How Samsara’s Attribution Model Turns Data into Gold

Tarush Aggarwal (00:00)
Welcome to another episode of People of Data, where we get to highlight the wonderful people of data. These are our data leaders from various industries, geographies, and disciplines, discover how companies are using data, their achievements, and the challenges they face. Today on the show, we have Kiriti Mani, the head of data strategy and analytics at Samsara. Samsara, as we all know, is an IoT platform that

Leverages data and helps organizations enhance their physical operations. Kiriti has been at Samsara for over six years He's grown into a data leader and he today plays a crucial role in helping shape the company's strategic directions and data driven initiatives Welcome to the show Kiriti Hey, thanks for having me. Are you ready to get into it? Yeah, let's do it. Awesome. So, you know

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Kiriti Manne (00:44)
Hey, thanks, Rooch. Thanks for having me.

Yeah, let's do it.

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Tarush Aggarwal (00:00)
What does Samsara do? How do you make money today and how does the data team play a part in helping Samsara do what it wants to do? Yeah, well, let me start with an overview of what Samsara does. Then we can go into how you make money and then we can talk about the data component. Perfect. So what does Samsara do? I always like to start with the customer.

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Kiriti Manne (00:11)
Yeah. Well, let me start with an overview of what Samsara does. Then we can go into how you make money and then we can talk about the data component. So what does Samsara do? I always like to start with the customer. So at Samsara, we're building technology for the world of physical operations. Think transportation, construction, field services and energy companies. These are industries that are asset and labor intensive.

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Tarush Aggarwal (00:27)
So at Samsara, we're building technology for the world of physical operations. Think transportation, construction, field services, and energy companies. These are industries that are asset and labor intensive. They want to operate smarter. They want to be more efficient and safe. And you can do that with data. And long story short, Samsara helps them get that data. We have a system of sensors. We do a lot of data processing.

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Kiriti Manne (00:40)
They want to operate smarter. They want to be more efficient and safe. And you can do that with data. And long story short, Samsara helps them get that data. We have a system of sensors. We do a lot of data processing and we give them the ability to take action on that. And I know you were asking, how do we make money? We're a B2B SaaS company. So we have a subscription-based model and customers pay either, you know,

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Tarush Aggarwal (00:54)
and we give them the ability to take action on that. And I know you were asking, how do we make money? We're a B2B SaaS company. So we have a subscription-based model, customers pay either monthly or annually, depending on the terms of their contract. And we're really charging for the software more than the hardware here because that's how customers can extract value from all the data that we're collecting.

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Kiriti Manne (01:09)
monthly or annually depending on the terms of their contract. And we're really charging for the software more than the hardware here because that's how customers can extract value from all the data that we're collecting.

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Tarush Aggarwal (01:24)
Exactly, the hardware is like a one-time cost, So it's probably a product line for you, but the interesting part of the business is the software. Exactly, it's how do we analyze all this data and then how can we actually improve our customers' operations in terms of safety or efficiency? And what's very interesting is fundamentally you're a business where data is your product. So when we think about how does the data team...

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Kiriti Manne (01:33)
Exactly. It's how do we analyze all this data and then how can we actually improve our customers' operations in terms of safety or efficiency.

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Tarush Aggarwal (01:52)
and how do you guys help Samsara in making, in being able to go build the software? I'm curious how it works because you have two separate data groups. You have data which is part of the product as well as data which is very typical data analytics in AI and ML. So how do you see this distinction and ultimately how does data collaborate to help Samsara hit its business objectives? Yeah, that's a great question.

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Kiriti Manne (02:16)
Yeah, that's a great question. So Samsara as a data company at heart, the data is core to everything that we do. And like you said, our value prop to our customers is the data that we provide. And not surprisingly, we have a very strong data culture internally as well. So yes, the product engineering team are very focused on data and launching data products for our customers. But all the other orgs like go to market sales and marketing,

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Tarush Aggarwal (02:19)
So Samsara, as a data company at heart, the data is core to everything that we do. And like you said, our value prop to our customers is the data that we provide. And not surprisingly, we have a very strong data culture internally as well. So yes, the product engineering team are very focused on data and launching data products for our customers. But all the other orgs like GoToMarket, Sales and Marketing, Support,

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Kiriti Manne (02:46)
support, customer success, all of these orgs also leverage day-to-day data in their day-to-day. So for me, I'm more on the go-to-market side, so that's where I have the most visibility.

