Yash From Momentum (00:00)
Hello and welcome back to Building Momentum, the show where we peel back the curtain on the exciting and often chaotic world of building a successful SaaS business. I'm Yash, your host for this show, where every episode we bring you the stories and strategies of founders who've been in the trenches, conquering churns, scaling their teams, and building products that people and businesses love. In this episode, we'll be chatting with Chirag from Blaze. Blaze uses AI and automation to scan millions of online signals to help modern companies
target the right potential customers, especially when they are ready to purchase. We're excited to hear their story and the lessons they've learned along the way. We'll be dissecting the wins, the losses, and everything in between. So buckle up, grab your headphones, and get ready to dive in the world of SaaS founders. Hey, Chirag. Thank you for joining in. How are you doing
Chirag Mahapatra (00:44)
I'm doing well and thank you, Yash, for the really warm introduction. I'm super excited to be here. So I had a look at some of the prior episodes and I hope this one lives up to the bidding.
Yash From Momentum (00:54)
Yeah, it would for sure. And so I want to start by if you can share something that helps us figure out where are we today, like just taking stock. So in terms of if you could talk a little bit more about what does and how long ago did you start, where have you reached today, and what are some short -term goals at Blaze?
Chirag Mahapatra (01:16)
Yeah, so I can share a bit about and I can start with the problem which we saw and this was we were actually experimenting with the prior idea and that's when we noticed this problem. Today the world of outbound marketing is extremely cluttered and if you look at it the problem is that like all of us get
potentially anywhere between like multi tens to hundreds of messages every single day. And this is just like business related messages. And then you have also like the people get personal messages and all that sort of stuff. People's attention is extremely fragmented. And it's very hard for someone who is reaching out to a prospective client to kind of break through that entire barrier. like lot of the times like the
Yash From Momentum (01:59)
Yeah.
Chirag Mahapatra (02:00)
messages will not be read or in case they're read, they're not interesting enough for the message to get mindshared. And we saw that as a huge problem. The second thing we saw is most people are not spending, people's attention are being diverted away from traditional business channels such as email and some of the business messaging channels to people are spending a more time on places such as X, LinkedIn, potentially Slack, Discord.
telegram. So user attention is really fragmented even in the business context is fairly fragmented. So these are the two insights which he had and we went back to the drawing board to figure out okay how can we help our customers.
reach their target users in a very targeted and direct way so that they're actually able to get the user to listen to them. And that's when we realized that, the way to break through the clutter today is by having hyper -personalized messages for the targeted user at the right time. The reality is every buyer is like, take for example, almost every company would need software compliance.
but it's not that they would need Software compliance at the exact same time. It depends on the stage of the business. So being able to understand where a business is and then being able to reach out to them at the right point of time is extremely critical.
So that's kind of the problem statement. What we started doing was started helping our customers aggregate a number of signals about their end users to be able to reach out to them at the right point of time. We started off with like with
Web3 as the industry and why we found Web3 is really interesting because there's a lot of data on social as well as public transaction data. And we kind of combined that to like be able to understand the end user and reach out to them. But we are slowly seeing that those patterns actually hold for a number of other industries as well. And that's kind of how we have been scaling. Yeah, that's how kind of how we have been scaling outward.
and kind of going from there. In terms of when we started, we started working on this idea around, I think it was April, 2022. So it's been a bit more than a couple of years. And in terms of where we are, so where we are, we're working with about hundreds of customers right now and have run over like thousands of campaigns on our product.
Yash From Momentum (04:22)
And you are also part of YC as I see.
Chirag Mahapatra (04:24)
Yeah, we went through the Y Combinator Winter 22 batch.
Yash From Momentum (04:27)
Yeah, I mean, that's a really cool thing. So, no, but this is also where I think, one of the things that I always sort of try to understand a little more if I can prove is that, are you like in the business of improving the salesperson's productivity so that they're able to focus on the right leads? Or are you in the business of helping salespeople
cast a wider net which is where they are able to reach out to more, like they get more responses or better conversion rates. So which or like is it both of
Chirag Mahapatra (04:59)
think the latter, the reality is the latter is the one which wins businesses, right? Today, like Yash, if you're doing a 2 % conversion rate and if I can help you get that up to three and a half, I'm sure that's something you'll pay me for. like the...
