Okay, I think we're set. Megan, thank you so much for coming on Metrics and Chill. This is, like I said, off mic, a huge honor for me. I've been following you for a while, and really excited to chat today.
So thanks for being willing to come on. Absolutely. Thanks for having me. I'm looking forward to the conversation.
So we're trying to do jump kind of right into the meat of it, and I will give a little intro. I feel like if you've been on LinkedIn and you don't know who Refine Labs is, like you probably haven't been on LinkedIn or paying attention that much. I'm like everybody at this point goes, but Megan is the CEO of Refine Labs, a leading B2B to Manage and Agency. Like I said, if you've been on LinkedIn, you've probably seen them, Megan's stuff being published.
Do you want to give like the 32nd elevator pitch for Refine Labs, like what you all do, and who's a good candidate to work with you? Yeah, the way I explain it to my parents is we help our customers get more customers. We are a B2B demand-gen agency. We partner typically with B2B SaaS companies.
I would say anywhere from 20 million up to, you know, one billion in revenue per year. And we help optimize efficiency for paid search, improve effectiveness of paid social advertising, all with the focus on driving more qualified web conversions, more opportunities, more close one revenue, helping our customers get more customers. Love it. I love it.
So Megan's graciously agreed to come on and chat with us about, today we'll be talking about how to leverage historical data to uncover opportunities for growth, and look at some forecasts, like be able to forecast growth for the future. So I'm excited to dig all into this, and we'll kind of get right into it. So you mentioned like when we were talking off mic, when you start to work with clients, the first thing you do is sort of review historical data with them. I guess a good way to start is like, how far before we get into like what you're looking for, how far back are you looking?
And like for listeners who want to try and emulate some of your playbook, like how far is too far to be helpful and maybe irrelevant? And how much is maybe not enough where they shouldn't put too much confidence in data if it's not far back enough? Yeah, it's a great question. So at this point in time, what we're finding is a trailing 12 to 24 months is the most impactful.
If you have the data from the beginning of 2023 until now, I think that's a really good data set. As we all know, we probably all experienced our version of the COVID boom in late 2020 and 2021. So there were pretty unusual market conditions in 2021. And then there was a bit of a retraction and a correction in 2022.
So if you are going to broaden your data set to include those years, it's kind of important to recognize that you might have some outlier data, both in terms of sort of over performance or inflated performance in 2021. And then maybe almost sort of under inflated in 2022. So we found that there was some normalization in 2023 and 2024 as the market stabilized a bit. So given that we're going into 2025, that's our kind of core focus and what we would typically recommend as you're thinking about 2025 and putting your forecast together as well as your go-to-market strategy and what you want to focus on to improve your results.
And I would imagine like the trailing two years, even without COVID probably is a good rule of thumb because you're usually starting like new marketing programs every so often, you're changing things, you're adding new products, like your SaaS company or whatever, or even like manufacturing whatever, like you're rolling out new products. So it seems like, yeah, I can understand how that would be the sweet spot of like far enough back to have a good sampling of things and not have like just six months be skewing all the results, but not be so far back that like, I can think about a data box for example, like there's a lot of stuff we launched in the last two years that didn't exist before then. So it would not be that helpful to look that far back. Yeah, that's typically a sweet spot.
I would say the minimum data set, you know, you can work with two to three quarters of data, but you also need to recognize that that's not giving you a full picture. And it's, you know, if that's all you have, that's better than nothing. And you should use, you know, use that and go through the process. But then also recognize that there might be some things relative to seasonality or just things that you're not gonna see yet because not enough time has passed in your data set isn't big enough.
So just, you know, every business is different and at a different stage, I'm always an advocate for leveraging historical data when you have it and just use critical thinking and common sense to make sure that you're not blindly following the data and understanding the relative context around as well. Yeah, 100%. When you, so once you look back and you're auditing a client, say 24 months of historical data, start us here, like what are you zeroing in on? What metrics are you looking at?