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Tarush Aggarwal (02:48)
customer success, all of these orgs also leverage day to day data in their day to day. So for me, I'm more on the go to market side. So that's where I have the most visibility. Yeah, that makes sense. And if you look at the culture of the company, being a data platform company, what stands out in the culture of the company? Yeah, I think.

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Kiriti Manne (03:12)
Yeah, I think a couple things and I think a lot of this comes from our founder or CEO, Sanjit Biswas. I remember I joined the company six to seven years ago and when I was interviewing at a bunch of companies and what really stood out to me about Samsara, even though it was just a series C company about 200 people, they

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Tarush Aggarwal (03:14)
A couple things, and I think a lot of this comes from our founder or CEO, Sanjit Biswas. I remember I joined the company six to seven years ago. And when I was interviewing at a bunch of companies and what really stood out to me about Samsara, even though it was just a series C company about 200 people, they had this sense of they knew like what they were doing.

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Kiriti Manne (03:40)
had this sense of they knew like what they were doing, but they had a certain humility to them, which was different from a lot of the other startups that I interviewed with where it felt like they were running with their heads chopped off and you know, there's like another fire they had to put out every other day. But some sorrow there is a like internal confidence to that company. And I see that today as well. It's a combination of building for the long term shooting.

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Tarush Aggarwal (03:43)
but they had a certain humility to them, which was different from a lot of the other startups that I interviewed with where it felt like they were running with their heads chopped off and you know, there's like another fire they had to put out every other day. But since Sara, there is a like internal confidence to that company. And I see that today as well. It's a combination of building for the long-term shooting for the stars, but

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Kiriti Manne (04:09)
for the stars, but also being very approachable, focusing on our customers, and then winning as a team.

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Tarush Aggarwal (04:11)
also being very approachable, focusing on our customers, and then winning as a team. Yeah. I love that. I love the humility aspect of a culture like that. I'm curious, what percentage of those 200 employees do you think are still there at the company today? Yeah, I don't know. It's a mixed bag across different orgs. If there was a percentage, probably less than 50%.

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Kiriti Manne (04:31)
Yeah, I don't know. It's a mixed bag across the different organs. If there is a percentage, probably less than 50%. Six, seven years is a long time in our age today.

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Tarush Aggarwal (04:40)
you know, six, seven years is a long time in our history. mean, anything more than like 15, 20% would be super impressive. Six, seven years, like life happens over there, right? People can get married and like, you know, all sorts of things. What's the culture like in the data team? Because, know, very often we see data teams have their own like microcultures and microclimates and it's just so multi...

it's so sort of collaborative. So I'm curious, what's the culture like, you know, in the marketing data group that you run and sort of in this sort of broader data group as well? Yeah. So I think we adopt a lot of the company-wide culture points like winning as a team, having a growth mindset, but something that's

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Kiriti Manne (05:16)
Yeah. So I think we adopt a lot of the company-wide culture points like winning as a team, having a growth mindset, but something that's maybe a little bit more specialized to the data side is our love for experimentation and our love for just trying out different things. And we have the fortune of sitting on top of pretty much all the data across the company.

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Tarush Aggarwal (05:28)
maybe a little bit more specialized to the data side is our love for experimentation and our love for just trying out different things. And we have the fortune of sitting on top of pretty much all the data across the company from a variety of first party data, which is our CRM, our product usage, our plethora of marketing data.

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Kiriti Manne (05:45)
from a variety of first party data, which is our CRM, our product usage, our plethora of marketing data, to a variety of third party data that we acquire from different vendors. So our toolkit of what's possible feels limitless. And we're constantly thinking of what are other ways we can activate this, either on marketing or sales, what are different ways we can connect the dots with their data.

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Tarush Aggarwal (05:55)
to a variety of third party data that we acquire from different vendors. So our toolkit of what's possible feels limitless. And we're constantly thinking of what are other ways we can activate this, either on marketing or sales, what are different ways we can connect the dots with their data. that culture of innovation is pretty strong within our team. And I know you, at this point,

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Kiriti Manne (06:15)
that culture of innovation is pretty strong within our team.

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Tarush Aggarwal (06:22)
probably have multiple different data teams. How, you know, do you have a unified sense of culture amongst the data group or, you know, just given, cause you know, the age old question of centralization and, and, so is sort of quite real. And the reality of it is it's not one or the other, it's both, right? Having a central function, but at the same time having data teams on the edge, especially for functions like marketing and, know, sales and product and things like that.

