Signal-Based Pipe Gen

[ 01:31:20 ] Julia Nimchinski: Kevin, signal-based pike, Gen.
01:31:22.980 –> 01:31:32.763
Kevin White: Alright. Yeah, it’s a tough follow following Patrick. I mean, he’s got I mean, he’s got really great Linkedin content. And I’m a big big fan of Patrick and Clay also.
01:31:33.543 –> 01:31:42.020
Kevin White: so I have. Okay, maybe I’ll I’ll draw on you, Julia. And Justin, of just like how to how to format this. So I’ve got like
01:31:42.490 –> 01:31:54.110
Kevin White: I try not to make too much presentation time. So I’ve got like 10 min of presentation. I also want to do like a pretty like in depth fun, demo and then leave some time for questions at the end. Is that, like generally a good format.
01:31:54.280 –> 01:31:54.660
Julia Nimchinski: Perfect.
01:31:54.660 –> 01:32:05.659
Kevin White: Cool, cool, cool. Okay. So I will just dive right into the presentation side of things. If I can figure this out. There we go. Okay, you can see my screen. Yes.

[ 01:32:05 ] Kevin White: my face. Okay, cool. So
01:32:10.440 –> 01:32:28.200
Kevin White: so great to be here. This is the second time I’m doing one of these events with the the Hsc. Team and I. My name’s Kevin. I lead the marketing team, and also the Sdr. Team, which is a different topic in itself. But really both teams, that common room kind of view ourselves as the pipeline engine for the company

[ 01:32:28 ] Kevin White: in my talk today is around AI prospecting methods. And it should be a good segue from from what Patrick just talked about into like these micro personalized campaigns, and how to do that at scale. It was like one of the last questions that came up. So seems like, really good really good sequencing here.
01:32:46.408 –> 01:33:03.059
Kevin White: The thing the place I want to start at is just the problem with personalization, today are like what’s been historically known as as personalization, or what you maybe think about is like, to be frank, just like shitty emails like this. I feel like we get these
01:33:03.070 –> 01:33:05.190
Kevin White: every day. They’re obvious. They
01:33:05.210 –> 01:33:11.710
Kevin White: they all are about what’s in it for me as the the sender, and not about it. What’s in what’s in it for the recipient.
01:33:11.970 –> 01:33:26.220
Kevin White: and they add not a lot of value, and I would argue that they offer, or that they are harmful to both the sender and your brand reputation. And one interesting thing is that AI is exceptionally great at doing this, which is, in the sense of
01:33:26.220 –> 01:33:48.850
Kevin White: it, creates highly highly scaled, lightly personalized, outbound messages that are commoditized. By commoditized. I mean, it’s using data inputs, to to get a bad output, which is the data inputs are just like what’s scrapable on Linkedin. It’s like what you know. Where do they work? You know, all this commoditized data. And so like, what we want to get at is something that’s a little bit better than better than that.

[ 01:33:46 ] Kevin White: I essentially see, like AI using AI for this. This approach that we all think is like we, we know, is kind of dead. The predictable revenue playbook approach as being like this way to scale a spam cannon which we know doesn’t work and so I think we all don’t want to like, we’ve experienced these types of emails. We don’t want to live in this world anymore. So all this begs the question of you know, what does a good email look like then?
01:34:14.960 –> 01:34:32.040
Kevin White: And so, I’ve crafted what I think is a pretty good outbound message here. And it does a few things differently from the previous example. It calls out someone of importance at the top. It references a company priority that has been researched.

[ 01:34:32 ] Kevin White: It shows the receipts in the form of a quote from the the stakeholder. It shows that the person on the other end did the homework like they know what’s going on in the business of the recipient? And then it ties a pretty strong educated guess to a pain points. And so, in short, I feel like this is this type of email is helpful and not self serving. And so the way, how do you like?
01:34:55.370 –> 01:35:06.862
Kevin White: I’ll go back. How do we get to this email? And I, I really like this new phrase, I also grew up with micro machines. So like this is the AI generated version of of micro micro campaign. So this is a
01:35:07.310 –> 01:35:32.499
Kevin White: a phrase that I’ll give credit to Brendan Short, who’s also a really great great follow on Linkedin. He kind of at least I heard about this term from him, and it’s essentially like the way that you get to these good emails is creating micro campaigns. And so what is a micro micro campaign? Let’s define this real quick and to me it’s a small, highly targeted list of prospects or accounts like call it 50 to 200.
