Product DemoAI-Powered Pipe Gen

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Julia Nimchinski: Welcome to the show.

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Kevin White: Hey? Thanks for having me sorry for crashing that last panel a little bit early, but I think we’re all good now.

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Julia Nimchinski: Real? Are you the.

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Kevin White: Real Kevin. Yes, it’s not the the AI. My AI counterpart is not joining me. I won’t let it let it, or I don’t know how to genderize it. But yeah, I won’t let it in for this this presentation. It’s gonna be pure human, Kevin here, and some help from common room, I guess. Common room AI.

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Julia Nimchinski: Amazing. Thank you so much for joining us today. I know it wasn’t easy.

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Julia Nimchinski: What’s new? Tell us more. What Demo are we watching today? Is it.

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Kevin White: Yo.

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Julia Nimchinski: AI!

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Kevin White: Well, so so I am in

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Kevin White: at an off site in outer Seattle area at a place called Suncadia. It’s very cold and snowy out here. I’m looking at the mountains, which is a nice view. And hopefully, the Wi-fi and all that stuff is working just fine. It seems to be okay. And I’m actually actually have a exclusive kind of demo today to share some of the stuff we’re building at Common room. It’s in beta, so may the demo gods be with me.

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Kevin White: and also have some offers for people who are who are tuning in. So yeah. Happy to just jump into things and we we got what like 15 min is that a I know we’re a little bit I’ll try to go fast so we can. We can maybe end promptly here.

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Julia Nimchinski: Let’s do it.

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Kevin White: Alright. So let me share my screen. I do have, like a a bit of like presentation to set the set the context so hopefully, that’s okay for folks. I’ll try and move through this fast and then get to the demo, which is more exciting stuff. So this is me. Hello! I lead the marketing team at Common Room.

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Kevin White: I want to talk about like a problem with the market right now, which is a bad personalized email. That’s almost a systemic thing that we see prevalent throughout the AI for go to market tech. And it’s you know, we’d like to think of it as like a spam candidate, essentially just looking at whatever’s crawlable on Google or someone’s Linkedin profile. And then using AI to personalize that outbound message which

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Kevin White: ends up being like pretty lightweight and not effective. And the big issue that we see with with AI not being effective for go to market. Especially for like warm, outbound or prospecting is a, it’s a, it’s a data issue. And so if you can pull in quality data, then your output and your use of AI becomes like a lot more, a lot more executable and effective. So

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Kevin White: that’s a that’s where we see an issue with common Room, and also how you know, we’re trying to build common room in a way to make AI for go to market super effective. And so what one area where we see that you can, you know, really bolster and and have a strong AI go to market motion is by capturing data. And I made this like 50 point checklist. That combines both your Crm hygiene. Different parts of enrichment. So you know the

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Kevin White: the person’s role, their company, their employee size, all that kind of stuff and then also the other thing that the common room does as a platform is capture signals from lots of 1st and 3rd party sources. And so like this is almost like the foundation of data that it’s at least like a really good starting point for AI is like, if you have this data foundation, then the AI AI can work well and be effective for prospecting, and also, you know, warm, outbound.

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Kevin White: Okay? And then, so I always pitch this. I always share this signal 100 signal guides. So if you. If you are data poor, or you need a place to capture data. This is a an effective resource to give you like a brainstorm of like. Okay, here, all the different places where I can pull in data so that I can train and use that as the infrastructure for my AI, a go to market motion to actually be effective and get those like outsized returns from AI

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Kevin White: and it just so happens that common room is really good at doing this stuff. So we capture here. On the left we capture a tons and tons of different types of data and signals.

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Kevin White: The other thing we do which is super important is both enrich, like, identify that person and accounts behind the signals, enrich it all with that. You know, this enrichment type of data here up in the top. Right quadrant

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Kevin White: and then. So you know the not only the actions that someone or some account has taken, but also the context of like, who’s behind that account. And if they’re a priority person or or if they’re like some intern in some region that you can’t sell to, which is a super critical factor to you know, prioritizing and making. You know, just running this whole types of new modern go to market motion.

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Kevin White: Okay? So that’s my spiel on setting the stage. We have like, 10 more minutes, I think so I can jump into Common room now, which is the exciting part, and show you some new stuff that we have been up to.

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Kevin White: So I’m going to jump here. And, Julia. We can see my screen here. Yes.

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Kevin White: all good. Okay, let me blow it up a little bit. There we go. Okay, so

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Kevin White: this is a view of Common Room. And specifically, we have we’re looking at a bunch of organizations that are sale that’s in our salesforce. Our our sales team can look at these organizations and like prioritize their book of business. And you know, kind of have have this as a starting point for, like, okay, what actions do I want to take within an organization? And so one thing I wanted to show, and I actually need to move this

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Kevin White: zoom. So I can see my screen a little better. There we go. So one thing we can see here is that if I click into any of these accounts, and I’ve kind of done this before. The demo here already. If I look at get Lab, for example.