So how do you look at that and the connection between the entire data function? Yeah, and that's really one of the challenges as a company scales is how do you stay connected across all of these different functions that are related but not 100% related? And just very recently, I was meeting with the data team in products and they were like, we're working on this really cool use case. And I'm like,

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Kiriti Manne (06:54)
Yeah.

Yeah. And that's really one of the challenges as a company scales is how do you stay connected across all of these different functions that are related, but not like a hundred percent related. and just very recently I was meeting with the data team and products and they were like, we're working on this really cool use case. And I'm like, wait a minute. There's like so many go to market applications for that use case. So we can do a better job of staying connected.

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Tarush Aggarwal (07:18)
wait a minute. There's like so many go to market applications for that use case. So we can do a better job of staying connected. I think sometimes we can kind of be in our own silo and operate on our own roadmap, but it's important to like pull our heads up every once in a while and then collaborate across all of our priorities to see what we can leverage and share.

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Kiriti Manne (07:27)
I think sometimes we can kind of be in our own silo and operate on our own roadmap, but it's important to like pull our heads up every once in a while and then collaborate across all of our priorities to see what we can leverage and share.

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Tarush Aggarwal (07:42)
are there any initiatives you do as a macro data function to make sure that everyone is in sync? Yeah, so some we try to be as centralized as possible. So there's some data sources that have use cases across the company, not just my team. So for those data sources, our central IT team is responsible for

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Kiriti Manne (07:48)
Yeah, so some we try to be as centralized as possible. So there's some data sources that have use cases across the company, not just my team. So for those data sources, our central IT team is responsible for the standardization, the pipelines and the governments to ensure that, you know, everybody is using the same data and it's a high data quality. So there are examples like that, but then when you get to like,

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Tarush Aggarwal (08:03)
the standardization, the pipelines and the governments to ensure that everybody is using the same data and it's a high data quality. So there are examples like that, but then when you get to like the more org specific activations, then the data folks in that org are gonna be running point on that. Got it, that makes a ton of sense.

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Kiriti Manne (08:18)
the more org-specific activations, then the data folks in that org are going to be running point-on.

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Tarush Aggarwal (00:52)
Very cool. Talking about the data team and the microculture over there, what's the strength of the, how big is the company? How big is the data team in the company? Yeah. So the company is, you need to know how many headcount we have now, maybe like three to 4,000.

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Kiriti Manne (01:04)
Yeah, so the company is, don't even know how many headcount we have now, maybe like three to four thousand -time employees. We've grown quite a bit the last six, seven years and there are different data teams throughout the company. So the data team that I lead is on the marketing side and supports more broadly go to market. There's a few data folks in the sales side. There's data folks in the product side. And then there is the central data team.

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Tarush Aggarwal (01:09)
full-time employees. We've grown quite a bit the last six, seven years. And there are different data teams throughout the company. So the data team that I lead is on the marketing side and supports more broadly the market. There's a few data folks in the sales side. There's data folks in the product side. And then there's the central data team in IT. So maybe across the company.

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Kiriti Manne (01:32)
in IT. So maybe across the company from a data engineering, analytics, strategy perspective, maybe like 40 folks, 40 to 50 folks.

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Tarush Aggarwal (01:36)
from a data engineering and analytics strategy perspective, maybe like 40 folks, 40 to 50 folks. That's super interesting. Very typically we see about 20% of a company's technology and about 20% of technology is data. So let's say you are a 4,000 person company, roughly in about 800 people in technology, out of that roughly another 150 to 200 people in data.

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Kiriti Manne (02:08)
like I mentioned, there's different like slices of data within Samsara. So there is the data team that works with our product and out different features in our product that leverage data. And then there's all the other teams that use our data for internal Samsara purposes, go to market support customer success.

So I think the 40 number that I mentioned is more the latter use case.

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Tarush Aggarwal (02:31)
I think the 14 number that I mentioned is more the latter, you see. Yeah, I think this, know, firstly, this is just sort of broad, you know, which is what we've seen very practically in doing a bunch of these shows. But also, I think what's very interesting is, sort of data, is companies where data is the product tend to always break this mold. I think we had a few other companies in season one of the show.

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Kiriti Manne (02:53)
Hmm.

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Tarush Aggarwal (02:56)
which we interviewed where everyone else roughly speaking fit this, but companies where data is your product, they always like way off. So that makes a ton of sense. Yeah. And I think part of it is also how the company operates. You know, we were talking about some stars culture and data is in our DNA. So just because your title doesn't have the word data in it doesn't mean you're not a data person. Yeah.

