Unlocking Marketing Attribution

Drew: Hello, Renegade Marketers. If this is your first time, welcome, and if you’re a regular listener, welcome back. You’re about to hear a Bonus Huddle where experts share their insights into the topics of critical importance to our B2B CMO community, CMO Huddles. The expert at this Huddle is Taran Nandha, Growth Advisor, Startup Investor, and Founder & CEO of Growth Natives and DiGGrowth. We tackle the daunting challenge of marketing attribution in this one, from how CMOs get it wrong to how AI might transform your attribution dashboard. If you like what you hear, please subscribe to the podcast and leave a review. You’ll be supporting our quest to be the number 1 B2B marketing podcast. Alright, let’s dive in.

Narrator: Welcome to Renegade Marketers Unite, possibly the best weekly podcast for CMOs and everyone else looking for innovative ways to transform their brand, drive demand, and just plain cut through. Proving that B2B does not mean boring to business. Here’s your host and Chief Marketing Renegade, Drew Neisser.

Drew: Hello, Huddlers. I’m excited to introduce you to Taran Nandha for a fresh look at marketing attribution. Taran is the Founder & CEO of Growth Natives and DiGGrowth. I’ve known Taran for almost a decade through our mutual friend Eric Eden. Eric, a martech wizard with at least four successful exits as a CMO, all the way along his career, has had a secret weapon, and that was Taran and his India-based team, first in-house at Cvent and later on his own at Growth Natives, a company Taran has grown to over 300 employees within like, four or five years. It’s really remarkable. So I’m excited to chat with Taran about all things martech, especially the state of attribution. So with that, hello, Taran. How are you and where are you this fine day?

Taran: Hello, Drew and the rest of the folks, I’m as good as it gets. And right now I’m actually in India, so kind of time zones apart, but connected through Zoom.

Drew: We really appreciate you staying up. It’s late. And where specifically in India are you?

Taran: I’m in Chandigarh, which is northwest of Delhi, the capital city, so about a couple of hundred miles northwest.

Drew: Got it. Yes, alright. Well, one of the things we’re doing more and more of is, you know, getting to some input, some ideas and information really quickly before the audience has to leave, or perhaps to persuade them to stick around. So can you offer three things many marketers get wrong when approaching attribution?

Taran: The number one thing I’ve seen is people getting sold on tools. But I truly believe that there is no one-size-fits-all solution when it comes to really deciphering marketing attribution or revenue attribution. So I think it is really, really important to keep that in mind, that whatever you do, the process, the tool, the people, they have to be aligned to your business objectives and how you’re going to measure that.

Secondly, I think we get a lot of reports by channel, which is great, but at the same time that is not real attribution. A lot of times people confuse between channel-level reporting and attribution reporting. I think for really true attribution reporting you need to have a consolidated view of your customer’s journey, and then also, not just the digital footprint, but also offline things, like events and anything that is being done offline, and the efforts that are being put by the sales team to really get a comprehensive, good picture.

Last but not least, marketing analytics is a journey and is not a destination. So a lot of times we feel that, okay, we’ve figured it out, but you cannot rest. It’s a journey and not a destination, so you have to continuously invest in it, continuously evolve it, and make sure that you stay on top of it.

Drew: That’s a great start. Let’s go through those one at a time. We talked about the fact that no one size fits all, but we’d love it if it did, and that it’s always about the people, the process, and the tools. So talk a little bit more about sort of orchestrating that, and then we can get into some of the other details.

Taran: So when I say one size doesn’t fit all, you have to have a great understanding of your business and then how and what you want to measure and what is really meaningful. Once you’ve decided that, then you kind of embark on that journey wherein, like, you know, what is the right tool that I need to invest in? And like, a tool alone will not do it. You gotta have the people, you gotta have the process, and you also have to have the patience. So you gotta put all this together, starting with, like, you know, some basic KPIs, and then having an evolution model in your mind as well.