What are you looking for in the data? Like do we mean company-wide data? Like that they could be tracking across, are these specific teams? Are these specific KPIs?
Are there certain metrics that rise to the top? Are you all visualizing? Like I know you all are sort of semi-opinionated on like, I don't know if you still track, like I think Chris came on a couple of years ago and talked about the hero metric. Like are you, one thing I, not gonna derail is a little bit, but one thing I liked in that like chat that John had with him at the time was like, you all began to like sort of say, well, hey, all these companies are defining pipeline like a different way and like all these metrics a different way.
So yeah, is there standardization that you do? Like when you come in, what do you do to get an accurate look and what exactly are you looking at? Yeah, it's a great question. Let me kind of break that down for you and then I can also speak to your own pipeline and some of the more specific perspectives that we have at Refine Labs.
So we call it a revenue performance assessment. So what we are looking at is we're typically gonna use the CRM Data Salesforce or HubSpot and then we might also be looking at data in your marketing automation platform, whether that's HubSpot or Marketo or Pardot maybe. So those are the two sort of sources of truth that we're looking at for this analysis. We are going to pull all lead and contact records of in bounds that have come in to the company over that period of time.
Ideally, we're also able to grab whatever the pipeline source was. So in many cases that can be really straightforward in terms of maybe paid search UTM, understanding a very specific paid search campaign that converted or that could be an event that people went to, whatever it happens to be. Everyone's data is different. Everyone has some gaps in their data, nobody has a perfect CRM.
Part of this process also, little tangent side note, as we go through this, we also try to identify where we see data gaps exist. And that's actually a benefit of this exercise because it's an opportunity to not only do the analysis, but identify where you can be tracking things in a better way. So going forward, you can improve just the data accuracy that you have. We will also be looking at opportunities.
So we're gonna wanna understand based on all those lead and contact records and any associated pipeline source data that we're able to gather. Let's follow them that contact or that lead through the funnel. So how many opportunities are created? How are those opportunities progressing through the pipeline?
How many of those are closed one? How many of those are closed lost? So those are the key records that we are analyzing within those systems. And so in terms of PPI's, one of the things that we're gonna wanna be immediately understanding is what is the inbound conversion sort of flow at a high level?
And then we conduct what we call a split the funnel analysis. When we begin to look at that inbound funnel and start to look at it and sort of slice it up by pipeline source, what is that telling us? Is it showing us that certain items are converting to opportunities at higher rates? Is it showing that maybe certain programs are driving a lot of top of funnel, but never materializing into opportunities?
The split the funnel analysis gives us a level of granularity of sort of the efficacy of different programs that you have. Now my other sort of disclaimer here is, marketing isn't intended to be measured perfectly. And that's not the goal here. The goal is to identify trends and come to hypotheses of what we think the data is telling us.
It's never going to be perfect. And when we think about pipeline sources, we're looking at sort of the tipping point of what influenced that lead or contact prior to the conversion. But we all know that there's a million things that that person experienced or interacted with before they came to lead. So whether search converted them or social converted them, or they just came to your website, direct traffic, we have to recognize that there's a lot of things that play there.
But directionally, this is going to give you some important insights that can help be one key input into your strategy. So the final output of the revenue performance assessment will effectively be a high level analysis of your full inbound funnel, splitting that up and looking at that by pipeline source. And then understanding from a close one and close loss, almost like a win loss analysis, what are we seeing in terms of trends of why we are winning and why we are losing? So those are sort of the outputs from that analysis.
In addition to that sort of identification of data gaps where we can say, OK, great. We know that we can track certain things. We're not tracking that here at this company and these systems. That's something that we can correct going forward.
I'll pause there. There was a lot of talking. No, this is great. My mind, it's like that GIF or the meme where all the numbers are swirling.
I'm just trying to like, I'm seriously taking notes here to come back and ask a follow up question. I'm curious when it comes to tracking pipeline source. I know this is what I know we're going to talk about. Things you all are opinionated about.
