Transforming Health Tech Go-to-Market: How Bonfire Analytics Drives Sales Efficiency

Speaker A: Foreign. Welcome to Product and Health Tech, a community of product leaders innovating in healthcare. I'm um, Chris Hoyd, principal at vynl. Today we're diving into the world of healthcare data analytics with Jay Epochuri and Vinay Nagaraj, co founders of Bonfire Analytics. These longtime friends turned business partners are on a mission to transform how health tech companies build and execute their go to market strategies. In our conversation we explore their journey from building a healthcare contact database to becoming an analytics platform as they navigate the winding road to product market fit. How they've helped a prosthetics company achieve sales team parity and a 3x increase in efficiency by targeting the right providers. Why volume driven targeting is often counterintuitive. And how Bonfire's data reveals surprising ideal customer profiles for different health tech companies. And how they're preparing to navigate healthcare policy uncertainties while turning potential headwinds into opportunities. Let's jump into the conversation. Hey guys, thanks again for joining today. Uh, let's just start with some background. Let's dive right in. How did you guys get into this space and what inspired you to start Bonfire?

Speaker B: So we're both co founders of Bonfire. We've known each other for a long time. We've been um, friends since college. And uh, yeah we got into the space like um, of like building Bonfire through like a shared vision that we both had. Um, it comes from a bit of my background as in data science and Vinay's background in um, health tech, sales and growth. Uh, so we both studied engineering together. But then after graduating I moved into the data science world, worked at a few different startups doing a lot of work in like machine learning, different working with different data sets. And Vinay worked at um, a health ah, tech, a digital health company. And um, it was a bit of like subside projects that I was doing around connecting disparate data sources, uh, that oftentimes data scientists like a lot of great publicly available data that is really useful for data science projects but data scientists don't leverage that much. And then Vinay feeling a lot of the pain points in selling into healthcare where we found a combination like uh, kind of ways to leverage what I was building for the pain points he was feeling. Um, yeah. Vinay, any thoughts on that?

Speaker C: No, uh, I think that was a good summary. Um, yeah, we've definitely both um, had exposure to the startup world as well, I think had some of that DNA, um, having worked at startups. So I think we were both kind of hungry for the next idea and when we kind of joined forces. The only other thing I'll highlight is I think it was nice to have a shared vision from the get go. I think both of us were interested in healthcare. Both of us had a similar zest for how data can be a really integral part of a product offering. Um, and then since then we've just kind of um, iterated on like Jim mentioned the pain point that we're solving for, um, as we've built Bonfire, and

Speaker A: that's a nice segue. Can you talk a little bit more about that pain point that you're solving for and how you guys uniquely address it?

Speaker C: Yeah, I can take that one. Um, and so in terms of the pain point, um, one of the things like Jay mentioned as part of my background is I was on the growth side, um, selling into healthcare. So I was at a digital health company prior to Bonfire called Roundtrip Health, which is a patient transportation platform for hospitals and health systems. And as I was going through that sales process, I realized selling into healthcare is just incredibly difficult. A lot of companies struggle with it and a lot of my peers who are on the commercial teams had very, very similar pain points. Is, we don't know where to start, we don't know how big our market is, we don't know who to prospect, we don't know how to build the right business cases for our prospects. So a lot of those things, um, came up over and over again and I think that's where we were. We realized that data and insights can be a really powerful way, um, to allow these commercial and sales teams to just be very targeted and not have to resort to what we call the spray and pray, which is you send a thousand emails, get 10 responses, one meeting. And that's what I did for a while when I was selling to a doctors physician group hospital health systems. And so, uh, that's what we're trying to eliminate with data is just having the data and the insights from the get go allow teams to just be a lot more efficient and effective.

Speaker A: I know that uh, AI is a big part of how you guys, uh, interpret the data for the customer. Can you talk a little bit about how uh, AI sort of fits into your product and what that does for your users?