01:35:33.238 –> 01:35:42.949
Kevin White: They’re generated using a very specific trigger or signal, if you will, combined with some criteria so it could be like there’s a
01:35:42.950 –> 01:36:05.749
Kevin White: a hiring spree going on in that company, or there could be. You know, they’re moving to a new location, or these types of things that are like moments in time. And that brings me to like the last point of what a micro campaign is like. They’re usually like short. They have a short half life, because, like, it’s essentially a signal that this company or this person is in market for something, and like that time period is probably like
01:36:05.750 –> 01:36:07.290
Kevin White: 30 to 60 days.

[ 01:36:07 ] Kevin White: And so when you have all these things, then you can create that gives you the the baseline to create a really good email like this. And so essentially like what AI can do. And where AI can help is like we can define the audience with AI and with software, and then fill in the blanks of
01:36:28.390 –> 01:36:45.189
Kevin White: these different areas where there’s like the brackets and like your value prop and stuff like that. And so this is like, actually what I’m going to go in and build today as part of the demo which should be fun. But there’s a few things that I wanted to cover before, like going directly into a demo of common room.
01:36:45.740 –> 01:37:13.060
Kevin White: So let’s also start with, like the areas that, like what AI needs to do to help to get to this qualify like really strong outbound message that should convert and so the things here that that the bar that we need to hit is the the data points that we need to hit. To get to this message is we need to do research. We need to like dig into articles or different different things, different signals that are happening within a company or or at the person level.
01:37:13.647 –> 01:37:38.939
Kevin White: We need to identify those. The signals that we’re getting from those companies could be a website visit. It could be, you know, they’re they’re opening new roles in a in a region. We need to find the right stakeholders. Once we have that information like. So not only is this thing happening, this priority happening at this company, but like, who is the person who’s a stakeholder that is driving that that motion, or it’s like the the person that’s responsible for that outcome.
01:37:39.265 –> 01:38:08.180
Kevin White: Then we need to like personalize a message combining all those things. What’s the signal? Who’s the prospect? And like? What message should we send to them that they will care about and react to? And then you need to be able to like action that you need to be able to send them an email, call them up on the phone with that context. Send them a Linkedin message you need to like, do some sort of action like this is kind of like the end to end prospecting workflow that AI can certainly help out with. And it can help you scale these like really good emails.

[ 01:38:07 ] Kevin White: And so
01:38:11.550 –> 01:38:19.740
Kevin White: I guess I kind of already covered this. But like, can these are the things that like check, check, check, check, check. AI can help out with all these things. And
01:38:19.740 –> 01:38:47.980
Kevin White: you know, I used to have this this slide I used to have would say, the AI can be a co-pilot of doing all these things. But actually, it’s becoming much more like the the invoke word is agentic now, so you can almost have the AI go out and like, do these things for you and kind of like, serve up these audiences on a platter. Suggest personalized, suggest personalized copy to use for outbound messaging versus like you typing it in. So AI is doing actually a lot more of the work here. And that’s also some of what I’m gonna share in the demo.
01:38:49.130 –> 01:39:07.939
Kevin White: so almost time for me to jump in there like not too much more presentation left, but I wanted to share, like a few concepts or prerequisites, if you will in order to make this whole thing work. And the 1st thing I like to highlight is just the concept of quality data in equals quality data out. And so, in other words, you need to train your model with quality inputs.
01:39:07.940 –> 01:39:23.399
Kevin White: That is like buying signals or and also the personas behind those buying signals for the AI to use. And this is really, really key to making sure that you don’t just get like the generic commoditized data to train the model which ends up in the spam cannon. We don’t want to end up in that area at all.
01:39:23.660 –> 01:39:29.973
Kevin White: And so this is some stuff that common room pride. We pride ourselves at Common Room like what we do is
01:39:30.400 –> 01:39:59.629
Kevin White: one is capture. These really interesting bespoke signals that are much more strong in terms of buying intent than just your typical like. Oh, I looked up your your Alma mater on Linkedin and also saw that you changed jobs recently or something like that. There’s a lot more signal and a lot more input that you should be feeding into both the account and the prospect level. And this is the quality data that you can use to personalize, outbound at scale in an effective way.