I think you have ways that you prefer to track this. Or probably, I'm sure this is one of the areas where there's gaps coming. And so pipeline source, how much weight do you put on? You've got people on one hand that are like, how did you hear about us?
And you're getting all these standardized answers of podcasts, CEOs linked in, whatever. And then you've got people that are just doing the UTM, or Google search or direct or whatever. If you see people that are more on the standard side of direct, organic, whatever, do you put a lot of stock in that? Or do you try and go through the CRM notes and try to uncover any nuances there?
Or do you start there and then try and move them more to the nuance? How did you hear about us or tracking other channels like that? Yeah, that's a great question. And so our general perspective on attribution is there's no one attribution model that is perfect or the best.
We believe that you should leverage multiple forms of attribution, again, as signals and data points to help make decisions, not as the source of definitive truth. So you're referring to a concept that we talk a lot about, self-reported attribution. The way I like to break that down is your software-based attribution is really helpful for what we would call demand capture programs. So paid search is sort of the most straightforward example.
You can set up conversion tracking in Google, and you can very clearly understand what campaigns are driving pipeline, which ones are not. As long as you are an expert in that, and your conversion tracking is set up properly, you should be optimizing paid search for efficiency and really only spending on campaigns that are converting and winning you opportunities. So that's a fairly straightforward setup. Now, when we think about demand creation programs, and we think about paid social, we don't believe in running direct response or gated content on paid social.
I mean, that's pretty much common knowledge now in LinkedIn marketing land. And the reality, though, is the reason people did that before was because it was easy to measure. And so when you deploy a more brand awareness, sort of demand creation, strategy on paid social, effectively what you're doing is you're ungating all your content, you're putting all of that out there, you're driving people back to your website to learn more, maybe some landing pages, maybe you're a podcaster, YouTube content, because you're trying to get the word out and overall just provide value and resources to your audience. Now, when software-based attribution tells you that 75% of your in-bounds came from direct traffic or organic search, OK, that's helpful to a degree.
But then what I always like to ask people is how do they know to go to your website? How do they know to go to your website? That's the question you actually want to answer. It's great that I know that they just came direct to my website.
But what I really want to know is how did they know about my website? So that's where self-reported attribution can answer the question for you. And so we recommend all of our clients at a required field. How did you hear about us at a primary conversion point, open text, required?
And we can capture that information. And we build what we call a hybrid attribution dashboard. And we'll show you. OK, 70% of your traffic or your in-bounds are direct traffic organic search.
Let's overlay the same self-reported attribution data to the same leads that came in and that are attributed in that way based on software. And let's see what they say. And that's where you get those more qualitative insights of, I saw your LinkedIn post. I listened to your podcast.
Also things like word of mouth, my peer, my former colleague recommended that I use you. I found out about you in a Slack community. All of these are really valuable insights that help answer that question of how did you go to direct to my website? Or if it's organic search, you basically are just passing through Google to get to my website.
How did you know to search for that thing? So both are really, really critical and really important in looking at that bigger picture. In terms of pipeline source, the logic or the reasoning behind the methodology is it helps identify what source becomes a common tipping point, essentially, to that final step before they get into the funnel and get on a sales conversation with your customer. And that can be helpful to understand that.
So if we see that many customers are still running a lot of the old school plays with gated content or direct response on LinkedIn, so when we do this analysis, very common conclusion is showing them, OK, look, you got 300 leads, last quarter from LinkedIn direct response or gated content campaigns, but only three of them became opportunities. So what we're trying to communicate to you is this LinkedIn strategy isn't really effective. The top of funnel performance might look good, but when you trace it to the bottom of the funnel, we're not really seeing the impact that we want. Now, we know, because we've done this with 300 other B2B SaaS companies, if we reset and redeploy your LinkedIn strategy, focus more on brand awareness, we won't be able to track this as much, but we know that we can drive more qualified, inbound web conversions on your site.