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Kevin White: I can see a bunch of different signals and contacts and prospects. That common rooms pulling in, which is great like this, gives you the whole context of this account. But we have this roomie AI common room roomy is the name of our AI agents. So we we have this

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Kevin White: new feature here, which is pulling in context on the account. And if I click into this roomie research Tab, it’s essentially you can see the the endpoint here, which is a account plan. That’s auto generated for the rep. So you have things like their business model risk analysis, different hiring trends, permographic summary. And yes, you can like go to chat, Gpt, and ask one to one of like, okay for Gitlab. Find me this stuff.

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Kevin White: but the the other the piece that’s missing that an Llm model just doesn’t have is all the like rich context data of is this account using your product who’s engaging on your website like these different signals are a really big differentiator to making your go to market prospecting and prioritization like, actually effective. Okay, so

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Kevin White: we have this here. I wanted to show how you can actually build this and modify it and customize it to your own. Go to market needs. And so

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Kevin White: I’m gonna click on Nvidia here. And hopefully, this demo works out so you can see here, Roomie AI, our agent is going and thinking, and it’s pulling together all of this info that we have set for what? What an account plan looks like for to get for a rep to get into this Nvidia account.

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Kevin White: Okay, so now I’m going to go to settings here and go to Roomie AI. And here we can see the account research Tab. There are all these different new research topics here. And you can see here we have a summary of hiring trends. We have investors and funding, we have primary competitors. So these are kind of all the different buckets that we have

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Kevin White: pre-built and preloaded as like this could be. Something that is helpful context for any one of your reps to understand like, what’s going on at this account. And how do I get a warm intro into that account? And I’m gonna click on edit here, so you can kind of see the what’s going on behind the scenes.

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Kevin White: And so here you can see this. This hiring trends research prompt. We give it a prompt. The other thing that’s really interesting that we do. And this is kind of specific to back to that data problem or that data issue with AI is that we’re referencing certain tags here like this is open engineering product and design roles within this company open, go to market roles in this company, and if I wanted to add another variable to this prompt

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Kevin White: this is where the differentiator of common room really comes into play. So I can look at like hiring trends, or strategic hires, I think, is one. Yeah. New hire announcements.

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Kevin White: So I can put in different tags here that we have identified as signals within this specific account, and use this to update my prompt. And so

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Kevin White: what this will do is give us a summary of, you know all the hiring trends within any given account that we want to research, and we do the same thing, for you know what’s their earnings call or you know who is invested in this company. And so when you do that.

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Kevin White: and and I and I click on one of the accounts, it’ll automatically, magically. Just kind of fill in all the blanks for these accounts. that that we want to prioritize, or the reps want to get into. So if I go back to Nvidia here. You can see that some of these have finished finished and have resolved, and so I can see like, Oh, what’s Nvidia’s business model?

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Kevin White: And then the other thing that’s really to me is really cool, at least is that we can start to. Then Daisy chain prompts on top of prompts. And so if I go back to this account, Research Tab here.

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Kevin White: I can click on this one that I created called Trojan Horse. And this one is essentially a conglomerate of all of the difference. So you can see here that we’re referencing that business model output. We’re referencing that primary competitors output. We’re referencing risk analysis. And so we’re kind of

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Kevin White: pulling in all of those different research prompts that AI is doing and and merging it together into this other prompt. That’s like, okay, take into into account all these different things that we know about this account, and then take into account what we know about our business, and then merge those 2 things together so that we can create an account plan, a strategic plan of attack. Hence the name Trojan horse for how we get into that account.

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Kevin White: Okay, so the last thing that I wanted to show is what that looks like. And hopefully it’s done with Nvidia, for now, if not, I can go to a different one. Last thing I wanted to show is going into Nvidia here, and hopefully we have our Trojan horse. Yeah, there we go. Our Trojan Horse account plan that references. All these other

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Kevin White: different research findings is now ready. And this is now a. The 1st thing I can do as a rep is, just jump into this account and say, like, Okay, here are the 5 different things that 5 different points of entry that I can get used to get into this account. Okay? And then there’s 1 more thing that I want to show which is our personalization feature.

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Kevin White: which allows you to. I think I did the HSE one here.

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Kevin White: yes, actually, no. It’s down down here, warm intro and account blind. If I click edit here, you can see. I say, this is for sending a warm intro into that account. I’m now using this organization. Trojan horse research output to then personalize an outbound email that we want to send to someone at that account. And so real quick if I go back into. I know we’re like getting close on time here. If I go back into Nvidia

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Kevin White: and I click on my contacts within Nvidia, I can see like, okay, look at all these cool people who are ideal prospects with different scores. How do I? What warm intro should I send to this person to get into that account.

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Kevin White: If I click on one of these people, Ari here and I go to draft AI message, you can see this warm intro message that we used in the previous prompt with a Trojan horse informing it. And I click here.

