About this episode
Eddie Reynolds and Rachael Bueckert, revenue operations leaders at Union Square Consulting, tackle the gap between having functional dashboards and actually using data to drive revenue decisions. Eddie's background includes driving go-to-market at Salesforce as the company grew from $5B to $10B revenue, and he now helps B2B tech companies close the gap between "workable data" and "actual decisions." Rachael brings operational rigor to the conversation, having worked through the challenges of metric definitions and attribution tracking across multiple companies and initiatives.
The core insight is simple but profound: Perfect data is not the goal; the goal is to have a dedicated person with time and accountability to turn data into insights that move the business forward. Most companies skip this step entirely, building dashboards that no one actually uses. Eddie points to Salesforce as a live example: even with near-perfect account and billing data, contact data was "an absolute dumpster fire" because it wasn't strategically important to the business. The same trade-off applies everywhere.
The reframe here is that companies should embrace imperfect data as a feature, not a bug. Rather than delay revenue impact to achieve data perfection, organize regular go-to-market council meetings with a dedicated analyst or rev ops leader who owns the insights. This person closes Slack, blocks their calendar, and sits with the data for three hours every Thursday. Yes, the first ten meetings will be arguments about where numbers come from and what definitions mean. But by meeting 100, you have a well-oiled machine that turns metrics into hypotheses into action. Eddie and Rachael explain why this discipline matters more than the dashboarding tool itself, and they walk through specific frameworks for doing it: segmenting close rate, deal size, and sales cycle by customer type; reading rep performance right-to-left to diagnose pipeline problems; and using AI to enrich raw data with call transcripts and email scoring. They also reveal how USC built their own proprietary AI-powered dashboard with Claude that can ingest data via Model Context Protocol (MCP) from Salesforce, HubSpot, Google Analytics, and other sources, without needing heavy integrations. The dashboard enables rapid toggling between time periods and custom bucketing, and Claude answers follow-up questions in seconds. The free PDF guide and demo dashboard are available for any B2B tech company to learn from and replicate.
What we cover in this episode
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1:40
The gap between data and decisions Why most companies build dashboards nobody uses, and what actually bridges the gap to revenue impact.
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6:12
Perfect data is the wrong goal How Salesforce grew $5B to $10B with messy contact data, and why imperfect data beats delayed insights.
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8:37
Who owns dashboard insights CRO vs. rev ops ownership, and why both need to live in the data with accountability and dedicated time.
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14:20
The GTM Council meeting structure Weekly or monthly cadence where a dedicated analyst brings hypotheses, not just dashboards, to revenue leadership.
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17:33
Why USC built an AI dashboard Moving beyond Salesforce limitations to see data across all systems and toggle metrics with AI speed.
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22:09
Data ingestion via MCP not integrations Using Claude to ingest Salesforce, HubSpot, Google Analytics, and custom data sources without heavy field mapping.
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31:16
Pipeline metrics by segment Close rate, ASP, and sales cycle analyzed across SMB, mid-market, enterprise, geography, and industry.
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35:28
Reading rep performance right to left Start with quota attainment, diagnose pipeline problems, then drill into activities and messaging with AI.
The lines worth sharing
The gap between good data and actual decisions is that you have a dedicated person with real accountability to produce specific outputs.
Eddie Reynolds · 6:06
There's this age old conflict: do you want perfect data or do you want to grow revenue? Salesforce grew from $5B to $10B with messy contact data.
Eddie Reynolds · 8:53
We don't have to do all these integrations and field mappings. We can just ingest the data via MCP and put it into the dashboard.
Eddie Reynolds · 26:39
The first place companies can improve revenue is by doing a better job of looking at their data and taking action on it.
Eddie Reynolds · 26:11
Common questions from this episode
What is the main gap between having a good dashboard and actually using it to drive revenue?
The gap is a dedicated person with time and accountability to analyze data and bring insights to leadership meetings. Without this, dashboards sit unused. This person should block weekly calendar time, dig into metrics, form hypotheses, and present actionable conclusions to the go-to-market council.
Why is perfect data the wrong goal for go-to-market analytics?
Perfect data delays revenue impact. Companies like Salesforce grew from $5B to $10B with messy contact data because they prioritized shipping and growth over data perfection. The trade-off is intentional: imperfect-but-available data beats perfect-but-delayed insights.
Who should own the responsibility for turning dashboards into decisions, the CRO or rev ops?
Both. The CRO has accountability for revenue and should live in the data. Rev ops should dedicate time to analysis and bring insights to the CRO. Zoom's former CEO Tim Strickland said: if rev ops doesn't bring insights to the CRO, they're not doing their job; if the CRO doesn't review that data, they're not doing theirs.
What is a GTM Council meeting and how should it be structured?
A weekly or monthly meeting with revenue leadership (CRO, CMO, head of CS, finance) where a dedicated analyst presents data insights, not raw numbers. The analyst brings hypotheses on what's working, what's not, and recommends actions. This creates accountability and forces the team to act on data insights.
How does USC's AI dashboard improve on traditional Salesforce dashboards?
It ingests data via Model Context Protocol (MCP) from multiple sources (Salesforce, HubSpot, Google Analytics, custom files) without heavy integrations. Users can toggle between time periods in seconds, ask Claude follow-up questions, and bucket metrics in custom ways that match the business model.
What are the three core pipeline metrics to analyze by segment?
Close rate, average deal size (ASP), and sales cycle. Segment these by customer type (SMB, mid-market, enterprise), geography, industry, and rep. Use right-to-left reading: if a rep isn't hitting quota, diagnose whether the issue is pipeline generation, conversion, or activities.
SEO meta description
Eddie Reynolds and Rachael Bueckert on turning GTM dashboards into revenue decisions: how to close the gap between data and action, why perfect data kills growth, and how to build your own AI-powered dashboard.
Target keywords
GTM dashboard
go-to-market metrics
rev ops analytics
pipeline metrics
sales cycle analysis
AI dashboard Claude
data-driven sales
Union Square Consulting
GTM Council meeting
revenue operations decision tree
close rate by segment
outbound sales metrics
leadership dashboard
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Read the full transcript · 67 KB
EDDIE0:00we want to make meaning of our data. And, like, how cliche is it to just build a bunch of dashboards that people don't look at?
EDDIE0:06I think we all need to embrace AI,
EDDIE0:08I want to embrace AI to drive revenue.
EDDIE0:10What I don't want to do for our clients or for ourselves
EDDIE0:13to go spin up a bunch of agents
EDDIE0:15that I can tell everybody how cool it is that we built a bunch of agents,
EDDIE0:18I've got nothing to show for it, no revenue.
EDDIE0:21the first place that I think companies can improve revenue is just by doing a better job of looking at their data.
EDDIE0:26go and fix all of that data in Salesforce,
EDDIE0:28in HubSpot,
EDDIE0:29et cetera, etc.
EDDIE0:31can take a monumental lift,
EDDIE0:32it can actually be a lighter lift to do it this
EDDIE0:36because
EDDIE0:36we don't have to do all these integrations and field mappings and all this other stuff.