Yash From Momentum (05:04)
Mm
Yeah.
Chirag Mahapatra (05:12)
So I know there are number of tools around like the productivity angle, but we are focusing on conversion. How can we get, how can we help our sales and marketing users who are using our product get better responses, get better responses and more responses.
Yash From Momentum (05:28)
And I think, just expanding on one of the things that you said that there are so many tools and platforms that claim to help with outbound outreach. And still, is the outbound outreach that I am done, which other salespeople are doing, is extremely poor quality. So it will be like the same copy
emails that they would have sent to a couple of my other founder friends as well. And so why do you think this is? Which is where there are so many number of tools and platforms, at the same time the quality of the outbound outreach that is happening is significantly poor, right? So I want a good, smart salesperson to reach out to me and sell me things because I might find something valuable.
But it's just not like the outbound outreach that happens. It's just a turn off right at that point of time itself. So why do you think that that happens?
Chirag Mahapatra (06:20)
Yeah, I think that's a great question. I think a lot of the times people look at this as an automation problem rather than a data problem.
And I think that's kind of what we think what we have been like as an industry gravitating towards, okay, how can we layer on a complex set of automations and those automations, especially on some of those tools, you can, you can go pretty crazy on it. And the thing is like, yeah, it's like those flowcharts almost like give me anxiety when I see those, but like, think that we have, we have looked at this. I think that as an industry, we have looked at this as an automation problem.
Yash From Momentum (06:39)
Yeah, yeah, Yeah, yeah.
Chirag Mahapatra (06:53)
At Blaze we're trying to reframe this as a data problem, is, sure automation is great and I think it's of course necessary, but can you reach out to the right user at the right point of time? Even within your business, it could be that if I might have a better chance of selling a product.
If I reach out to someone else, like who has actually the time, who has the budget, who is actually on the lookout for the thing, then think. And especially after the company goes beyond 50 people, it's not that everyone has the context to everyone, and it's not that the founders would have time to respond to all of those.
So being able to find like use a database like data related approach in which you're finding the right person who has the right budget and the right time using the signals with the company themselves. Most companies are talking a lot about themselves publicly, right? You have your Twitter, have LinkedIn, have like different social channels, you have your website. Companies are actually giving a lot of these signals, right? You can like look up a company and then figure out the tech stack and see what's missing. So all of those
technologies are available. And then like what we looked at, okay, can we take a data first approach in which we understand as much as possible about a target company and then use that information to reach out to do outreach with of course, layering in good automation as a when required. yeah, first and foremost, looking at it as a data product. And I do think like, especially with the like advances in AI over the last few years, I think like that's, that's the approach which we think will win in the long term.
Yash From Momentum (08:23)
Yeah, and this is like really a great way to think about it. So in outbound or cold outreach to an extent, automation is necessary condition, but it is not sufficient. So you need to have intelligence, you need to have data sources that tell you that and the T in B, A and T like stands for time. Is this the right time to approach?
Chirag Mahapatra (08:33)
Yes, exactly, yes.
Exactly. And you can even like, I'm just like, and one of the interesting features and like, I'm not going to plug my product in.
the interesting things which you're also looking at is looking at patterns as to what is the right time to send a message to a given person, right? Because then it comes to the top of the inbox at that point of time and things like that. And then like all of those things also matter because nobody's looking at them inbox 24 seven, like they have certain parts or at least most people have certain points of time in the day in which they're looking at their inboxes across social or email and all of that sort of stuff. So even figuring
out the right time to send a message to a person is also valuable. I think figuring out all of those things, I think will actually increase conversion rates to much more than what we have been seeing earlier.
