Transcript of Transforming Health Tech Go-to-Market: How Bonfire Analytics Drives Sales Efficiency
Product in Healthtech
0:08Welcome to Product in Healthtech, a community
of product leaders innovating in healthcare. 0:13I'm Chris Hoyd, principal at Vynyl. Today we're
diving into the world of healthcare data analytics 0:18with Jaya Pokuri and Vinay Nagaraj, co-founders of
Bonfire Analytics, these longtime friends turned 0:25business partners are on the mission to transform
how health tech companies build and execute their 0:29go to market strategies. In our conversation, we
explore their journey from building a healthcare 0:35contact database to becoming an analytics platform
as they navigated the winding road to product 0:40market fit, how they've helped a prosthetics
company achieve sales team parity and a 3x 0:46increase in efficiency by targeting the right
providers, why volume driven targeting is often 0:51counterintuitive, and how Bonfire's data reveals
surprising ideal customer profiles for different 0:56health tech companies, and how they're preparing
to navigate healthcare policy uncertainties while 1:00turning potential headwinds into opportunities.
Let's jump into the conversation. Hey guys, 1:08thanks again for joining today. Let's just
start with some background. Let's dive right 1:13in. How did you guys get into this space
and what inspired you to start Bonfire?1:19So we're both co founders of Bonfire. We've
known each other for a long time. We've been 1:25friends since college, and we got into
the space of building Bonfire through, 1:33like, a shared vision that we both had. It comes
from a bit of my background as in data science 1:40and Vinay's background in health tech sales and
growth, so we both studied engineering together, 1:46but then after graduating, I moved into the data
science world. Worked at a few different startups, 1:51doing a lot of work in like machine learning,
different working with different data sets. 1:56And Vinay worked at a health tech -- a digital
health company, and it was a bit of like some 2:05side projects that I was doing around connecting
disparate data sources that oftentimes data 2:12scientists like a lot of great, publicly
available data that is really useful for 2:17data science projects, but data scientists don't
leverage that much. And then Vinay feeling a lot 2:21of the pain points and selling into healthcare,
where we found a combination, like ways to 2:28leverage what I was building for the pain points
he was feeling. Yeah, Vinay any thoughts on that?2:36I think that was a good, good summary. Yeah,
we've definitely both had exposure to, like, 2:42the startup world as well. I think had some
of that DNA, having worked at startups and, 2:47you know, and so I think we were both kind
of hungry for the next idea. And, you know, 2:51when we kind of joined forces, the only
other thing I'll highlight is, I think we 2:55it was nice to have a shared vision from the
get go. I think both of us were interested in 2:59healthcare. Both of us had a similar zest for,
like, how data can be a really integral part of, 3:03like, a product offering. So yeah. And then
since then, we've just kind of iterated on, 3:09like Jaya mentioned, the pain point that
we're solving for as we've built Bonfire.3:13That's a nice segue. Can you talk
a little bit more about that pain 3:16point that you're solving for and
how you guys uniquely address it?3:20Yeah, yeah, I can, I can take that one. And
so in terms of the pain point, you know, 3:26one of the things like, you know, Jaya mentioned
as part of, as part of my background, is I was 3:30on the growth side, selling into healthcare. So I
was at a digital health company prior to Bonfire, 3:35called Round Trip Health, which is a patient
transportation platform for hospitals and health 3:40systems. And as I was going through that sales
process, I realized selling it to healthcare 3:45is just incredibly difficult. A lot of companies
struggle with it, and a lot of my peers who are on 3:50the commercial teams had very similar pain points.
Is we don't know where to start, we don't know how 3:54big our market is, we don't know who to prospect.