EDDIE0:40We can just ingest the data via MCP
EDDIE0:43put it into the dashboard.
EDDIE0:44there's so much that we can do
EDDIE0:45with AI right now to just take our data and deduce it and normalize it and put it together.
EDDIE0:51That can save a lot of time and effort that we would have had to put in just a few years ago to get the same basic insights.
EDDIE1:06actionable playbooks from the trenches working as go to market strategy and rev ops consultants for our clients here at Union Square. Consulting and candid conversations with revenue leaders in the market that have been there. Now let's get into it.
RACHAEL1:21We've talked a lot before about how to make your data reliable, so you can actually trust the insights coming out of it. Today, we're getting into the gap between having workable data and actually turning it into decisions that help build your go to market. And a little bit about how we ended up building our own proprietary AI powered dashboard a well.
RACHAEL1:40Hey, Eddie, how's it going? Hey. Yeah, my Canadian Rachel. Yeah, yeah.
EDDIE1:48I'm good. You threw me there. I'm excited to get into this. I think it's such an important topic.
EDDIE1:53Because, yeah, we want to make meaning of our data. And, like, how cliche is it to just build a bunch of dashboards that people don't look at? So let's talk about how to turn that into actual revenue growth.
EDDIE2:04It's so interesting. It's like even I remember seeing like Jason Limekilns like 27 types of like VP's of sales, which somehow translates into a graphic only showing like four types of upsells. I'm not sure I ever understood that. And one of them is he calls Mr. Dashboard and and it's not the penultimate of crows. And so it's an interesting thing to think about, that there is a much higher level way to lead a revenue team, and I totally agree with it.
EDDIE2:30So I'm excited to dive into how we take these dashboards and turn them into something that's really driving revenue.
RACHAEL2:36So how often do you work with a client that has a functional dashboard with technically accurate or workable data, but it gets completely ignored six months after the launch.
EDDIE2:45So I think that there's a wide spectrum, right? I don't think any company and I include Salesforce in this. When I worked there as they grew from 5 billion to $10 billion and kind of, you know, set the benchmark for how to do things like this. No company has perfect data. I'll even use Salesforce as an example, where I think in many ways the data was as near-perfect as it could get, such as like the account structure, especially for customers, usage data, billing data.
EDDIE3:14I mean, Salesforce had things dialed in marketing engagement data, but then like contact data, for example, was just an absolute dumpster fire. Why is that? I don't know, but maybe because Salesforce decided that for all the things that they were trying to accomplish as a business, you know, knowing who their actual customers were was paramount. But knowing every single contact and not having any duplicate contacts in every single account, including prospect accounts, just wasn't a priority that they wanted to invest their resources to fix versus the million other things that they could do to try to grow as a company, including acquiring other companies, building new products, hiring more salespeople, etc..
EDDIE3:52So I think there's this really wide spectrum, and we definitely see that with our clients. We see that with ourselves. And I think the spectrum goes from really, really bad data to really, really good data. It goes across every different data point that you have in go to market, from the data that feeds marketing to the data that feed sales, to the data fields, customer success, to finance the product.
EDDIE4:12I mean, hopefully a lot of your financial data is more accurate because it's pretty important. And then it also goes over a spectrum of time. So what you see is, is like things get fixed when you pay attention to them, and when you pay attention to things, you start to see better ways that you can do things.
EDDIE4:30So for example, if you are laser focused on outbound pipeline generation, you might start with a certain dashboard that shows your activities, your meetings booked, pipeline generated, close one revenue, for example, and then you discover we have a lot of people that are not showing up to the meetings that we book. Maybe we should track not just meetings booked, but meetings held.
EDDIE4:55We need to go figure out how to do that. What technology are we going to use? How are we going to store the data? Is that like completely automated or is that human driven? If it's human driven, do we get people to actually check the box that they held the meeting? Do we check that they have notes like, what exactly are we going to do?
EDDIE5:13And this is probably a bad example because hopefully you're not having to do all that. But what exactly are we going to do to get this accurate data. And then you have to like go back and like I don't want to say reinvent the wheel, but you kind of have to like sharpen the ax again. And this is just continuous, ongoing forever.
EDDIE5:29And I've personally been working on this in my company, in companies that I worked directly for in the past, for almost 20 years now. And it feels like every single day we're still running into something that we need to update and improve and augment. Especially with AI. Now the capabilities are much higher. So now that the capabilities are higher, we want to take more advantage of it, which creates more work for us.
EDDIE5:52As we have experienced in building this AI dashboard that we're talking about today.
RACHAEL5:56Absolutely. And what do you think is the actual in practice gap between having good data and making better decisions because of it?
EDDIE6:06The simple answer is that you have a dedicated person with dedicated time and real accountability to do this, and produce specific outputs. And an even simpler way to describe this would be to have a weekly go to market council meeting. Now, this could be monthly, but what I will say is, is the higher the frequency that you do this, the faster that you're going to iterate.
EDDIE6:30And in a go to market council meeting, some people call it a pipeline council meeting. You have somebody who is tasked with turning data into real insights, bringing those insights to the team, the team being revenue leadership, CRO, CMO, head of CS, maybe products, maybe finance, maybe CEO, etc. the piece of sales and saying this is what the data is telling us.
EDDIE6:55Now, if you try to do this as we have done the first time, you're just going to immediately walk into this. Just how do I say this? There's going to be a lot of where did this number come from? And that number is wrong. I don't know any way around it. Like, I've been doing this for 20 years, and every time we do this, Rachel, you've been on these calls that we do this internally.
EDDIE7:18How much have we argued about? Where did this number come from? This number is wrong. Oh, the number is not exactly wrong. But we need to change the definition because that number doesn't represent what we wanted to say.
RACHAEL7:28Yeah.
EDDIE7:30It just it's constant. Right. So the first meeting that you have is just this like dumpster fire of that and the second meeting and the third meeting and the fourth meeting,
EDDIE7:43but hopefully by like the 10th or 100th meeting, you've got most things pretty dialed in and there's still going to be this, like, oh, we did this new thing, we launched this new initiative, we have this new data point, and now people don't trust it.
EDDIE7:55Where did this come from? Why is it like this? But that's what I've experienced, I don't know. Let me turn this back on you. You've been intimately involved in this. What have you seen with our own data?
RACHAEL8:05I mean, pretty much exactly what you just said. And it's hard to sometimes when you want to ship new initiatives and try new campaigns to projects, and it has to go live before you're able to really nail down exact attribution for it. Or like all the definitions for everything for it, because you need to ship quickly. And then it comes time to look at the reporting and it's like, oh, we didn't get a chance to like really figure out these metric definitions.
RACHAEL8:31So it's now now we're on the spot trying to figure it out and make the reporting more accurate.
EDDIE8:36Yeah. And this goes back into like this age old conflict of like do you want perfect data or do you want to grow revenue. Right. So I could go and criticize Salesforce and be like these idiots. I can't believe that they had such a mess in their CRM. And you're like, yeah, these idiots grew from 5 billion to $10 billion in the three years that I was there.
