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This podcast will help you grow your B2B company quarter after quarter—with confidence, clarity, and data-backed decisions.
In each episode, you’ll learn proven strategies, practical frameworks, and first-hand insights from GTM leaders, RevOps pros, and seasoned B2B executives. They’ll walk you through how they use data to set smart targets, forecast accurately, overcome growth plateaus, and build high-performing sales and marketing engines.
You’ll hear stories of real challenges, real results, and the data-driven moves that made all the difference.
The best B2B companies don’t just look at metrics—they use them to take action. Move The Needle will help you do the same.
Databox (00:01.429)
Hello everyone and welcome back to another episode of Data Box's podcast, Move the Needle. Today I have a very special guest, Yash Tekrewal from Clay. Clay is a data orchestration software company that helps you enrich and orchestrate your data for your sales and marketing so that you can do much more improved targeting, personalization, et cetera, et cetera. We're actually customers, I don't know if you know.
That Yash, so we use Clay here at DataBox. Yash, yeah, now you do. And then Yash is responsible for ecosystem growth there. They have a term that they termed at Clay called go-to-market engineering. So Yash is responsible for making sure that the ecosystem of go-to-market engineers is growing. And I think I did it justice. Anything you want to add about what you do or what Clay does, Yash?
Yash Tekriwal (00:33.102)
I didn't know that. Yeah.
Databox (01:00.438)
to get going.
Yash Tekriwal (01:00.694)
No, I think you did a great justice, especially given that I gave you the lowdown five minutes before this. So you captured it well.
Databox (01:07.293)
I'm somewhat familiar with Clay. Like I said, we're customers so I'm somewhat aware. I'm sure there's things about Clay that I don't know but have a good decent handle on it. And I love ecosystem stuff. think you may or may not know but yeah, you do know because I know we spoke years ago about building out the partner. So I think a few years back somebody, one of your investors said, hey, you should talk to Pete and we ended up
Yash Tekriwal (01:27.788)
Yeah, exactly. This is a good full circle moment.
Databox (01:36.213)
talking early on when you were just getting your partner ecosystem going. And so now you're probably like two years into it now doing that.
Yash Tekriwal (01:44.974)
Two years into it now, yeah, I remember we talked to you. We also talked to Brian Signorelli, both very helpful conversations, because you were both very early on the partnership side. so, yeah, it actually did inform a lot of how we thought about our early decisions, who we reached out to, how we built the program. And it was foundationally helpful. That partnerships motion is now really starting to actually hum as of this year. And I think because we got the foundational pieces down pat, I'm not as nervous about how it's going to scale, which is going be great.
Databox (01:50.102)
yeah. Awesome. Yeah.
Databox (02:06.635)
That's awesome.
Databox (02:10.613)
Yeah.
That's great to hear. That's great to hear. If I ever go off and become a full-time consultant helping SaaS companies with their partner programs, be my first endorsement there. So thank you. So today we're going to talk a little bit about the role of ecosystem development and growth and how that kind of impacts a broader business at Clay. And also,
Yash Tekriwal (02:15.766)
Yeah.
Yash Tekriwal (02:25.441)
Happy to be fry the testimonial for sure.
Databox (02:43.531)
how the work you're doing connects to the broader strategy at Clay there as well. So maybe we start with a little bit of that strategy on Clay. Maybe give us a little bit more history of Clay. I know you guys have this huge and growing following, but I imagine there's still a lot of people out there that have no clue what you actually do and all that. So maybe just start there.
Yash Tekriwal (03:07.253)
Yeah, so I think for sure there's actually now an increasing number of people I think that like know our taglines or have seen our marketing or know the brand and still have no clue what we do. So that is 100 % true. I think like a fun story I can share there is that we had a had a debate about the like ROI of Subway ads in New York.
Databox (03:17.771)
Yeah.
Yash Tekriwal (03:27.725)
you know, in the winter of 2025, we ended up doing them. And I think they were massively successful, brilliant designs from the creative team. The tagline for all of them was, insert X artists, you know, Monet had paint, GTM has clay. Or like, know, DaVinci had blank, GTM has clay. Things like that, with a very subtle, great clay branding on it. And so we got...
Databox (03:44.224)
Okay.
Databox (03:47.753)
OK, yeah.
Yash Tekriwal (03:52.942)
rarely positive reception on those ads, and I kept sending our team screenshots of texts from people that would be like, that's your company, right? And so from an awareness standpoint, super great. And then also, our head of finance once asked me, he was like, do any of them know what we do? And I was like, no. He was like, I don't know what the ROI on that is. I was like, totally fair, but at least they know who we are, right? That's the first step.
Databox (04:00.157)
Yeah.
Databox (04:09.325)
No, not again.
Are you from New York City? Is that why you had all these text messages?
Yash Tekriwal (04:20.021)
No, I just, think New York is a clustering of a bunch of people. Yeah, I'm from Virginia, actually. But, so that was fun. And I think just to explain it for the people on, like you did it justice, but like.
Databox (04:23.339)
Yeah, that's it. Okay.
Databox (04:28.705)
That's cool. While we're telling stories, it reminds me of a story. So I worked at HubSpot forever. once HubSpot got big and IPO'd and everything, if I had a t-shirt on, I would get, oh, do you work at HubSpot? Do you know HubSpot? I got used to hearing that, right? Of course, I live outside of Boston, so also people knew it from an employer perspective. So I've been at DataBox for nine years now.
Yash Tekriwal (04:46.764)
Right.
Databox (04:54.689)
And so was playing soccer the other night, indoor soccer the other night, and my team would just wear black t-shirts. So I had my Databox t-shirt on. So at the end of the game, one of the guys on the other teams comes over, he's like, do you know Databox? And I'm like, in fact, I do know Databox. And he was a customer, and I didn't know. it's kind of cool when you run into those scenarios. It doesn't happen as much at Databox as it did at HubSpot, but we'll get there. Yeah. Yeah.
Yash Tekriwal (05:13.813)
Yeah!
Yash Tekriwal (05:19.661)
I think it's still wonderful when you do it. I often tell my parents this, because my parents still have no clue what I do. But now they're hearing it from their friends. like, yeah, Like, gosh, is that Clay? That's this company, right? And I think the fun thing about that, for me at least, and I imagine for you as well, is that we do operate in this very specific SaaS AI bubble. And I know that there's a whole world outside of that.
Databox (05:25.985)
Totally.
Databox (05:32.575)
Right. Yeah.
Databox (05:41.601)
Totally.
Yash Tekriwal (05:44.609)
But it's cool to see when our bubbles get large enough that we start to actually bleed into other ecosystems. And it feels like that makes the work more real. Yeah.
