A Product Market Fit Show | Startup Podcast for Founders
00:18:39 · Daniel Saks, Pablo Srugo
We can prioritize who you should target and then we can have your reps reach out to the high value targets, call it with a phone call or even have your execs go in person but then automate some of the lower tier list. And that's driving 2 to 4x connect rates and meetings booked rates across our network. So seeing incredible value in terms of the time savings so reps don't have to list build anymore or manually think about a lot of the email and LinkedIn and that way they can be more focused on doing what they love and do best which is being in front of customers. And we're seeing the killer outbound teams be people who are very calling centric or in-person centric and they're trying to use GTM engineering via Landbase and Claude or alternatively to us, maybe a Clay to essentially automate a lot of these workflows so they can be very targeted in who they spend time with. [00:19:31] S2: I was going to ask you specifically about Clay like their ripping I mean they just raised like a $7 billion valuation seems to have been what do you think they got right right in the world of like ZoomInfo and Apollo and all these other players like what did Clay get so right in your opinion?
00:19:31 · Pablo Srugo, Daniel Saks
[00:19:31] S2: I was going to ask you specifically about Clay like their ripping I mean they just raised like a $7 billion valuation seems to have been what do you think they got right right in the world of like ZoomInfo and Apollo and all these other players like what did Clay get so right in your opinion? [00:19:43] S0: I think the key thing was there was no infrastructure to be able to aggregate different data sources and therefore orchestrate or automate capability. So in the past you had to have a user going into ZoomInfo or Apollo with a cursor and like make their own judgment on who they go after and therefore it's just a really manual fragmented process and no one data vendor has the best call it access to understand who you should go after and to have the right contact info. So I think what Clay got right was really leveraging the creativity of sellers in a way that they could act as engineers to orchestrate data in new ways and I think Kareem was like super ahead of his time it's amazing what they did because they really started this pre-AI but were able to leverage a lot of the AI to take it to the next level.
00:19:43 · Daniel Saks, Pablo Srugo
[00:19:43] S0: I think the key thing was there was no infrastructure to be able to aggregate different data sources and therefore orchestrate or automate capability. So in the past you had to have a user going into ZoomInfo or Apollo with a cursor and like make their own judgment on who they go after and therefore it's just a really manual fragmented process and no one data vendor has the best call it access to understand who you should go after and to have the right contact info. So I think what Clay got right was really leveraging the creativity of sellers in a way that they could act as engineers to orchestrate data in new ways and I think Kareem was like super ahead of his time it's amazing what they did because they really started this pre-AI but were able to leverage a lot of the AI to take it to the next level. [00:20:34] S2: Walk me through this like you're a go-to-market person and you're going to use ZoomInfo or you're going to use Clay and then we're going to bring in land-based but I just want to understand just from that perspective I mean ZoomInfo is you know pretty straightforward probably like Apollo like you just you said filters like I want businesses there's many employees this geography you know this industry whatever and it builds you a list at least that would be my simple understanding I've played a little bit but I'm not an outbound GTM person right so I don't go super deep with Clay though what would be how would that work what's so different about it?
00:20:34 · Pablo Srugo, Daniel Saks
[00:20:34] S2: Walk me through this like you're a go-to-market person and you're going to use ZoomInfo or you're going to use Clay and then we're going to bring in land-based but I just want to understand just from that perspective I mean ZoomInfo is you know pretty straightforward probably like Apollo like you just you said filters like I want businesses there's many employees this geography you know this industry whatever and it builds you a list at least that would be my simple understanding I've played a little bit but I'm not an outbound GTM person right so I don't go super deep with Clay though what would be how would that work what's so different about it? [00:21:01] S0: Yeah so on ZoomInfo you would be manually you know filtering within your interface and it's very time consuming to build a list and you get charged credits every time you like download a list and then you call that list and the list might have very low efficacy the challenge with Clay is that you need to be very technical you need to be a GTM engineer you need to have sophistication in order to build these lists but essentially what they did is they connected to the Zoom Infos and Apollos and many others and allow someone to import data from many different data sources play around and prioritize that data even potentially like validate that data through online sources and then push it out to your sequence or call it push it out to instantly or push it out to outreach or whatever that might be.
00:21:01 · Daniel Saks, Pablo Srugo
[00:21:01] S0: Yeah so on ZoomInfo you would be manually you know filtering within your interface and it's very time consuming to build a list and you get charged credits every time you like download a list and then you call that list and the list might have very low efficacy the challenge with Clay is that you need to be very technical you need to be a GTM engineer you need to have sophistication in order to build these lists but essentially what they did is they connected to the Zoom Infos and Apollos and many others and allow someone to import data from many different data sources play around and prioritize that data even potentially like validate that data through online sources and then push it out to your sequence or call it push it out to instantly or push it out to outreach or whatever that might be. [00:21:41] S2: So it's similar but it's just better because it ties to many different data sources is that a simple way to think about it?
00:21:46 · Daniel Saks
[00:21:46] S0: Yeah that's a good way to put it is that ZoomInfo has their own data and you can only use your own data and you need to be interfacing it on the ZoomInfo you know call it software whereas with Clay you can pull in the data sources you can play around in tables and then you can push them to what you need and what's different about Landbase because we started Agentic Native is that we actually built it for agents to reason over so instead of a GTM engineer needing to call it like spend a lot of time with Clay credits and Clay tables essentially Landbase can go and learn a lot about you we have a lot of information about every business but you can give it access to your CRM or to your other data and we can then autonomously identify and score who your top accounts should be suggest how you go after them suggest messaging and we're built native within Claude Code and Codex so one of the challenges with Claude Code and Codex is that if they're recalling info from an LLM they're sharing what's available on the public internet but that's nowhere near the comprehensiveness of what exists in terms of people and companies so with Landbase plus Claude Code we're giving the Claude and Codex context to call it 40 million businesses 400 million business profiles but 1500 fields on each of these areas that you can use as custom signals so if you wanted to say one query that I just did is that hey we want to target companies that have over 100 SDRs or prospecting companies in the US no one else has the data in order to or is able to write the SQL in a way to do that analysis but Landbase in Claude Code can do that in minutes and give really good evidence the other thing that we did recently and we're going to actually be sharing at Pavilion coming up in September in New York is we did benchmarking so just like there's the software engineer bench that OpenAI pioneered where you can benchmark the efficacy of co-gen players to create engineering workflows we did the same thing for prospecting where we looked at how do you have the most precise list that's going to drive the best outcome and we tested Landbase, Apollo, Zoom, Info, Clay and our precision is more than 2x and I think that correlates with the results that our customers are seeing but the way we were able to get there is to index all this info so agents can reason over data and therefore it makes it e