01:40:00.885 –> 01:40:02.035
Kevin White: Oh, also,
01:40:02.750 –> 01:40:30.350
Kevin White: I don’t know if we can link this in the in the notes here. But I often get the question of like, Okay, what signal should I start with like, how? What signal should we capture? And so, I created this this resource that people tend to like, which is 100 signals that you can use. So we can share this, or you can just go to this. URL. I think it still works. And this is a great starting point to see like, oh, here are all the different inputs that we could consider that we want to capture to power our, you know, personalized, outbound or prospecting motion.
01:40:31.110 –> 01:40:46.659
Kevin White: Okay? And then the last thing here. The other last thing here, as a prerequisite is not only do you need to know the signals like the the inputs, the triggers that are happening at the account in person level. But you need to like, unify all of those into
01:40:46.660 –> 01:41:10.980
Kevin White: a single user profile and then roll that up into a single account. So when you’re looking at the account level, you have this holistic view of like, here’s all the people from this account who have visited website. Here are all the people on this account who are using our product or are engaging with our with our CEO on social channels, or have changed jobs recently or former product users. And so when you can aggregate all this into one place essentially as a
01:41:10.980 –> 01:41:29.310
Kevin White: repository, the data foundation, if you will. That’s why I have the mountains here is like this is the data foundation for the AI to to do its job. You have that all in one place, and it’s unified so that you’re not getting these like one off. Weird unpersonalized AI spam canopy type things. And so
01:41:29.900 –> 01:41:47.309
Kevin White: so those are the prerequisites. And now let’s kind of like jump into common room to show how you can do this. In a demo. So I’m going to stop slideshow presentation here. And Julie, can you see this screen here, like, common? Okay? Great.

[ 01:41:47 ] Kevin White: okay. So this is, I’m gonna start at the end here a little bit in common room. So here we have. These are essentially like packaged up. Let’s call it micro campaigns for the sake of this talk. And so here we have pricing page visits from economic buyers in the last 28 days.
01:42:03.580 –> 01:42:26.729
Kevin White: You know, we’re packaging all this up with pulling in signals via AI, and then recommending putting these these prospects if you will, on a platter for an A an Sdr team, or someone to like reach out to these people. So you know, you can do this at scale, you know. Send them to an outreach sequence. And like that is essentially like a workbench, or like a prioritized view for reps to go through their day.
01:42:26.730 –> 01:42:51.030
Kevin White: But to get to the example that I was sharing more in in the in the deck in the presentation. We, you know you can stack rank all these 2. But we have this view here of, or it’s an org view. It’s an account view with Sdr. Roles who are open that are open in Europe. And let’s hope this loads here, there we go. And so from here I might want to do a little bit more research and personalization, so
01:42:51.160 –> 01:43:18.110
Kevin White: I can click on any given account. Here, let’s click on amplitude. And then from here I can say, Okay, here’s all the context. Here’s this like unified view of what’s happening within amplitude as a rep. I might want to like, get this context to personalize an outbound email. But let’s say, like, okay, because we know that they’re already in this micro campaign, like we know someone we know. Amplitude has open roles from the S. In the Sdr. With Sdr. In the title in in Europe. Then we can just
01:43:18.110 –> 01:43:25.660
Kevin White: craft that personalized email. And so I can click on contacts here and see which of the contacts that I might want to reach out to
01:43:26.214 –> 01:43:38.860
Kevin White: we don’t have different filters here like this is just all the contacts that we know we found signal on from amplitude, so I can add another filter here for, say, make sure that we’re reaching to an economic buyer pretty powerful stuff.
01:43:38.860 –> 01:43:57.329
Kevin White: And then, you know, maybe Jessica looks good, or Francis looks good. I can click into Jessica. I can see that unified view of all of her signals and what we’ve what activity we found on on Jessica. And then this is kind of like the the magic AI part. We can click on this. Generate a message for me based on these signals.