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Kiriti Manne (03:07)
Yeah. And I think part of it is also how the company operates. You know, we were talking about some Samsara culture and data is in our DNA. So just because your title doesn't have the word data in it doesn't mean you're not a data person. And really everybody kind of at some Sara uses data in their day to day. So that's why like it's tough to say, you know, who's specifically just a data person.

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Tarush Aggarwal (03:24)
And really everybody kind of at Samsara uses data in their day to day. that's why it's tough to say who's specifically just a data person. How about in your interview process, for non-data roles, I'm curious is that something which you evaluate?

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Kiriti Manne (03:44)
Yeah, definitely. And it really depends on the role and what we're looking for in that role. So one example, some of the stakeholders that my team works with is the growth team, the growth marketing team. And they're responsible for a lot of top of funnel campaigns to generate demand and capture demand. And a lot of that is also powered by data. You need to...

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Tarush Aggarwal (03:44)
Yeah, definitely. And it really depends on the role and what we're looking for in that role. So one example, some of the stakeholders that my team works is the growth team, the growth marketing team, and they're responsible for a lot of top of final campaigns to generate demand and capture demand. And a lot of that is also powered by data. Yeah.

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Kiriti Manne (04:08)
understand who your customers are, what they care about, where in the buyer journey they are, what messaging works. And then you need to evaluate success and performance of your campaigns. So we partner with that to provide them data, some BI tools and some ROI analysis here and there. But they also need to understand, how do I evaluate success? What data should I use? What's good? What's bad?

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Tarush Aggarwal (04:08)
Understand who your customers are what they care about where in the bio journey they are what messaging works And then you need to evaluate success and performance of your campaigns. Yeah, so we partner with that to provide them Some BI tools and some like ROI analysis here and there but they also need to Understand, you know, how do I evaluate success? Yeah, what data should I use? What's good was bad?

Tarush Aggarwal (04:37)
What does the data stack look like? Yeah, so this is point evolution for the last six, seven years, we've gone from startup to mid-sized company, now like mid-sized public company. So right now, our first and third-party data come from a variety of sources. So Salesforce is the classic CRM. We get customer.

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Kiriti Manne (04:37)
Yeah, so this is a point of evolution for Samsara Over the last six, seven years, we've gone from startup to mid-size company, now like mid-size public company. So right now, our first and third party data come from a variety of sources. So Salesforce is the classic CRM. get customer usage as well. And then all of this data

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Tarush Aggarwal (04:59)
usage as well. And then all of this data is aggregated in our data warehouse. And it's right now a combination of BigQuery and Databricks, which causes some friction throughout the company. But eventually we're trying to migrate everything into Databricks. So we have all of our data in one warehouse. And then We migrate it from Salesforce and everything else into Databricks or GCP.

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Kiriti Manne (05:03)
is aggregated in our data warehouse. And it's right now a combination of BigQuery and Databricks, which causes some friction throughout the company. But eventually, we're trying to migrate everything into Databricks. So we have all of our data in one warehouse. And then we pipe.

Yeah, exactly. there's a lot of pipelines that the central Vistek team builds out from these tools into Databricks.

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Tarush Aggarwal (05:24)
Yeah, exactly. So we, we, there's a lot of pipelines that the central Vistek team builds out from these tools into Databricks. Do build your own pipelines or do use some sort of automated ingestion tool? It's a combination and it really depends. So for Salesforce data, I believe we use a common ETL tool to pipe that data over. There are other data sources. So for example, on the marketing side, which has its own

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Kiriti Manne (05:37)
It's a combination and it really depends. So for Salesforce data, I believe we use a common ETL tool to pipe that data over. There are other data sources. So for example, on the marketing side, which has its own set of complexities, we leverage a long tail of channels and each channel has its own data. So we need to connect all these channels to Databricks and

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Tarush Aggarwal (05:52)
set of complexities, we leverage a long tail of channels and each channel has its own data. So we need to connect all these channels to Databricks. And sometimes we can leverage off the shelf ETL tools. Sometimes it's a custom pipeline that we have to build. Got it. And once it has a Databricks, you use a lot of sort of Databricks modeling and orchestration or do you use things like DBT or Airflow and DAX on top of it?

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Kiriti Manne (06:04)
Sometimes we can leverage off the shelf ETL tools. Sometimes it's a custom pipeline that we have to build.

Yeah, I think we use all those tools that you mentioned and also try to leverage as much native Databricks functionality as possible.