Like, you know, in the next three to six months, I want to really start getting the data together. Then I want to start running some basic reporting, and then eventually, like, you know, I’m going to get to a certain level of granularity. And that granularity, you know, you can’t just stop at, “Okay, I got 10-level metrics.” You can go down to, for example, if you’re doing Google paid advertising, you can get down to what are the ad sets that are working for me? Within those ad sets, what are the ad copies that are working for me? And then not just stop at the ad copies, but also, what are the keywords that people are clicking on that are actually leading to revenue?

I think I covered all three: don’t keep it limited to being very single-channel focused. Make sure that you are doing cross-channel analytics and kind of collecting all that data, having a plan which is long term, and then obviously not over-relying on just a tool, but making sure that you’re putting all the muscle behind it as well.

Drew: It’s so interesting. And having worked with clients for well, a long time, the attribution is so tricky, and what I think is so interesting, and what you’re describing is there’s a lot of, “Okay, we’re gonna focus on leads,” and so we’re gonna focus on cost per lead, for example, which we know is so problematic, right? And it’s a really interesting place to start and get into in this conversation because it seems like it’s a logical thing. You’re paying Google to bring somebody in there. You pay on a cost-per-lead basis. But the truth is, a lead has no value, right? And you don’t know if it has value until it sort of works its way, until it finally becomes an opportunity, and then it’s really doesn’t have value until it closes. So how have you weaned your clients off of cost per lead as a metric?

Taran: The number one thing we prescribe is vanity metrics to start with. What’s important for the business? Does your CEO or the Board of Directors really care about how many leads you’re getting? They care about pipeline and revenue. What are the bookings? What is the revenue? What is the ARR? Are we growing the ARR? Those are the metrics that you start with, and then you track your way.

Yes, leads are important. I’m not saying that leads are not important, because that’s a leading indicator. But just relying purely on leads and trying to just reduce the cost per lead—I can reduce your cost per lead in less than 30 minutes. If I go, let’s say, broad match on Google advertising, your cost per lead will go down from exact match to broad match, but you will start getting a lot of junk leads, which are meaningless if they’re not becoming opportunities, if they’re not becoming revenue at the bottom of the funnel.

So I think it’s really, really important to make sure that whether you’re tracking channels or tracking campaigns at the top of the funnel, having an economic value to that lead, and trying to make that economic value… So more than the cost per lead—trying to bring the cost per lead down, increasing the value of the lead at the top of the funnel. You’ve got like 5,000 leads that convert into – So 100,000 visitors, 5,000 leads, 1,500 MQLs, 450 SQLs, so on and so forth, and you have 40 closed-won which gave you $400,000 in revenue. So basically, what it means is that each visitor that you had on your website (because those 100k visitors resulted in 400k in revenue) each visitor is valued at $4, each inquiry or lead is valued at $80. Now, that is the number like you have to look at making sure that the economic value of the lead at every stage is getting higher and higher, and not just the cost. I hope that made sense.

Drew: I think so. So, let me cut to the chase on this one. Of those 100,000 top-of-the-funnel visitors, 5,000 were leads, so only about a third of those were actually MQLs. And it’s really probably better to just say SQL is about 10%. I only want those SQLs. So help me think about attribution, not at 100k in terms of leads, but 450 in SQLs, and how you can use the tools, and what are people doing to sort of get to more SQLs, if you will.

Taran: So think about the top of your funnel. You’ve got like, those 100k visitors. They’re coming from different channels. They’re coming from different campaigns. They’re coming from all these different things that you’re doing, both outbound and inbound, to bring them into your funnel.

As they engage with you, they fill out a form, they attended an event, whatever—they become a lead at that point. And as they’re moving down there, if you are not able to stitch who came from what campaign, who came from which ad, which keyword, which event, webinar, whatever other tactics that you are deploying, you have to be able to stitch it back to the original source.