We don't know how to build the right business 3:59cases for our prospects. So a lot of those things
came up over and over again, and I think that's 4:04where we realized that data and insights can be a
really powerful way to allow these commercial and 4:11sales teams to just be very targeted and not have
to resort to what, you know, we call the 'spray 4:16and pray', which is, you know, you send 1000
emails get 10 responses, one meeting. And that's 4:20what I did for a while when I was selling to,
you know, doctors, physician groups, hospitals, 4:24health systems. And so that's what we're trying to
eliminate with data, is just having the data and 4:30the insights from the get go, allow teams to just
be a lot more, you know, efficient and effective.4:36You know, I know that AI is
a big part of how you guys, 4:41you know, interpret the data for the
customer. Can you talk a little bit about how 4:46AI sort of fits into your product
and what that does for your users?4:50I feel like there's a lot of buzz around AI right
now, and it's sometimes hard to understand what, 4:56what it encompasses like it seems to
like touch a lot of different things. 5:00A lot of what we do is leveraging a lot of machine
learning techniques to build a lot of these 5:08insights. We try to really provide actionable
insights to these health tech companies. And 5:17we'll build these insights using like machine
learning techniques. We also use different AI 5:22tools to help accelerate these processes.
But, yeah, I think there's just generally, 5:29there's a lot going on with AI now, and there's
probably more that we can do and we are planning, 5:36but I would say right now a lot of it is really
focused on ml, and then also leveraging a lot 5:42of the tools that are existing to help
accelerate our like product development.5:47Can you maybe talk us through a recent, you know, 5:51I understand you might not be able to
divulge all the details, but a recent 5:54sort of case study of how you've helped one
of your customers leverage your platform?6:01Yeah, I'm happy to share some detail there. We
actually did share a customer story recently, 6:06and so it's, you know, good timing. We can
talk about them. So we work with a company 6:11called Point Designs. They're sort of a mid market
prosthetic device company targeting prosthetists, 6:18orthotists, you know. And they have devices
that help, essentially, amputees, and so when 6:24we started working with them, initially, you
know, their pain point was they weren't really 6:30sure about their market. These patient populations
who need the kind of device that they've built 6:36or the devices they've built. Where are they?
Who are the prosthetists and orthotists who are 6:40serving those patients, and how can they reach
them more effectively? And it was a little bit 6:45more of like the casting a wide net and see what
comes through, sort of approach. And yeah, since 6:51we've been able to work with them, and you know,
they've been a really great partner to work with, 6:55and have also provided feedback as they've used
our data on our product, it's been really cool 7:01to see that the data has helped them in terms of
identifying, you know, where are these patient 7:06populations, targeting these prosthetists and
orthotists. And then ultimately, what we saw 7:12was a 3x increase in sales efficiency, which is
the sort of resources they've dedicated to sales 7:18and marketing. They've been able to 3x that in
terms of the return they've gotten, which is, 7:24which has been really exciting. And another thing
that they also mentioned was their sales team has 7:28a lot of parity now, which is also an interesting
thing, right? Is being on the sales side, 7:34parity is something that's tough for sales leaders
to get to, because, you know, you don't want to, 7:39you want to sort of assign and territories
and regions to reps in an equitable way, 7:45but it's hard to do that without the right
data, and so that's something they shared, 7:48is now they're a point where different reps
occupied this kind of spot at various times 7:53in the year. So yeah, and just generally,
we've looking forward to growing with them, 7:59and they actually gave us a great kind of insight
in how they've used their platform, which might 8:04be another topic we talk about, is, you know,
they see us as the windshield and the CRM as 8:08the rear view mirror, which I thought was a great
kind of summary of our positioning is, you know, 8:13we want to help them with getting to their market
more quickly. The CRM helps, obviously, track a 8:20lot of that and make sure their sales processes
are optimized. That's an example of how we help, 8:26you know, a medical device company, but we also
work with other digital health companies as well.8:30I also read your recent post about GLP-1 market
strategies, which I thought was fascinating, 8:37both, you know, for the healthcare insights
and for how it kind of showcases your guys' 8:41approach to data analysis, if you don't mind,
I'd like to dive into that for a minute here. 8:48I think the article demonstrates you know how
you use provider-level pharmacy claims data 8:53to generate strategic insights, right? So
can you talk a little bit about your unique 8:58approach to acquiring, processing and