01:43:57.573 –> 01:44:15.380
Kevin White: And as you can see here, it looks like you have 23 open roles in Europe exciting times, and then we kind of it’s not the perfect email like I shared before, because Common room doesn’t have like a Gdpr like specific type of thing. But you can. You can get to it. You can start to see that you get to this point of personalization where you create these really
01:44:15.450 –> 01:44:30.489
Kevin White: specific bespoke emails. And you can like emails or outbound messages. Then you can. You can send that at scale. Okay, so and also, I wanted to share that. You can also like, add this to outreach to it’s not just generating a message you have to like, copy and paste. So
01:44:30.790 –> 01:44:42.460
Kevin White: okay, so how the question is like, how do we get to this? How do we generate? Get to that generated message? And how do we get to this micro campaign? Audience? And so that’s where kind of like the power of
01:44:42.670 –> 01:45:08.600
Kevin White: aggregating all these signals into one place, and then unifying the identity or the filling in like doing the waterfall enrichment behind the scenes with AI is what common room does and where you can like actually filter down to this specific micro campaign. So as I click on this view of organizations. We have the same thing, for you know, contacts and organization objects. So contacts would be, you know, we found signal on this person organizations are we found signal on this organization like
01:45:08.600 –> 01:45:25.919
Kevin White: they’re hiring, or they’re they’re moving to a different region or something like that. But we, you can see here we have 57,000 organizations in this view, which is, you know, information overload. You want to narrow this down to the specific micro campaign, so that the AI can like do its thing on top of that specific
01:45:25.920 –> 01:45:36.639
Kevin White: cohort of organizations. So to to do that we can add all these filters on both the criteria of the accounts we’re looking for, and the signal that we’re seeing from those accounts.
01:45:37.060 –> 01:46:04.849
Kevin White: So I’m going to say, add filter here. And I’m actually going to create a filter group which is multiple filters in one thing, I think you get the point as I go through this. But I’m click on organizations here, and I’m going to say so. We want to see organizations who have open Sdr roles. And so I’m going to click on. Add filter here. And there’s this menu item of like all the different things you can filter by. But it’s nicer to type things in versus trying to find them on this list. So I’m going to say, title job title listing
01:46:04.850 –> 01:46:11.650
Kevin White: is any of Sdr, I’m gonna select all of these, and I’m gonna add another one for sales development
01:46:12.240 –> 01:46:15.200
Kevin White: job title listing is sales development.
01:46:15.640 –> 01:46:22.990
Kevin White: I’ll do that. And then maybe let’s add another one for business development or Bdr business
01:46:24.240 –> 01:46:29.150
Kevin White: Dev. We have some of them already showing up there cool. And then I’m gonna apply this filter.
01:46:29.350 –> 01:46:43.159
Kevin White: When we apply that, we’ll see this number of organizations go down to only those with a opening of a job opening that has this title in it. There’s 3, 57 still a little bit too high for a micro campaign here.
01:46:43.423 –> 01:47:10.559
Kevin White: We just added some new Ui improvements, and I’m seeing a little bit of like a off thing here. But I’m just being the picky about our product. So that’s what you’re seeing here. It’s just a little bit of Ui issue. But the other thing we want to do is click on. This is to add that they’re hiring for these roles in a specific region, because that region is also compelling to the person reaching out to who’s like hiring in that region. So to do that we can add another filter here for like
01:47:10.560 –> 01:47:37.700
Kevin White: for the we’ll call it like a news category. And so we’ll look at this is like a grouping of, like all the announcements and public data from this company that’s announced externally. So we have grouped these into like AI groups, these into like different cohorts of actions that are happening like fundraising. Or they’re they’re hiring. Or they’re expanding offices into somewhere. So let’s choose those. So I’m gonna say, expands offices in
01:47:38.213 –> 01:47:43.860
Kevin White: expands offices to opens new location increases headcount expands facilities. Now.
01:47:44.519 –> 01:48:11.219
Kevin White: just that alone is not interesting because it could be did any region we haven’t specified on the region yet. So I want to narrow this list down even further to the specific region. I can do that with with this which we call it a a conditional filter. So I’m gonna add another filter to this, which is news keyword and just say anything that any of these news mentions that that include keywords like Europe.