Every one of them who actually becomes a sales-qualified lead, and not just at that point of entry, but also all the other touches that enable them to become an SQL, and then once they have become an SQL, what is the journey beyond that as well? Because, yes, as a marketer, it is very important that I am providing marketing-qualified leads and sales that become sales-qualified leads, and there’s a healthy ratio there. But guess what? If those SQLs are not converting to pipeline and revenue, I will not be the favorite of the salespeople or the management anymore. So it’s really important to keep the eye on the eight ball, which is really the revenue and the pipeline.

Drew: I understand, and I’m with you to a point, and then I get completely confused, which is, at this moment, we’re down to between SQL, and you call them close-won, there were 10, 20, 30 touches. There were three months, six months, there were different people coming in. When Gardner looked at it, it was this spaghetti chart of a journey. I know you have attribution dashboards that help marketers make decisions because it’s easy to say, spend more money to get top of the funnel. That’s not what we’re trying to do. We’re trying to get to marketing where you know this combination of things leads to revenue, right and so what does multi-touch look like right now? And how precise can you get? Easy question.

Taran: Yeah. Easy question, and an easy answer to it as well. The not-so-easy answer is that it actually it’s tough. You may not be able to get 100% insights, but I think you can get up to like 70, 80% insights accurately in that you know you’re doing analytics at two levels. One is the individual, and then roll there, roll it up to the account level. So as we know, in a B2B world, there’s usually a buyers committee, especially like for large ticket purchases, there’s probably, like, three, maybe five, maybe seven, people that are actually going to influence or make that decision, and all of those books are going to interact with your brand at certain point at different stages. So one is really that all the individuals, their individual journey within your marketing landscape, then aggregated to the top at an account level. So you need to have both the individual level analytics and then rolled up to the account level. And then you also have to have like, you know, the byways of the artificial intelligence and machine learning, it is actually easier now to identify what are the most preferable paths of least resistance, meaning what is the most commonly taken path by your customers that leads to revenue? So for example, like leads that are coming in from a webinar, they are likely to go download a white paper. Once they’ve downloaded the white paper, they’re likely to go book a meeting with you, or more preferably respond to your sales outreach. Once they’ve reached that point, they’re going to consume a case study. I mean, this all is actually visually available to you through modern analytics, where you can see, like you know, what are the steps that were taken by an individual and by an account before they became a lead, before they became an SQL, before they became an opportunity, and finally, before they became a closed-won.

Drew: That sounds like journey tracking. I know that you’ve been developing tools to help with that. One of the things is it’s about, you have to connect a lot of different things in order to do it. Talk a little bit about, and then we can get to AI in a second, but so to really track the journey with some precision, what has to be connected?

Taran: So the plumbing and data collection is the most important aspect. So the plumbing includes, if you’re running advertising, you’re getting data from Google, Facebook, LinkedIn, you know, all the other channels that you could be leveraging for running advertising. Your cost data for advertising is residing there, but they will never tell you, like, what is the revenue that is coming from that cost? Then those folks are becoming leads. They are probably being nurtured by you through a marketing automation platform. So there’s a lot of touch points that are stored there, and then eventually they’re handed off to sales once they are like, either marketing qualified or whatever as your stage definition, and then they go through the sales funnel. So at a minimum, you need data from all your ad platforms, all your events, all your like, you know, top of the funnel activities that you’re doing. Then you need data from all the touch points that are being activated through your marketing automation platform. And then finally, the revenue data is coming from your CRM, and also certain touchpoint data is coming from your CRM. So you have to have like you have to at least all these systems have to be connected. All that data has to be synchronized, normalized, so that like that person that came to your website is tagged and dragged throughout their journey, and that’s how you build that data set. And then you do reporting on top of that. And what we have done is basically we built connectors for the entire ecosystem, be it your ad platforms, be it like Salesforce or Dynamics or CRM, and then also, like all the popular marketing automation platforms. And what we are doing is we’re aggregating all that data, normalizing it. We have our own, you know, AI-driven models that we run on that data. And then we’re running, like, visualization on top of that as well. That gives you, like, pretty good, deep insights.