Yash From Momentum (09:33)
Fair point, That's meaningful for sure. so the next thing that I want to understand, and I think you also mentioned that there are some amount of tools and technologies that exist. And so one of the challenges that becomes when we are building a product in a competitive market is that it is only after
you know, customer signs up, uses it for a few weeks or months is when they realize whether the product is valuable or not. But to get people to the website and then some website to sign up and then getting their first credit card in, at that point of time, specifically in this industry, the customer doesn't like they are just at the time that they are putting in their card, they're just hoping that the software will perform. And so in those cases,
What I've seen in my limited experience happen is that the organizations that have more capital, that have the larger microphone, like the larger speaker or whatever we choose to call it, they tend to attract a lot more than early stage companies with better products. So given that, and I would qualify this as that industry, that there are tools which may not be great,
may not be the best ones out there, but they have more resources, more capital, a larger mic. How do you think about growing in that as an industry? So given that you are early stage, you raise some amount of seed, are part of YC. And so when your competitors have more resources, how should a founder think about scaling or growing their business?
Chirag Mahapatra (11:02)
Yeah, so that's a great question. the first thing, yeah, let me actually talk about the first point which you made, which was really interesting. It's that onboarding, the first onboarding flow for a given user is extremely critical. Like first impressions matter a lot. And hence, like what we actually do for about at least like most of our customers is kind of onboard them manually.
get them to think, and in fact, almost try to run the first campaign for them. And we have seen that that leads to extremely high levels of success. Now, once the customer sees a level of success, that they're seeing responses coming, they're seeing the demos being booked, they're
like meetings being set up and things like that, that gives them a level of confidence in which they want to invest further and explore more strategies. But that first impression, and I think we will probably try doing this till like this breaks, is like, it's like someone on our team is essentially manually helping the customer out and setting that entire campaign up from thinking to the creatives to thinking through the thinking through
like exactly, like a campaign sequencing, what the automation should be, how to get the data, enrich the data and all of that sort of stuff. And I think taking out that friction from the end customer has worked really well for us. And I think that's
And I think we, we learned this from like, think the Stripe founders, think early Stripe founders, what they, what they were doing was like, whenever they were on a sales call or something, like they would essentially like turn, flip the sales call and go ask the customer to go and install Stripe right now. And I think, I think we kind of think about it the same way. yeah, I think of course there are like number of big players in the space, I think at the end of the day, customers want their problem solved. They are not looking like, I mean,
Yash From Momentum (12:33)
Yeah, yeah.
Chirag Mahapatra (12:44)
Nobody gets paid for using a tool, Like people get paid for getting meetings, people get paid for demos, both outcomes. And we try to work as hard as possible to have the best possible first interaction with a product. if that means us doing anything and everything possible, we'll do that.
Yash From Momentum (12:48)
Yeah.
for outcomes.
Got it. It sort of reminds me of, I think Paul Graham's do things that don't scale, right? Especially in the early stages, which there's a great segue for me there because that's what I think Airbnb founders also did. And you spent a great deal of time at Airbnb and then followed that with, you your year at Meta
Chirag Mahapatra (13:12)
Yeah. Yeah.
yeah.
Yash From Momentum (13:28)
And so, so having spent like half a decade at Airbnb, a year at Meta, you know, really great companies to work at. Why give that up? Found a company. know, mean, sure. Doesn't matter whether you're part of YC, what insight you have, how great of a coder you are or great of a marketer you are. It is a grind. It puts your life in an uncomfortable position for sure. Like, why do that?
Chirag Mahapatra (13:48)
Yeah. Oh, 100.
Yeah, think that's a, yeah, I mean, okay. The jobs at Meta and Airbnb are phenomenal. Like I don't think you can go wrong with that. Personally for me, was, I think there are two aspects. One is
I definitely saw that data was being underutilized in lot of businesses around. And I saw that as an opportunity. then I think, of course, how exactly data can be utilized for different functions. That's something which you defined after we started the startup. even at my... The way people were thinking about data and machine learning back in Airbnb and...
Meta was not how most of the industry was thinking about it. And I think I saw that as an opportunity kind of where I can have an impact. So I think that was the opportunity which I saw. And then I think the second piece is like, I wanted to put myself in a position where like I was uncomfortable and was like learning on a day to day basis. And I think nothing does that more than like building your own startup. And I think like, for me, like that was, that was the challenge which I was really looking forward
So think those are the two catalysts kind of held, which essentially like pushed me to like start this. And I think like both of those have held out. I think like, especially the data hypothesis is actually like getting worse like
inefficiencies getting worse because the big companies are actually like phenomenal at using data and like companies like Meta, Airbnb to the extent, et cetera, like amazing. Like they're not just mining the internal data they have, like essentially modeling the entire world with the data they can have, right?