analyzing that type of healthcare data?9:03In that article, it was a lot of work with like
prescription claims data. We also work with a lot 9:10of other types of data, like medical claims data,
SDOH data, which is and more like population level 9:19data. So it's a lot of different data sets from a
lot of different different sources, and so there 9:26can be a lot of challenges when working with data
from these different sources, because oftentimes 9:32we're trying to create insights at a specific
level of granularity that isn't necessarily - we 9:40may directly get from any one source, and so
we have to, like, abstract it to the level of 9:47granularity we're trying to develop insights
on. For example, the prescription claims data, 9:53it's all at the patient level. And then there
might be other other data sets, like SDOH data, 10:01social determinants of health data, which might be
at the population level, like at a zip code level, 10:05county level, and we have to figure out how to
connect these together at a different level of 10:11aggregation. In the case of the article you're
talking about, it was at the health system level, 10:16while taking into account like different biases
and and figure out figuring out complicated 10:24relationships in the data. For example, a really
complicated relationship to figure out from 10:31that's not easy to figure out from data is like
affiliations data understanding for given health 10:37systems. What are all the like the entities
that are encompassed by that health system is 10:45constantly changing, like every I feel like every
month we hear about different like acquisitions, 10:51roles, roll ups between different health systems.
Sometimes we don't know which clinic is owned by 10:57a health system or whether it's independent, and
that data is not directly like in the prescription 11:03claims data or like some and there's no data set
that just like tells you. And so a lot of the 11:10that work that you were talking about and and
like getting to that health system level data, 11:15as we did for the GLP article, of showing transit
the health system data comes from, like connecting 11:23all these different data sources and trying to
predict things that just go beyond even the the 11:29prescription, like the prescription trends. Even
before doing that, we have to try to predict - 11:34okay, what are all the organizations which are
part of that health system and that in itself 11:41can be a pretty complicated process, yeah, does
that answer at least some of some of the question?11:47Yeah, that was great. Thank you. Okay, so you
just, you know, described some of the deepest 11:52complexity, I think, of the American Health
System, right? Like disparate data sources, 11:58difficult to discern interrelationships
between them, even more difficult to like 12:04turn, you know, the data or the relationship into
an insight. Can you talk a little bit about how 12:11Bonfire is, you know, uniquely differentiated
to to get to that insight is that the access 12:19to data that most others don't have,
is the way that you analyze it? Is it 12:24the way that you get to the insight?
Can you talk a little bit about that?12:27This is a good question. I think it's a
combination of the different items that 12:32you mentioned. One obvious barrier is access to
data - like a lot of the data is not publicly 12:43available, is you'd have to, like, purchase it
from different sources, like, whether it's a CMS 12:49or from clearing houses, and that data can be
quite expensive, but then that only gets you so 12:57far. Then there's also a lot of, like, publicly
available data, which may or may not be easily 13:03accessible, like it lives a lot of the relevant
public data lives across a different variety of 13:12of like sources, like it might be the CMS has a
lot of lot of great data sets. Other organizations 13:20have a lot of great data sets. Sometimes it's
like trying to scrape certain websites. It's 13:26not in a necessarily, like a clean, extractable
format. And so one, I think, just one aspect of 13:33it is being able to beyond, like, first of all,
being able to purchase data. But one aspect of it 13:39is knowing where to pull data, like what data is
valuable to, like, help achieve a certain like, 13:49insight that we're trying to build, and then where
to find that data, how to pull that data off these 13:54processes take a lot of domain knowledge, and it's
not, definitely not something we've always had, 14:01like, there's been a lot of trials that we've
had to go through of, you know, talking to, 14:06talking to experts in the field, ideating
with, with mentors and stuff. But I think, 14:14like we've learned a lot about the different data
sets that are available and how they all interact 14:20with each other, what the pros and cons are,
and that's led to, like, I was able to, like, 14:26build towards a lot of these insights that a lot
of, like a lot of companies, don't have access to.14:34It's a great summary of, like, how data itself
is super powerful. But interestingly enough, 14:40what we've also found is data alone is
also not enough, right? Which is why, 14:43when we talk about insights, that's like
important and also not just insights, what kind 14:48of insights? And that's where we talk about the
application of the data. Because with the data, 14:53we could actually go in probably several different
