Hey everybody, welcome to the second growth podcast. I am Tori Kinlick, a VP of Demand Generation at Refined Labs, and your host for today's episode. On this episode, I sat down with my friend and colleague, Sam Keanley, and we talked about shifting budget from demand capture channels to demand creation channels. In particular, Sam walked through a framework that he has built and refined over the years when analyzing paid media campaigns, in particular paid search campaigns.
And this framework allows you to quickly analyze which of your keywords and campaign groups are creating the most leads, opportunities and revenue, and which ones are not. And you should likely pause and ideally use to free up some of that budget for some of those demand creation efforts that those of us at Refined Labs are often talking about at NOSM. So I hope you find the episode helpful, and I hope you enjoy listening as much as we enjoyed recording it. The second growth podcast has been talking a lot about budget doing more with less, just kind of the current state of the economy, a lot of the challenges that marketers are being faced with right now.
Very relevant topic. And so one of the things that I wanted to bring Sam on to talk through is some of the conversations and approaches that he has taken or helped other marketers take when trying to shift some budget away from demand capture channels, ideally to free it up so that you can utilize it on demand creation efforts, a little bit more that longer term play. And so that's not always the easiest conversation to have. There's so many leaders out there that either only have a very fundamental understanding of marketing.
Maybe they're really focused on direct attribution. And right now with budget cuts going on, it's going to be rare for many of us to find ourselves in a situation where we can say, OK, this is the budget that we have for search right now. We want additional budget to go spend on some of that future facing awareness and demand creation efforts. Typically, what we're going to be looking at is, OK, we've got a pie here.
The size of the pie doesn't change. We need to figure out how we can utilize more of it in a little bit more of a strategic fashion. And so Sam's got some really cool ideas and a bit of a framework that we'll talk through that will help marketers look at their current spend in AdWords and figure out, is there any access? Is there any waste, which ideally could kind of free us up to reallocate that budget elsewhere?
But before we get into some of those details, I think we'll zoom out just a little bit and talk about this big question that we're trying to answer here. And it's about how to get your boss or your leadership team on board with shifting that money. For many of you that have been following along, the demand creation versus demand capture or discussion debate, whatever you want to call it, it's been everywhere lately. And rightfully so.
I think the most important thing to understand is it's not quite so binary of you should be doing demand creation or demand capture. Ideally, you're doing both, and you're doing them both efficiently. But there's not a lot of non-marketing leaders that are going to be able to really understand that thoroughly if you put it that way, right? Talking about things like you want to win before Google.
That's a phrase I like to use often that really seems to resonate with people. And I think that all sounds great. But like we said before, you're not just going to be able to get that additional budget handed over to you. So I think that if you're in a situation where you need to make the case, really, first things first is understand your audience.
Are you talking to marketing leaders or non-marketing leaders? For non-marketing leaders, the conversation might actually be a little bit easier, assuming that you've got a good level of trust there. And so I think simply quit, right? It's about the reactive versus proactive advertising.
Reactive, those search channels, you're waiting for someone to search the relevant terms that you're bidding on, waiting for them to know that they have a problem, and take to a search engine to try to identify that problem, or a solution to that problem, I should say. Whereas proactive advertising, a lot of this kind of more demand creation that we often talk about, that's what you need that long-term mindset. And I think that there's a good way to approach this. And there's a not so good way to approach it.
So Sam, I know that you kind of had some thoughts on, let's say we're talking to some of these non-marketing leaders. And what do you think is the best course of action there? You want to go in and start talking about cost per leads, cost per conversions. You think that stuff's going to resonate?
Yeah, I've made this mistake plenty of times. I come prepared with all this data. Here's our click-through rates. Here's our cost per leads.
And I watch the exact size just glaze right on over. They don't care about cost per lead, to be honest. It's a good metric. At the end of the day, their concerns are the business level.
What's driving pipeline? What's driving revenue? So if you want to be adding cloud conversations on them, to be taking you seriously, you need to be showing the impact at that level, not at the cost per lead, quickly, right level. Yeah, no doubt.