Databox (05:49.826)
Totally. Yeah, yeah. For most of time, most of my time at host about my people asked my dad what I did. His response was, you know how when you go to Google and you have to like sort through all the crap to find what you want. would say, that's what my son does. bugs up. Like, because it probably taught me a lot. Anyway, I get back to play what the hell is playing?
Yash Tekriwal (06:05.985)
Yep.
Yash Tekriwal (06:15.117)
So that is an incredible explanation. Yeah, yeah, that's true, Clay. So Clay, I would say data orchestration is the simplest way of thinking about it. It's a buzzword, I would say, for the biggest problem I think any salesperson, any marketer, any customer success person knows is that you've got all your teams trying to put all the data into one central store of truth. No one's doing it in the same consistent way. It inevitably gets messy. Hygiene is an issue. Adding in external data to keep buttressing the system becomes a tertiary issue. And it all compounds.
So I think what data orchestration often means is we get to use all the enrichment providers we partner with, all the AI tools that are now available to people to help you build systems that do a lot of the cleaning for you so that your team doesn't have to spend time worrying about data quality or data accuracy. They can just spend time thinking about the human relationships, the activities that actually go generate and retain revenue.
Databox (07:05.929)
Okay, so I knew about the data enrichment and that's primarily what we use you for, right? So like we'll get somebody to sign up for our product and we don't get much information. We've tried to get more information. It ruins our signup rate. So we, you know, when we get a company email and name, we then use you to go and do a bunch of things to enrich the data. So that helps us with our outreach, helps us with personalizing the in-app experience is better. So I knew about the data enrichment side, but talk to me a little bit about the data quality side. I'm not familiar with how you guys help with that.
Yash Tekriwal (07:23.127)
Right.
Yash Tekriwal (07:34.178)
So data quality, think, sort of comes in three various formats. think format number one is the simplest, which you're already familiar with. So I think data enrichment is a key piece. But oftentimes, in a pre-clay world, I actually think people forget to give us the credit for really inventing the waterfall concept as well. People were like, I was doing this, manually uploading and downloading CSVs between every single data provider that we had in order to increase match rate. So that's one element of data quality that is really coverage.
Databox (07:51.477)
OK.
Databox (07:57.61)
Yeah. OK.
Yash Tekriwal (08:02.603)
Then I think the second element of data quality is standardization. And that has two sub-elements. So with standardization, you have a bunch of rules and expressions. So simple one would be NYC versus New York versus New York City versus Brooklyn, all as location names in your CRM. You probably want to treat those all the same. And it's probably better to standardize them so that people aren't missing swaths of your data by only searching for one of the four.
Databox (08:17.665)
Okay. Yup. Yup.
Yash Tekriwal (08:27.211)
You can do that via this code and formulas and cleanliness for the standardization of it. And then you run into that second subset of standardization, which is things that are a little bit more complicated than a formula. So let's say I have a bunch of job titles in my CRM. I want to group those people by seniority. Like simple approach is let me try and take out the word head or director or VP or something out of the job title to grab seniority.
Databox (08:27.348)
Okay.
Databox (08:52.031)
Yeah. Yeah. Yeah. Right.
Yash Tekriwal (08:52.993)
But these days, people call themselves a bunch of things. Like, where do you put a GTM engineer on seniority, or like a senior GTM engineer? So you can then run AI, either to go scrape the web or to reason on your data in clay at scale. And it can go pick out those data points for you, or it can help clean up those data points for you. And so those are the three prongs of data hygiene I think about.
Databox (09:00.598)
Yeah.
Databox (09:06.581)
Okay.
Databox (09:13.451)
Got it. Okay. It's the same. Yeah. You remind him. It reminds me of a few other tools that I'm familiar with. Are you familiar with like in cycle, which helps people like with duplicate, filling in missing areas, standardizing fields like titles or locations. So it sounds like you're doing elements of that. And then what you, the third thing you described kind of sounds like N8N where you can create like kind of a workflow where you're saying like,
Yash Tekriwal (09:20.908)
Yeah?
Yash Tekriwal (09:33.512)
Thanks.
Yash Tekriwal (09:37.889)
Yep.
Databox (09:41.565)
execute this code when this happens and and you could feasibly I guess use N8n to do what you're talking about where it go find a contact right but but I imagine you make that a whole lot easier because you have all the integrations with the data providers and as well as the LLMs and all the other stuff right
Yash Tekriwal (09:48.353)
You could, 100%.
Yash Tekriwal (09:58.646)
Exactly. I think I know both the tools you just mentioned. And I would say the other thing I'm always honest about is, N8n is a good example. There are many things that you can do in KLEI that you could replicate in N8n. It then just becomes a preference of platform workflow versus a table format, the native integrations, and some of the external features that are bundled around it. N8n is a very horizontal platform. We are very specifically purpose-built for GTM.
Databox (10:02.037)
Yep.
Databox (10:10.741)
Okay.
Databox (10:22.721)
Right.
Yash Tekriwal (10:26.357)
So there are like certain elements of the GTM side that are native to us.
Databox (10:27.393)
Yeah, and specifically, contacting that company data and like processing that at the row level, right? Like, and that's more efficient in your system than it would be in NNN. My perspective, I assume it would agree.
Yash Tekriwal (10:33.783)
Exactly.
Yash Tekriwal (10:37.569)
Yeah, but I also use N8n. My personal favorite, actually, I'll shout it out, is Gumloop of all the workflow automation tools. Yeah, I find it, used it, so I was a Zapier user for a long time because I used to run, actually this was another area where I used DataBox. I ran my own low code agency and then gave people efficiency analytics that we streamlined from an Airtable that I had linked up to all the automations we were running, visa via dashboard. That was made in DataBox. And then,
Databox (10:43.982)
okay, I'm not familiar.
Databox (10:52.586)
Okay.
Databox (10:59.883)
Okay.
Okay.
Yash Tekriwal (11:05.933)
In that agency, use Zapier a lot. And then I went from Zapier to N8n, actually. And then I went from N8n to Gumloop. And I would say that I think of Gumloop, N8n, Zapier, all of these workflow automation tools sort of as last mile automation for the type of work that we do. Because there's a lot of benefits to the GTM and the visibility part that you get in Clay for orchestration. But then there's like...
Databox (11:22.88)
Yep.
Yash Tekriwal (11:30.677)
running anything that needs to run in large parallel clusters or where I'm doing lots of conditional formulas and so I'm having branching logic where now all of a sudden half of my table is not running. I don't need to look at half of that table anymore and it's slowing everything down. So for those types of flows or for more complex code types of flows, I send the data from Clay to a Gum Loop or N8n, process it there and then we'll bring it back.
Databox (11:52.468)
Okay.
