offering, it’s more restricted on the dataset side, in terms of what information is available to the user. But it still allows users to easily connect different forms of more readily available alternative data to financial markets.
What I alluded to earlier is that when we first started the business, we didn’t have very much capital or resources to spend on datasets. So we took more readily available data, something like Google Trends, and applied that data to financial markets.
The lower-priced offering takes those more readily available datasets and gives users tools to easily connect that to financial markets, so they can use it to track companies in their portfolio or get new ideas of what stocks to invest in, without having all of the more complex infrastructure that we keep for the advanced offering.
Bryan Wagman: Got it. And so then what are the tiers after that? Is there one or two tiers after that, or how’s the rest for you?
Adrien Navarre: Above that, we have what we call the KPI Forecasting Suite. That offering is more directed toward professionals: asset managers, funds, corporates, and private investors.
Our KPI Forecasting Suite is a larger set of data sources, some of which are premium and only available through limited, if not very high-priced, offerings. We have unique datasets, and then also some of the social data and our own datasets that we’re collecting, all mixed into that offering.
A majority of our infrastructure is taking all of those unstructured datasets and translating them into actionable insights, primarily through KPI forecasting. So something like Yipit or M Science, but based more on this pre-purchase intent data, where we take pre-purchase intent data, translate it into how that will impact a reported metric, and then do that for hundreds of public businesses and increasingly private businesses as well.
That way, investors can have a direct answer to how this social event or pre-purchase intent data will impact a public reported metric from different public companies.
Bryan Wagman: Sure, that makes sense. I’m curious if there are any big pivots that your business has made over the course of the past couple of quarters. If there’s one that stands out in your mind as most recent, what would that be?
Adrien Navarre: I think one of the biggest pivots is maybe how we’ve thought about the integration of some of the language model tools into our product.
I’ve seen many data businesses take different approaches to integrating language models, and I think we have a very different approach. Most of it we don’t talk about or advertise or mention anywhere on any of our public materials. That’s quite intentional, because around some of these hype cycles around new technology, there can be many companies overusing certain words, and it tends to drown out the actual value of what they’re providing.
We’ve taken the inverse strategy, which is don’t mention it at all, but have all the benefit of it. Initially, we were thinking about how much of this is something we want the user to be able to interface with, versus how much of this is something where we’re telling them a structured output or answer.
Many of our competitors, or many other data businesses, go the route of giving the user tools to generate their own structured outputs. My opinion is that this is the wrong direction. As much as it’s very cool to be able to go into a chat interface and ask any question, ultimately, very often our clients don’t want to have uncertainty or variant interpretation, where one time you ask it a question and it tells you one thing, and the next time it tells you something else.
Very often, clients want the best answer, and they want that answer for many different things. The value that we provide is giving them that answer. So the approach we’ve taken instead is to utilize this infrastructure to benefit everything on the back end of our business. But in terms of the client’s interface and final output, they only see the final answer. We’re giving them the answer, while still having all of the benefit of that more malleable infrastructure that allows us to scale and grow much more quickly.
The way we’ve integrated language models into our business is different than many other data businesses. But so far, I think with the market validation we’ve received, it’s been the right way. It’s provided a material, significant increase in efficacy to our business by giving our clients structured outputs from language models.
Bryan Wagman: Yeah, that makes sense. I guess it’s the same data, though, and I’m just trying to think about this from a strategic perspective. The differentiating factor is the customer experience there, is that what you would say? By wanting to go structured instead of the tools approach?
Adrien Navarre: Yes. I think the user experience matters because in the data industry, determinism is very important, and accuracy is very important.
We’ve always operated in a way where it’s kind of like human-in-the-loop infrastructure. It’s maximal automation, but with a human in the loop to verify very high-importance accuracy that our clients expect.
The way clients interface with our data and information is that they view structured outputs, consistent structured outputs that answer the questions they have. How is this thing going to impact this company? What things matter for this company? What’s the performance of this business? We answer all those questions.
The way we implement automation into answering those questions is all on the back end. It doesn’t require the user to self-query or self-structure the information themselves. We’re not just throwing a bunch of unstructured information at them and letting them figure out what’s important from it. We’re telling them what’s important, how to interpret it, and what the best interpretation is of that information.
Bryan Wagman: Is the KPI Forecasting Suite, does that all apply to that suite as well, or is there more of a user-directed approach there?
Adrien Navarre: Yeah, that’s all available in the KPI Forecasting Suite. We do have tools that allow users to view more unstructured data if they decide to, but we’ve always seen that clients far prefer structured outputs and answers over unstructured data. The tools are there if they want to use them.
Many of our clients subscribe to, or have, many different datasets or pieces of information competing for their attention. They would prefer to have the best structured output at all times, rather than have to dig through, search for, and interpret the information themselves to then come to some conclusion. It’s far better for them to have those structured outputs from the very beginning.
Bryan Wagman: Going back to some of the financing discussion from the beginning, it sounds like you’ve just raised capital that one time. Is that correct? And I’m curious how you’ve thought about that as you continue to build out the business.