So, as like, so you are, and most small businesses, startups, even like mid -market businesses, like do not have that same luxury or have access to that same level of data or the.
analysis skills to be able to do interesting things. And it's not just collecting a proprietary internal data set, but the way you get value out of the data set is actually joining with hundreds of touch points from other different sources to a piece that I've passed on. So I think that has been, and of course, people are growing to appreciate that. But then there's still a requirement for a talent and skill set to build that sort of
build that out and build that out at scale. And I I suspect that this hypothesis will play out like in every single like function for the next decade or so. Like I think.
Like if you look at it, like I think probably like 20 years or 30 years back, like learning English was fundamental. And I think then probably like learning at least like basic understanding, like software was fundamental. think like, yeah, going forward, like understanding data and like how like different data sources, et cetera. It's not going to just the function of like data scientists is going to be big, be part of everyone like from HR, HR marketing, sales.
and even like, and of course, like definitely like technical related professions. And I think that's kind of the world which we are heading towards. And I think that's the opportunity for not just me, but I think lots of entrepreneurs out there.
Yash From Momentum (16:56)
Interesting, interesting that you say that, right? And so the conventional wisdom or assumption is that given that we have AI, we now don't need to learn a lot more. I so there are, people who are, I am not entirely sure as to how this happened,
But we've turned AI into a debate where AI is going to essentially just do everything. And so one of the things that I've also been reading and hearing is that, so I would say that there are folks who would very quickly call a demise of an industry or call a demise of a company or call a demise of anything as soon as there's something new that comes in and they just find a way to sort
plug that in. But over here, I've never been able to actually come up with a good argument. So like one of the things that I've always been reading and hearing, and I'm sure folks who are watching this have also been exposed to things like this is that you don't need SaaS in the age of AI. In the age of AI, you'd be able to just build your own SaaS. And everybody will be able to write code and build their own softwares.
And SaaS will be disrupted. And I'm sure it might be disrupted. It might change the way that it will not continue to function the way that it does today. But it is a little hazy, the future. So you don't really know as to what will happen to SaaS. Do you have any thoughts on what's the future of SaaS with AI?
Chirag Mahapatra (18:22)
I think those are some great questions. The way I... Yeah.
Yash From Momentum (18:25)
I don't even think that this was a question. I think this is not even a question. This is just like a confused rant probably because I am just getting in a lot of signals around this. But whatever you could make out of it if you can help us.
Chirag Mahapatra (18:38)
Well, I think you may have a great point. AI accelerates a lot of people to do things. But think about this. If AI has accelerated someone who doesn't know how to build software, AI is also accelerating someone who is excellent at software and who was already a 10x engineer earlier. And that's how I see it. Sure. What I think the gone is gone up is the bar has gone up.
The bar has gone up in terms of, okay, so now what a 10X engineer looks like is different, but there are still like, at the end of the day, it's a power law and there will be some people who are outliers. And those outliers are also using AI. So if someone who hasn't built software is using AI, the outliers are also using AI to accelerate themselves and build even more interesting things. The next thing is, even if you look at like GPT, GPT has primarily been trained on publicly available data sources,
think. And I suspect the publicly interesting publicly available data sources, probably like one to 2 % of the total information available. Like there's enormous amount of data within enterprises, within internal knowledge bases and all that sort of stuff, which hasn't been tapped. And the future data oriented products are the ones which are going to like leverage all of those sort of knowledge bases to actually like add value.
Yash From Momentum (19:46)
yeah.
Chirag Mahapatra (19:58)
And I think that is where most of the alpha is going to come where like you have these LLMs, but like you're fine tuning those LLMs with your data to create value for your customers or for your own internal purposes. that is where most of the new value will be created, which again comes back to a data problem. Once a lot of these best practices around like how to...
fine tune LLMs and all that sort of stuff becomes like available. And then I think it comes down to, okay, what is the unique data set you have to where you can extract value out of it.