Okay, got it. So there's still a role and it sounds like you maybe have a decent number of customers that are using both Klay and a tool like Gumbly or NADAM to do certain
Yash Tekriwal (12:03.647)
Yeah, I think these days all the rage is Claude Code plus Clay. Or some people even are like Claude Code can replace Clay. And we can have that whole debate if we want as well. I think there are cases for and I think there are cases against.
Databox (12:07.413)
Yeah.
Yeah. Yeah. Yeah. Yeah. I think Claude is like, is everybody's saying, Claude can do this, Claude can do everything. So like, think some people have this vision of like Claude just replacing their whole stack, which I don't think we're quite there yet. We're heavy Claude users ourselves, of course. yeah.
Yash Tekriwal (12:33.591)
Same, I will say I love Claude, but my soapbox that I'll die on for this one is everyone's talking about build versus buy. And I actually wrote about this maybe yesterday or earlier this week. I think it's a false dichotomy. It's not just build versus buy, it's like build and maintain and debug and create and innovate and scale versus buy. Because you buy a lot of other things beyond the build. So, yeah.
Databox (12:41.408)
Yeah.
Databox (12:46.537)
Yeah.
Right. Yeah, for sure. we're very much a build on top of the systems we have, right? To augment them or to tailor them to our needs. But yeah, we've canceled very few software contracts at this point, even though I would say that quite a bit going. Yeah, exactly.
Yash Tekriwal (13:04.801)
Same.
Yash Tekriwal (13:13.783)
Same here. anything, we have added many more. So yeah.
Databox (13:20.011)
We're somewhat ruthless about like, you know, not using three of the same tools and picking one and centralizing, but beyond that is definitely great. Cool. So strategy for the company, maybe give us a little background of like, you know, when the company started, how big you guys are now. Cause like a lot of people probably who don't know you or don't know how impressive Glaze is.
Yash Tekriwal (13:26.603)
As you should be, as you should be financially,
Yash Tekriwal (13:48.182)
Yeah, no, I appreciate it. think that, so for context, I think very few people actually know this history. We were actually started about nine years ago. So that would be, think 2017 is when Kareem, our CEO and founder started the company. And it was a horizontal automation tool, similar to N8n and to Zapier, but like in a table-based format for.
Databox (13:56.885)
Okay.
Yeah.
Databox (14:10.804)
Okay.
Yash Tekriwal (14:12.203)
the first six, seven years. It still is that today, but I think a lot of the momentum that we've picked up over the last three years in particular, I joined in January of three years ago. know, correlation, causation, I'll let you decide. But, yeah, I'm kidding, to be very clear. But...
Databox (14:16.554)
Yeah.
Databox (14:29.697)
I'm
Yash Tekriwal (14:32.147)
It has been the GTM focus, actually, right? Like, think focusing in on a specific enough problem with a broad enough aperture that people resonate with what we're doing and then we keep building features that sort of expand and grow from our initial audience. So the journey of growth for Clay was having a consistent but slow-growing churn-heavy data, sorry, user base for the first five, six years. I think around year five or six is when we started doing more of the like...
Databox (14:35.328)
Yes.
Databox (14:46.486)
Yeah.
Databox (14:57.227)
Okay.
Yash Tekriwal (15:01.463)
go to market, cold outbound agencies, service and support. And I think the big insight there is that even though Clay has a learning curve to it, it's not a simple tool. It's also not rocket science. So it lands somewhere in between. If you can promise somebody like a 10x improvement on their work, truly, then the incentive is there to go spend the time learning it. And so I think we focused a lot on flexibility and power and functionality.
Databox (15:10.185)
sure. Yeah.
Databox (15:15.018)
Yeah, agreed.
Yash Tekriwal (15:28.193)
And then we won over a lot of those early users. And we were also very stubborn about trying to build a PLG motion. so like Varun has talked about this, our other co-founder on several interviews, does, he like sort of invented this reverse demo format where when we were hopping on a call with a customer, instead of us just showing them the software, we would have them create an account, pull it up on their screen. And then I would annotate and point to where they need to click in order to do the thing that they wanted to do.
Databox (15:49.835)
Yeah.
Databox (15:53.835)
Got it. Yeah.
Yash Tekriwal (15:55.63)
Because the point there is to demonstrate in 30 minutes or less, I can get you further along the path than you think. You'll have enough confidence to start getting more value out of it. And wherever customers were doing something incorrectly or it was impossible to explain, that's the best product feedback loop for our engineering team to then go fix those features, change the button placement, look at what we should be actually doing to foster that growth. So.
Databox (16:10.123)
Totally.
Databox (16:13.985)
Bye.
Got it. For those listening, I'm going to say this is going to sound rude, but you're not old enough to claim you've invented that. But I totally get it. We used to do that early days HubSpot. I'm sure we weren't the first either doing the reverse demo. And yeah, it forces, it forces them to like feel it and feel accomplished, right? It's not just like, I'm going to show you how to do it. And you have to remember all this. It's like, let's just walk you through it. It makes it feel simpler to them. And then they also have learned it. So it's great, great tactic. I can, when you're selling.
Yash Tekriwal (16:26.561)
You're good.
Yash Tekriwal (16:41.953)
Yeah.
Databox (16:44.171)
products that aren't rocket science, like you said, but do require some learning curve and maybe a little be a little intimidating. It's a great technique. So cool. That's good. So give us a company is now in some metric.
Yash Tekriwal (16:52.043)
Yeah, I'm happy to be humbled on that. Yeah.
Yash Tekriwal (16:58.717)
Yeah, so I would say when I joined, were about eight or nine people in like a one bedroom apartment in Williamsburg. And we are now. Yeah, a long time ago. And then we're about to be 360, 370, and we're moving into 11 Madison Square in August, which we just announced, which is going to be super fun in New York. So yeah, it's going to be like our first like true foray into being like a real, true big tech company. It's going to be crazy.
Databox (17:05.215)
Okay, you were not allowed in there.
Databox (17:14.619)
wow. OK.
Databox (17:18.291)
fancy. Very cool. Yeah.
Databox (17:26.047)
Where are you now? Where are you right now? Like physically.
Yash Tekriwal (17:28.779)
I'm currently in our flat iron location. So we have two floors at a building. I'm in New York, yeah.
Databox (17:32.298)
you were in the area. Okay. Yeah. Okay. Cool. Nice. Long, long time ago, I consulted or advised a startup company in the Flatiron area. I know the area. Yeah. They didn't make it. But hopefully you guys do. I'm sure you will. So, okay, so company's done well. It sounds like a long period of time of like slogging it.
Yash Tekriwal (17:37.293)
But yeah.
Yash Tekriwal (17:44.267)
nice, there you go.
Yash Tekriwal (17:49.055)
Unfortunate. Appreciate it. Yeah.