Drew: When you say normalizing, can you just explain what you’re actually doing?

Taran: So within normalization, one is really, like, you know, stitching that data together. For example, like, you know, a person coming to your website without filling out the form, we know their IP, basically what IP they’re coming from. The moment they fill out the form, we know that it’s Drew, and now all the touch points from that IP are now actually being able to be attributed to Drew. And then Drew actually goes, you know, through the funnel, eventually either becomes a customer or not. That all stitching of the data needs a certain level of normalization and certain level of manipulation before it can be used for healthy analytics.

Drew: Part of the role that you guys play, and this is an advantage because you’re in India, and your cost per hour is lower, is you actually do that stitching together, right? And the normalizing behind the scenes, if someone doesn’t have a rev ops department, and because, unless all the systems are connected and coming through, and, you know, collecting the data in a common format behind the scenes, you’re pulling all this data together and stitching it. Now, before I forget, well, it’s just AI to work needs good data, really good data, right? So we’ve stitched all this data together, we’ve normalized it, and then what is the role of AI? And, you know? And what is that that’s not an LLM at that point, it’s something else. What is it?

Taran: So, AI, basically at that point, it is not. At that point, it really is, once you have, like, that data that is stitched together, you can run different types of models on that. They are AI and machine learning-based. And as you are, like, running those models, the beauty is, those models are actually self-learning. So as in any other model, you have to, like, do a lot of manual intervention, and over time, you have to be updating them all the time. With the advent of AI, a lot of these models, once you’ve implemented them, they actually are self-learning, and they self-govern themselves, and there is less engineering that is required beyond a certain point. And you’re absolutely right to run successful AI models. You have to have really, really good, organized data. And I think that’s where, like, a lot of heavy lifting is required. Once you have that data, then the models and the visualization can take off.

Drew: We have a wide range of CMOs in CMO Huddles who have, some have huge budgets, and some have very modest budgets, and they don’t have—they may have a modest tech stack. Obviously, they’re going to have a CRM, most likely Salesforce or HubSpot. They’re going to have a marketing automation platform, and they may have a few other tools, I guess. The question is, they may not have the in-house expertise to pull it together, and I’m wondering if additional tools are required, and what does it cost to do this. Because, look, if you had to spend $100,000 to figure out whether or not you know it would be what’s it actually cost to build a decent attribution system and how long does it take?

Taran: You’re right. I mean, the skills can be a bit of a challenge, especially in internal marketing teams, because traditionally, marketers have had to lean on the CIO’s office to do any kind of technology project, especially that require like, you know, data and visualization and analytics. But the rule of thumb that I use is deep marketing attribution analytics is not for companies that are not spending—you have to be spending at least half a million on, you know, paid media, basically. That’s kind of the general guideline that I have. Like, if you’re spending close to half a million on paid media and other campaigns, then you should be willing to spend anywhere between five to 7% of that budget to actually measuring the success. The other thing I’ll tell you like, you know, the cost of running analytics, we ignore it when we are doing it internally. So, you know, I’ve seen so many times companies, they have people that are like, spending about a week out of their month, doing nothing but building reports and analytics. If you take that cost of like, you know, let’s say the average cost of that individual is about like, you know, 10k, so you’re already spending about $2,500 a month, which is about $30,000 a year. And like I said, you know, if you’re spending for half a million, you should be allocating about five to 7%—that’s what it is. And there are tools out there in the market that can enable that. Yes, there might be some initial cost in building out the plumbing and setting up the system. So let’s say, like, the cost of the first year could be about 10% of half a million, but then eventually it will go down to about 5% of that half a billion. And as you can spend more, you know, your needs may go, but it’ll always keep like, you know, the percentage that you’re spending off that budget will keep reducing.