And I kind of see that as a second direction where the world is going. And I think the third direction is I think the future of SaaS is going to be, think it seems to be like going very outcomes oriented. But now that people have like, as I said, like the bar is going up earlier, people were paying for automation. Now I think people will be or automation or be having a system of record. Now I think people will be okay. Yeah, sure. All that is good. like, can my final
outcome is to make money or my final outcome is to hire a candidate or my final outcome is to close a book a demo. How much of that are you doing? And then I think there might be some interesting business models around that. Where like you people have instead of paying for, instead of paying for, let's say for an SEO agency, instead of paying for the number of blogs created or the number of page links added, can you pay for the, what my Google ranking is and all of that sort of stuff.
Yash From Momentum (21:27)
Outcome is a service, right?
Chirag Mahapatra (21:28)
I think exactly yes. think that those are probably some of the trends which are going to accelerate over the next three to five years.
Yash From Momentum (21:35)
Interesting, and so this is great, because one of the things that we as Momentum Ventures are also working on is essentially offering a service of fine -tuning or verticalizing the LLM on data set. But interesting that you mentioned it as well. So another question that I have for you, which I wanted to understand a little
is that for a product like Blaze, it touches a couple of different departments in an organization. It talks to marketing and sales primarily, but then maybe also from ads to social media to search, like a couple of different things. so what we've seen traditionally is that pricing models for platforms that typically talk about
or typically offer value on ads versus pricing models for things that help with email campaigns are very different. But you are one product. And so how have you thought about your pricing where in some cases it is per seat, in some cases it is per 1 ,000 impressions or whatever the case may be, or some cases even outcomes. So how do you think about your pricing?
Chirag Mahapatra (22:41)
Yes,
OK, thank you so much for asking the question. That's actually super helpful. So our ads product is actually something which we realized, especially when we were doing campaigns on X and LinkedIn, is that if a user actually sees your ad, then they're a lot more likely to respond to your outbound. And I think that was a very interesting insight which we had, is because if you look at the
a funnel, user funnel, awareness, concentration, and conversion. If a user knows nothing about you, has never seen you, et cetera, it's very unlikely for them. So ads help in building awareness. especially what we try to do is build very custom audiences where we're targeting ads to specific audiences, whom we are going to do outbound to later. So it helps us become top of mind for the... It helps businesses become top of mind for their end user. And then
After that, do the outbound.
Let's say like in a certain time period after that. And that is a thesis which we have played out. So for us, ads, we use ads as a part of the awareness piece. like our primary business model is like, so we have a base plan, which is primarily okay if you're self -serve. I think that's a great plan for you. If you are on the plus plan, it's primarily like if you have teams and things like that. And then premium, if you're like using some of the like API or complex features, SSO and all of that sort of stuff.
final one is what the enterprise is when enterprise needs active help from our data team to actually create audiences, well as run some of the campaigns, monitor, do data analysis and reporting, and then also help them inform on strategy. that's kind of how we have baked it. And what we realized, again, we don't really charge for ads as a per unit thing. We rather bake it in as part of the product and focus on helping
getting closer to the outcome piece.
Yash From Momentum (24:38)
So would it be fair to say that the way that your pricing has been designed is, I mean, there could be limits in everything, but the way that the pricing has been designed is like these are built for different personas, Early stage, like single founder doing code, doing outreach, and then a small company and so on and so forth.
Chirag Mahapatra (24:56)
Yeah, think that's definitely one way of looking at it. I think the other way is also from a level of time perspective, because we do have some early stage startups also using the enterprise plan, primarily because they don't have an internal team to execute on, design and execute on campaigns. So that's where we add a lot of value there.
So yeah, it's kind of also like the someone who sells service, basically someone who knows, understand the strategies, knows exactly what they need to do and is executing at a high level.
And the pro plan is someone where like you have a team and you need multiple people. The premium plan is someone like who has the time and effort to like build complex workflows using some of the APIs, fetch data, join it with some internal data sources, which they do not want to upload to our data systems and stuff like that. And like that, so they have already thought through a bunch about that. And enterprises again, like basically like, okay, they want to execute in complex strategies, but probably like do not have any sort of
time, et cetera, et cetera, do it, and they need us to do a lot of the heavy lifting. I would look at it from that perspective.