Speaker B: I feel like there's a lot of buzz around AI right now and it's sometimes hard to understand what it encompasses. It seems to touch a lot of different things. A lot of what we do is leveraging a lot of machine learning techniques to build a lot of these insights. Um, we try to really provide actionable insights to these, um, health tech companies. Um, and we'll build these insights using machine learning techniques. We also use, um, different AI tools to help accelerate these processes. Um, but yeah, I think there's just generally there's a lot going on with AI now and um, there's probably more that we can do and we are planning. But I would say right now a lot of it is really focused on ML and then also leveraging a lot of the tools that are existing to help accelerate our product development.

Speaker A: Can you maybe talk us through a, uh, recent. I understand you might not be able to divulge all the details, but a recent sort of case study of how you've helped one of your uh, customers leverage your platform.

Speaker C: Yeah, um, I'm happy to share some detail there. Uh, we actually did share a customer story recently and so it's good timing. We can talk about them. So, uh, we worked with a company called Point Designs. Um, they're sort of a mid March market prosthetic device company targeting uh, prosthetists, orthotists, you know, and they have devices that help, um, essentially amputees. Um, and so when we started working with them, uh, initially, you know, their pain point was they weren't really um, sure about their market. You know, these patient populations who need the kind of device that they've built or uh, the devices they've built, um, where are they? Who are the prosthetists and orthotists who are serving those patients and how can they reach them more effectively? And it was a little bit more of like the casting a wide net and see what comes through sort of approach. Um, and yeah, since we've been able to work with them and they've been a really great uh, partner to work with and uh, have also provided feedback as they've used our data on our product. Um, it's been really cool to see that the data has helped them in terms of identifying where are these patient populations targeting these prosthetists and orthotists. And then ultimately what we saw was a 3x increase in sales efficiency, which is, um, the sort of resources they've dedicated to sales and marketing. They've been able to 3x that in terms of the return they've gotten, uh, which has been really exciting. Um, and another thing that they also mentioned was their sales team has a lot of parity now, uh, which is also an interesting thing, right, Is um, being on the sales side. Parity is something that's tough for sales leaders to get to because

Speaker A: you um,

Speaker C: want to Sort of uh, assign and territories and regions to reps in an equitable way. But it's hard to do that without the right data. And so that's something they shared is now they're at a point where different reps occupied this top kind of spot at various times in the year. Um, so yeah and just generally we're looking forward to growing with them. Um, and they actually gave us a great kind of insight in how they've used their platform which might be another topic we talk about is you know they see us as the windshield and the CRM as the rear view mirror. Um, which I thought was a great kind of summary of our positioning is you know we to help them with getting to you know their market more quickly. The CRM helps obviously track a lot of that and make sure their sales processes are optimized. But um, yeah that's an example of how we help you know a medical device company. But we also work with other digital health companies as well.

Speaker A: I also read your recent post about GLP1 market strategies which I thought was fascinating both you know for the healthcare insights and for how it kind of showcases your guys approach to data analysis. Um, if you don't mind I'd like to dive into that for a minute here. Um, I think the article demonstrates how you use provider level pharmacy claims data to generate strategic insights. Right. So uh, can you talk a little bit about your unique approach to acquiring, processing and analyzing that type of healthcare data?

Speaker B: In that article it was a lot of work with um, like ah, prescription claims data. Uh we also work with a lot of other types of data like medical claims data, um, uh, SDOH data which is, and more like population level data. So it's a lot of different data sets from a lot of different sources. Um and so um, there can be a lot of challenges um, when working with data from these different sources because oftentimes we're trying to um, create insights at a specific level of granularity that isn't necessarily we may directly get from any one source and so we have to abstract it to the level of granularity. We're trying to uh, develop insights on for example the prescription claims data. It's all at the patient level, um, and then there might be other data sets like SDOH data, um, social determinants of health data which might be at the population level like at a zip code level, county level. And we have to figure out how to connect these together at a different level of aggregation. In the case of the article you're talking about it was at the health system level while taking into account, um, different uh, biases and figuring out complicated relationships in the data. For example, a really complicated uh, relationship, um, to figure out from that's not easy to figure out from data is like affiliations, data, um, understanding for given health systems. What are all the um, uh, entities that are encompassed by that health system? It's constantly changing. I feel like every month we hear about different acquisitions, roll ups, um, between different health systems. Sometimes we don't know which clinic is owned by a health system or whether it's independent. And that data is not directly in the prescription claims data. There's no data set that just tells you a lot of that work that you were talking about in getting to that health system level data, as we did for the glp, um, article of showing trends with the health system data comes from connecting all these different data sources and trying to predict things that just go beyond even the prescription, like the prescription trends. Even before doing that, we have to try to predict, okay, what are all the organizations which are part of that health system. That in itself can be a pretty complicated process. Um, does that answer at least some of the question?