Drew: And so the thing that’s interesting is, if we’re basing this on advertising and the optimization of the advertising—the assumption, then, is that we can just keep optimizing the advertising, both the message and the media, in order to drive the business, when we’re really talking about multi-touch attribution and the combination of things.

Taran: The reason I use that is because you said, what is the number? I usually use, like, you know, like, whatever is your paid media spend, and if it is not paid media, if you’re like, you know, leveraging media partners, if it is like, you know, any kind of outbound activities that you’re doing, whatever is actually your program spend, you know, about 5% of that is what you should be reasonably allocating to running analytics. So if you are, like, spending a million dollars in marketing, then it should be about, you know, 40 to 50k as you go up like as I know that percentage keeps going down. So if you’re doing like, you know, let’s say your program budget is about 10 million, then you’re not spending half a million on it. Probably be spending by maybe a couple $100,000 on running analytics on it. 

Drew: And in theory, that money, that investment, is making you significantly more effective, and so it’s paying for itself.

Taran: Absolutely, absolutely.

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Drew: So we have a question. Do you need to use a tool to mine prospect intent data, like Sixth Sense or Demandbase? Or can you mine intent data using other tools? Or, you know, as you’re building these attribution, I would think intent data would be part of this story, right?

Taran: Yes. ABM analytics is part of the story. So be it Sixth Sense, be it like, you know, Demandbase, there are signals, there are inputs that we can incorporate from, and one should incorporate from, like, you know, whatever is part of your tech stack and wherever there is spend. So as you’re building out these attribution reporting, there is some level of reporting that is available in Sixth Sense and Demandbase as well, but it is usually very limited to like ABM type of reports, and it does not include data from all the other sources, basically.

Drew: So I’m imagining a CMO is not going to be doing this work themselves. They’re either going to have someone on their team, or they are going to outsource it to a company like yours, and they’re going to need to know what questions to ask, right? Because they can’t get in. They’re not going to be connecting the things. But they need to know questions to make sure that when you’re building something like this, it’s built in a way that you know, because it’s garbage in, garbage out, right? So, and we’ve talked about data clarity, but I still feel like there are some questions that we could equip the CMOs in the audience to ask when trying to set up a multi-touch attribution dashboard like you’re describing.

Taran: The most important questions to ask, if you would say, like, you know, if you’re just getting started on this journey, one is, like I said, “What is important for you to measure?” Most people do have a fair understanding of that. Then really mapping out, like, you know, what data do I need to enable this level of insights? And then where does that data reside today? How do I collect all this data? And truly, I also believe that one has to have some level of consulting to run and build this successfully.

We’ve run into so many situations where companies have either bought, like, you know, Sixth Sense or other tools, and they are just not either implemented right, or it was a decision that was taken that we should move forward with it. But then there was no real consulting. There was no real like somebody who actually understood how to build it. And then it really became, like you said, you know, “garbage in, garbage out” basically.

So the choice of the tool is important, but it is also very important that you know who are the people that are going to be actually implementing it. And you also need to have like, you know, champions internally. So it can’t be just your KPIs. It’s not just what you think is important. You have to understand, like, you know, the ultimate goal of marketing attribution or mission analysis is also to bring sales and marketing closer together. And to do that, you’ve got to have a very active dialog with sales, like, you know, in terms of what is important for them, in terms of KPIs. What are the common KPIs that you agree upon? And then how do you get to a point where you can actually report against those KPIs? And then obviously, like, you know, continue to have that dialog and keep improving things as you go forward.