EDDIE8:53And now they're like, I don't know what their revenue is now like 30 billion. Yeah. You know, we can there's lots of things we can criticize about Salesforce, but there's some things that they've done. Right.
RACHAEL9:03Yeah, I definitely think there has to like you said, has to be a good balance between not getting in your own way and being able to ship things and actually, like, make money for the business versus trying to work on the business at the same time and ensure that you are able to measure the right metrics and get the insights that you need in order to make decisions about those campaigns that you're trying to run.
EDDIE9:29Yeah. And I feel like whenever we're trying to make decisions about campaigns, we're trying to run horse sales initiatives or customer success initiatives. At the end of the day, like we are always operating with imperfect data. I want the data to be more perfect than, than otherwise, but it's never a perfect right. Even if you had the absolute perfect attribution data, for example, that data still doesn't really tell you where people came from and why.
EDDIE9:58Because people come from multiple channels and you can never, even with perfect data, know what thing influence them the most, because you'd have to get in their head to understand that. And things like that persist through sales and customer success as well. But ultimately, I think that if an organization, for example, says, all right, we're going to take our head of Reb ops, or we have a dedicated analyst or somebody in addition to the cross, etc., that are also doing this when they have time.
EDDIE10:29But we have one individual who has at least part of their job is dedicated to go to market analytics. They're going to go and they're going to carve out time every single Thursday morning, for example. And they've got three hours where they close slack. They don't have any meetings. Nobody's calling them. And they can sit in front of the data and really look at it.
EDDIE10:51And then the unfortunate reality of that is, is that the first time you do that, just like I said, you run into all these issues of where did this data come from, is this accurate, etc. then once you get the data accurate, you start doing really basic and uninteresting like things. You start to come to conclusions that are kind of like, yeah, duh, we knew that.
EDDIE11:11So then you got to push through that before you can get to the silhouette stuff. And what I mean by Aja, so what is it's so easy to present revenue figures to someone like a crow whose job is on the line for these numbers and to say, hey, this is what's happening with your metrics. And they're like, I already know that.
EDDIE11:35So what? Who cares? What do we do with that information? And that takes real effort. And I don't want to just go on a soapbox here. I'll let you ask some more questions, but we can dive into that later in the podcast on how you actually do that. And there's a lot of ways to leverage AI to do that too.
RACHAEL11:54We can get into that pretty soon here. But like, first, who actually are you supposed to own that problem of whether data or whether a dashboard provides useful insights because you said somebody has to have like dedicated time to do that, who should that person be?
EDDIE12:08I think it's multiple people. So we had the folks from zoom info on our podcast, actually multiple folks multiple times, but we had Tim Stem trickling. Tim, strictly, what is up with us today, I don't know. Tim Strickland, the former CEO of zoom, info on the podcast, and Scott Sutton, one of his former VP's of robots. If I remember the title correctly and they had a big team, right?
EDDIE12:34Lots of data analytics, etc. but what Tim the Crow said to me that I thought was really interesting is rev ops should be doing this, and if they're not bringing insights to me as a crow, they're not doing their job. But if I'm not looking at the data and I'm not bringing insights back to them, I'm not doing my job.
EDDIE12:52Something like that. Don't quote me word for word. And I thought that was really interesting because at the end of the day, the crow is responsible for driving revenue and that person is going to lose their job if they don't deliver. So they have the accountability already. We don't need to add anything to that. And I think that if crows want to save their jobs or really excel in their jobs, they should really own the data.
EDDIE13:17Not that that's the whole job, but it's really important. However, I also think it's really hard. Crows are really, really busy, especially crows that maybe don't have too many layers of management between them and wraps, and they're getting involved in deals, hiring, onboarding, etc. you know, there's different levels of crows and, you know, operating at the level at zoom info, maybe it's a little bit easier to find time to really live in the data.
EDDIE13:42But if you're spending all day closing deals and on calls with reps, it's harder to do that. Either way, I think it's really valuable to have someone in Rev ops who can dedicate all of their time or some of their time to this, and they have real accountability to say, look, we have these meetings scheduled at this time.
EDDIE13:58This is what I expect to see from you. And the good news is, with everybody stretched so thin, there's a lot that we can do with AI to try to start that process and to try to do a lot of that heavy lifting so that the human in this scenario, be it the crow or the person rev up.
EDDIE14:16So it's a VP of Rob ops or it's a rev ops analyst can do that work better and faster. In addition to the other folks like the CMO, the head of Customer success, all of those people should be looking at their own data as well. And when I say their own data, they should be looking at revenue data.
EDDIE14:31Like if a CMO is not looking at what revenue their campaigns are producing, there's something really, really wrong with the metrics and the systems and most importantly, the culture of go to market in a company.
RACHAEL14:44So before we get into the meat of like figuring out these, so what insights and how you come to these insights? I want to talk a little bit more about what the dashboard needs to look at in order to be able to get these insights. And this is related to how you're just saying, like revenue leaders need to be looking at their dashboards themselves.
RACHAEL15:02So how does the dashboard need to look in order to get the quickest,
RACHAEL15:10most accurate insights possible for like a revenue leader, CRO, CMO who's very busy and they only have so many minutes a day to pull up a dashboard, what does it how does it need to be configured?
EDDIE15:21So this is such a great question and it's such a hard one to answer.
EDDIE15:24I think the first simple answer is, is that you don't want to have too many metrics, because if you're just looking at a wall of metrics that you can't make rhyme or reason of it, it just confuses people. Anybody, no matter how analytically analytical you are.
EDDIE15:37And I'm a pretty analytical person. So for me to say that, like, I really like numbers, but I still get overwhelmed when I'm looking at a wall of, of metrics. Because the important thing is you have to take action on these metrics, right? So if you're not going to take action on them, what's the point? So I'm about to just start listing off a bunch of metrics that are really, really basic, right?
EDDIE15:59Like we want to know how much revenue we have. We want to know how much revenue we have closed in new business and what net revenue retention looks like right at the highest level. We want to know what our pipeline looks like, how much we've generated, how much we have. We want to know if we're pacing towards goal or not.
EDDIE16:17We want to know what like what's contributing to pipeline in terms of like marketing leads, outbound meetings. ET cetera. On the net revenue retention side, like how many expansion meetings and opportunities have we set up, things like that. Right. And this is where it gets unique to every company, right? Like maybe you have a partner motion like we do, and you start looking at a lot of metrics for partnerships.
EDDIE16:41Not every company has a partner motion and every company has a motion, etc. but what I think is really important is that you have one set of metrics that you look at to understand how the business is working, that are succinct enough that you can wrap your head around them and try to make decisions. So, for example, if you haven't generated enough pipeline, then the action item is we need to go generate more pipeline, right?
EDDIE17:05That's where you start to drill into like next level layers. Right. So if I say all right, we haven't generated enough pipeline, then I want to know, well where is our pipeline coming from right now. And I want to have the ability to dive into the numbers. Now, the reason that we built a dashboard with AI instead of just in Salesforce, as we've been doing for we being our collective team for like 20 years since I started this company in many years before that, is that Salesforce just has limitations on what you can see.