Speaker A: Yeah, that was great. Thank you. Okay, so you just, you know, uh, described some of the deepest complexity, I think of the American health system. Right? Like disparate data sources, uh, difficult to discern interrelationships between them. Uh, even more difficult to like turn uh, you know, the data or the relationship into an insight. Can you talk a little bit about how Bonfire, um, is uniquely differentiated to get to that insight? Is it the access to data that most others don't have? Is the way that you analyze it? Is it the way that you get to the insight? Can you just talk a little bit about that?

Speaker B: That's a good question. Um, I think it's a combination of the different items that you mentioned. Um, one obvious barrier is access to data. Like, um, I think, um, a lot of the data is not publicly available. You'd have to purchase it from different sources, whether it's a CMS or from clearinghouses. And that data, um, can be quite expensive. But then that only gets you so far. Then there's also a lot of publicly available data which may or may not be um, easily accessible. Like a lot of the relevant public data, um, lives across a different variety of um, uh, sources. Like it might be the CMS has a lot of great data sets. Other organizations have a lot of great data sets. Sometimes it's like trying to scrape certain websites. It's not necessarily A clean, extractable format. And so one, I think one, just one aspect of it is being able to. Beyond like first of all being able to purchase data. Um, but one aspect of it is knowing where to pull data. Um, like, where what data is valuable to like, help, uh, achieve a certain uh, like insight that we're trying to build and then where to find that data, how to pull that data off these processes, um, I think take a lot of domain knowledge and it's definitely not something we've always had. There's been a lot of uh, trials that we've had to go through of talking to experts in the field, um, ideating with mentors and stuff. Um, but I think we've learned a lot about the different data sets that are available and how they all interact with each other, what the pros and cons are. And that's led to, I was able to build towards a lot of these insights that a lot of, um, if any other, a lot of companies don't have access to.

Speaker C: That's a great summary of how data itself is super powerful. Um, but interestingly enough, what we've also found is data alone is also not enough. Right. Which is why when we talk about insights, that's important and also not just insights, what kind of insights? And that's where we talk about the application of the data. Um, because with the data, data we could actually go in probably several different directions, you know, just with the foundational data. But I think what we've seen is again, the pain point for commercial teams is like especially strong. Um, and I think our positioning is those commercially actionable insights are kind of our focus and our bread and butter. Like business questions that come up over and over again, you know, are what's our market size? Um, who should we prioritize when we do outreach? Um, how should we allocate our sales and marketing resources? How do we tell a story for ROI to a physician group? Right. A lot of these questions are answered through those insights. And I think that's where we've been able to get to that insight faster. Rather than give a sales team a lot of data to say, hey, now go forth and you know, manipulate the data yourself. Right. So I just wanted to add the application of the data towards these kinds of insights is where we've positioned ourselves.

Speaker A: I love that and I love the, the piece which, you know, I guess we'll, we'll, we'll link to it when we post this. But um, it was a cool article because it took kind of a consultative approach, right? It was like, here are some strategic frameworks alongside data insights. Not for any specific customer, but it's kind of like here's the level of insight that we can drive. Some of it's uh, counterintuitive. It was kind of almost a thought leadership piece. Right. Um, so I'm curious how that sort of, can we expect more of that? Uh, is that part of your growth plan?

Speaker B: Yeah, um, we definitely want to do more thought leadership pieces. That's um, something that we've been talking about a lot. And I think it's helpful, um, for people who read those posts because, um, I think at the core of that glp, one post is showing how like, based on uh, different ICPs that different companies might have, the health care organizations that they should be targeting can be very different. Um, there's a lot of, um, it's not a lot of time. Like I think generally, uh, in the, in the market right now, it's very volume driven. Like, uh, no matter what, like whatever a health tech company is building, they go to the, to the, they try to sell into the organizations or the providers that have the most volume. But that's probably counterintuitive to what we try to tell is that there's more to it than that based on what you're building. There's a lot of other factors, um, that are not necessarily related to volume that should be, um, taken into account. And so hopefully we can write more pieces like that for different segments.