And so I think you have spent more time than probably most of the folks that I work with here at Refine Labs, really analyzing a lot of paid media, spend, and results. So what are some of the trends that you're seeing right now? And how does that relate to the topic at hand here? Yeah.
It's the blessing and curse of Google, right? It's directly attributable. So people know exactly what they think they're getting out of it. It's the race to how can we be as efficient as possible with lower cost per lead, higher click to conversion rate.
And while I love Google, I hate Google because they trap marketers in thinking that believe the conversion is the end of the deal because that's all that it usually connects to most standard marketers end up hooking it into. So what I'll do is I'll audit a number of different B2B SAS accounts that we work with. And they come on and while they're coming in, sometimes they're excited. We have a really low cost per lead.
This is working. I'll go on and look. And it's not uncommon for me to see 60% to 75% of them are spending money on keywords that, although they convert, they're not turning into pipelines. They're not turning into revenue.
So while it looks like it's doing well from a platform standpoint, it's ultimately really inefficient for the company. Yeah, you would think that view, that understanding is critical for all marketers to be taken a look at. But oftentimes, that's not the case. Too many other marketers, other agencies, other digital campaign managers are oftentimes optimizing exclusively for those conversions.
And what we know is that those conversions are almost a bit of a cosmetics stat. Certainly, they can be a good indicator for success. But what's really important is what's happening to those leads, those conversions, once they enter your sales funnel, once they get into your CRM. And so analyzing things exclusively on the paid media channels is going to only give you a fraction of the picture.
And the better approach is to look at that information, but couple it with what's happening inside the CRM. And so at a 50,000 foot view, that makes a lot of sense. It sounds really easy to do. But I know that there's a whole lot more nuance to it than that.
And so in some of the conversations that you and I have been having recently, I know that you had an example that you like to run through, which I think is really going to provide a little bit of this framework, this guidance for our audience here. And I think, if, correct me if I'm wrong, but you might even have some visual aids here to walk through any of those that might be catching this on video. But I'm sure you'll do an effective job at talking through for folks that are just listening in today. Yeah.
So I love a good framework, love a good template. How do we make this repeatable for customers as we want to continue to do this? So yeah, I'll walk through. I'll do a screenshot with you and I.
Hopefully the team will be able to get that in the video format. We'll also post a link to it for anyone who wants to steal it. Just clone it and you can do whatever you want with it. So I'll pull that up.
And then I'll also make sure that I'm talking through it so for those who aren't watching on video are not missing a beat. So while this is loading, I believe you should be able to see a beautiful little template coming up. So to tee this one up, the core things that we'll need in order to run this type of analysis is that that's the one beauty of Google adds is that since it is direct response, you should have a good bit of data already to work with since you're using SRIFS, UTMs, other things to capture this type of information. So this is where we say, if you want to get your Boston board, use data to your advantage.
So when we get into the Google side of it, what we want to look into is we have an example here. And I'm using the example of Drift. They're not a customer. Just most people know Drift, the category of chat bots, conversational AI, conversational marketing, however, want to be known as.
So using this as an example for people to just see how we like to structure accounts and how we'll want to look at the analysis itself for getting into each of these campaigns. So a typical on-paint search structure, if you're looking at column A here, we usually look at four different campaign levels. The first is Brand Keyword. So this is going to be things like Drift, or where they're using the brand name as part of the search.
The second is high intent non-brand keywords. So these will be more at the category level or the software pane level. So it's going to be something like chat bot software. Third category is the load of mid intent non-brand keywords.
So that's going to be something just like chat bot, sales enablement. So the difference between high intent and low intent is people may just be looking for what is a chat bot instead of I want chat bot software. So they're looking to solve for that pane. And that's the big difference there.
Now the fourth one, if you have a budget, we often see a lot of customers and organizations with B2B space going after some competitor terms. So in this example, Intercom is a common competitor to Drift. So we have in some keywords at that level. So now we've got that outline laid out for what the account structure is often going to look like.