Yash From Momentum (26:00)
Got it. Interesting. So this brings me to the last part of this conversation. And so one of the things that we do is we ask each founder to ask a question to the guest that we are going to have next time. And so typically, this question is something that you, in your company or in your journey of building a SaaS company, are sort of battling with or trying to understand how to go about it.
So while you think about the question that you have, I'll ask you the question that our previous founder, was Lena from UnSchooler, had for you, which was that, and so they have an LMS product, which is an AI -based LMS product, so it helps course creators create content faster using AI, and then the standard LMS that you typically have. And so one of the things that,
that they were thinking about is that there are different industries that they could target. So an LMS is great for universities, for schools, and even for organizations of a particular scale. Even within those organizations, there are certain sub industries that will find an LMS more useful than the others. And when you have so many different industries that could use the product, how do you actually decide
Which are the lower hanging fruits and how do you figure out that, hey, you I'm going to dedicate the next six months to 12 months or whatever the case may be. And this much dollars or this much team to just doing GTM for this particular industry. So how do you sort of niche down when you have a couple of options?
Chirag Mahapatra (27:33)
Yeah, that's a great question. I mean, again, like, think a lot of,
I think there might be a lot of interesting answers out here. What we have typically done is that we talked to about like five to 10 people per industry and then see how many of those actually convert into customers. At the end of the day, there's nothing like money or data, which kind of like thing, which helps validate your hypothesis. So what I would do is like, yeah, if we have a, and I think we did run a few of these experiments in the past as well. and where we will
in like three, in B2B, it's like three subcategories. What we did was within each of the subcategories, we identified like, let's say, okay, how do we, like we first said, okay, you need to do a certain amount of experimentation, right? So we were like, okay, we need to get 10 demos booked for each of these vertical industries. And of those 10 demos, how much are we able to convert?
And typically that has been a very good prioritization function. It's always almost never, at least for us, it's never been that it's been equal. We would see that one industry like has a significant larger pull than other for whatever reason. And sometimes we would know we would have an hypothesis side of them. Sometimes it's a reason which would, which would really surprise us. And then we would start focusing on that for the next six to nine months. And I think, and then again, yeah.
Yash From Momentum (28:50)
So like complete qualitative approach.
Chirag Mahapatra (28:52)
Yeah, actually just like look at the, like, see what works and kind of do that. And, by you fortunate by using our own tool, like we have been able to like, so for us, like getting those initial, like conversation set up have been fairly like straightforward. So we have been actually like able to do that. So I think.
Yash From Momentum (29:09)
interesting.
Chirag Mahapatra (29:09)
What we tell you, think like just being experimental about it, trying to get data from the market and using that to inform your decision making is probably the best approach.
Yash From Momentum (29:18)
And what's your question? And from the quality of this conversation, I'm confident that you'll make it really difficult for the next founders. So what's your question? What's the thing that you're battling with?
Chirag Mahapatra (29:24)
No
Yeah, I think one of the questions which I have been intrigued about and would love to hear more about is especially in early stage startups, need people, not just the founders, but you need for all the employees and team members to also be adorning multiple hats.
But at some point of time, that can stretch into where that can end up creating inefficiencies. So my question for the next founder would be, how do they identify when a team member is stretched versus when they can take up new challenges and take up new challenges for the company? And then when does it make sense for someone to have a specialization or versus when does it make sense for someone to take on multiple things?
Yash From Momentum (29:49)
Yes.
Interesting, it's a great question. And with that Chirag, I thank you for joining me on this conversation. For all the folks who've been listening this, watching this on Spotify or YouTube, wherever you are, you'll be able to find the link to Blaze, which is withblaze .app in the description. You can go ahead, check it out, make your outreach have better conversion rates.
with Blaze. And thank you for joining in today and we'll see you until next time.
Chirag Mahapatra (30:44)
Awesome. Thank you, Yash. Really appreciate your time.
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