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Tarush Aggarwal (06:20)
Yeah, I think we use all those tools that you mentioned and also try to leverage as much native data rates functionality as possible. So you're very, very of best in breed, sort of going and playing around with everything out there. And in terms of BI, what's the BI tool across the company? Yeah, Tableau is the standard, but we also use Looker dashboard for some use cases, primarily because of how licensing works.

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Kiriti Manne (06:37)
Yeah, Tableau is the standard, but we also use Looker dashboard for some use cases, primarily because of how licensing works.

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Tarush Aggarwal (06:47)
Awesome. Very interesting. And you see some sort of consolidation on data breaks. So longer term, at least, the GCP component is not going to be as relevant. Yeah, I think there is a lot of value in unifying our data in one warehouse. And it will really unlock a lot of use cases across different orgs. How about AI? I'm sure AI is on everyone's mind. Do look at the AI world of infrastructure as you

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Kiriti Manne (06:57)
Yeah, I think there is a lot of value in unifying our data in one warehouse, and it will really unlock a lot of use cases across different orgs.

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Tarush Aggarwal (07:14)
different, our Databricks still going to be a unified layer across this? So you're looking at this separately because you have AI inside your product as well. wondering if you have a, know, sort of how you think of this strategically. Yeah, so that is something we're trying to figure out as is every company, right? How do you really leverage AI in your tech stack with your customers internally?

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Kiriti Manne (07:29)
Yeah. So that is something we're trying to figure out as is every company, right? How do you really leverage AI in your tech stack with your customers internally? And we have been leveraging AI with our customers for years with all the trillions of data points that we collect in the field. And recently we're trying to leverage AI internally as well. So in terms of the infrastructure, we're still, you know, frankly,

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Tarush Aggarwal (07:39)
And we have been leveraging AI with our customers for years with all the trillions of data points that we collect in the field. And recently we're trying to leverage AI internally as well. So in terms of the infrastructure, we're still, you know, frankly, trying to figure that out. Can we like leverage some of the infrastructure we built for our customers for internal use cases, or does it have to be separate?

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Kiriti Manne (07:58)
Trying to figure that out, can we leverage some of the infrastructure we built for our customers, for internal use cases, or does it have to be separate? A lot of AI is the quality of the data, which is, think, that's where the focus is right now. What's the internal data that we want to feed these models, and what are the use cases that we want to unlock?

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Tarush Aggarwal (08:09)
you know, a lot of AI is the quality of the data, which is, think that's where the focus is right now. You know, what's the internal data that we want to feed these models and what are the use cases that we want to unlock? That makes a ton of sense. What's one achievement which you're very proud of in, you know, inside the sort of marketing data team? Yeah. So there is,

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Kiriti Manne (08:34)
Yeah. So there is a lot of use cases that we built out over the years and definitely shout out to both the Giga engineering team and the strategy and analytics team. They're filled with extremely talented and fun people to work with. So a lot of this credit goes to them. One thing that I'm really proud of is our attribution model. So

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Tarush Aggarwal (08:37)
a lot of use cases that we built out over the years and definitely shout out to both the Giga engineering team and the strategy and analytics team. They're filled with extremely talented and fun people to work with. a lot of this credit goes to them. One thing that I'm really proud of is our attribution model. So marketing spends millions of dollars a year to generate demand.

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Kiriti Manne (09:01)
marketing spends millions of dollars a year to generate demand. And you need to understand what's working, what's not, so you can really fine tune the top of funnel engine. And attribution is how we do that. It's how we make capital allocation decisions. And we ingest all of these touch points across all of these channels and campaigns with our customers. And then we feed them into this Taylor algorithm that we built.

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Tarush Aggarwal (09:06)
And you need to understand what's working, what's not. So you can really fine tune the top of funnel engine. And attribution is how we do that. It's how we make capital allocation decisions. And we ingest all of these touch points across all of these channels and campaigns with our customers. And then we feed them into this Taylor algorithm that we built that's tailored to some stars business model.

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Kiriti Manne (09:31)
that's tailored to Samsara's business model. So everything is in house. It's customized. It accounts for edge cases. And the output is really, a Google paid search drove X million dollars in pipeline. LinkedIn drove Y million dollars. And it unlocks so much visibility for the org and a lot of insights into what's working where.

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Tarush Aggarwal (09:35)
So everything is in house, it's customized, it accounts for edge cases, and the output is really, Google paid search, drove X million dollars in pipeline. LinkedIn drove Y million dollars, and it unlocks so much visibility for the org, and a lot of insights into what's working where. How big was the group which built this feature?