01:48:11.835 –> 01:48:19.939
Kevin White: For say, like Gdpr compliance or London. I think the Dublin is a hotbed for Sdrs. I’ll do Germany
01:48:20.080 –> 01:48:21.570
Kevin White: Netherlands.
01:48:21.930 –> 01:48:28.840
Kevin White: and I think you kind of get the point. Just keep on. You can continue adding more and more here to to cover the areas of Europe that you’re interested in.
01:48:29.230 –> 01:48:56.189
Kevin White: I’m gonna say, apply conditional filter. And then from here we get this organization list. Oh, down to one. Usually it’s a little bit higher. I probably just didn’t include enough things here. And then the next thing that we can do is not only do we want to see the number of job listings open for Sdr, we want to see, like the quantity we can kind of see the quantity here. But to create this specific field.
01:48:56.461 –> 01:49:18.699
Kevin White: That is mentioned. I should go back. I’ll go back to and show you how like where it’s mentioned in the in the next as I like. Go through more of the demo here. But you can. You can create this number of like number of Sdr roles open through an advanced feature called Calculated Fields, and so to do that we can say, we can create a new custom field. And we’ll say, like Sdr roles, open
01:49:19.690 –> 01:49:24.350
Kevin White: oops. I spelled that wrong roles role openings.
01:49:25.030 –> 01:49:28.319
Kevin White: and then we’ll say calculated next.
01:49:28.872 –> 01:49:41.540
Kevin White: And we’ll say job listings. And then you can define that same criteria that we had. So we can say, job listing title is Sdr and then we can save this. That’ll create this new field.
01:49:42.165 –> 01:50:04.509
Kevin White: I’m not gonna save it now, because I’ve already created it, and what that will get you is to a place where you’ll get this number of Sdr roles open. I can click into this and just validate that you know these are the Sdr. Roles that amplitude has open. And so once we get to this place, we can save this as a micro campaign, which is what is then showing here. And then we get to this place of like, okay.
01:50:04.510 –> 01:50:27.499
Kevin White: we’re gonna we’re whoever owns these 23 accounts. We’re gonna have them like, go in and create those really bespoke emails. Via AI, using that using that calculated field that we just created. And the last last thing I wanted to show is like, Okay, how do we actually use the right prompts to get that output of that, the email? And so. We have this feature. We call it roomy. AI.
01:50:27.640 –> 01:50:52.430
Kevin White: And I created this prompt here for this HSE demo and here you can see that we’re using this this custom field. We want the AI to reference this field that we just created, which is that they have 23 open Sdr roles, and we have a lot of other prompting here. That you can use to to. So the the AI can look at his prompt and that create the kind of message that you want, that like really quality message that I shared in the presentation
01:50:52.988 –> 01:51:08.760
Kevin White: and then also shout out to we have a a prompt library. If you go to Common Room, and you want some prompts like this, prompt here is inspired by the prompt our prompt library. That you can just copy and paste and use these for your own prompts, whether it’s in common room or somewhere else.
01:51:09.226 –> 01:51:17.290
Kevin White: And so so once you have this prompt then you can kind of connect all the dots of for this micro campaign.
01:51:17.640 –> 01:51:31.400
Kevin White: and we can get to this place where where I showed in the the initial the initial demo. So the thing I wanted to highlight is here. We have this Sdr roles. Open that 23 number here. And then the more magic is when you
01:51:31.480 –> 01:52:00.539
Kevin White: go to outbound to this person. And you reference that specific that specific like data point that was calculated. And you can see here that it says looks like you have 23 open Sdr roles in in Europe. And that’s like the hyper, personalized, like specific micro campaign thing to call out in addition to this other context here. And so you know, hopefully that ties together all the kind of things that need to happen in order to send these kind of like really quality messages at scale.

[ 01:52:00 ] Kevin White: And yeah, it’s really cool to see that like you can, you can do all this in in one platform and like these things are all becoming possible now, so I’ll pause there, and we can take questions for the next 5 or so.
01:52:14.610 –> 01:52:21.609
Julia Nimchinski: Really love the holistic approach here, Kevin, and the educational aspect, and generally that you can do it
01:52:21.710 –> 01:52:28.129
Julia Nimchinski: all together in one platform we have a question from Russell Sherwin, and the question is.