directions just with the foundational data. But I 14:59think what we've seen is. Again, the pain point
for commercial teams is like especially strong, 15:05and I think our positioning is those commercially
actionable insights are kind of our focus and 15:11our bread and butter, like business questions
that come up over and over again. You know, our 15:15what's our market size? Who should we prioritize
when we do outreach? How should we allocate our 15:21sales and marketing resources? How do we tell a
story for ROI to a physician group? Right? A lot 15:27of these questions are answered through those
insights, and I think that's where we've been 15:32able to get to that insight faster, rather
than give a sales team a lot of data to say, 15:37hey, now go forth and, you know, manipulate the
data yourself, right? So I just wanted to add the 15:43application of the data towards these kinds of
insights, is where we've positioned ourselves.15:48I love that. And I love the piece which I, you
know, I guess we'll link to it when we, when we 15:54post this. But it was a cool article because it
took kind of a consultative approach, right? It 15:59was like, here are some strategic frameworks
alongside data insights, not for any specific 16:06customer, but it's kind of like, you know, here's
the level of insight that we can drive, you know, 16:12some of it's counterintuitive. It was, you
know, kind of almost a thought leadership piece, 16:17right? So I'm curious, can we expect more of
that? Is that part of your your growth plan?16:25Yeah, yeah. Like, we definitely want to do more
thought leadership pieces. That's something 16:32that we've been talking about a lot, and I think
it's helpful for people to who read those posts, 16:40because I think at the core of that, that
GLP-1 post is, is showing like, how like, 16:46based on different ICPs that different companies
might have, the healthcare organizations that they 16:52should be targeting, can very different. In the
market right now, it's very volume driven. Like, 17:04no matter what, like, whatever a health tech
company is building, they try to sell into the 17:11organizations or the providers that have the most
volume. But that's probably counter intuitive to 17:18what we try to tell is that there's more to it
than that based on what you're building. There's 17:23a lot of other factors that are not necessarily
related to volume, that that should be taken 17:29into account. And so hopefully we can write more
pieces like that for different different segments.17:35There's like, you know, companies that
either earlier stage, and then there 17:38are companies that are more mature. So you
know what, Jaya had talked about, the ICP, 17:42that's ideal customer profile. And so a lot of
companies may not know what their ideal customer 17:48profile is up to a certain point. And so if we
were to share more, you know, pieces like this, 17:53it might give them a better sense of like
- oh, yeah. How do I build my ICP in a more 17:58thoughtful way? And then for companies
who've already had a lot of success and 18:02proven out their ICP now it's like, how can
data help me maximize that ICP faster, right?18:10That's a beautiful way to capture it, all of that.
Okay, cool, yeah. And it's back to the windshield 18:15analogy, right? Like, looking to the future for
Bonfire, what do you guys think is next, sort of, 18:21in the, you know, near to intermediate
term. And what's the long term vision?18:26Near term, you know, we really feel like
the portion of the the market that we want 18:33to serve. And, you know, we've seen that
the pain point is, again, really strong is, 18:38you know, with digital health and medical
device companies, and there's, you know, 18:42certainly think that need for the commercially
actionable insights. So I think for us, 18:47near term success is, how can we find more
partners to build with, to help them, obviously, 18:53build more data driven go to market and establish
kind of more proof points for a lot of these 19:01metrics that we look at, you know, in terms
of sales, efficiency, qualified leads, 19:04business cases, I think we want to continue doing
that in the near term. I would say long term, 19:10our mission really is to accelerate Health Tech
adoption through, you know, these analytics and AI 19:16driven insights, right? So we want to help those
companies get their products to market faster. So 19:21get them in the hands of patients, providers,
payers, faster than ever before. So long term 19:26success would be like, we're the go to kind of
commercial insights platform for health tech.19:31Covered some decent ground
there. Do you guys have an ICP?19:36Yeah, I think it would be. You know,
if we're not we don't know our ACP, 19:41then we shouldn't necessarily be prescribing
other people to find theirs. I think, yeah, 19:46we, we've done a little bit of like, I think just
evaluation of where have we seen a lot of pull, 19:52and also, where can we immediately add value? I
think, you know, those things. And I would say 19:57there's, there's probably three buckets - within
digital health, there's two -- so there's regular 20:03SaaS digital health. So these are software
tools that are being sold into physicians, 20:08physician offices, clinics, hospitals, et cetera.