We'll want to start getting into the data itself. So again, since it's easily attributable, you should be able to pull this out of your CRM if you've been leveraging tracking scripts to things like HubSpot or UTMs and then plugging that into your marketing automation platform. So what I like to do is start at the CRM level. I like to filter for all leads, which are the conversions that Google's calling them from the past 12 months, at least, the K through paid search.
So this will account for longer sales cycles, seasonality for the whole year, and then export that out. And so what you'll then have is the life cycle stage. So how many of those that came in through those UTMs turned into leads? How many turned into opportunities?
How many turned into customers? One thing to keep in mind is that the number's going to be smaller as you work down the way. So although there were five customers, that means that technically that keyword did generate 80 leads in this example of Drift. So you'll just want to make sure that you're not being literal with that.
And then working back to say, OK, if there were five customers, that means that we're going to be doing 80 leads in the first line example. So Sam, just for a point of clarity here. So earlier we were talking through how not all of the data that's going to be critical to this analysis is going to be available inside the platform. So for those of us that are just listening in today, maybe you could just give a quick overview of the structure of your template here and some of those data points that you're looking at lead opportunity and customer, and where you would typically source that information from.
Yeah, great question. So those would be coming straight out of your CRM and marketing automation platform. So often when you have a lead or an opportunity record, when it does become a closed one deal or a closed loss deal, you'll have this lifecycle stage that's in there. And so that's going to be updated as it goes on.
So as a lead is converted, there's com reports and sales for so lead with opportunity object information, opportunity with desktop information. So you can go through and start to look at that. So you can start to align how many, when we get into like a pivot table type thing, how many of each stage are mapping over to the individual keywords or campaigns? Yeah, thank you.
That's really helpful. And I think the thing that's probably most important to understand here is that the lead that we're talking about is truly one that is identified as such inside your CRM. If you're looking just at conversions on the platform, actually this is kind of a relevant example, feeling very meta right now. But for instance, for those of you that have drift or another chat pod on your website, you might be tracking conversions for when that chat window opens and fires.
That might be looked at or tracked as a conversion inside your AdWords account. Probably not the ideal one, right? Because you want someone to complete that. If you have a thank you page, it's a much better way to track that conversion.
But most important thing to understand is that a conversion on the Ad platform is not always equal to a lead inside your CRM. So I think that that's really important for everyone here to understand and certainly is going to make the analysis here much more accurate if you are pulling this information from your CRM, because that way you at least know that whatever happened when that person converted on the AdWords campaign, you know that it was at least good enough to make it into your CRM if that's where you're pulling your information and sourcing your data from. So yeah, I think that that's a great starting point here that we're looking at is just understanding that some of this information is going to come from your CRM. Some of it might come directly from the Ad platforms, but it's best to try to marry these two together.
Exactly. And that's why I'm far from Excel master by any means. This is a basic pivot table. And that's exactly what we're doing is we're marrying the CRM data.
So the UTMs, the life cycle stage. And then we're mapping and pulling over some of that platform data so we can understand the spend, the conversion, everything else from there so we can see how effective is at the end of the day. So what you'll see in the visual here is we have campaign keywords, life cycle stage. And then after that, we add in some additional rows or additional columns that speak to things like keyword spend.
So for each keyword, how much was spent during the time frame? If the aggregate campaign level, how much was spent on that whole campaign? And that's going to allow us to look at what's the cost per lead once we know how many leads there are by the keyword and do some additional calculations, which is where this is going to start to get interesting. So what I like to do is start with the campaign level.
So as we said earlier, say we have four campaigns, brand, client, non-brand, loan, client, brand, non-brand, and competitor. What we'll do is we'll look at in that time frame, I'll go into Google Ads and see how much was spent on brand campaigns. So for the example of here, I say, OK, they've spent $36,000 during this time frame on branded keywords. And then what I want to know is how much of that $36,000 was spent on each individual keyword.
So if you're running a single keyword or a single theme ad group, you should be able to get to this data. So say we've got three different branded keywords that have at least triggered some type of lead for us over the past year. So say we spent $20,000 on Drift, $10,000 on Drift Chatbot, $5,000 on Drift Demo. You would see that that means that those three keywords that have generated leads make up $35,000 out of 36,000 total campaign spend.