EDDIE17:33As we all know, one of the things that I really hate is it's very difficult to like balance between different time periods and different filter criteria. And so what we've built with Claude enables us to see this same data that we have in Salesforce. But to just like with a press of a button, toggle between weeks and months and quarters and years and change the weeks, quarters and years.
EDDIE17:52And yes, you can do this in Salesforce, especially if, you know, reporting. But Claude can make this so much more powerful. And so we have a dashboard where we can see all these things that I'm listing off, and I can just quickly toggle between different time periods and other filters so that I can see this information at the snap of a finger, but that's just surface level.
EDDIE18:12The next place I want to look is like, I want to see detail, right? I want to understand what's going on. So what are the things? What are the things that are contributing to our pipeline generation? Where are we off? Did we drop in terms of outbound? Did we drop in terms of inbound? Did we go up in terms of outbound?
EDDIE18:28How do I dig into that. Right. And then the next layer that I want to look at is segmentation. Is there something specific that we're doing that's working better than something else? Is there a particular industry a particular type of customer, etc., etc. that is driving more pipeline or driving more revenue, or where we have better close rates or conversion rates?
EDDIE18:48That's something I skipped over, is I want to look at conversion rates as well, but I can just continue to list off, like every metric under the sun, that you could easily see if you just schedule a demo with Salesforce or Clary or what have you. But it's unique to each customer, and I think it's really, really important that we narrow it down to the most important metrics that we're actually going to look at and take action on, and then we can have a separate set of time where we say, like, all right, we really need to fix this one thing.
EDDIE19:17For example, we really need to fix new business pipeline generation more specifically with outbound. Let's start to really, really dig into what's working on what's working and what's not with generating pipeline and outbound.
RACHAEL19:29so since we built this AI powered dashboard for ourselves, and I'll just explain it a little bit more for our listeners, essentially, Jerry and some other people on on his team, I don't know their actual names are AI development team built this proprietary dashboard for us that gets clawed to well, it doesn't clod doesn't actually calculate any of the revenue numbers for us as one of the big rules of the system.
RACHAEL19:55Those numbers are collected, I think, directly from Salesforce. And but we're essentially able to ask Claude anything we wanted. It can pull those numbers accurately and be able to tell us what's going on in the business. And we can see the dashboard at a glance. Actuals versus targets for any date range that we want to do for the specific metrics that we find important for our own business.
RACHAEL20:17So how did it come about that you decided to build something like that, or wanted to get our team to build something like that? And how has it helped you so far in the insights that you've been able to gain as a revenue leader?
EDDIE20:31So I think the impetus for us is that we always want to eat our own dog food. And so I'm thinking through the lens of a 50 or $100 million B2B tech company and what they need most. And there's all this pressure right now. Go implement AI, go implement AI, go implement AI. And I ask myself, what's the one thing that if we could do anything with AI that would move the needle the most?
EDDIE20:53I'm kind of torn between two things. One is understanding our data better. I just firmly believe that any organization, at any level of scale, is going to perform far better if they are looking at their data on a regular and consistent basis in depth than one that is not separately. I think that if we were targeting the right accounts, that's really critical as well.
EDDIE21:15But those two things kind of go hand in hand because like, what are the right accounts? So the right accounts are the accounts that convert into leads and meetings and opportunities and customers and renewals and expansion opportunities. So how do we even define that if you can't look at those metrics clearly? So I wanted to work on that because I wanted to solve the problem that I think is most important for our clients.
EDDIE21:42And also just.
EDDIE21:46Coincidentally or fortunately, it benefits us tremendously as well. Like, I'm a big believer of living in the data. And so as you have experienced, Rachel, we have been talking about our metrics every single week for I don't know how long now. And there's always these challenges that I shared that we run into. And this dashboard solves some of those challenges.
EDDIE22:09It enables me personally to prep for that meeting where I'm not the one presenting the numbers. It was you. Now it's a little bit more Jerry, who kind of functions as our internal VP of Rav ops.
EDDIE22:25It enables me to prep for that meeting ahead of time and understand what's going on. And then I'm skipping ahead and instead of asking, like, where are we at with pipeline generation? Where the number come from? I'm asking, yeah, I get it. I understand that our pipeline generation is here, our leads are here, our website visits are here.
EDDIE22:42So what? That's one piece. Another piece is that this dashboard is able to pull information in from multiple systems. Right. So we don't have a Tableau or some other tool like that partially just due to cost. And we're able to essentially quote unquote vibe code our own thing because we can MCP into not just Salesforce, but also HubSpot, also Google Analytics, and then all the other systems that are more unique to us since we have such a big content engine like we can't MCP into LinkedIn, but we can MCP into the LinkedIn analytics tool that we've signed up for.
EDDIE23:20We can MCP into the Excel downloads that we drop into Google Drive that show other LinkedIn analytics. We can MCP into Spotify. I think actually, I don't remember how that works, but we have the Spotify data in. I think we actually have to manually export that. But then we can MCP into the file that holds that data. I think there's a way to MCP into YouTube.
EDDIE23:42I think we're trying to figure that out next. And I'm able to see our entire revenue engine all in one place. And then I'm not only able to see this dashboard, but I'm able to go in and ask Claude a set of questions. And so that brings us to like the next layer, which is the first thing is I just want to see the raw numbers.
EDDIE24:02The second thing is I want AI to answer the obvious questions. Why is pipeline generation up or down?
EDDIE24:10Why our website visits up or down? Why are these types of website visits up or down? And then what I also think is interesting about using an AI tool is we can even bucket different things, different ways. They're pretty hard to do in other systems.
EDDIE24:25So for example, like we completely customize the way that we look at our website analytics by bucketing different sources into one. Because for us, we kind of know where everything is coming from, when it might show up otherwise, like organic search or direct, it's probably coming from our LinkedIn since that drives the bulk of our traffic. So why not just bucket that together and look at that in the way that makes sense for our business, instead of the way that it comes out of the box from Google Analytics.
RACHAEL24:52And I know we built this not just for our own usage, but the idea was to build it for our clients as well. Where did the idea for that come from? Rather than just use the systems that we've been using for as long as the company has been running?
EDDIE25:07Well, I think first and foremost, we have never been like married to any one system, like we started as a Salesforce shop and we continue to be a Salesforce shop because every client that we work with, every company, B2B tech company of a certain size is pretty much all on Salesforce. So so be it. We're on Salesforce, they're on Salesforce.
EDDIE25:24We work on Salesforce. Great. But what I've seen over the last ten years is that companies are increasingly buying other products and not buying Salesforce for their marketing tool, for their customer success tool, for their analytics tool, etc., etc.. And so we've never been married to Salesforce for those solutions. We've been implementing the other solutions for years. So this is just the next evolution of that.
EDDIE25:49I'm also like firmly on board that I think we all need to embrace AI, but I want to embrace AI to drive revenue. What I don't want to do for our clients or for ourselves is to go spin up a bunch of agents so that I can tell everybody how cool it is that we built a bunch of agents, and I've got nothing to show for it, no revenue.
