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Revenue Attribution: Mapping the Customer Journey to Revenue

Uncovering Repeatable Patterns in Your Customer Journey

Steffen: I used to be the CRO, which is chief revenue officer but as of this year, I switched to just chief marketing officer because we’ve hired our first sales leader now at Dreamdata. So I’m a B2B marketing leader by background. I’ve been doing that for about eight years. So I’ve always been in B2B and always been in somewhat growth-related roles, more and more as time goes and you get smarter, you grow into leadership positions.

But to make a long story short, in my last company, we were selling to schools and businesses and we were trying to grow really rapidly and ran into a ton of attribution pains because we wanted to grow super fast, but we also didn’t want to waste our money. And initially, I just put in 10,000 euro in that, but you have a good idea about what comes out of it. But when you cross 100,000 euros per month in ad spend, you don’t really have the best idea of where’s the last 10,000 euros going.

So I got really into all this attribution stuff without really finding any proper solution. But at the end of my last tenure, I met my two co-founders who had a very odd, but actually, a very valuable alpha version of the product, which is now Dreamdata, which is what we call a B2B revenue attribution platform. And what that means is on behalf of our customers, we build a data warehouse that contains every single touch that you have available for any account.

And we plot all of these touches into a timeline, so you can see what was the first touch from this account? What was the last touch? How many internal stakeholders were involved? How many sessions did we have with them, and so forth? Curious people would then use this information to find out what are the repeatable patterns in our customer journeys? Is the stuff that is never-ending with us making money? This is something we should just stop right now and so forth. Basically, I’m just interested in this whole attribution, whole customer journey thing because I want to win. So I want to understand as much as I can about every customer journey.

Barry:  I can definitely relate to the attribution pain points. That’s something that, as a marketer, I’ve experienced at multiple companies from two fronts, one, the attribution, to understand where we’re spending money and what’s the most important way. But also, to project and to make the C-suite executive team happy also with our spend. Even if we’re confident about it, but also for the C-suite that isn’t day-to-day marketing.

So for today’s podcast, I think we’re going to discuss two topics. One is Dreamdata attribution, which is your bread and butter, but two, something that I’m sure is connected to it. And this is where I want to start, about mapping the customer journey towards revenue. This is interesting for multiple reasons for me, and I’m sure multiple reasons for you and our audience, but for me, it’s interesting because the customer journey is so different for every company.

So even companies that do the same thing may have different types of customer journeys. And I’m curious to understand how to create customer journey maps, why to map the customer journey and when people should be mapping the customer journey. So let’s start with the why. Why do I need to map the customer journey?

Why You Should Map the Customer Journey to Revenue

Steffen: I think when we talk about B2B, you have to acknowledge that it’s a different game than B2C. There’s a lot more touches. It’s a lot longer. There’s a lot more people involved in the journey. And that’s just a pain for us, if we speak particularly about marketers, because we are typically the first part of the journey and not the last part, which is typically handled by sales because they’re the ones closing the deals. But that contains that problem, that all of the activities that we are involved in, is not that transparent with what is good and what is bad? And what I mean in my world when I say good or bad, that means activities that end up producing pipeline and ideal deals, as well.

The reason why we want to map the entire customer journey, is to find activities that are repeatable and preferably scalable, as well. Meaning that, if I do these things, they end up becoming sales qualified opportunities, and they end up becoming deals that we’re actually able to win. And then you could also have another bucket of things that you do that never, ever produces any revenue. So you probably should question why you want to keep doing those things. So for me, it’s never about finding something that is 100% the truth. It’s about finding statistical evidence for certain activities that are repeatable, which you can scale and you can expect to profit from.

I think that just as a disclaimer, no one at Dreamdata would ever say that, “We are going to tell you 100% of the truth.” And I think that’s when people bash attribution on LinkedIn, for example, it’s because they think we’re obsessed people that don’t see any nuances in the world. We do, what we talk about is just, it’s a game of competitive advantage. And if you know more than your competition about your customer journeys, the likelihood of you winning, no, we’re never going to tell you 100% of the truth, but if we can make you go from say, 10%, 20% to 50%, 60%, your likelihood of success is going to go up. So, that’s my background for this whole interest of why you should be interested in customer journeys. And then take a breath of air.

What to Map in Your Customer Journey

Steffen: To answer the second part question, Barry, what is it that you want to map then? Essentially, you want to map anything that touches the account, that is mappable, you can say. So typically most B2Bs would have a CRM system that contains calls, that contains emails, et cetera, going out towards specific people at specific companies.

Then you would also have a marketing automation tool. You would maybe have a customer success tool, maybe even an outreach software that all of these places, people from your company are actually engaging with accounts. So what we do is, we pull out all of this historical data out of the platforms into Dreamdata and then we organize all the touches into a timeline. Now this timeline might contain four different people that do something with the account, but it’s all part of the same account, which is why I started out by saying that, you have to almost religiously commit to that.

There’s something called an account timeline and not just a lead or an individual timeline, which is typically where the attribution gets very wrong if you do lead-based, individual-based, attribution in B2B. And then I’ll say then, the last two components just mentioning before I’ll let you talk, Barry, is we also then provide a script that you put on your website that tracks every single page view, every single session, every single user, and join that into the account timeline, as well.

So you know when you have activity on your website, where is it coming from? What is it doing on the website? Does the same use of return repeatedly and so forth? And then the last component just before stopping, we also pull in all the data from all the ad platforms you use. So you can compare cost to the deals you win, which gives you that accountability for revenue, but also transparency to what activities build revenue? That was a long story.