So what that's telling us is that $35,000 generated leads spend, $1,000 did not generate any leads over that first course of time. So that's your first area that you can start to look at. That's some independent spend. We can probably pull that out of over a course a year.
None of those keywords contributed anything. So they're not going to show up in this report since this is calling CRM data. So what you'll do is you'll hop into Google AdWords, and you'll see, OK, if Drift, Drift Chatbot, Drift Demo, all generated leads. And we also have Drift Pricing, Drift Alternative, or something else in there.
You can pause those keywords effectively if they haven't done anything for 12 months, or you can think about how should you be optimizing those? Maybe your landing page experience isn't as good. But what that signaling is, that's some inefficient spend, where if you wanted to pull something out, you could pull it from that one versus some of the others right off the top. So from there, what we'll do is we'll look at leads into opportunities, pipelines.
So this is where we're going to start marrying back over to the CRM side. And so what we'll do here is we'll say, OK, of the 80 leads that came in from Drift, we knew that 20 turned into opportunities. So of those, that means that we had a lead to opportunity conversion rate of 25%. And now we can calculate out the cost or opportunity of that.
So we knew that we spent $20,000 on that keyword. If we had 25% convert to opportunities, 20 opportunities, that means that we have a $1,000 cost per opportunity. So looking down a level to start looking at efficiencies past the cost per lead, we start getting into qualified odds, pipeline, everything else. And then, similar to what we did with the leads, we can see, were there any keywords in here that did not turn into opportunities?
And if so, find those in Google Ads, and you can start to pull it down and say, OK, well, they might be good at driving leads, but not opportunities. We don't see this often in brand. Brand is usually pretty good at driving leads, opportunities, and pipeline. You can see that through this whole sheet.
But when you get into some of the lower-entent non-brand terms, competitor terms, you'll often see a very big drop off between how many are leads, and then do any turn into opportunities. So a really strong example of that would be comparing chatbot software, which is high-entent, to just chatbot itself. So in the example here, what we say is that during a time frame, chatbot software, we spent $40,000 on that keyword, it drove 40 leads. So $1,000 cost per lead.
Chatbot, we say we'd spend $30,000, and it drove 120 leads. So that's the top saying that we spent $250 at a cost per lead. So if you're comparing those like most marketers, this was me not too long ago, you'd say, chatbot is way more efficient and effective than chatbot software. We're getting four leads for the price of one chatbot software.
We should put more money into that. And this is where the light bulb usually goes off when you start tracking it further down the funnel, because if you follow those, what you'll see is that, OK, so if there's 120 chatbot leads, you might see 4% convert to opportunities being a little bit generous. So there's 120 leads now turn into five total opportunities. So what that means is to take $30,000 to divide that by five, and now you're looking at a $6,000 cost per op.
Compare that to chatbot software, where you probably have a much stronger conversion rate from lead opportunity, since that's high in time, usually it's about 30%. So all those 40 leads, full opportunities. And so what that then number comes out to is you take the $40,000 keywords then, divide that by 12, and now you're looking at just over $3,000 cost per op. So that quickly flips, right?
You have chatbot before it, four for one on chatbot software. But now, chatbot software, you're doing two ops for the price of one op that chatbot. And this is such a prime example of exactly why people should not be optimizing for cost per lead. This is all spelled out right here for us.
You're looking at cost per lead for some of these low intent keywords, and everything looks great until you start looking at how those things are converting into legitimate opportunities, and ideally, sales qualified opportunities even further down the funnel. And at that point, now, when you see it all mapped out like this, you might start to follow why so many of us ever fine labs are constantly re-emphasizing that you want to be focusing on high intent keywords and high intent modifiers on Google, not the low intent ones. And it's not because you're not going to generate leads. Because you're not going to be generating pipeline from it.
And I think that this is just a beautiful example of exactly that. Yeah, now you're spot on. And so you can continue that further. So as we scroll over, we can see the same thing, opportunity to close one.