Now, let's take a look at that. Wow, 25% of your web conversions actually result in an opportunity. And 50% of those move to close one. That's a reliable, predictable funnel.
If we can drive more of those, that's going to improve performance. And we believe if we reset this strategy in this way, we can accomplish that objective. So that can be a pretty typical conclusion that we can come to after conducting the analysis, especially if a lot of companies still have a lot of those old school tactics running in their mix today. And when you mentioned at the end, you'll do a closed one and closed loss analysis as part of this revenue performance assessment.
And what are you looking for there? Is it more of that example that you just shared of the pipeline sources and how they're impacting closed one and closed loss? Or is it more getting into the product things, and maybe features or functionality that are missing? Is it more product oriented or more marketing oriented if that makes sense?
Yeah, both. You can get insights for both. What's really interesting about the closed one and the closed loss analysis is that, in my view, it helps really crystallize your ideal customer profile, and therefore your overall targeting approach. And if you have multiple product lines or business units, which ones are really being accepted by the market?
So some of our most interesting conclusions from the win loss analysis are often, people have a perspective of, oh, this is our ideal customer. This is who we're going after. When you conduct a win loss analysis, you can say, oh, this is really interesting. You think that you want to win in the enterprise space, but 70% of your closed one revenue is from the mid-market.
So that's actually your sweet spot. That's how you can acquire customers. You have really strong ACVs, fast sales cycles. That's the majority of customers that you're acquiring.
We can still try to acquire the enterprise segment, but you're clearly really good at this, and there's still a ton of room to grow in the mid-market segment as well. This could also show itself in specific industries. Oh, wow, we find that the manufacturing industry is the primary industry focus. Should we go back to our targeting and see if there's opportunities to adjust our targeting to really focus on the industries or the personas or the segments that are buying the most from you?
So those are often, the win loss analysis often really surfaces some good insights in terms of how are we targeting? Who are we targeting? Where should we adjust that so that we're maximizing on what's working best and the opportunity? And it's not to say that you still can't dedicate budget to maybe new segments or new products that you want to promote, but that you should really think through how you want to prioritize that to hit a growth target.
You're more likely to move the needle on achieving a growth target by doing more of what is working than doing something that is unproven. Yes, okay. My last question on this first point is you mentioned that there's often data gaps you see, and I think in some ways listeners will breathe this eye of relief because the pressure is off, there's no perfect, no one's got this perfectly figured out. What are some of the most common data gaps I'm just curious that you see when you come in?
Yeah, so number one, most people just don't have straightforward conversion tracking for paid advertising programs set up correctly. It's a little bit of a lift to get it done both in platform and with various integrations with Salesforce or HubSpot, but there's no excuse that you shouldn't know exactly what is happening with paid search, for example. And so that's typically whether it's conversion tracking within the Google Ads platform, setting up appropriate UTM tracking, making sure that anything that's in Marketo or HubSpot is actually integrated and passing through into Salesforce, that will flow often is not set up or not set up properly to collect everything. Most companies still are not using self-reported attribution.
This is a great insight to capture really important information. And then additionally, a lot of people have not designed a system to categorize all of their inbounds and build out this concept of pipeline sources. This one typically, everyone has sort of some of it done, but not all of it. So I think those would be the top three data gaps that we see.
And then I would say, usually they're sort of like the basic integration setup with the map in the CRM, but oftentimes there are issues or gaps within that integration where maybe actually, wow, you're collecting everything on the lead record in HubSpot or Marketo, but you're not passing those variables over into Salesforce. So we're losing the full funnel view that we want. So it's like, oh, you're collecting the data, but we're not passing it through effectively to understand bottom of funnel impact. So that's another common thing that we see a lot that we work with our clients to correct.