Databox (18:00.11)
And then when you focused in on the go-to market, the sales kind of use cases, that's when the company kind of found product market fit and started scaling up, raised money, grew the team, et cetera.
Yash Tekriwal (18:13.901)
Correct, yeah, I think there were like a trio of like really important product features. I sort of described the journey as like pre-PMF, we had a product, but we didn't have like enough of a platform. So we were trying, exactly. Yeah, and then I think once you build your core set of functionality, you have an actual platform that you can sell. And then from that platform, you start to expand into other use cases if you can sell the core platform, and then you need to.
Databox (18:23.125)
Yeah. Yeah. You didn't have enough retention. Yeah, it's very common and sad. Yeah.
Databox (18:36.149)
Mm-hmm.
Yash Tekriwal (18:41.375)
sell adjacent use cases and like expand upstream or downstream or adjacent. And I think, yeah, in 2023.
Databox (18:46.123)
Right? What were those three features, the trio of features you said, what were those?
Yash Tekriwal (18:55.521)
Yeah, it's funny. was just reminiscing on this with one of our other early folks the other day. It was essentially for people that have used Clay, this might be hard to believe, but for quite a while we did not have a company's people jobs data set. We had a couple of like very specific starter lists. Like you could go look at the like series A companies we were able to pull down from like Crunchbase or something else. And that was it. So most people, like 95 % of people were uploading CSVs that they were pulling from like an Apollo.
Databox (19:17.537)
OK. Yep.
Yash Tekriwal (19:25.163)
or a sales now scrape or something else into clay manually. So I think we had a single engineer whose name is Roger, who I think someone was telling me at some point that Roger did the work of like what would take 20 people at Amazon two quarters to do in like a month and a half. So thank you Roger, incredible engineer. He stood that up himself. And I think that was a big inflection point for us in terms of feeling like you could actually do the end to end motion in clay.
Databox (19:28.201)
Okay, got it.
Databox (19:50.752)
I kind of remember it was like the first time there was like a tool that connected your systems and it lets you like pull the pull but had the data store as well. Like I remember like for decades we've basically been scrounging lists together, putting them together in style and uploading to our CRM. So you guys were the first ones to say like that should be together. And it was a foreign concept. It was a foreign concept for people. But like,
Yash Tekriwal (20:12.47)
Yeah!
Databox (20:17.141)
Your data must suck or your tool must suck one or the other. Like nobody believed that you could do both and do both fairly well. So what was the second one?
Yash Tekriwal (20:25.493)
Yeah, no, it's a good observation. think that it's just funny in hindsight, right? Like how obvious it feels, but it was hard at the time. The second one is what I sort of touched on already, which is waterfalls. So, and like with waterfalls, there's a sub point in there that becomes like AI formulas, because all a waterfall is, especially for like someone who's never touched clay before is let's say I want to get data from 10 different enrichment providers. I'm looking for someone's email. If I use one provider,
I see this all the time, my coverage is like 60%. That's like 40 % of people that I'm never getting. At two providers, maybe it goes up to 70. At three, maybe it goes up to 80. Then you get all the way to eight, and we're actually hitting 90 or sometimes even 100 % on certain data points. So it's worth it to have all those subscriptions, but it's really painful. Like we had all these integrations before. We didn't have the native waterfall concept. So what I was doing as an early customer of Clay is I would set up each of the email providers
Databox (21:06.785)
Okay.
Yash Tekriwal (21:22.925)
and then I would go in and write JavaScript into each column cell to then say only run if this result is this valid into the next cell. And I would do that manually for every single provider in that waterfall. And then I would recreate that manually from scratch every time. And this is when I come back to like, the pain is still so large that it was worth it. I did not, I cannot take credit for coming up with that. I believe it was Brian.
Databox (21:40.307)
time. So did you come up with this? Did you come up with the waterfall idea? Okay, you just you had the pain. You knew what the pain was. Yeah.
Yash Tekriwal (21:51.734)
Yeah, but now you can automatically insert 10 providers at once. We'll do the conditional routing and the logic for you. And this kind of leads into the third point. AI really was a big accelerant for us because now you can use AI to auto-generate those small snippets of code that are linking together your columns so you don't have to understand JavaScript. really, like, R specific. It's a ClayScript. It's a Frankenstein version of JavaScript, to be specific.
Databox (22:02.006)
Yeah.
Databox (22:17.673)
Yeah. OK.
Yash Tekriwal (22:20.809)
and technically TypeScript, but whatever. Sorry, I'm getting too technical. And then the other big thing, yeah.
Databox (22:23.457)
Yeah. So the AI can allow you to execute the waterfall without sitting there saying do this then and in the JavaScript is what you're saying.
Yash Tekriwal (22:33.493)
Yeah. So AI sort of played us like, you know, a secondary function to waterfalls in terms of making conditional formulas easier to use. But then AI is also a third category entirely of its own because the ability to let's say run an open AI call at scale across 10,000 rows in clay is massive for people. And then also we built the web scraper that has become one of our like biggest standout features where people can now fill what I would call like the last mile data problem, no matter how much enrichment data you get.
for your specific industry, company, ICP, there are probably nuanced important things that you know move the needle on your sales process, but no data provider's gonna give it to you. So you're gonna have to have a human go grab it. But now with the AI web scraper, we can start to fill that last mile of data.
Databox (23:09.909)
Yep. Yep.
Databox (23:18.559)
You can go to the prospects website or whatever and ask the right question and figure it out from the way they're what's on their website or how their website structured or what they do.
Yash Tekriwal (23:28.937)
Exactly. Yep. You hit the nail on the head.
Databox (23:29.825)
Yeah. Cool. Yeah, no, it's, it's interesting because yeah, all this, all these problems we've in my career, I or someone on my team have done very manually. And I, can remember data providers, like we would, we would buy, we would do one and then we'd be like, all right, that one sucks. Let's try the next one. And that's, and then, you know, we realized, okay, well to get the good data, we just have to pay zoom info because they have the best data set and they're, but they're really freaking expensive. So like, will try it for a few months to see if it helps. Right.
And so you guys came along with this waterfall concept and solved that problem as well.
Yash Tekriwal (23:58.819)
Yep.
Yash Tekriwal (24:05.705)
Exactly. think that's another thing that we don't talk a lot about, but I really think is such a simple but important innovation in the space is because you know this well, you can also remember that like for any data provider worth their salt years and years ago, you had to pay an annual contract or they tried to force you into one. It's five to six figures. Yeah, it's so much money.
Databox (24:21.171)
And it was expensive. Yeah. Yeah. Right.
Yash Tekriwal (24:25.909)
And then you like, aren't really sure if you're actually using all of your data or the seats or getting the value out of it. And now you're like throwing money into a sinkhole of like the data is just important. So like it's worth it, but is it optimized?