EDDIE26:08And the first place that I think companies can improve revenue is just by doing a better job of looking at their data. And to go and do that, let's say a company is really messed up from a data perspective, which is most companies, let's be honest, to go and fix all of that data in Salesforce, in HubSpot, et cetera, etc. can take a monumental lift, and it can actually be a lighter lift to do it this way, because we don't have to do all these integrations and field mappings and all this other stuff.
EDDIE26:39We can just ingest the data via MCP and put it into the dashboard. Sure. Of course, like the data still needs to be accurate in Salesforce. And so we need to do that work. Don't get me wrong, but I don't need to integrate HubSpot and Salesforce anymore so that I can match my leads in HubSpot to my accounts and Salesforce this, that, and the other.
EDDIE26:58Like, I can build out, I mean, we already have that integrated, so that's a bad example. But like if I want to look at how many subscribers are on our newsletter, I don't need to build objects in Salesforce for that. I can just pull that into our dashboard and see exactly what we want to see. And just anecdotally, what I'll say is I did learn this lesson the hard way.
EDDIE27:19In my last job, I was very much drinking the Kool-Aid of like, if it doesn't exist in Salesforce, it didn't happen and everything has to be in Salesforce. And I worked for a company that had a very large Postgres database. We were tracking about 4000 rows per day of customer interaction with our app, which were really important pieces of information to tell us what was going on with our business.
EDDIE27:43But I foolishly pushed all that information into Salesforce and tried to build a Salesforce dashboard and basically just broke the thing. And sure, like that was a junior mistake on my part. This was a long time ago. In my defense. But we just don't need to do that anymore. I think that there's so much that we can do with.
EDDIE28:02Sure. Like companies still need like larger tools when they have larger data sets. But there's so much that we can do with AI right now to just take our data and deduce it and normalize it and put it together. That can save a lot of time and effort that we would have had to put in just a few years ago to get the same basic insights.
RACHAEL28:22And for everyone listening, we actually put together a full step by step guide on how we built this AI powered dashboard. We have it in PDF for you download for free. We're going to have the link to directly download the PDF in our show notes. And I'm also going to make page to host a PDF on at Union Square Consulting.
RACHAEL28:46Dashboard. And that's going to be all one word.
EDDIE28:49I'm excited about this Rachel. It's funny, you know, sharing this with the world on this podcast because I feel like we're only in the first step, like Jerry and I already probably have mapped out like the next five major updates that we're going to make to this thing. But I also want to add like another thing, and I don't want this podcast to come across as a sales pitch.
EDDIE29:06First of all, we're not selling this dashboard. So like means if you're listening to this like take this, go check out the source, build it yourself and Claude vehicle, why don't ever call us hire us anything like it's yours for free to take. I'm not trying to pitch you, but if I was going to pitch you, one of the other benefits that we have here is that we can now codify this across our entire client base.
EDDIE29:28So assuming the client is okay with this, we can then MCP into their Salesforce. We can apply all of these rules. We can apply all this logic, ask all of these questions. And we essentially are building a AI data analyst that is not going to give you the end all be all of what is working and not working in your go to market, but it will do a massive amount of the heavy lifting so that we don't necessarily need to build our clients like 1,000,000 hours to do this, and we can get insights as fast as possible.
EDDIE29:59My ultimate goal is when we take on a new client, that we can get insights on what's working and what's not working or go to market, and what actions they can take to improve things on day one. Am I going to get there? I don't know, we're going to go in there. We're going to see all this junk data.
EDDIE30:14We're going to have to clean it up. We're going to have to define things. We're going to have to ask people like, how do you define MKL? Where is this written down? Because I'm looking in Salesforce, or I'm looking at HubSpot or Marketo and it looks like a dumpster fire. Yeah, these things are still going to need to be worked out,
EDDIE30:29I think that we can get through them much faster by using AI.
RACHAEL30:33Quick pause. Everything we talk about on this show. Diagnosing go to market ops. Prioritizing projects for revenue. Impact processes. Metrics. Insights. Building a predictable go to market engine. We've built frameworks for all of it. They're free and undated on our website. Union square consulting frameworks. The link will also be in the show notes, so make sure you check that out.
RACHAEL30:55All right. Back to the episode.
RACHAEL30:58All right. Now that we've talked about the dashboard at length, Eddie, let's get into the actual insights part. You were saying there's different ways that we can look at this data and identify different insights from it to actually take actions in the business. So how do we go about doing that?
EDDIE31:16The simple answer is you spend time on it, right? But the more detailed answer is case by case dependent, whether we're looking at our ability to close deals or we're looking at our ability to generate pipeline.
EDDIE31:32That's pretty much all go to market, right? That's new business and expansion. It's not like renewals is a little bit different, but I'm not going to go deep on that right now. So let's just talk about pipeline management as an example to start with. Right. The first problem that we face is do we have clean pipeline and also do we want clean pipeline?
EDDIE31:53I was doing a prep call yesterday with the folks over at Crow Collective. We're going to do a podcast on what Crowe should do in the first 90 days to build their go to market engine, with a focus on the change management that's needed. So, for example, you walk into an organization and I'm thinking of real clients that we have right now, and it's like, all right, well, we have a 70% close rate because reps don't put deals into their pipeline.
EDDIE32:20But you can't just tell the reps to go put all the deals in their pipeline to their fired, because these reps are crushing it, and the organization needs them to stay in the organization and keep crushing it. And you have a change management management issue on your hands. What do you do about that? So this is a real problem that many companies face.
EDDIE32:38we can still look at other metrics. If these are inbound leads, we can look at well what's our lead to win rate? I mean, at the end of the day, if you have an inbound lead that you can measure and a certain percentage of it is close, one business, the conversion rate to qualified pipeline and from qualified pipeline to closed one is somewhat meaningless and arbitrary.
EDDIE32:59It's meaningful in the sense that your team is spending a lot of time working that deal. So it would be better if you had a really high close rate than if you had a really high lead conversion rate. But at the end of the day, if you can generate, if you can close X percentage of leads, that's pretty great.
EDDIE33:17Same thing could be said of meetings. So I'm always thinking about how do we work with a set of imperfect data and how do we automate things so that there is less onus on humans to do manual data entry that they don't want to do? Then you have this change management aspect that I won't get into on this podcast of we got to get them to update their pipeline.
EDDIE33:37So where am I going with this? Well, the insights that I want, if I have perfect data, is that I want to look at the three basic things with pipeline management, I want to close rate, average deal size or ASP. And I want to look at sales cycle, those three things. Right. And what I want to look at is I want to look at that across the organization.
EDDIE33:59If we have different teams, I want to look at different teams like SMB, mid-market enterprise. I want to look at different geographies, different industries. I want to segment this data in different ways. And what I want to find out is what is working best in our business if we're closing, if our close rate for tech companies is twice what our close rate is for consumer packaged goods, maybe because of the tariffs, for example, it could be a hypothesis.