And we can help them just kind of go to market, 20:15find their top prospects and kind of that entire
funnel of insights that we talked about. Then 20:22there's med device, which is, you know, also
in a similar vein of digital health SaaS, 20:27they're selling to providers and oftentimes for
device companies, there are specific codes that 20:32are very relevant, whether they're, you know,
diagnosis codes or procedural codes that you know, 20:36a physician might be billing directly under.
You know, we heard remote patient monitoring 20:41a lot. There are a lot of devices that fall under
that fall under that bucket. So we can help drive 20:46those insights from a claim standpoint, and, you
know, helping them build that commercial strategy, 20:51etc. The third bucket is going back to
digital health. There's tech-enabled services, 20:55which is pretty interesting. So you know,
a lot of these platforms that are aiming to 21:00connect patients to providers faster, and so
they do actually provide care. But you know, 21:05technology is kind of the intermediary. How we're
serving them is not necessarily to enable them to 21:10sell to providers and payers faster. It's actually
to help them optimize for patient acquisition. And 21:17so what that means is they're looking for ways
to get patients referred into them, right? And 21:22we have insights around patient referral patterns.
So where are patients going to and from? They show 21:27up at a PCP and then go to some specialist. What
does that patient journey look like? And we're 21:32seeing a lot of that use case come up too. And
so we can help those companies by understanding 21:38who are the high volume refers that they should be
targeting. And then also, separately, you know, we 21:42do have payer level data too, and so if they have
a contract with the payer. Now, the question is, 21:47how do you activate that payer relationship? Who
are the providers under that payer who might be 21:51seeing patients that might be, you know, ones that
they can serve? And so a lot of those insights, 21:56you know, we have that can help those companies
too. So yeah, summing up, within digital health: 22:01there's digital health SaaS, there's digital
health tech enabled services, and then there's 22:05med device, and then we're playing in the
kind of SMB mid market for those segments.22:10You know, maybe from the product side
or the technical side, what's on the, 22:14if you can talk about it, or whatever
you're willing to share. What's on the, 22:18you know, sort of near term roadmap there.
What are you excited to be shipping soon?22:24Yeah, absolutely. Yeah, that was a good summary
of our of our ICP there. And there's a lot that we 22:32we want to be building, and a lot of things that
we are currently building. One thing that we've 22:37been thinking a lot about is integrating into
existing workflows, because right now we have a 22:45Bonfire platform. We have this web app where users
can come in get access to a lot of the data, and 22:53sites interact with the data in different formats,
like visually, seeing data on a map, etc. But it's 23:00another login, right as like, we have like, you
know, users like it. They can access the data, 23:06but ideally, all the information that they need
is in one place for them. And a lot of times, 23:14you know, sales people, they live in their
CRMs spend a lot of time in their CRMs. And 23:20so we're thinking about a lot of those types of
integrations, and it's something we're working on, 23:26on incorporating and building out right
now actually, is more of these, like 23:32CRM integrations, for a lot like many aspects of
our data, that it can be directly - a lot of the 23:38relevant information is directly pushed and
integrated into the workflows and CRMs that 23:46our users are currently leveraging.