Speaker C: There's companies that either earlier stage and then there are companies that are more mature. Um, so what Jay had talked about with the icp, that's ideal customer profile. And so a lot of companies may not know what their ideal customer profile is up to a certain point. And so if we were to share more pieces like this, uh, it might give them a better sense of like, oh yeah, how do I build my ICP in a more thoughtful way? And then for companies who've already had a lot of success and proven out their icp, now it's like, how can data help me maximize that ICP faster? Right.

Speaker A: That's a beautiful way to capture it. I love that. Um, okay, cool. Yeah. And it's back to the windshield analogy, right? Looking to the future for Bonfire. What do you guys think is next? Sort of in the near to intermediate term? And what's the long term vision?

Speaker C: Yeah, near term we really feel like, um, the portion, uh, of the market that we want to serve and we've seen that the pain point is again really strong is with digital health and medical device companies. And there's certainly I think that need for the commercially actionable insights. So I think for us near term success is how can we find more partners to build with, um, to help them obviously build a more data driven go to market and establish kind of more proof points for a lot of these um, kind of metrics that we look at in terms of sales efficiency, qualified leads, business cases. I think we want to continue doing that in the near term. Um, I would say long term. Yeah, I think our mission really is to accelerate health tech adoption through these analytics and AI driven insights. So we want to help those companies get their products to market faster, faster. So get them in the hands of patients, providers, payers faster than ever before. So long term success would be like we're the go to kind of commercial insights platform for health tech.

Speaker A: Covered some decent ground there. Do you guys have an icp?

Speaker C: Yeah, um, I think it would be, you know, if we're not, we don't know our icp then we shouldn't necessarily be prescribing other people to find theirs. Um, I think, uh, yeah, we've done a little bit of like, I think just um, evaluation of where have we seen a lot of pull and also where can we immediately add value? I think, you know those things and I would say there's, there's probably three buckets. Um, there's within digital health there's two. So there's regular SaaS digital health. So these are software tools that are being sold into physicians, physician offices, clinics, hospitals, etc. Um, and we can help them, you know, just kind of uh, go to market, find their um, top prospects and kind of that entire funnel of insights that we talked about. Um, then there's Med Device which is uh, also in a similar vein of digital health SaaS. They're selling to providers and oftentimes for device companies there are specific codes that are very relevant whether they're diagnosis codes or procedural codes that a physician might be billing directly under. Uh, we hear remote patient monitoring a lot. There are a lot of devices that fall under that bucket. Right. Um, so we can help drive those insights from a claim standpoint and you know, helping them build that commercial strategy, et cetera. The third bucket is going back to digital Health. There's tech enabled services, uh, which is pretty interesting. So you know a lot of these platforms that are aiming to connect patients to providers faster and so they do actually provide care but you know, technology is kind of the intermediary, um, how we're serving them is not Necessarily to enable them to sell to providers and payers faster. It's actually to help them um, optimized for patient acquisition. And so what that means is they're looking for ways to get patients referred into them. Right. And we have insights around patient referral patterns. So where are patients going to and from? They show up at a PCP and then go to some specialist. What does that patient journey look like? And we're seeing a lot of that use case come up too. Um, and so we can help those companies by uh, understanding who are the high volume referrers that they should be targeting. And then also separately, you know we do have payer level data too. And so if they have a contract with the payer, now the question is how do you activate that payer relationship? Who are the providers under that payer who might be seeing patients that might be ones that they can serve? And so a lot of those insights we have that can help those companies too. So yeah, summing up within Digital Health there's digital health SaaS, there's digital health tech enabled services and then there's Med Device and then we're playing in the kind of SMB mid market uh, for those segments.

Speaker A: Maybe from the product side or the technical side. What's on the, if you can talk about it or whatever you're willing to share what's on the you know, sort of near term roadmap there. What are you excited to be shipping soon?