Databox (24:30.581)
Yeah, right.
Databox (24:38.229)
Yeah, is it optimized? it up to date? How are they keeping it up to date? Are they relying on our, are they stealing what we do and sending it back to other people? There was a whole, what was the of the Jigsaw? I think it was Jigsaw that did that. They ended up selling to Salesforce. Jigsaw's whole business model was like, we have lots of customers that are updating their contact data. So then it's up to date for you. Like, great. So you're selling our work back to us. Thanks.
Yash Tekriwal (24:45.889)
The answer, by the way, is yes, yes.
Yep.
Yash Tekriwal (24:59.959)
Right.
Exactly. But I think that like we did the partnerships with individual data providers so that you could pay as you go and pay as you go. Then slowly became more and more of an industry standard. And I would actually say like outside of AI again, that's something that like, I'm proud that we were able to shift a little bit, which is data that you pay as you go. Yeah.
Databox (25:09.353)
Yeah, gotcha.
Databox (25:15.879)
Okay. Yeah.
Databox (25:22.015)
after. Yep, makes sense. Very cool. All right, so I think we had enough of like, what you guys do and the strategy to get there. Like, what's what's next? I noticed that you not too long ago, you'd launched something around ad targeting or personalization. So like, what what do you guys see next? Where are going next?
Yash Tekriwal (25:30.678)
Yeah.
Yash Tekriwal (25:39.66)
Yep.
Yash Tekriwal (25:43.596)
So I think that we have branded ourselves as the go-to-market company for a while, and it's been mostly true, but we focus on very specific use cases. The journey as I see it has been, we started with truly just data enrichment, and then we migrated into outbound. And then we started migrating into inbound and CRM enrichment, and now we're starting to really get into marketing, which contains like...
Databox (25:49.098)
Mm-hmm.
Databox (26:03.361)
Yep.
Okay.
Yash Tekriwal (26:07.501)
ads, contains lifecycle emails, it contains like how do I organize large chunks of my data, that's the audiences feature that we recently launched. And what I would say without giving away things that I can't give away is that I think we're going to continue to try and do our best to really just become the toolkit of choice for different people in different parts of the go-to-market profession. So you can imagine at some point, yeah.
Databox (26:16.052)
Okay.
Databox (26:20.288)
Mm-hmm.
Databox (26:29.907)
Okay, so expanding the new use cases within the go-to-market world and the stuff that is out is the ad stuff. Tell me about the audiences. What's that?
Yash Tekriwal (26:39.025)
So audiences is this idea where we'll hook up to your CRM or your data warehouse or both, then we will provide to you. There's truly honestly like an incredible engineering feat by the team to do this. But you can now add filters. imagine the pain that audiences solves is at a large enough company, if you've been there in the past, if you as a marketer want to get.
a pull of customer data from your data warehouse or CRM, you have to go ask the data team because they have to go write like a custom SQL query to make sure that they're pulling the right data from the right fields with the right accuracy so that you're not mass emailing the wrong people or like targeting ads towards an incorrect audience. With audiences now, ironic that the terms worked out there, you can actually have a data person set up the entire infrastructure connected into Clay and now on
Databox (27:19.329)
course.
Yash Tekriwal (27:33.955)
as like a marketer can go in without having to know SQL and I can just add a bunch of those like you've used it in like notion or air table before data box has these filters as well where it's like yes I can know for company greater than one opportunity or you know greater than 10 impressions or in between a to B or contains X
Databox (27:51.682)
You're talking into the database to create segments of the audience of the database to then do your targeting on your ads. Okay. And does that help with like look alike audiences as well so that you can, you can train the ad systems to go and like look for people like this that meet these criteria, this audience criteria. Okay.
Yash Tekriwal (27:59.394)
Yes, exactly.
Yash Tekriwal (28:11.181)
It does, yeah. So it sort of helps with everything downstream of it because now that you're operating at enough scale, if you can just quickly get a slice of that data and then take an action or hypothesis on that data, whether it's outbound, audience for an ad, looking up lookalikes, it becomes much easier.
Databox (28:16.225)
Mm-hmm.
Databox (28:24.479)
Yeah. Okay. Got it. Very cool. Nice. So you're just basically expanding the use cases of the core product and building from that. So that's awesome. Cool. All right. And then, so let's step back and talk a little bit about how you guys manage strategically. Who decides and how do you decide where you're going 12 months from now? What's that process look like? I'm not asking you to share the secrets of what you're doing, just more of like...
Yash Tekriwal (28:31.447)
Thanks.
That's the dream. Yeah.
Yash Tekriwal (28:46.514)
Yeah.
Databox (28:52.673)
How do you guys arrive at these things and how do you indicate them internally and all of that?
Yash Tekriwal (28:55.455)
No, for sure.
Yash Tekriwal (28:59.341)
Well, so I think the funny thing about this, because I am being fully honest when I say this, is that the only thing that we think about in terms of a year timeline is what's our revenue goal that we want to hit. Even the how we get there, we do enough of a forecasting exercise to try and break it down quarter by quarter to make sure that we have targets that we can hit from, again, just a broad revenue perspective. But I think what's fascinating, even at our size today, is that
We don't do a lot of year-long product progress planning mapping. We don't do a lot of year-long, for example, even my team in ecosystem. I sort of know the general direction that I want us to head in and I know the most immediate short-term things that are top of mind. But if I told you I had a year-long plan for where I want the team to end up exactly, I'd be lying.
Databox (29:46.658)
Yeah. Do you have like a quarterly plan? how, how much? Okay. Yeah. That's good. Otherwise I was going to start repassing, but totally makes sense. You guys have the revenue target, you have smart people, you're fast growing already. So like you can have some wiggle room around what you do three quarters from now, right? You don't need to have that, that foresight. And plus if you try to, you might get it wrong. Um, but, uh, but on a quarterly basis, each team is,
Yash Tekriwal (29:51.125)
I do have a quarterly plan, yes. Yeah.
Yash Tekriwal (30:10.879)
Exactly.
Databox (30:15.221)
like putting together like what are their key objectives or projects or how do you guys think about it? Yeah.
Yash Tekriwal (30:19.369)
Exactly.
Yeah, so like on a quarterly basis, like I'll give you this example from our team, because I think it's shareable, right? Like there are within the ecosystem, six sort of broader subgroups. There's like what I call innovation, which is like our startup programs and our campus programs. There's community, which is our digital community, our IRL play clubs. There's 84 of them around the world. And then our like clay cup, which is our annual competition. Then we have live trainings, which happen digitally and in person. We have async content on the university. And then we have a certified
Databox (30:39.637)
Bye bye.