Drew: I think the keep improving part is where I keep getting stuck. And I get stuck because of the complication of this journey, and so you talked earlier about journey mapping and visualization, and I’m trying to wrap my mind around that, so that we really understand what good looks like, or great looks like in this world. Because, you know, it’s so easy to just do first touch or last touch and just call it a day, which is why so many people do that. And it’s so problematic to do first touch and last touch, right? I mean, it’s like the salesperson who has this wonderful dinner with a customer, and then they end up closing. Was it the drink, the fine wine? Was it the chocolate mousse? What was it? And they can’t. It was just a great dinner, and it worked out, and so they felt comfortable, and they ended up closing the sale.

But the truth is, they didn’t even get to that dinner unless they’d gone to the webinar, and they had gone to the trade show and they had read the white paper. Help me sort of wrap my mind around what feels like unmeasurable and infinitely complicated and somewhat irrational, maybe with a specific example of where you sort of looked at attribution for a client and found, “Oh, we were thinking about this, but it became that.”

Taran: So I’ll give you a very specific example. B2B companies tend to spend a lot of money on events, both by hosting events and also by attending a lot of industry events, by spending on having presence there—booths, speaking slots, advertising at events, and usually they are pretty big ticket items.

So we had one of our customers that they love going to trade shows, but they were never able to figure out what’s the real value that’s coming back to them. So what we did is we helped them build cohorts. In fact, it became a part of our product eventually. We built cohorts around events. For example, like you went to Dreamforce in 2023 or 2022, and your average sales cycle is, let’s say, two years. There were people that came to the booth, they dropped their business cards, you got their list. But you never are able to tell, like, you know, what was the revenue impact of that?

You usually say like, “There were 500 quality leads that we got from the people that showed up at the booth.” And then sales people are usually complaining and saying they just came for the freebies. But what we did is, like, we made that into a cohort. So it’s a cohort of all the interactions that you had at that event, and we tracked that cohort over time, and how many of those eventually became customers, and what was the true ROI of that event? Because typically, like, large events don’t give you ROI right away. So that is one example where we’ve actually, like, really done wonders in helping our customers understand the true economic value of those cohorts. And now those cohorts are not just limited to events. There can be cohorts, for example, like, you know you’re running search advertising. Anybody that is coming from search advertising today? I mean, you’re looking at what was my pipeline this month, and then you’re also looking at revenue this month. But that revenue, mind me, is actually coming from leads from the past. It’s not the revenue from the leads that you generated this month. So let’s say you spend like, 50 grand this month on Google advertising. You want to see how those the leads for that month actually performed over time. So you need to have like, you know, these cohort analysis, and then obviously, when you’re getting that granular when you’re putting all this data together, you can actually run any kind of journey mapping, journey analytics across this data, for example, like, you know, for all the people that came to the Dreamforce event that I just mentioned to you, as an example. What percentage of them actually attended a follow-up webinar? What percentage of them actually downloaded something that you emailed to them? So you can actually build out really good, sophisticated touch point analysis for these cohorts as well, and not just like you know, the general.

Drew: You talked earlier about patience, and now I’m really appreciating that. We went to Dreamforce in 2022, we’ve tracked those folks, and we closed them in the first quarter of 2024. You have to go all the way back, and then you could track their journey to be more efficient in your spending now. So you can’t get started on this stuff soon enough, because this is one of the challenges with that. You know, this is why cost per lead is so easy to do, because it’s real-time, but it’s not accurate, right? And so you’ve got this really interesting conundrum with attribution, which is, in fact, there’s a lag to getting it to the point where you really know what worked, right?

Taran: Yes, there is some analytics that you can run that look back in time and do some level of analytics. It’s not as general. If I use the paid advertising example, I can actually look back and say what channel, what campaign yielded me what level of revenue? Because I’m actually able to, you know, through UTMs, I’m able to pass down that information. I may not be able to attribute revenue down to the keyword level that I’m advertising, but looking from that point onwards, the moment I’ve started tracking that data, I can actually start going granual.

So you know, when you first implement Attribution Analytics, your level of granularity is not that deep, but since you are collecting more data, since you are actually making it more meaningful, you are able to drive more meaningful insights. And you’re able to keep getting more granular.