So that's one exciting thing, 23:50I think, that we hope will more seamlessly
incorporate our data into existing workflows.23:58So I'm curious now this is that, you
know, push back on this if you disagree, 24:03but in my tenure in health tech, this feels
like a particularly, let's say, volatile time 24:11which way the federal government might go on
different issues with the way AI is evolving, 24:18you know, both as a new tech paradigm that can,
you know, drive new startups and innovation and 24:25new businesses, but can also be leveraged within
incumbents in exciting new ways. Everything's 24:30changing right? So I'm curious if you guys, could,
you know, describe what you think are the sort of 24:38headwinds right now? What are you sort of working
against what's working against you? What are the 24:42tailwinds? What do you think might serve to
kind of help Bonfire over the coming years?24:47I think we don't know what data will
be accessible, and to what extent, 24:52at least my, you know, sort of optimistic line
of thinking is even if certain public data access 24:59gets taken. In a way that it'll come back in a
different form, maybe behind a paywall. So because 25:04that data has a lot of intrinsic values, so I
would hope that, if the data is going to go away, 25:10that at least, you know, having some more budget
to spend can at least unlock that. I agree that is 25:17definitely a headwind, I think, separately, taking
a step back even like macro economically now, I 25:23think we're obviously beyond the age of like, just
limitless SaaS spend right from for companies, 25:31and I think that's affected how companies have
raised. It's affected how companies operate. 25:36And so that creates both a challenge and an
opportunity for us. The challenge obviously, 25:41being budgets are just harder to unlock.
But at the same time, the opportunity is, 25:47you know, if we are able to build something
that's super defensible and has, you know, 25:51an ROI, then it's going to be more sticky. So
I think that's probably something we're looking 25:57at. And I think the tailwind there, you know, in
contrast to having companies operate leaner is, 26:05you know, if they're not going to scale
up a sales team like they once did, 26:10how can they still increase their revenue? And I
think the answer that we're betting on is better 26:16data and better insights. So I think the data
liquidity that you know, we've been able to see 26:22and be able to incorporate, and also show that
having the data can make teams operate leaner 26:30and be still be effective. I think we want
to continue to ride that tail end, at least.26:35It's unfortunate, but some public data
sets are already being taken down, 26:39like I know there's some, there's
some public data sets around, like 26:44racial diversity and like these, like social,
like, more socio economic, like things related 26:51to race that have been taken down. And a lot
of these are really impactful for especially, 26:57like a lot of researchers use. And so we've
already started seeing some of these public 27:03data sets getting taken down, and hopefully
it doesn't expand like too much from there.27:09Data alone is, you know, the the access is, is a
headwind, but I think how the healthcare system 27:15and the policy landscape is going to change,
that's a huge question mark right now too. You 27:20know, I think we, there are some talk about the
coding system that we use right now, the ICD 10 27:25and CPT, and there might be reform there that also
directly has implications for us, and obviously 27:31every provider and payer too. So I think those are
things that we'll just have to navigate, you know, 27:36and hopefully we can help companies think
through that, you know, if there are any changes.27:41Okay cool. Thanks, guys, thanks for exploring
that with me. I want to talk a little bit about 27:47your guys journey as founders of of Bonfire.
So I suppose rather from you know, rather than 27:53looking forward and sort of speculating about what
might come, I think the the journey of starting a 28:00company trying to scale it is certainly something,
you know, I admire. We at Vynyl work with startup 28:08founders pretty often. Many of the leadership at
Vynyl have founded companies in the past. It's 28:14kind of in our DNA, and we just love to work with
entrepreneurial folks. So, you know, I think Paul 28:20Graham once said, founding a startup is like
getting punched in the face repeatedly. Can 28:27you guys talk a little bit about that? You know, I
think you guys started this in 2022 is that right?28:34Yeah, we found it at end of 2022 but we were
like, kind of ideating on it and and even, 28:40like, trying to, like, do, like, proof of concepts
even before that. I think it's like that journey 28:48to product market fit can be kind of a, as I'm
sure you know, Chris, can be kind of a windy 28:54road. And even, like, I'm not even sure we're
completely know if we're there. Yet it feels like, 29:02it definitely feels like we're getting closer and
closer, but it's hard to know when we're actually 29:06there. When we started Bonfire, we were addressing
the a lot of the same pain points we are now, but 29:14our solution was actually kind of different, and
so it took a little bit of time to get to where 29:19we are now, which was more of this leveraging
the data, like a lot of those claims data, 29:25social determinants of health data that we are now
to to help with, like help sales teams with their 29:33go to market strategy on the analytical side.