Awesome. So first thing you'll do, just to recap is this revenue performance assessment. You'll look at the leads that come inbound, pipeline source opportunities, you'll follow the opportunities all the way through, close one close loss. You'll do this analysis and you'll come away with suggestions on how to refine targeting, which channels you might lead into more how you can optimize different channels, and then fill any data gaps, make recommendations on filling data gaps.
At this point, then what are some other, so specifically, how are you using this data then to forecast moving forward? And how would this relate to hitting a company's targets? Like let's say a company goes through this exercise now, and they have a goal for 2025. How would that play into that?
Great question. So when we look at the historical track record of performance, we effectively want to start with a baseline of just a linear progression as if that performance were to continue. Then what I always say is, if you want to do better than you did last year, something has to change. You have to improve some aspect of your funnel.
So the baseline will be effectively a linear progression of the historical track record of what we see. Let's take things into account like seasonality and those types of norms in our business. You don't just have up into the right necessarily every single month. Then let's identify what we found in the analysis.
Wow, we really need to improve our conversion rate from form complete to opportunity created. Something is wrong there. If we literally just convert 10% more of our inbound to opportunities, we can hit 20% more revenue next year. So it's actually identifying what are our levers.
And so when you think about it from a demand gen or a paid advertising perspective, you can spend more money. So if we were to spend more money on advertising and we were to drive more results because of the increase budget, what would that look like and how would that impact our performance next year? How much better would it be? Then what are other levers?
Typically things like volume of web conversions, conversion rate from web conversion to opportunity created, conversion rate from opportunity created to one. Where are we seeing opportunity across that funnel? And can we invest more? And then can we figure out what we need to do to improve one or two of those conversion rates?
And that's really, you then can model out with logic and reliability that if we were able to improve those, how that would actually impact your results. And that's where we like to focus on with our clients. It's like, don't try to boil the ocean. Oftentimes, if you have two or three levers that you can pull to change things, you can hit your growth targets year over year.
So I find that people often try to overcomplicate it or try to have like, we're going to do these 10 things. That's going to help. Actually just really zero in on the two or three things that are really going to move the needle and focus on those for three to four quarters. And then do the analysis again and pick your next two to three things and focus on those for the next three to four quarters.
So that has reliably worked. Especially what I would say is we've been working with more like really larger companies. I would say anywhere from like 250 million to 750 million in annual recurring revenue. These companies are successful.
They have typically been around for 10 plus years. They're typically still running a lot of the old school plays. They have a lot of data that we can analyze and we can look at. They're spending a lot of money on paid advertising.
Anywhere from 250 to a million dollars a month. All of these companies that fall in this bucket insane opportunity to be more efficient and more effective. And their relative success to date has not put them in a position to adopt a lot of the things that probably feel like old news on LinkedIn now. Oh, of course, no one gates content.
Or of course, no one does this. And it's like, oh, a lot of people still do that stuff. There are different ways to modernize it. So there's a huge opportunity for that particular segment.
And we're seeing that most of our new customers are kind of in that range. And we're able to immediately drive improved efficiency and effectiveness with their paid advertising. When companies come to partner with you in this respect, are they often bringing their own target to you? Sort of saying, OK, we want to hit this revenue goal next year.
What are some levers we could pull to get there? Like, what would we have to do in order to get there? Or are you coming to them and saying, if you do these things, like these are your opportunities, and if you do, then we estimate it would get you X amount of revenue and that should be your target. So are you setting a target based on what you think is realistic and feasible looking at the data?
Or are you trying to reverse engineer their goal in the most efficient way to hit it? We do both. We'll provide what we call a demand planning analysis. And so based on that historical assessment, here are all the levers that we see.
If we focus on improving these levers, here is how we can improve performance. What's really interesting is when you do that exercise, and then you understand the goals that they're providing, and then what we'll say is, OK, if these are your goals, and this is the baseline, this is what you have to believe to be true in order to close the gap. And that will also help facilitate an honest dialogue if the goals are just totally unrealistic. Maybe they're aggressively achievable.