01:52:28.400 –> 01:52:35.760
Julia Nimchinski: see similarities in your messaging with ample market. How do you compare or contrast your offering with them and others.
01:52:36.830 –> 01:52:50.890
Kevin White: Yeah, I will get this question a lot, because, like, the good thing about being in this market is that there’s just like tons of competition. Or I don’t know if it’s a good thing, but it’s it’s it validates that there’s actual market here. And so we see companies like
01:52:50.890 –> 01:53:07.620
Kevin White: ample market, or koala, or unified. Gtm, come up a lot. I would say, like the the thing that I always go back to is more of these foundations of what Common room does really? Well, and so there’s the the 2 things
01:53:07.620 –> 01:53:20.439
Kevin White: that are. These prerequisites are mostly differentiated from, or I mean, I would say a hundred percent differentiated from a lot of the companies out there. I would say the breadth of our signal capture, things like open source signals.
01:53:20.440 –> 01:53:45.390
Kevin White: job change signals, product data signals. These are things that a lot of other vendors don’t offer this like full suite or full spectrum of signals. I know ample market does offer certain things like Linkedin data. And maybe, like job change data, but not the full breadth of these, you know, tons and tons of signals. And I think that’s really key to like, be able to pull in everything together so that you can kind of get like a full holistic lay of the land
01:53:45.390 –> 01:53:49.679
Kevin White: end of like what buying signals accounts are, accounts are people are showing.
01:53:49.910 –> 01:54:17.640
Kevin White: and then the other part that is often differentiated in Common Room. Is this like this identity resolution piece? So it’s not only knowing the signals, but like, if the signals are all siloed or they’re like this is one signal from one user with this unique unique handle or unique identifier. And you can’t connect the dots to like this other signal with this other unique identifier, so that you can move, move it all, move, consolidate all the signals into one persona or one account.
01:54:18.016 –> 01:54:36.369
Kevin White: That piece, if we call it identity resolution is actually really challenging to do and so we, we have invested a ton of R&D into doing that at the at the forefront. And so typically, like, we’re really good at that match rate and identity resolution which is really key to being able to execute these types of micro campaigns.
01:54:37.940 –> 01:54:44.190
Julia Nimchinski: Next question, what’s an example of AI helping a team? 10 x their Gtm output.
01:54:45.750 –> 01:54:50.132
Kevin White: What’s an what? So I don’t know if this is 10 x
01:54:50.670 –> 01:55:13.351
Kevin White: but it’s it’s pretty high. So this customer story from that we have with Semgrep. Jason is actually a person who I worked with at segments and we essentially started like back in the day we were building these micro campaigns off of product data and using tableau and all these tools that are not super optimized for this. But he kind of like is was trained on this background of, you know,

[ 01:55:05 ] Kevin White: identifying these triggers and signals and then like using them to do outbound effectively. And so what his team at Semgrip is doing. He’s the. He’s the Sdr. Team lead there.
01:55:24.090 –> 01:55:47.900
Kevin White: and they’re they’re running 6 different signal based plays from open source signals from product signals from signals on Linkedin. And the way that they 10 exit is their methodology, which is essentially like, okay, we have this really strong hypothesis of, you know, these product led signals are the signals that we want to start with, because they’re like closest to the end of the buying funnel, and those are the highest converting. And so we’re going to run
01:55:47.900 –> 01:56:10.750
Kevin White: different plays and validate those plays through Sdr team. And then once we feel like we’ve optimized those signals. Then they’re going into other external signals like open source signals because they’re an open source developer tooling company. And then they’re running that same kind of like growth, experiment, methodology, and stacking those different plays on top of each other. And so what Jason and Team have done is, I think, that they have 15 different signal based plays.
01:56:10.750 –> 01:56:39.930
Kevin White: And they just keep on like running that same playbook over and over again to get like, really, really strong pipeline returns because they’re doing it in this systematic and scaled way. And so like, I think that methodology is a way to 10 x versus, you know, trying to like, do everything at once or like. There’s no magic bullets, and they’re just doing things the right way, and they’re able to predictably scale by doing that. So I would read this story and use that as a blueprints, for you know how to how to get the most pipeline and revenue out of this approach.