How long did it take and which iteration are you on now? Is this iteration number six? Have you been doing this for years? Yeah, I wish we had a defined iteration number. It's been something that we've been developing for the last maybe four or five years in the house. And we constantly make improvements based on feedback. one example, the holy grails of

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Kiriti Manne (10:09)
Yeah. Yeah, I wish we had like a defined iteration number. It's been something that we've been developing for the last maybe four or five years in-house and we constantly make improvements based on feedback. So one example, the holy grail of marketing measurement is a combination of attribution, marketing, mixed modeling.

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Tarush Aggarwal (10:31)
marketing measurement is a combination of attribution, marketing mixed modeling, and experimentation. So one example of how all of these feed together is we run a lot of experiments in the field, different kind of AP tests that really fine tune our understanding of how this channel is performing. We take that as an input into attribution. So we're constantly refining.

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Kiriti Manne (10:37)
and experimentation. So one example of how all of these feed together is we run a lot of experiments in the field, different kind of A-B tests that really fine tune our understanding of how this channel is performing. We take that as an input into attribution. So we're constantly refining the assumptions and the weights that we have in attribution based on experiments and other things we're seeing in the field.

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Tarush Aggarwal (10:59)
the assumptions and the weights that we have in our attribution based on experiments and other things we're seeing in the field. Yeah, that makes a lot of sense. What is one challenge which you've had and how are you working to resolve that? Yeah, so, Simstora is a ever-evolving company and we're growing quite fast at scale, which is very exciting.

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Kiriti Manne (11:14)
Yeah. So, Samsara is a ever evolving company and we're growing quite fast at scale, which is very exciting. But the next stage for us is the multi-product platform. Historically, we've sold a couple of products and now we have a plethora of products to sell. And with that comes a lot of complexity and a lot of growing things. And one of them is how do we equip our direct sales motion?

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Tarush Aggarwal (11:25)
But the next stage for us is the multi-product platform. Historically, we've sold a couple of products and now we have a plethora of products to sell. And with that comes a lot of complexity and a lot of growing things. And one of them is how do we equip our direct sales motion to be successful? So we serve so many industries, segments, now we get all of these products. As a seller, it's overwhelming to be on top of everything.

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Kiriti Manne (11:44)
to be successful. So we serve so many industries, segments. Now we have all of these products. As a seller, it's overwhelming to be on top of everything. How do you know what to sell, when to who? So we're trying to use data here to make it as easy and as prescriptive as possible for our sellers. So we haven't solved it yet, but there's a crawl, walk, run here. And the run is going to be

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Tarush Aggarwal (11:53)
You know, how do you know what to sell when to who? So we're trying to use data here to make it as easy and as prescriptive as possible for our sellers. we haven't solved it yet, there's a crawl walk run here and the run is going to be standardized automated workflows, prescriptive signals with, you know, machine learning models that just predict.

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Kiriti Manne (12:10)
standardized automated workflows, prescriptive signals with machine learning models that just predict what accounts should you target now, what's the next best action, what do they care about? And that's the problem. I think we have the right vision in place and we're trying to focus on the execution.

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Tarush Aggarwal (12:18)
What accounts should you target now? What's the next best action? What do they care that's the problem. I think we have the right vision in place and we're trying to focus on the execution. That makes a lot of sense.

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Tarush Aggarwal (12:36)
And what's one piece of advice you have for anyone who wants to work at Samsara?

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Kiriti Manne (12:36)
Always think about the customer. That's one of our core motives. know, at some sort of different level of customers, there's our actual end customers, you what do they really care about? How can we help solve their problems? For my team, our direct customers are actually our stakeholders in marketing and sales. So we're constantly thinking about how can we help them? What are problems that they're facing? And I think I just love that like framework of

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Tarush Aggarwal (12:36)
always thinking about the customer. That's one of her core know, at some start there's different level of customers. There's our actual end customers. You what do they really care about? How can we help solve their problems? For my team, our direct customers are actually our stakeholders in marketing and sales. So we're constantly thinking about how can we help them? What are problems that they're facing? And I I just love that like framework of

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Kiriti Manne (13:03)
of prioritizing and problem solving.

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Tarush Aggarwal (13:03)
of prioritizing and problem solving. Yeah. And one last question, are you hiring right now? Awesome. Thank you so much for being in the show and adding so much value to our listeners.