Yash Tekriwal (30:51.043)
and badging ecosystem, and then a talent marketplace. So those are like the six subgroups of the ecosystem that we currently have. And then the way that we are, because we're actually in the middle of finishing up Q2 planning, so it's a timely ask. Right, so for each of those functions, there's a couple of sub-functions within them. Yeah, yeah, exactly. We're tech companies, right?
Databox (30:58.401)
Gotcha.
Databox (31:04.074)
Yeah, yeah.
That's funny. People tell you they're we're doing the same thing, though. I keep telling my team, I'm like, we should just do it three times a year. Because that's basically we're always about behind.
Yash Tekriwal (31:19.455)
Exactly. Well, so for us, we moved this year to start our fiscal quarters on the February. Yeah. So we had a long quarter. This was our four-month quarter this year. But.
Databox (31:24.36)
yeah, you could do that too. That's another way to see. Yeah, I got you.
Yash Tekriwal (31:33.037)
Yeah, so I have basically, and I'm happy to talk about them if you'd like, like plans for each of those sub-functions in terms of how we want to grow, what the scale we want to hit is, and some of the question which delves into maybe what you want to know next is like, what are the metrics that we hold ourselves accountable to and that are most important for driving that component piece forward?
Databox (31:37.857)
each of those things.
Databox (31:48.162)
Oh, you're like, tap ahead. You must watch the podcast. So yeah, pick one. Pick one of those six areas. Yeah. And I'd love to hear what, you know, what are you measuring? I'd love to hear the badging and certification one, if you don't mind.
Yash Tekriwal (32:03.157)
Yeah, so the badging and certification one is interesting because we launched a process back in, gosh, September, October of last year. And I'll say that I have never seen a badging or a certification process that I have liked as a learner or an educator.
Databox (32:13.568)
Okay.
Databox (32:21.129)
Okay. Yeah.
Yash Tekriwal (32:23.519)
Inevitably what happens is the whole purpose of a credential is to bypass some of the like, I need to see your work, I need to quiz you, I need to understand if you're actually good at this thing. Yeah. But then, are you really gonna get a good sense of that from like a couple of multiple choice questions and like a proctored assessment of what someone does? Probably not.
Databox (32:31.691)
Sure. It's a third party of the US side. Yep.
Databox (32:42.305)
Probably not.
Yash Tekriwal (32:44.781)
So it becomes this credential that gets gamed very quickly in industry and then it just starts to mean nothing. So I had a very strongly held opinion that we should try and do it differently and the way that we should do it differently is certify people based on the work that they've actually done. So we launched V1 of our certifications process in the winter of last year.
Databox (32:49.503)
Sure.
Databox (33:01.898)
OK.
Yash Tekriwal (33:06.661)
And it was purely build your clay tables, submit your clay tables, and then we will process, read them, evaluate them, and then return back a certification.
Databox (33:10.881)
That's all.
Databox (33:14.593)
So they were doing it like in a demo account or their own account or something and submitting it and say I did what I'm supposed to do. Okay Yeah nice
Yash Tekriwal (33:21.037)
correct and then we would grade it. The unfortunate reality is people still found, this is what I mean, people will find ways to game your system. And so we started to see that happening like.
Databox (33:28.737)
Yeah, Copy.
Yash Tekriwal (33:32.659)
someone made a post about like, can get you clay certified in 15 minutes. And then he was like linking three templates. And I was like, okay, fine. Yeah, if you like change the variable names and then it's hard for us to detect if it's exactly the same table. And then we just got into this race where like for a month, I was trying to build with the team a bunch of different like anti-fraud flags. And then we took a step back and we're like, maybe we should just redesign the whole thing. Maybe we're like, didn't meet our original objective.
Databox (33:36.033)
Got it.
Databox (33:42.987)
Right.
Databox (33:50.633)
Okay. Yeah.
Databox (33:55.426)
Well, what you really underappreciated, Yash, is that people were actually doing it. Most companies that launch a certification program, it's crickets. So you're fortunate in that you had demand for sure and interest. there's a business model, right? Like there's a business model for them to get certified and then they can get work, right? And implementing Clay, as you talked about earlier, is not rocket science, but does require a learning curve. so like even when we...
Yash Tekriwal (34:01.022)
I set up.
true.
Yash Tekriwal (34:17.025)
Exactly.
Databox (34:23.327)
when we took clay on, like we put a person on it. He has a few other smaller responsibilities, but it's a good portion of his time is that but we also still hired a consultant to help us help him like on board, right. So, so like there's a need there. So, so how'd you change it? How did you use the certification? How'd you make it a little less fraudulent?
Yash Tekriwal (34:37.773)
Yes, sir.
So we're in, so we hit pause, which was actually to your point.
I do appreciate just how much the community really wanted them. There was some incredible backlash and that all things went like as well as handled. I people understood what we were going for. And then someone on my team has been doing an incredible job of rethinking and redesigning like what this could be. And we're still going to try and take a courageous bet on what we think it should be. So I'll give you a little preview of it because it's going to be really soon, but there's going to be multiple levels to the certification. We're going to be assessing multiple dimensions of what we think makes a good a market engineer.
Databox (34:52.319)
Yeah. Yeah.
Databox (35:03.467)
Okay.
Databox (35:11.901)
Okay.
Yash Tekriwal (35:13.295)
So one dimension is going to be, do you know how to think about these problems like an engineer that has an understanding of the tools in this landscape? And that's going to be a live assessment that we're going to have conversationally from like a case study fictional scenario. Yeah, and like you can't game that, you know, so that's part of the design there.
Databox (35:21.43)
Yeah.
Databox (35:24.929)
wow. Interview. Okay. Yeah.
Databox (35:32.703)
Yeah, unless you've seen those commercials where like somebody's doing an interview and they like smear the peanut butter on their camera and then they bring up Chatuchakiria clodders. I think it's literally a quad. Somebody will figure it out. But yeah, it's harder to game for sure.
Yash Tekriwal (35:42.701)
Yes, I have seen those. Yeah.
Yash Tekriwal (35:49.152)
Exactly. people will eventually do it and I think like to your point, that's a sign of a good program, so I'm okay with it. But I just want it to be hard to game. I want it to be like you had to have tried... Like it should be so hard that in the time that you took to game the system, you could have just learned the skill. That's what I want it to be. And then...
Databox (35:51.746)
Yeah. Yeah. Yeah, it's a little harder than 15 minutes. Yeah. Yeah.
Right, right, makes sense.
Yash Tekriwal (36:11.019)
Yeah, like the other two layers are going to be live building. So you can't just templatize and use someone else's work. We're going to see if you can apply that creativity from the verbal sort of critical thought reason to a live build as well. And then there's going to be a third level of like, have you done it for real? Have you done it for a customer, for yourself, for like an employer? And then that will be peer reviewed by the community. yeah, let's see. Yeah.