Speaker B: Yeah, um, absolutely. Um, yeah. That was a good uh, uh summary of our, of our ICP there. And um, there's a lot that we want to be building and a lot of things that we are currently building. One thing that we've been thinking a lot about is in integrating uh, into existing workflows because uh, right now um, we have a bonfire platform, we have this web app where um, users uh, can come in, get access to a lot of the data insights, interact with the data in different formats, like visually seeing data on a map, et cetera. But it's another login. Uh, users like it, they can access the data but ideally all the information that they need is in one place for them. And a lot of times um, you know uh, sales people, they live in their CRM, spend a lot of time in their CRMs. And so we're thinking about a lot of those types of integrations and it's something we're working on um, incorporating and building out right now actually is more of these like um, CRM integrations for like many aspects of our data that it can be directly. A lot of the relevant information is directly pushed and integrated into the workflows and CRMs that um, our users are currently leveraging. So that's one exciting thing I think that we hopeful will more seamlessly incorporate, uh, our data into existing workflows.

Speaker A: So I'm curious now this is uh, push back on this if you disagree. But in my tenure in health tech, this feels like a particularly, let's say, volatile time. Which way the federal government might go on different issues with the way AI is evolving, uh, both as a new tech paradigm that can drive new startups and innovation and new businesses, but can also be leveraged within incumbents in exciting new ways. Everything's changing right now. So I'm curious if you guys could describe uh, what you think are the sort of headwinds right now. What are you sort of working against? What's working against you? What are the tailwinds? What do you think might serve to kind of help bonfire over the coming years?

Speaker C: I think we don't know what data will be accessible and to what extent. Um, at least my sort, uh, of optimistic line of thinking is even if certain public data access gets taken away, that it'll come back in a different form, maybe behind a paywall. Um, so because that data has a lot of intrinsic values. So I would hope that if the data is going to go away, that at least having some more budget to spend can at least unlock that. Um, I agree that is definitely a headwind. Um, I think separately taking a step back, even like macroeconomically, I think we're obviously beyond the age of like, uh, just limitless, uh, SaaS spend right from, for companies. And I think uh, that's affected how companies have raised, it's affected how companies operate. And so that creates both a challenge and an opportunity for us. The challenge obviously being budgets are just harder to unlock. Um, but at the same time the opportunity is, you know, if we are able to build something that's super defensible and has, you know, an roi, then it's going to be more sticky. Um, so I think that's, that's probably something we're looking at. Um, and I think the tailwind there, um, you know, in contrast to having companies operate leaner, is, you know, it's, if they're not going to scale up a sales team like they once did, um, how can they still increase their revenue? And I think the answer that we're betting on is better data and better insights. So I think the data liquidity that we've been Able to see and be able to incorporate um, and also uh, show that having the data can make teams operate leaner and still be effective. I think we want to continue to ride that tailwind at least.

Speaker B: It's unfortunate but some public data sets are already being taken down. I know there's some public data sets around um, uh, like racial diversity and these um, more socioeconomic things related to race that have been taken down and a lot of these are really impactful um, for especially a lot of researchers use. Um, we've already started seeing some of these public data sets getting taken down and hopefully it doesn't expand too much from there.

Speaker C: Data alone is uh, you know the access is a headwind but I think how the healthcare system and the policy landscape is going to change, that's a huge question mark right now too. I think there were some talk about the coding system that we use right now, the ICD10 and CPT and there might be reform there that also directly has implications for us and obviously every provider and payer too. So I think those are things that we'll just have to navigate and hopefully we can help companies think through that. You know, if, if there are any changes.

Speaker A: Okay, cool. Thanks guys. Thanks for exploring that with me. Um, I want to talk a little bit about your guys journey as founders of, of Bonfire. So I suppose rather from you know, rather than looking forward and sort of speculating about what might come, I think the, the journey of starting a company and trying to scale it is certainly something you know I admire. We at VYNL work with um, startup founders pretty often. Many of the leadership at VYNL have founded companies in the past. Um, it's kind of in our DNA and we just love to work with uh, entrepreneurial folks. So you know I think Paul Graham once said founding a startup is like getting punched in the face repeatedly. Can you guys talk a little bit about that? You know I think you guys founded this in 2022, is that right?