EDDIE34:25I don't know anything about tariffs or CPG, but I could imagine that that might be tough right now.
EDDIE34:32Then we might say, well, let's divert more energy in some more to be to be tech or professional services than CPG. That's a perfectly logical hypothesis and conclusion. Right. So however, I can segment things I want to do that. I would also want to look rep by rep. So if I'm let's just say I have like a team of ten sales reps and that's it.
EDDIE34:55Which sales rep is closing the most deals. Which one is not? Now my first question is can I really trust that data? If I can trust the data, then I want to use that as a coaching moment. I want to go to the wrap and I want to explain to them, hey, like this is where your close rate is and this is where others close rates are.
EDDIE35:11And I really like the way that Kevin Dorsey approaches this from a coaching perspective. He likes to sit down, or he's at least shared that. He likes to sit down with a wrap and kind of do the math with him and like, well, hey, if you want to hit quota based on this close rate, this meeting conversion rate, this conversion rate from coal call to meeting, you need to make 300 calls a day.
EDDIE35:30So to get to quota you could make 300 calls a day. Or we could work on improving one of these conversion metrics. What would you like to do? Unsurprisingly, the rep is like, I don't want to make 300 calls a day. Sounds awful. Okay, cool. Like, well, where do you think that you could improve the most? Here's how you're benchmarking against the rest of the team.
EDDIE35:50Like, to me that's really powerful, right? All right. So I'm trying to answer your question, which is a big question. So I've touched on pipeline. That's how I would want to look at things. And so we can do a lot of that with AI.
EDDIE36:03Especially if we are then enriching all of our fields by like taking our call transcripts and translating them into fields. On Salesforce, we're looking at emails, we're looking at meetings set, et cetera, etc. we can build this really rich data set in Salesforce, and then we can analyze that data set with AI to identify hypotheses and come to some initial conclusions before humans have to do anything, and then the humans can do the work thereafter to take it the last mile, which I think is incredibly powerful.
EDDIE36:35So the next thing I want to look at with inbound is I'm going to look at how do leads convert into pipeline and closed one business and ultimately LTV as well. It's not forget about that. Anytime I can look at LTV like that's a lot more interesting.
EDDIE36:49then I want to look at what's working and what's not working.
EDDIE36:51Like, where is this? Where are these leads coming from? What's our attribution look like? What like where these leads coming from in terms of like the industry or the, you know, the segment, etc., etc., etc., what's working and what's not working? I'll tell you, in our own business, like everybody, LinkedIn has been hit really hard. That's taken a hit on our entire website traffic.
EDDIE37:14And so we're looking really hard at that. And we're saying like, is there a way that we can fix LinkedIn so that we can get back to where we were last year, or do we need to go and invest more in other channels, such as this podcast, which has grown 500% this year? So that's been a nice uptick.
EDDIE37:30Where do we invest our time, money and energy in marketing to get back to where we were last year in terms of website visits and website conversion rates, those are the kind of things I want to look at, like, where's traffic going to the website. How is traffic converting from the website to leads? How are leads converting the pipeline?
EDDIE37:48How well is our team following up on leads and how are we doing it closing those leads out? And if I can segment that in any way by looking at different teams, different products, different geographies, etc., then I might identify that this one thing is working better if that other thing is not working as well. And so let's do more of that.
EDDIE38:05So case in point, we're not a marketing agency. So I'm getting a little bit out of our depth here. But we do spend a lot of time marketing. So I'll share this. We look at our podcasts and all of our podcasts have somewhat the same level of like plays and downloads within a ballpark, but there's a drastically big difference in terms of the percentage of people from one podcast that go to our website versus another, and that has to do with certain themes.
EDDIE38:32And whether it's you and me, Rachel, doing this podcast and talking about like, specific frameworks and best practices versus when we bring a guest on the show. And that makes perfect sense, because when we bring a guest on the show, they're just sharing their experience, which I find really valuable. But it doesn't give people a lot of reason to go to our website.
EDDIE38:50So then we have to think about how we do the mix of producing podcasts. All right. So I've touched on inbound. Now let's go to our bound.
EDDIE38:58Outbound is really basic. We look at activities, meetings, pipeline generated
EDDIE39:02closed one business right.
EDDIE39:03We do the same segmentation that I've now probably repeated too many times on this podcast.
EDDIE39:07The thing that I want to look at is like where is the conversion rate good or bad, right? So I love the idea of like reading from right to left.
EDDIE39:16If I've got a rap that's like crushing quota, then it's like, okay, cool. Like there's not no problem to fix, but where could that rep do better?
EDDIE39:22If they're not hitting quota, are they generate enough pipeline and not closing pipeline? Well then my first question is is this real pipeline?
EDDIE39:28And then if it is real pipeline and they're not closing it, what's going on.
EDDIE39:31Where in the state like stage by stage where things dropping off. What can I look at. And again, the more that we can enrich the data,
EDDIE39:36the more we can ask AI to help us identify why this rep is losing deals,
EDDIE39:40If we have call transcripts, like we got a lot of Intel there.
EDDIE39:44And then if they're not generating enough pipeline, then I would look at what's the meeting to pipeline conversion rate
EDDIE39:49what might be going on there. And if it's not that, then I look at what the activity to meetings is. And if it's not that, then they're not generating enough activities.
EDDIE39:58Here's another example of where we've used AI to help with this.
EDDIE40:01So we have a new ref on our team Zeke. And he's ramping up.
EDDIE40:05And so we couldn't do this last year when we hired our last rep. And the first thing I want to do is like just get the messaging down right. Like a new rep. I'm doing the opposite. I'm reading from left to right, like just log activities in book meetings.
EDDIE40:19Right?
EDDIE40:20Well, it's really hard to go and read every reps. Email like that is an incredibly time consuming task. So I built a project in Claude to pull all the emails from Salesforce via MCP, analyze every single email, and produce a PDF every day that says, here's your emails. This is your score for the day. This is how many emails you logged.
EDDIE40:40This is the average score across all those emails. And then here's each individual email scored from top to bottom, best to worst. And you can see what you did well and what you can improve upon. And that has been game changing. Like I have seen his emails go from being night and day, different from what I wanted to see, to being exactly what I want to see in the span of like a week or two, which is just fantastic.
EDDIE41:09And so then it's like, all right, cool. Now he's starting to book meetings, so then we move on to the next step. Everything I've said could be applied to expansion, sales, or in this case I'm actually talking about partner sales. Non. But it's fairly similar for our for us at least. So I hope that answers your questions. Really long answer.
EDDIE41:29But I hope that I went into enough detail that our audience gets value here.
RACHAEL41:31Yeah. Yeah, I think so. There's like a lot of moving parts to it and I know it might be
RACHAEL41:37it potentially it could be a little bit overwhelming for resume leaders to like, listen to this and hear like, all the different ways that they could be implementing this and not really know, like where to start on Monday or what the first thing that they should look at and the first thing they should do would be so what would your advice be for them?