Databox (36:33.385)
Okay. wow. Okay. That's cool. I like that. Yeah. I think that last one is the key, right? And I like as a, as a program matures, you'll have more and more people that have like, I'm sure you already do. You already have people that are probably implemented clay a hundred times, right? And so like, clearly they should be the ones that are at the top of the list. So, so that's good. Cool. And then, so how do you measure the success of that program? Like what metrics do you guys track internally to say like,
Yash Tekriwal (36:51.351)
Exactly.
Databox (37:02.175)
This is worth the effort. This is having an impact on the business. This is the impact. How do you do that?
Yash Tekriwal (37:06.837)
Yeah, so think first, I think about the dimensions across which we would measure success of that program. So I think the most rudimentary dimension is, are we getting people certified? So what's the number of people going through a certification process and how many of them are emerging actually certified? Now, the caveat I'll add to that number is.
Databox (37:11.339)
Mm-hmm.
Yash Tekriwal (37:25.719)
We don't necessarily want it to be a high number. This is where the second dimension of the tracking will come in, which is of the people that we certify, how many do we then see placed at either an enterprise or mid-market company hiring for that talent because we know that that's a need from our sales process and GS programs. So we are actually going to try and use these certified individuals to also help address some of the requests we are constantly getting from companies looking to hire this type of talent. And then the third dimension, which is like a long
Databox (37:42.89)
OK.
Databox (37:51.649)
Got it.
Yash Tekriwal (37:55.616)
longevity metric is checking in, seeing how it goes, right? Like think we want the certification to be accurate, which the only way to check for that is once we get someone certified, once we place them in their next role or they start their next thing, are they doing well? Checking in on like what well means. And so that's a little bit more of a qualitative assessment from like the employer standpoint of across these dimensions that we also measure on the certification side. How do you think this person is holding up?
Databox (38:20.683)
No. Gotcha. Cool. Have you been able to tie it more closely to any of your financial metrics? Like are these people out there, are they reselling the product or are you measuring retention of the customers that they work with or anything like that that makes a more direct financial tie? I'm sure you've gotten that question from the CFO at some point.
Yash Tekriwal (38:41.667)
for sure. Well, think there's basically for the direct, so almost everything that we do is both incredibly important to the financial metrics and also indirectly like unimportant. And like I'll give you the certification example and then I'll also give you like another example I think will make even more sense because attribution is just hard, especially from a marketing standpoint, which you know better than anyone. Thing number one is that we can somewhat
Databox (38:56.393)
Okay. Very hard. Yep.
Yash Tekriwal (39:08.973)
for certifications at least, get at the financial impact because we have historical data on companies where we don't have a strong individual champion or someone has left the organization are like 60, 70, 80 % more likely to turn at time of renewal. And so like a number of people we can place successfully into companies that have a gap there comes back to a retention metric for us that is, you know, not directly impactful, but like very responsible for.
Databox (39:21.556)
Okay. Gotcha.
Databox (39:27.327)
Yeah.
Databox (39:34.795)
Gotcha.
Yash Tekriwal (39:34.848)
And then I think also the thing that I'm really excited for actually, we don't have this yet, but we will try and plan it is as we add more clay users or GTMAs really to companies that have more use cases they want to be expanding with us into, do we see a correlation between time of hire and time of expansion of usage of contract in the directions that they're asking for? Because a lot of times it's a skill issue. So like that's one way we think about on the certification side, but then like to give you a totally different, but also like
Databox (39:53.503)
Okay. Gotcha.
Yash Tekriwal (40:05.191)
interesting example, we also run these IRL workshops. So we'll invite 20 target customers, typically champions. Yes, IRL means in real life. too millennial for my own good.
Databox (40:09.217)
Those that aren't right here, you mean in real life? We have some older people that listen to the podcast. I don't want them to get lost.
Yash Tekriwal (40:23.137)
Thank you for checking me on that. And so these IRL workshops, very, very, very, very, very hard. And in real life workshops, very hard to, yeah, very hard to.
Databox (40:31.669)
Yeah. Yeah, you can find once
Yash Tekriwal (40:39.263)
attribute back to revenue because it is just one of many many many types of that we might have with the customer along the journey. There is the subjective anecdotal evidence that we get from customers or sorry prospects who come to these events that are like now I understand Clay can do this and this and this so I'm actually going to add all those back into my POC and now we see contract size grow up. Or now that we've built faith in a technical champion who's actually excited to use the tool even though we've gotten there conceptually the deal moves along much more quickly because you actually
Databox (40:46.944)
Yep.
Yash Tekriwal (41:09.167)
actually have people that are like, need this to do my work now. Are we able to accurately measure any of that? Not really. But what we're going to try and get to is we do know how much pipeline is in a room because we know the average opportunity sizes from when we run that workshop. I can poke holes in that number nine ways till Sunday, but it's fine. Yeah. And then we can see how that increases or velocity changes.
Databox (41:12.053)
Gotcha. Yep.
Databox (41:16.353)
Yeah.
Databox (41:24.321)
Yeah.
Yeah, totally. Yeah. Have you, do you all do self-reported attribution where you ask your lead to signups or your new customers, like how they heard about you? No.
Yash Tekriwal (41:41.537)
No, not really. I mean, I think we do on onboarding, but I am a huge skeptic of those surveys, if only because like...
Databox (41:47.73)
if it's open text, you get good answers. It's not representative. It's not like, you know, perfect tracking where you can say, we know where everybody came from or what they all their touch points are. But it gives you some indicators of what's happening in the influence.
Yash Tekriwal (41:51.295)
OK, well, so there I agree with you. Yes, if it's open text.
Yash Tekriwal (42:02.029)
It gives you a directional. That is true. Yeah. I would say the best attribution we get for that, ironically actually, is because we have such a strong social media flywheel. People will post on LinkedIn all the time about like, oh, I went through this, this, this, and this, and then ended up at Clay. Actually, if you Google like 16 touch points Clay attribution on like LinkedIn's website, you'll probably find this post. Someone made a really great post about there were like 16 different moments that Clay interacted with him.
Databox (42:11.188)
Okay.
Databox (42:16.447)
Yeah.
Databox (42:23.046)
huh.
Databox (42:29.181)
they actually stepped back in their own touch points before they bought your product. Okay. Yeah. Got it.
Yash Tekriwal (42:33.195)
Yeah, exactly. They were like, I created an account, didn't want to do it. Went to an event, didn't want to do it. And like the workshop was one of them towards the end where he's like, I finally got it. And so anecdotally, things like that help us know that it works.