Speaker B: Yeah, we found it at end of 2022 but we were like kind of ideating on it and, and even um, like trying to like do uh, like proof of concepts even before that. But yeah, I think it's like that journey to product market fit is not can. It can be kind of a um, as I'm sure you know Chris can be kind of a windy road. Um, and even like I'm not even sure we're completely know if we're there yet. It feels like uh, it definitely feels like we're getting closer and closer but it's hard to know when we're actually there. Um, when we started Bonfire we were uh, addressing a lot of the same pain points we are now. But our solution was actually kind of different and so it took a little bit of time to get to where we are now which was more of this um, uh, leveraging the data like a lot of those claims data, social determinants of health data that we are now to, to help um, with like help sales teams with their go to market strategy. On the analytical side we were actually starting trying to think more about contact information which we don't do at all now. We were um, thinking about okay, how can we put together like you know, compile and maintain the best healthcare um contact information database. Um and that's what we spent like the first few months to some amount of time when we were working on Bonfire trying to address and we had some really early customers, they didn't pay a lot but that we were trying to build this out for. Um, but then that was I think like a challenging space also not it didn't seem like there was as much need there. A lot of like the companies we were working with especially the, the later stage beyond the startups, they, they were, they were like oh we, we can figure out contact information on our own or contact information like it doesn't really help as much. We, we can meet them in person or find them on LinkedIn or, or like figure out other ways to get in touch with them. It was more the startups that were interested in contact information and so, so after working on that for a while first we started pivoting more to the analytics side um because it would help us target these larger companies like maybe mid market SMB to mid market to even more enterprise rather than focusing on the startups where there was a more need for insights for their go to market strategy rather than just having the contact information. And so that was like one of the early kind of um, shifts and trying to figure out what we're building that we did was we just one shifted away from like trying to build this like healthcare contact database to morph a analytics and insights. Um and then also uh, trying not to focus too much on those early stage startups and going a little bit more up market to um, mid market in SMB. Um and yeah that's what where we've been building, building now. Um, yeah and uh, yeah I'll let Vinay share some thoughts too on that.

Speaker C: The product market fit journey is, is certainly very I think uh circuitous and it's uh, tough to know when you're there but you, all you can do is kind of lean on signals where you are and iterate based on feedback. Um, I think going back to that uh, Paul Graham quote, um, that's a, uh, that's a funny one and I think a lot of founders can relate to that. Um, is. I think at the early stage you just don't know what you don'. Um.

Speaker A: Right.

Speaker C: Like. And I think that's part of why we went through an accelerator. You know, as first time founders, you know, having some level of structure, mentorship, um, you know, and sort of processes that have been tried and tested in the past, uh, were really helpful. I think we would have, you know, without that kind of initial support, uh, could have wasted a lot more time, you know, building the wrong thing for the wrong type of segment. I think we were able to at least, you know, um, use some of those like frameworks to think more strategically about who is our customer and based on feedback, how do we get to that point sooner? Um, so that's something to just highlight, um, as well. Um, and I think generally, yeah, uh, when you're a small team, I think just there's a lot to do all the time. So you never feel like you're completely uh, I think uh, through the entire to do list there's always more. And so it's a matter of I think knowing it's a marathon and not a sprint and being able to prioritize really effectively. So I think just uh, that's something, something at least I felt and still feel. Uh, it's just important to know and to be in a tight feedback loop with the team on what to focus on and kind of go from there. Um, but it's been, I think for both of us been very exciting. Uh, I think we've learned a lot and continue to be very excited about what we're building and the value that we've been able to introduce.

Speaker A: Awesome. Yeah, it seems like an incredibly cool product. You guys seem like you're anticipating, you know, the, the market movements and the tech paradigms, uh, really well. And I think you've just shared some wise words for any, uh, you know, future health tech founders that might be listening. So thanks guys. Love the conversation. I really appreciate you joining products in health tech today.

Speaker C: Yeah, thanks for having us on, Chris.

Speaker B: Thanks Chris.

Speaker A: You can also connect with us on LinkedIn, YouTube or on our website if you have ideas or suggestions of what you'd like to hear on a future episode or if you'd like to be a guest, please just shoot us an email@infoproductsandhealthtech.com.