EDDIE41:54Well, it's so interesting because it's actually talking to a crow that's been following our content for a long time. I think it was yesterday or the day before, and we're having a similar conversation about where to start. And I went into our
EDDIE42:07go to market ops decision tree.
EDDIE42:09If you could fix one thing in your business, what would it be?
EDDIE42:13New business or net revenue retention. And by that, what I mean is, if you could take one of those things from where it is today to where you think it could be,
EDDIE42:21which would give you more of an uplift in revenue, short term and or long term.
EDDIE42:26And his response is like, wow, that's a really good question. Like, and this is a smart guy.
EDDIE42:30He I think he follows our content because he's the type of person that really thinks about this stuff a lot.
EDDIE42:36he was like, oh, that's an interesting question. Like, I really want to think about that. And then you drill down all the way down to like, okay, like we need to fix our outbound activity so that we can book more meetings.
EDDIE42:45Right? I skipped a bunch of layers. But if you're interested in reading through this, we have
EDDIE42:49we have that content on our website.
EDDIE42:50And so for me, like that's the first place that I want to start,
EDDIE42:53right? I think a lot of these metrics are not new to people, but the distinction is, are we going to look at the metrics and then take action on it?
EDDIE43:00Right. So let's just say let's go with this like problem that we're booking a bunch of outbound activities, but we're not booking enough meetings. There could be any number of problems with that from the messaging not being good. The research process before we message not being good, we're sending out like we have. We're telling reps to target too many different contacts.
EDDIE43:20We haven't done a capacity plan or territory plans. Well, we haven't defined our ICP while there's so many things that could be going into that. Right. So then like that gives you like another layer to dig into the data, like ICP, for example, do we have our ICP defined? Do we have the like data on our prospects to tell if they fit our ICP or not?
EDDIE43:40And is there a way that we can see if we have reached out to the right accounts or not? By definition, your ICP tells you which companies are going to be easiest to sell. So if you're trying to sell the companies that are not in your ICP, no matter how good your messaging or efforts are, you're not going to convert those accounts, because if you did, then they would be in your ICP,
EDDIE44:01right?
EDDIE44:01Or at least if you did enough.
EDDIE44:03So that's where I would want to go. I want to start there and then. I like the idea of having a regular meeting, because it's a forcing function that says, look, there's accountability here. Someone needs to do all this work, drill into something and bring something specific. And so, for example, for me, a really good VP of ops or rev ops analyst is bringing this to the meeting and saying, I've identified that we are booking enough, we're logging enough activity but not booking enough meetings.
EDDIE44:32And here are the reasons why I think this might be. Let's discuss what to do about it.
RACHAEL44:37Awesome. And that's so you're talking about the pipeline cancel or go to Market Council?
EDDIE44:42Yes. This isn't the only way to accomplish this, but any regularly scheduled meeting and go to market council is really good for bigger companies. Like if you're a crow and you've got like seven sales reps and one person in rev ops and one person in marketing, I don't know that you necessarily need a pipeline council. You could just sit down with your head of marketing and your head of rev ops and just discuss this, but having a regularly scheduled meeting is a force function to say there's accountability for a clear deliverable.
EDDIE45:11You, Mr. or Mrs. Head of Rev Ops or Rev Analyst are responsible for coming to this meeting. Having looked at the data, analyzed it, formed hypotheses, tested those hypotheses, and come to this meeting with some actual conclusions on this is what I think the this is the action I think we could take to generate more revenue. And let's discuss this and decide what to do about it.
RACHAEL45:35And you mentioned a piece of content before. I just want to make sure which piece of content were you referring to? I just want to make sure you can put it in the show notes.
EDDIE45:43There's the go to market decision tree, and then we also have that like kind of baked into our go to market ops diagnostic framework.
RACHAEL45:49Okay. And yes, those will be in the show notes. And you can find all those frameworks on our website at Square Consulting Frameworks. Awesome. So I just had one last question on this topic for you, Eddie. Do you have any favored story or example that you haven't shared yet so far in the podcast, either within our own company or with a client company where we've been able to adjust their
RACHAEL46:18metrics, definitions, adjust their data to a point where we can actually see
RACHAEL46:23or define insights from accurate data that we've now been able to build, and what those insights were, and the effect that I had on the company.
EDDIE46:33Oh, man. I mean, like so many, right? I think that almost every single company comes to us and their metrics are a mess because their definitions and process are a mess. And this is the first thing that we work on. So it's like literally every single customer,
EDDIE46:45I'll give you an example of one.
EDDIE46:46I'll do two companies. So one company and I can't drop names because it's always difficult. Like nobody wants to share their dirty laundry publicly. Right. Which is why it's always hard for us to generate these customer stories.
EDDIE46:56I don't want to drop names and say, hey, look at how bad this company was before they hired us. But we had a company that was brought to us by their private equity firm.
EDDIE47:04There were at $100 million in revenue, and so I can't say that they were all bad, because if before, like before, I tell you what, I'm about to tell you, the fact that this company grew to $100 million in revenue was virtually no go to market. Metrics is incredible. They have an amazing product that is really, really well liked in the industry that they sell it to and well known, and they're crushing it.
EDDIE47:30They were crushing it before they worked with us, and they're crushing it even more now. But when they came to us, their private equity firm said, we can't even see how many leads or how many opportunities they've generated because they have no clear definition, definition of what a lead is and what an opportunity is, and no adherence to updating that data in Salesforce.
EDDIE47:54So at the instruction of the private equity folks who are some really smart people that know what they're doing. So I didn't take objection to this. We went back and forth on it and it's like, well, you know, there's a lot of work to fix this. They're like, no, but we need this, like tomorrow. So what's the like the bare minimum we can do?
EDDIE48:11Can we just say that every single, like book a call or book a demo is a lead and every single demo delivered is an opportunity. Now, is this the perfect data? No. But is this better than what they had? Yes. So this is what we did on like day one. Got that information over to the PE firm.
EDDIE48:37In this company's defense, they didn't really have Crowe or anybody in red ops. So it's understandable why they were in this state. And then we went and cleaned all this stuff up to the point that now data is still not perfect because they've got, you know, certain challenges that many companies face, but it's as perfect as we can get it.
EDDIE48:55And there's has real visibility into what's working and what's not working. They're going to market. And this company has grown to I believe they're at over $200 million now. And so this is in tandem with their VP of ops. I hope I'm not sharing too much information here. But these are like really, really great folks to work with because they get it and they've come in, they've taken over this company that had a really good product and really good go to market, but not necessarily the best operations and built an operational engine around it.
EDDIE49:23I'd like to think that we've helped a bit with that, and now they have real insights into their data, and they've also doubled revenue, which is pretty great. Another company would be Techstars. I think I can name drop here because we do have a story for this, and one of the biggest challenges that they had was that they their sales team was not focused in on the right accounts.
EDDIE49:51So this is somewhat metrics and this is somewhat process. So they were reaching out to just a large number of accounts and trying to set meetings. And the conversion rates were just not good. Right. So we could see that in the metrics. So not only do we help clean up the metrics that weren't good, but we also help them narrow in on the accounts that they were focused on, which then leads to more metrics.