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Kevin White: It’ll then create an email. It’ll it’ll build an email for me that includes things like

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Kevin White: You know how Nvidia compares to its competitors a different case study and like their their go to market strategy. This email might not be perfect. Take some prompting to make it better, even better over time. But you can kind of start to see the power of what’s possible by combining all these different reference points of AI and having AI build this orchestration for AI to, you know, do a lot of the heavy lifting that your reps are doing

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Kevin White: and if you were to do this as a rep manually, it probably take maybe an hour to do all this. We just did it here in the last, like 5 min. So pretty cool, powerful stuff. And

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Kevin White: yeah, that is

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Kevin White: pretty much it. If you want to demo this or try this or get early access. Make sure to mention sign up for common room. You can create a free account and mention that you saw it, and we’ll open up access to you.

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Kevin White: and that’s all I got.

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Julia Nimchinski: Amazing as always, Kevin. Let’s just address the questions that the community submitted before here. One of them is how common rooms AI distinguish between genuine buying signals and random customer behavior.

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Kevin White: Can you repeat that? How we distinguish between

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Kevin White: random buying signals and customer behavior that would.

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Julia Nimchinski: Between genuine buying signals and random customer behavior.

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Kevin White: Oh, okay, okay. So I think the

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Kevin White: it’s kind of in the eye of the beholder. Here. Is. What I would say is that, you know, one signal could matter a lot to you like a website, visit or product data. And that could be like really high intent. And that is some signal that your team uses and knows and that that works well, whereas, like, maybe you know, a keyword intent data, or like a Gt review is.

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Kevin White: you know, it’s it’s a signal. Yes, but it’s not like super actionable. And so like, I feel like, what you need to know is just kind of like what signals work for your company. We have Roi reporting that tells you, okay, these signal plays are converting at this rate. So you can kind of like stack ring and understand that over time. But yeah, I think the starting point is actually just knowing your your business and like, what signals? What triggers? Someone to be a good customer, and then using that signal as a starting point.

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Kevin White: But we can pull them all in is the thing. It’s just the the eyes and the beholder of who, how to prioritize that.

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Julia Nimchinski: Quite subjective. How soon will AI surface Gtm. Opportunities before teams even look.

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Kevin White: Sorry. Can you repeat it again.

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Julia Nimchinski: Yeah. How soon will AI surface Gtm opportunities before teams even? Look.

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Kevin White: Yeah, I mean, interesting question. I think

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Kevin White: eventually we’ll get there. I mean, you know, with common room essentially like we’re pulling in all that signal, and then and then, once you unleash the AI on that. It’s like, okay. We know from this pattern matching that like these plays have worked in the past. Therefore, like we find this other signal, then, you know, here’s the accounts that we’re recommending you get get into and like that’s the kind of like the vision we’re building to, for sure. So I think you know, in the near future, you know. Call it

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Kevin White: 6 months to a year like it’ll just be like, Oh, log in! Here are the accounts that you care about, you know. And here’s how to go. Go get into those accounts, you know.

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Julia Nimchinski: Exactly. That’s what we were discussing with G. 2 CEO today, Goddard

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Julia Nimchinski: out there revamping the whole ui ux. And it’s really uncertain what will happen to cannabis?

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Julia Nimchinski: Yeah, categories. Yeah.

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Kevin White: Yeah, it’s a fun market. Everything’s kind of like mixing and melding together. And you know it’s fun. You get lots of competition, which is hard, but then it’s also kind of fun, because, like, there’s a market here, and everyone’s kind of like chasing after the same thing. So it’s fun.

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Julia Nimchinski: Exactly last question here to be respectful of your time. What other tech stacks

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Julia Nimchinski: tech stack tools does this integrate with? And how do you make it even more powerful?

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Kevin White: Yeah, yeah, I mean, so the

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Kevin White: there’s a few I mean, there’s a lot that we integrate with. There’s both like these signal providers like Linkedin, or your website visit, or your Crm, or you know, snow like a data warehouse or communities like these are all signal part

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Kevin White: technology that we pull signal in like, these are all different things of people interacting on these different platforms. That’s good signal for you to take action on and then there’s like the data out piece, which is, you know, I have this signal based play. I now wanna like run a sequence like an outbound sequence using outreach or sales locked, or Hey, Jen? Or something like that. So yeah, I think I mean, there’s a pretty vast library of integrations we have here. So I would say, check out common room, and you can kind of tinker and explore there.

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Julia Nimchinski: It was a great demo, as always. Kevin. Thank you so much, and looking forward to see you again tomorrow at gentech gta.

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Kevin White: Oh, yeah, that’s great. I’m on a panel tomorrow. So plug for that. Thanks. Everyone have a good one.

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Julia Nimchinski: Thanks.

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Julia Nimchinski: A. I. Nbc.