Adrien Navarre: Yes. Since the very initial pre-angel investment that we received at the beginning of the business, we haven’t raised any additional external capital. Prior to that, when the business was a side project, it was purely bootstrapped from personal capital.
Much of the growth in the business now has all come purely from growth and revenue of the business. As we’ve accelerated in growth, we’ve started to potentially explore other options. I very much come from the side that, since we’ve been able to grow and operate the business for so long with very minimal external capital, I believe it’s very possible for us to continue that path moving forward.
At the same time, there are certain strategic opportunities that come up at different points, which can have the dual benefit of giving us capital and also accelerating the growth of the business. Those are the things that we consider at times.
We’ve never been in a position of requiring external capital. We never ran out of money, and we didn’t need some money to get to the next point. We’ve been able to do all of that just through the growth of the business. But I think it’s very possible in the future that different strategic opportunities might come up where it makes sense for us to receive external capital to continue growing the business even faster, since we’ve hit such an inflection point.
Bryan Wagman: Sure, yeah, that makes sense. One other thing that you mentioned earlier, when you were talking about the entrepreneurial journey, was how learning to delegate was important for you. Is that something that came naturally for you, or is there anything that you picked up along the way that made it easier? Maybe it just has a lot to do with hiring good people, but I’d be curious to hear your experience in that realm.
Adrien Navarre: I think with delegation, I’ve always viewed it as being able to replicate, if not surpass, my own personal skills and ability through leveraging the knowledge of others.
Often, delegation can be hardest when you’re not confident that someone else might be more skilled than you at a certain topic or task. But what I’ve found is that as I’ve hired, I’ve always found people I believe to be better than me at these different roles. So it makes it easier to delegate the responsibility of those roles to those people while still retaining my own influence on the roles.
That’s how I’ve viewed it: find people who are better than you at the specific roles, but then you can still influence the direction and focus of those people within those roles to make them even more effective.
That comes from a long time for me personally, just because I’ve always been so time-constrained in terms of focus. I’ve needed to delegate even from very early on. When I was starting Ticker Trends initially, I was working at the robotics company, and most of the day I couldn’t work on Ticker Trends. I already, at that time, was paying a software engineer a full-time salary to work on Ticker Trends while I was doing my own job, because I knew I wouldn’t be able to make the progress I wanted if I were working on it alone.
The importance of that delegation has only increased over time. As the aspirations of the business have grown, I’ve realized that I need to keep offloading many of the responsibilities I’ve taken on, because if not, we wouldn’t be able to get to the next levels of growth for the business.
That’s been a constant process as the business has grown: pushing myself on the side of delegation, but also pushing myself on the side of creativity, of where Ticker Trends can go, to keep accelerating the growth of our business, which so far, fortunately, has been relatively successful.
Bryan Wagman: These days, when you’re looking to hire, how do you identify talent, and what does the interview process look like?
Adrien Navarre: In terms of identifying talent, most often, since these are primarily technical roles, and I don’t necessarily look for people who have worked in the industry, although we’re not biased against this, most of the time we’ve just tended not to hire from people who’ve worked in the industry.
Often, we’ll look for people who are newer software engineers looking to change into a new industry, and talent that’s very excited about technology and very familiar with new software. One thing I’ve always looked at is what people do in their free time, because that is what they’re truly passionate about. So we look for people who, when they leave the office, are doing software engineering-adjacent things or interests. That shows that they’re really naturally passionate about what they’re doing.
In the hiring process, something I’ve taken up from advice from others and some mentors I’ve met throughout growing the business is the importance of trial tasks and onboarding tasks, where you’re throwing the most difficult simulated challenges at someone during the hiring process and seeing how they respond to those challenges.
That’s been very effective: seeing an actual simulated work product and being able to evaluate that for a very difficult question, or maybe even a question that might be impossible, and seeing how they approach it. That has always allowed me to have a much better gauge of hiring before they start full-time in the position.
Bryan Wagman: Yeah, that makes sense. Well, this has been awesome, Adrien. Is there anything else that you’re really excited about for Ticker Trends, or that I should be asking about?
Adrien Navarre: No, I think for Ticker Trends, what’s been exciting about our future direction and some of this scaling is that as we’ve had more resources and as we’ve built up this technology stack, the applications for our technology have grown equally. That’s been really exciting for us.
Something we’re always interested in is when people have new ideas for our data or applications of our technology. We have some of the largest infrastructure now in monitoring these pre-purchase intent datasets across such a wide variety of data and applying it to public and private markets.
It’s been very fun to work with people who have new ideas in those realms. If anyone ever wants to come to us and collaborate on making this data and information effective for their problems, then we’re very excited to work with them and to get them using Ticker Trends.
Bryan Wagman: Yeah, awesome. Well, thank you so much for your time today, Adrien. I really appreciate it.
Adrien Navarre: Thank you so much, Bryan.
Bryan Wagman: Alright.