Maybe they're not aggressive enough, but it opens the door to that conversation. If the goals are kind of like way out there, we explain, like we'll show them. And we're like, look, like the math isn't adding up. Like you could five X your investment in paid advertising and we could assume significant improvement in every conversion rate across the funnel.
And you're still not gonna hit that goal. And so we can show them with math and logic, what is realistic to predict. The other part of this that we do look at and that is important is it's not just about the data. Like if you're a company that's struggling to grow, you need to kind of zoom out a little bit.
Do you have product market fit? Is your positioning and messaging correct? Do you have a shitty website? Like is your target market big enough?
How do you stack up relative to your competitors? These other factors are critical for a company to grow, right? So it's not just the marketing sort of demand, generate, or paid advertising strategy. So those are also important factors to look at that need to be part of the discussion as well.
I guess one of my last questions on this is, do you see any common trends of like the, when you look for those top one or two levers to pull? Do they often overlap client to client you keep seeing like these same things come up? Or does it really completely depend? Like some companies it's totally like targeting refinement and they need to do better with like they're targeting based on what you're seeing in the closed one or closed losses and other ones it's like, yeah, your opportunity conversion rate is abysmal, right?
And so you're gonna go through it. Or are there a couple that constantly surface? Yeah, so of course it depends, but we definitely see some pretty common trends across a lot of our clients. So I would say spending too much on paid search.
It's a really easy channel to overspend on, especially if you don't have proper conversion tracking setup and looking at your conversions in a full funnel view. So typically we're able to drive the same or better results with 30 to 50% less budget on paid search. So that's one, paid search efficiency is number one. I mean, Google has created a very successful business because they get everybody to spend all their money on Google ads.
Paid social effectiveness. So usually people are doing some type of paid social advertising but they're just not doing it well. And that can be because of targeting, that can be because of the ad creative, that can be because of the channel selection, that can be because of the actual execution on a particular channel. So I'd say the broad trend is like sort of not effective for paid social advertising, but there can be a lot of different reasons of why that's the case that we really need to dig into.
And then third is lack of a sort of measurement framework that makes sense for that business. So a blind spots, not really knowing what works or doesn't work and not having a clear perspective on how they can measure success. So I'd say those are the top three trends that we see. And those are the three things that we aim to solve when we work with our customers.
And my last question is, is there anything, like when all this process is done, let's say you go through this a year, are you then repeating this process? So like every year you'll do a revenue performance assessment. Okay, now we've been running impression based LinkedIn ads and not direct response or whatever, like to capture leads. And so now we're gonna see what that looks like for a year.
We're gonna refine this and you just kind of keep going. You're over here filming it. Absolutely. Yeah, you should be running your revenue performance assessment or split the funnel analysis every nine to 12 months.
Doing it on an annual basis in advance of annual planning is the right cadence in my opinion. And this is what I tell people, growing a business is usually doing the unsexy things really well over and over and over and over again. So that's what it comes down to. And so that is the process we run with our clients.
We've worked with a lot of companies and had a lot of customers for three, four years now. And we can continue to compound on the improvement and the growth year over year by taking that approach. I love it. Megan, this has been awesome.
Thank you so much for walking us through some of the framework source steps that you take at Refine Labs. Where can people, let's start here. Where can people hire Refine? Like where do they go to hire you and learn more about working with you and specifically like where to go to just follow along with you?
I think you're probably LinkedIn right for you. Like you seem to be pretty active there. Yeah, yeah. Just check out Refine Labs.com and you can check out what we do.
We also have our content product called the Vault. So if you're not ready to hire us as your B2B Digital Agency, you can actually get access to all of our playbooks and frameworks from the Vault. So it's a great resource for those that are just interested in learning more. And then yes, I am not a social media person personally.
So LinkedIn is the only social media. Awesome. Love it. Thank you so much for coming on.
We'll link to all those things. Really appreciate you being on the show.