Databox (42:43.145)
Yeah, no, get all these anecdotes, like you may or may not know, but I publish on LinkedIn pretty much every day. so like, thank you. So, so we constantly, I constantly hear from the team, like, they've been following you forever. Or, you know, that in fact, there was somebody that like, on the management team that like ran into a random person and like, I follow your CEO on LinkedIn. Right. So it's like, it's all that stuff matters. It's just, it's, it's hard to track. Right. Yeah. It's hard to adjust.
Yash Tekriwal (42:48.457)
I love your LinkedIn posts. I read them all the time. Yeah.
Yash Tekriwal (42:55.181)
Right.
Yash Tekriwal (43:06.379)
Yeah.
Basically, yeah.
Databox (43:13.055)
Yeah, cool. But it sounds like there's a culture there where like, not everything needs to be tracked. They understand the understand that like, this kind of stuff has influence and has impact even if you can't perfectly and correlated to close deal. Yeah.
Yash Tekriwal (43:29.239)
correct, I would even go so far as to reframe that into, we almost do track everything, but there are two types of data that we will take in. One is what everyone is used to. It's the financial metrics, the tracking, the planning, the spreadsheet. And then the other is just the feeling. I think that the Subway ads, these programs, these workshops, you can tell if you're doing something well for the most part, are you feeling the pull of the market? Are you feeling the pull of customers?
Databox (43:35.967)
Yeah.
Databox (43:43.819)
course.
Databox (43:49.067)
Yeah.
Databox (43:53.024)
Yeah.
Yeah. Got it.
Yash Tekriwal (43:56.482)
And I think we just pay attention to that more than the average company does instead of second guessing whether or not it has a direct return on bottom line.
Databox (44:00.546)
Yeah, definitely more than we do at DataBox. Definitely more than we did at HubSpot. HubSpot, Darmesh had a phrase that he would frequently say in meetings, which was basically, in God, all others bring data. So like, we don't trust your fucking feelings. In fact, there was a meeting where Halligan, like I shared some feelings about something and Halligan's like, we don't really do good with feelings. Let's get to the numbers.
Yash Tekriwal (44:15.56)
Hahaha!
Yash Tekriwal (44:27.627)
Yeah, totally fair.
Databox (44:27.713)
So, yeah, it's a unique culture, enjoy it. Like it's nice that they give you that grace and they trust your, trust what you're hearing and feeling and all that stuff. That's good, it's important. Yeah, enjoy. Cool, anything else you wanna add about what you're doing or what Glaze doing that you think our audience might appreciate?
Yash Tekriwal (44:40.181)
Yeah, I'm very thankful for it, for sure.
Yash Tekriwal (44:54.029)
I don't know. think the only thing I'll add is just that I think it's really exciting time.
To be in these spaces. I think it's really easy to feel overwhelmed and intimidated by AI So if there's like one thing I can add it's that like I often tell our customers this it is my literal job To know the platform in and out and I still feel behind in terms of all the things that are coming out So I don't think it's possible for everyone to stay fully on top of things. But the reason I still think it's so exciting is If you are someone who has the curiosity the drive and the willingness to adapt and learn The world is your oyster right now
Databox (45:01.365)
Mm-hmm. Yeah.
Databox (45:08.852)
Yeah.
Databox (45:14.197)
Right.
Databox (45:21.45)
Yep.
Databox (45:29.141)
Yeah, totally.
Yash Tekriwal (45:31.201)
Like you can open up Cloud Design, can open up a Plexity computer, you can just test and figure things out and build skills that I didn't even, I could never have even dreamt of being possible years ago. And so yeah, we're just trying to like nerd out and have fun with tech here and see how we can do things for fun. So if you're interested in that ever, come be a part of the ecosystem. That's what I would say. Yeah.
Databox (45:41.013)
Right, totally.
Databox (45:50.324)
be part of the ecosystem. Exactly. So ways they can do that then they can get certified clearly, which lines them up it seems like to essentially get referrals or even job opportunities through you. What other ways can they get engaged in your community?
Yash Tekriwal (46:01.141)
Yep. Yeah, so I think there's probably three big ones I would send people to. I think number one is if you're interested in learning more about the product, the use cases, where it goes from, university.clay.com. Number two is on that university website. You'll see a lot of async content and all the other parts of our ecosystem. But I think the other thing that people find exactly.
Databox (46:22.379)
videos.
Yash Tekriwal (46:25.107)
a lot more valuable are the live learnings. So like we do live digital programs and we do cohorts and then we also do these workshops. So like feel free to go there and the other beautiful thing about that.
Databox (46:33.501)
Is the live in person or virtual or both? Both. Okay. Cool.
Yash Tekriwal (46:37.325)
Both. And then that leads into the third piece, which is the community. So we do have these clay club chapters in a lot of major cities. There's 84 around the world. If you're in LA or SF or New York or London or Barcelona or I'm not going to list all 84. But there's a ton of them. Yeah, exactly. And so it's fun to meet other people IRL. I think as much as we get more and more digital, IRL still is magical. And we have a digital Slack community. Yeah.
Databox (46:46.239)
OK. Yeah.
Databox (46:53.107)
Yeah, please do.
Databox (46:58.891)
Yeah. Yeah. Yeah.
Yash Tekriwal (47:06.049)
True, good call out.
Databox (47:07.371)
We're getting all the acronyms out. So cool. Thanks, Josh. And then if they want to do, do you publish regularly anywhere? you like, where, should people follow you?
Yash Tekriwal (47:18.133)
Yeah, so if people are interested in following what I talk about or what I'm thinking about, I write pretty regularly on LinkedIn as well. And I have a substack. It's substack.99 %yosh.com. If you Google that, you'll find it. Yeah.
Databox (47:24.339)
Okay. you do? Sweet.
Databox (47:31.529)
Okay, got it. If they Google your name, I'm sure are you the only Yash Tekra wall on the internet or is another one? Congratulations. There's another Peter Caputo the fourth, believe it or not in Germany. So like, thought I had it nailed all my life. And then I found this guy. He's not very active online though. So like I squashed first service. Yeah, I got him covered.
Yash Tekriwal (47:37.419)
I believe so, it's great for SEO. I've always talked about that from a young age, yeah.
Yash Tekriwal (47:44.885)
No way.
Yeah, so you got them beat.
Nice.
Databox (47:55.755)
Cool man, well thank you for taking the time and joining us here and sharing a bit about Clay and what you're up to and how you think, how the company operates. Really appreciate the insights. Congrats on the success. You're on a cool ride, man. Enjoy it. It doesn't take take for granted, do you it? yeah, I don't know about that. I think you've innovated on the playbook, so nice job and good work. Cool man, well thanks.
Yash Tekriwal (48:09.473)
Thank you. I appreciate it. Thank you as well for the advice two years ago for everything that's happening now and for the continued gems. Yeah.
Yash Tekriwal (48:21.485)
Appreciate it.
Databox (48:26.114)
Don't leave. There we go.