Drew: So I’m imagining this scenario, you’ve implemented this program, and now we’re looking at opportunities or closed-won, and you’re able to actually go back because you have all the data points, and that’s when it really starts to go, “Oh my god. 10 of our last 20 closed-won opportunities actually were at that Dreamforce event, yay. It took a thing we were there. Let’s make sure that we keep investing there.” So again, the payout for this is, once you have the right data in place and all the tracking in place, it might take time to really optimize against opportunities. You probably can optimize upfront about spending a little bit, but you’re not going to be able to optimize for a while depending on your sales cycle. It’s really going to be determined on that. Okay, I think I’m with you. I want to get to a couple questions. Is there a standard approach to incorporating sales touch points into this attribution analysis?

Taran: There’s no standard approach, like I said, no one size fits all. Whatever your sales touch points are, they should be included in the whole journey of the customer, and then you’re able to run analytics against it. The standard is that whatever touch points your sales team is recording in your CRM should be made part of this analytics data. So for example, if they’re setting up follow-up calls, if they are sending emails, if they are inviting them to events—like all those actions that they are taking and the resulting action from those customers or prospects should be available in the data that we are advising.

Drew: We have another question, which is, how do you typically incorporate a half-life where a touch point isn’t attributed after a certain amount of time? And then they offer an example, if someone attends a webinar two years ago and hasn’t interacted with any additional marketing, and then attends a trade show and gives their business card to the team and then closes. I would think it’s much more the trade show that closed it than the webinar, but you know, again, you got awareness, which is why they stopped at the booth.

Taran: So typically, whatever is your average sales cycle, I prescribe that you go 3x of that time. So if your average sales cycle is like, you know, six months, look at touch points for at least a year and a half, don’t go beyond that, because beyond that, like, you know, it could be meaningful or not. That’s what we prescribe. Is like, you know, at least whatever is your typical sales cycle from first touch to close one use like a 3x Window, because some of them would be like, you know, taking longer. Some of them could be taking less time, but it rarely would be going beyond the 3x of your average.

Drew: You know, there’s a famous saying, which is, folks have a tendency to overestimate the things they can measure and underestimate the things they can’t. And one of the things that folks in our world of B2B tend to under-invest in is measuring brand or reputation, depending on what word is acceptable at your company. Where does that fit in your attribution and metrics model? Where does reputation fit in, and how do you approach tracking that?

Taran: To be very honest, we don’t do any kind of reputation scoring, but what you have to look at is your reputation these days is largely driven from your social media attributes. So, you know, we do have great tracking available for that, which is, like, you know, how well engaged is your audience on social media, and what kind of referral channels are activated? A lot of these referral channels are not something that you’ve activated. It’s just because you’ve published good content, if you created good brand awareness. But if you ask me, like, you know, we don’t focus on giving a reputation or a brand score. We focus more on what’s actually driving revenue.

Now, we also look very deeply into what is often ignored: the contribution of your social media channels to your revenue. Everybody has social media channels. And it’s not just about having paid advertising on social media channels. You’re publishing content very regularly. People do come there. They click on it. They then come to your website. We’re tracking all that, and then we are actually able to attribute it back to those social channels. Over time, if your reputation is increasing, your social influence, and the influence on revenue from that social influence should be increasing as well. That we track, but what we don’t track Drew is, you know, what is the value of your reputation or brand.

Drew: And there are other tools out there for that, and we’ll be talking about that in future Bonus Huddles. So that’s a good place, but it’s good that you’re focused. And ultimately, what we are talking about is how do you place your bets, not just in terms of where you spend your advertising dollars and your marketing dollars, but also where you place your bets in terms of the sequencing of your messaging. So we’ve got spend and we’ve got story, and eventually there’s this path in what you’re really doing in all of this attribution is helping the marketer both show the journey to revenue, which is what we’ve been talking about this whole time.