We were actually starting trying to think more 29:38about contact information, which we don't do at
all now. We were thinking about, okay how can we 29:46compile and maintain the best healthcare contact
information database? And that's what we spent, 29:55like, the first few months to, like, some amount
of. Time when we were working on Bonfire, trying 30:02to trying to address and we had some really early
customers. They didn't pay a lot, but that like we 30:09were trying to build this out for. But then that
was like a, I think, like a challenging space. It 30:17didn't seem like there was as much need. There
a lot of flip companies we were working with, 30:23especially the later stage, beyond the startups,
they were, they were like, oh, we can figure out 30:28contact information on our own, or contact
information, like, it doesn't really help as 30:34much. We can meet them in person or find them on
LinkedIn, or, like, figure out other ways to get 30:40in touch with them. It was more the startups that
were interested in contact information. And so 30:45after, like, working on that for a while, first,
we started pivoting more to the analytics side, 30:51because it would help us, like, target these
larger companies, like maybe mid-market test 30:58SMB to mid-market to even more enterprise,
rather than focusing on the startups where 31:03there was a more need for insights, for their
go to market strategy, rather than just having 31:09the contact information. And so that was like,
one of the early shifts and trying to figure out 31:16what we're building. That we did was we just one
shifted away from like, trying to build this, like 31:21healthcare contact database, to more analytics and
insights, and then also trying not to focus too 31:31much on those early stage startups, and going a
little bit more off market to mid market and SMB, 31:38and that's what, where we've been building,
now, I'll let Vinay share some thoughts too.31:47The Product Market Fit journey is certainly very,
I think, circuitous, and it's tough to know when 31:53you're there, but you all you can do is kind
of lean on signals where you are and iterate 31:57based on feedback. I think, going back to that
Paul Graham quote, that's, that's a funny one, 32:02and I think a lot of founders can relate to that
is, I think at the early stage, you just don't 32:07know what you don't know, right? Like, and I think
that's part of why we went through an accelerator, 32:12you know, as first time founders, you know,
having some level of structure mentorship, 32:17you know, and sort of processes that
have been tried and tested in the past 32:21were really helpful. I think we would have,
you know, without that kind of initial support, 32:27could have wasted a lot more time, you know,
building the wrong thing for the wrong type of 32:30segment. I think we were able to, at least,
you know, use some of those like frameworks 32:36to think more strategically about who is our
customer and based on feedback. How do we get 32:40to that point sooner. So that's something, you
know, to just highlight as well. And I think, 32:46generally, yeah, it's when you're a small team.
I think just, there's a lot to do all the time. 32:52So you never feel like you're completely like,
I think through the entire to do list, there's 32:58always more. And so it's a matter of, like, I
think knowing it's a marathon and not a sprint, 33:03and being able to prioritize really effectively.
So I think just that's something at least I felt, 33:08you know, and still feel, is just important to
know and to be in, like, a tight feedback loop 33:14with the team on what to focus on and kind of
go from there. So yeah, but it's been, I think, 33:19for both of us, been very exciting. I think
we we've learned a lot, and continue to be 33:25very excited about what we're building, and, you
know, the value that we've been able to introduce.33:32Yeah. It seems like an incredibly cool product.
You guys seem like you're anticipating, you know, 33:37the the market movements and the tech paradigms
really well, and I think you've just shared some 33:44wise words for any you know future health tech
founders that might be listening. So thanks, 33:50guys love the conversation. Really appreciate
you joining Product in Healthtech today.33:54Yeah, thanks for having us on Chris,33:56Thanks, Chris.33:58You can also connect with us on LinkedIn,
YouTube or on our website. If you have ideas 34:02or suggestions of what you'd like to hear on a
future episode, or if you'd like to be a guest, 34:07please just shoot us an email
at info@productinhealthtech.com
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