01:56:40.800 –> 01:56:51.360
Julia Nimchinski: Super cool question from Claudia. Can common room help identify prospects, for example, find certain companies based on certain criteria, for example, industry, location, etcetera.
01:56:51.720 –> 01:56:54.186
Kevin White: Yeah, I didn’t really get into this in the
01:56:54.900 –> 01:57:23.200
Kevin White: in the demo. But we do have the ability to use all of our filters and enrichments to identify different prospects. Based on, like all sorts of different types of criteria. And so one really cool use case for this actually to to automate it is we run, we run this plan. Our customers run. This play is like, we’ll see an account that’s visiting our website that’s in our like, our identified like target account list.
01:57:23.492 –> 01:57:48.059
Kevin White: But we don’t know the person behind it at some times, because, like we, we don’t have like identity. IP address information on that person or whatever and so we’ll we’ll track that as a signal on the account level like, oh, this account visited your website, and then we’ll call this prospecting tool as an automation to say, like, Okay, within this account, here are the people who are most likely to be visiting our website and care about our value prop
01:57:48.060 –> 01:57:54.840
Kevin White: or whatever. And then we’ll prospect those people, and then we’ll we’ll also build an outbound automation to those people as well. And so
01:57:54.840 –> 01:58:02.230
Kevin White: the combination of being able to get an account signal and then prospect is something that common room does. And it’s also a super powerful way to, to automate things.
01:58:03.730 –> 01:58:08.990
Julia Nimchinski: Another question, how clients in the cre industry use their products.
01:58:09.727 –> 01:58:34.380
Kevin White: I’m actually not familiar with the cre cre industry. I will say, though, that the best fit customers for us right now are the ones that have a lot of signal, and so that is oftentimes the ones like the customers like Zemgrep, which have, you know, open source and product led signals like this is just like a wealth of signal that you can use if you have lots of website visits, or if you have a strong Linkedin presence.
01:58:34.380 –> 01:58:52.499
Kevin White: our social presence. That’s another source of signal. And so that’s typically where we find companies have like a lot of success. And it tends to be these like open source companies, product led companies or companies that have, like a sales led approach, but lots of awareness and other signals that are generating across different breadcrumbs across the Internet.
01:58:55.850 –> 01:59:03.899
Julia Nimchinski: Trying to select the last question here. So what’s the key to turning unified signals into actionable insights for teams.
01:59:05.963 –> 01:59:06.830
Kevin White: Okay, so
01:59:06.960 –> 01:59:17.499
Kevin White: this, this is more to me of a marketing question of, like, you know, our go to market strategy question, which is like, what’s the key to to making signals work. And I think it’s
01:59:17.500 –> 01:59:40.909
Kevin White: having empathy like having empathy for the end user is the key to all of this is like, if you put yourself in the other person’s shoes, and you understand what their pain points are, then you can figure out what signals are they showing that infer that they’re experiencing those pain points and then reach out to them in a way that’s like here. I’m here to be helpful and solve those pain points. So I think about it more from like a 1st principles point of view, and to me it all comes down to empathy and understanding, your your audience.
01:59:41.960 –> 01:59:49.560
Julia Nimchinski: Thank you so much, Kevin. This has been a blast we shared your repository of signals in the.
01:59:49.560 –> 01:59:49.920
Kevin White: Awesome.
01:59:49.920 –> 01:59:50.970
Julia Nimchinski: You see? Slack?
01:59:51.400 –> 01:59:55.359
Julia Nimchinski: What else? Any updates? Where should our people go?
01:59:55.430 –> 02:00:18.480
Kevin White: Oh, I have this slide here. Follow me on, Linkedin. I’m talking and posting about this stuff quite a lot. I do a lot of like live demos via loom to show new features and like cool use cases. And so that’s pretty much it. And then you also, we have a we have a free trial of common room. Anyone here? That signs up and mentions my name or mentions the the
02:00:18.540 –> 02:00:29.730
Kevin White: the event here, or Hsc. I can extend that trial to 30 days for you. No prob. So be sure to like mention that when you’re filling out the form to sign up, so appreciate everyone and and thanks so much. Team.
02:00:30.460 –> 02:00:31.320
Julia Nimchinski: Thanks. Kevin.
02:00:31.800 –> 02:00:33.070
Kevin White: All right. I will leave.