Even if they have a pretty similar op that closes right away, usually the higher intent, have a slight higher win rate, because they are more prime, so they're ready to talk. So 25% win rate on those, say, you know, have a killer sales team and say that that chatbot won, was the lower intent, win that at 20%. By the time you work out to the customer acquisition cost, you're looking at about $30,000 per chatbot versus just over $13,000 per chatbot software. So this is where I say, this is what's going to intrigue your boss, your executives, the bottom line of what does mean for our business, reply blind for customer acquisition costs, because say your product costs $15,000 ARR.
Looking at these two customer acquisition costs, that's the difference between having a payback in two years versus having a payback in less than one year. So if you know how long you usually retain customers, that's a very different margin model. That's what your leadership is going to care about. And then they will help to invest more, or start to educate them on where should we be putting money if this isn't efficient at the lead level.
Yeah. And when you were kind of teaming up the subject before, right, we talked about that, especially when you're talking to non-market leaders, they don't care about our click-through rate metrics and our cost per lead metrics. But finance executives will absolutely care about the cost to acquire customer metric. That one is probably the exception for the one metric that marketing leaders and finance leaders can all agree on matters a whole lot.
And that's how much you have to spend to acquire a customer. And again, the data here, it's plain as day, much lower cost to acquire customer for these high-intent keywords, the branded keywords, of course. And not so much the story for those low-intent. And I would say pretty rare that you're going to see some of those competitive keywords also showing that direct blind attribution.
There is a certainly play for the competitive campaigns, but that might be a topic for another day. It's always time for them. But yeah, I mean, that's what's tricky is Google is a business. It would be on the day, so they want you to spend with them.
So being able to go and follow the data all the way through is what's really going to help empower you as a marketer with this. So back to earlier when you were saying, are there any insights or anything else? So when I said, usually 60% to 70% of spend on non-pipeline, you'll see in here there's a very large difference between brand and high-intent non-brand and low-mid intent non-brand and competitor in terms of what does not turn into pipeline. So the brand, the high-intent non-brand, they turn into pipeline at much higher rates.
There's less volume here. So that's why people always say we want more. But knowing that they're going to turn into pipeline for you is why this is always better than the low-intent, the competitor, where you'll get a lot of leads. But one out of four, maybe we'll turn into pipeline.
So that's where we have to really do that comparison, and understand what is best for my business and my marketing strategy at the end of the day. Yeah, absolutely. And so for all of our listeners here today, you now have the framework to look at all of the spend that you're investing into these search channels and quickly identify where you're being a little bit wasteful, where you can free up some budget for other areas, whether you're just in a cost conservation mode, whether you're trying to make the case to focus more on some of those proactive channels and demand creation efforts. This is a great way to do it.
And so with that, Sam, any high-level takeaways or summary points you want to offer everyone here before we wrap it up? Yeah, I'm definitely going to get repel it with us. But focus on down funnel more than the cost per lead. I mean, that's what's keeping your business driving at the end of the day.
So being able to get to that is what we'll take you to that level to marketer. And then thinking from your boss, your leadership standpoint, when you go into these types of meetings, is knowing like, what do they care about? And how do I get to the data that's going to validate this so that they understand where I'm coming from? Like, put the rates in what's going to convince them.
It's going to be able, it's going to be the ability to show this type of data, this type of insight. So here's the data, here's the insight, here's the recommendation. If you can go to them with something like that, that's when they're going to be onboard with making this type of change. Yes, sir.
Yes, sir. And Mikey takeaway is if you're not already doing it, follow Sam keenly on LinkedIn because he's dropping knowledge bombs like this on a daily basis on the LinkedIn platform and asking nothing in exchange for it. So a great marketer and all around good guy too. So Sam, thank you so much for joining today, walking everyone through the framework, and we really appreciate it.
Yeah, this has been fun. Looking forward to hearing from you all. So if you all do this analysis, listen to how it goes. Thanks everyone.
Bye. Bye. Bye.