EDDIE50:13So as an example, I'm a really big fan of having a count stages and contact stages, not just opportunity stages. So what you can do is you can take your entire set of accounts. You can even we have this fully automated so that there's no human updates, and you can take them from a stage like new, which means we haven't reached out to working, which means we have to engaged, which means they replied to open opportunity, which means that we have an open opportunity to customer or former customer or stale.
EDDIE50:43This can be fully automated by just looking at like meeting and email data. Right? That is all just like sync to Salesforce automatically with the right tools. And then you can look at this and you can say, okay, which accounts are assigned to which reps are in which stages. And then you can also run against that account and say, like, how many times have we reached out and who have we reached out to?
EDDIE51:02This is something that I'm doing with our new wrap, Zeek. This report is only valuable if you've actually all agreed on which accounts you're targeting. Because if you say, like, all right, here's like 10,000 accounts, just have at it. You pull up this report and you're going to see that 9500 of these accounts are new, 500 of them are working.
EDDIE51:20And then when you look at all the working accounts, you've just emailed them once each and they didn't respond. And that's why you haven't gotten a meeting. I mean, like, it's amazing how often I see this, right? So what I really love is the idea that, like, we really narrow in on the accounts that we're targeting, and then now we can build metrics around specific accounts that are more meaningful, which is what stage each of these accounts in.
EDDIE51:39How many contacts have we hit? How many times have we hit them, and which of these have converted and not? And then if we're also like tying all of our messaging in and we can MCP into Salesforce, for example, then we can go and look at that messaging and say which of these accounts have converted to meetings, pipeline closed one and what messaging worked best with them.
EDDIE52:01And we can literally tell the eye to go look for that. And then how does this compare to the folks that like, we didn't get meetings with? And that's a really, really great way to save a lot of time doing analytics on a large data set. Because the larger the data set is, the harder it is to do this analysis manually.
EDDIE52:16Like, how long would it take to read all those emails? And now we can identify like this is the messaging that is resonating with folks. And this is the messaging that's not
EDDIE52:23awesome.
RACHAEL52:24I love those examples and I love this topic so much. It really is just like to me, the crux of go to market operations and go to market in general. Being able to look at data and make actual decisions on that data that impact the company, and then seeing that revenue impact happen in real time is really cool.
RACHAEL52:41I love that data analysis.
EDDIE52:43Yeah, I mean, I do too, and I'm so excited with what we can do with Claude now because like, it's just there are things that are virtually impossible to do manually that we can now do with Claude. Like, I'm excited. We've got our podcast manager is going to have all of the transcripts for all of our podcasts uploaded soon.
EDDIE53:03We've already got like the first 50 on our website. Yeah. And so I can literally just go in, Claude and I can say like, look at our Google Analytics, look at our podcast performance, look at the transcripts and tell me which podcasts are popping the most. What are we talking about? What's going well, what's not, and which of these are leading to people visiting the website or not?
EDDIE53:25That's an incredible thing to be able to just ask like, hey, odd, can you can you go figure this out for me? Yeah. And we're doing these exact kinds of things. And no surprise, a lot of the feedback is, hey, by the way, Eddie, do more podcasts with Rachel where you guys share actual tactics on how to do this stuff because people seem to really like it.
RACHAEL53:44And here we are. Here we are. Yeah.
EDDIE53:47Take an action on those insights.
RACHAEL53:49Exactly. Your own dog food. But I'm glad you mentioned the podcast pages on our website. I feel like I should mention that for our listeners as well, because it's a little bit different than we've been doing it before. You're probably listening to this on Apple or Spotify, or maybe even YouTube, but if you go to our website, you can score consulting.
RACHAEL54:06You can see all of our previous podcast episodes, and each one has its own page. That gives. I think you can get the transcript now on it. The links to all the.
EDDIE54:16Reasons can get the transcript.
RACHAEL54:17Yeah.
EDDIE54:18Billings tool. There's like a summary.
RACHAEL54:19Yeah, yeah, there's a summary as well. And plus a full transcript of each episode and like a links to all the resources and everything. So it's kind of like a little web page for each podcast episode. And I thought that was really, really cool. I'm glad we have that now. So if you want to catch up on on the different episodes that you might have missed, a great way to do it.
RACHAEL54:37If you're like me and you feel like you can read faster than you can listen, all the transcripts are there to to binge on.
EDDIE54:42I don't mean to plug our website too much, but I'll tell you another thing that, like our agency developed that I just absolutely love is now you can go to our our newsletters, which by the time that this podcast is produced should be Union Square Consulting, backslash newsletters.
RACHAEL54:59Oh newsletters single oh.
EDDIE55:02Oh you're right newsletter singular. Yeah. We can search every single newsletter, every single framework. And I believe we're going to put this on the podcast page as well. So you can literally just type in like
EDDIE55:12outbound analytics and it'll tell you like which content we've done on that topic.
RACHAEL55:17Yeah, I'm really excited for that. We've always been like taking our posts and stuff, hoping for one day. We developed the website to the point where you could search for those tags, and now we can finally do it.
EDDIE55:28So I love the way you say tags.
RACHAEL55:30Tags, tags.
EDDIE55:33All right. Anything else?
RACHAEL55:34Yeah. No, that was wrapping. Yeah, that was it. Yeah. There were a lot of resources mentioned in this podcast about more than our most recent podcast, for sure. So be sure to check out the show notes for links to all those if you want to dig deeper into this topic. And yeah, thank you so much, Eddie.
EDDIE55:50Yeah. No problem. And I'll just say to our listeners, if anybody wants to like me on LinkedIn, if you're not interested in hiring us to do all this, then you just want to ask them questions like, by all means, we're also looking for feedback. It feels like there's an infinite number of things that you can put on a dashboard.
EDDIE56:02We've got our demo dashboard that's out there open to the world. We'll put a link to the show notes there as well so you can go and play with it. It's a fictional $100 million company with like standard metrics that one would expect for go to market in that company. So you can kind of play with it. And like there's a million things we could and will add to this dashboard.
EDDIE56:21But I always love people's feedback. So, you know, like, and we got the whole instructions on how you can build this thing yourself if you don't hire us to do it. Yeah.
RACHAEL56:29And speaking of feedback, if anyone listening to this is like wanting more content on a specific topic or maybe something that we haven't talked about yet, we're also always open to hearing from our audience and what you guys would like to learn about.
EDDIE56:41Yeah for sure. Well, Rachel, thank you so much for putting this together. Thank you.
EDDIE56:46Thanks for listening to the episode. If this resonated. Please give us a five star rating and a follow. It helps us reach more people, and you get our latest and greatest content without having to search for it. And if you're looking for hands on help and go to market strategy and or rev ops, please reach out to us.
EDDIE57:01We help our clients with everything from annual planning to improving processes and go to market, implementing systems to support those processes and go to market AI. We're always happy to offer a free consultation to help you identify the best opportunities to improve your go to market engine, with or without our help. You can find us at Union Square consulting.com and the info will be in our show notes.