Here’s a question from our audience: My sales team, on a good day, will assign themselves a task, complete it and make notes in CRM, but their touches are pretty lost in a mix of notes and tasks. I guess I’m wondering if teams require their sales teams to track those touch points in a way that can be extracted without adding more data entry, that sales simply won’t do well. So that’s the problem. Sales is notorious for not keeping up accurate data.

Taran: Good thing is there are AI tools that can actually extract the entire conversations and notes that your sales team is having and decipher them into meaningful insights, and then those can be actually included in your analytics models. For example, like, you know, they took a note and they said, “I met the person,” or “I called them,” and they feel good about pricing, but they still have some questions. That can be extracted through AI models or machine learning, and can actually be used as a set of input. If it is important for you, you should invest in a tool that can actually decipher that and what actions were taken.

Drew: Okay, we’re running out of time, so let’s just summarize with two do’s and one don’t for better marketing attribution.

Taran: I would say you have to try to be as granular as possible. Don’t leave it at just a single touch. Don’t stop your journey at multi-touch. It’s a journey. So, keep trying to go as granular as you can. That’s the number one thing. If you are able to decipher how many MQLs you’re getting today, go down to the next level, which is how many SQLs from those MQLs, and then to the next level: what are the opportunities, the revenue. But then where is that revenue coming from? Going down to, if you’re focused on search advertising, you should be able to understand what keywords are your money keywords, which are driving the most revenue so that you can invest more in that.

The other thing I initially said is that these models have to keep refining them. So, one is really try to be as granular as possible and keep refining these models and your stack. The one thing that you should not do: don’t stop at vanity metrics. Don’t just focus on clicks. Don’t just focus on MQLs or leads. Move away from vanity metrics, move to what is important for the business, and then be able to tie the revenue to the tactics or whatever is important to your business.

Drew: This is not a set-and-forget process. This is a journey that you have to get started on, and you need some expertise. If you don’t have that in-house, I really encourage you to set up some time to talk to Taran and his team at Growth Natives. I also want to thank Taran and Growth Natives for their support of CMO Huddles. They’ve done our full HubSpot implementation. They’ve integrated it with our Wild Apricot instance. It’s kind of crazy how many different integrations they’ve done. They also amazingly migrated Renegade.com to RenegadeMarketing.com in just an amazingly seamless way. So if you have back-end work that you need as part of your tech stack, from Salesforce to Marketo or whatever it is, at least talk to Taran, because his team is amazing. So thank you, Taran for joining us. It’s GrowthNatives.com, right?

Taran: GrowthNatives.com and DiGGrowth.com as well.

Drew: What is DiGGrowth?

Taran: DiGGrowth is our marketing analytics platform that I was talking about, that we have developed. So, for the first two to three years, we were solving so many attribution problems and doing custom analytics for so many customers, we eventually developed our own platform. And again, it’s not one-size-fits-all. It comes with a lot of consulting and services. The great thing is, like we’ve packaged it into a single price, so we call it a solution, and not just a tool. So engage with DiGGrowth like we provide the consulting, we do the heavy lifting, and then we get you to that state of Nirvana, if possible.

Drew: I love it. Well, thank you, Taran, for joining us. 

If you’re a B2B CMO and you want to hear more conversations like this one, find out if you qualify to join our community of sharing, caring, and daring CMOs at CMOhuddles.com.

Show Credits

Renegade Marketers Unite is written and directed by Drew Neisser. Hey, that’s me! This show is produced by Melissa Caffrey, Laura Parkyn, and Ishar Cuevas. The music is by the amazing Burns Twins and the intro Voice Over is Linda Cornelius. To find the transcripts of all episodes, suggest future guests, or learn more about B2B branding, CMO Huddles, or my CMO coaching service, check out renegade.com. I’m your host, Drew Neisser. And until next time, keep those Renegade thinking caps on and strong!