Barry: Yeah, it was a short question, but deserved a long answer. So there’s a few things that caught my attention. So one, to summarize, I guess in one sentence, it’s important to first identify everywhere your users could have seen your website or your branding, and then have that connected to technology. Those pieces still have to be tracked, maybe siloed, but they have to be tracked, whether you’re using your platform or not using your platform. The other thing I thought was interesting was that there’s a lot of times we talk about lead attribution in marketing, and here, we also talked about account attribution. I don’t know if that was your language?

Steffen: Yeah.

Barry: But not just looking at the lead, seeing how many times they saw a LinkedIn ad and how many times that they went to your website, but also looking at other people at their company, whether it be a champion, whether it be an IT person that’s looking for security, whether it be anybody else at executive, it’s important to see the bigger picture, to see who needs to be involved earlier, the process leader, the process, so that you can predict revenue also. So is there scoring with this that helps, is that another way that people can be using revenue attribution?

Building Lead Scoring Based on Customer Touchpoints

Steffen: So, we’ve not built it into, you can say, our application, but we’re actually just about releasing some content analytics stuff that will tell you, which pages are viewed when people buy and which pages are not viewed. So you can work your way back from when you win deals, they look at these pages. We also look at the opposite way and then say, when they start buying, they’re viewing these pages.

So with that, you could build some really strong lead scoring. For example, what we found was that a lot of people who buy look at integrations. But more funnily, what we also found out, that those who buy, they very typically hit a 404 page on the website, which means that you are really ticking around to find a 404 page on our website, but that is easily a sign that somebody’s actually doing proper research.

What we also confirmed was that, we have an upgrade button inside of the product, if they click that, it actually correlates with them ending up buying. And then the last two I just want to mention, is that we can see the about page always gets checked and we have our community page, which also often gets checked. So all of these things are stuff that are important in the buying journey, and we should both, you can do leads points on it, but you can also consider whether you should make those pages better or make more content like those pages. So it’s a really good thought, Barry, that you could actually do some proper leads going on this.

Barry: Well, I think it’s hilarious, the 404 example. And I also love that you’re doing a, how Dreamdata uses Dreamdata session. The 404 thing I think is really funny, because usually marketers are embarrassed about 404, but it’s your weird customer behavior indicator that people are curious and that they’re looking around and I love that.

Steffen: Yeah. I haven’t looked at the other accounts yet, but at least for me, it kind of makes sense because yeah, you have to struggle to find a 404 page on our website. And if you do find it, that means you’ve invested significant time in our company.

Barry: Right, logically it makes sense. Can you tell me more about mapping the journey of prospective customers and the different touchpoints? Dreamdata creates its own data, but also aggregates data from siloed platforms? So when I’m referring to the touchpoints, what’s the technology that is capturing that, even if it’s siloed? 

Steffen: There were a lot of questions there. Let me start. So essentially, Dreamdata is a data warehouse to which you connect all the tools that you use. So the data lives inside of Dreamdata, so that’s the data layer. And then on top of this, we have our own application that would typically answer 80% of questions that B2B marketers would have, but the data is also open and accessible. So you can connect whatever tool that connects with Google BigQuery and Snowflake and Amazon Redshift to the Dreamdata. So we take all of this data you have available and then extract it and clean it up and put it into nice tables.

So it’s very easy to use for BI people. That you could put back into your CRM system, so you could have an original source field and then you can have the Dreamdata source field, as well to compare what is actually true. You could put it into ad platforms, so you would inform Google, Facebook, LinkedIn about who are actually the accounts that go very far down your pipeline. You could build lead scoring models on top of it, et cetera. So, the foundation is the data model that contains every single touch of every account. So we do all that heavy lifting of that data cleaning for you. Now, what was the first part of your question?

Mapping Customer Journey Activities

Barry: So now let’s go into more detail on customer stages and find out actually how your company uses data and the key touchpoints you’re collecting into your data warehouse.

Steffen: So that’s typically, what I use the most, as you can say, a marketer, is information about activities that I can repeat, activities that consistently start journeys. So an example for this could be, Google search ads. If people are typing a specific thing, is that correlating with you buying more of our product? Then I would analyze in there, where you could see what’s the impression share of this keyword and if it’s very low and you’re already making money on it, then if you put more money into this keyword, it’s most likely you’re going to correlate with your company making more money because the intent of the keyword, the anomalous stays the same way, whoever types it in.

Barry: Do you have any more examples maybe, I’m kind of curious, just like the top of the sales funnel things, maybe things off your website, through tracking, which Dave Gerhard’s talking a lot about these days, is the dark funnel. Is there a way to track Slack communities? Again, not necessarily just on your platform, but in general, for anyone that is doing marketing?

Steffen: Yeah. So I’ll get to the dark social thing afterwards, but just to explain Dreamdata. So we only work with first-party data, which means data you have collected yourself. So typically, people will have to be on your website or you would have to create the digital touch inside the CRM system for us to map it. So in that way, I think the technical term is deterministic, it means that stuff that you can actually measure.

So we’re not guessing attribution, we’re only doing trackable attribution. That means this whole dark social thing, the way that you’ll measure that, is that if they arrive at your website containing a certain UTM, then we could take that into account. If they just arrive at your website with our green data IO and nothing else in the URL, then we won’t track it.

Barry: Yeah, that makes sense.

Steffen: But just the last point, I just want to tell a little bit about also, is that, what we can typically see across all our customers and that’s almost 1,000, is that the known journey time is very often the same length as the research phase where people remain anonymous. So what companies typically are aware of, is from that first conversion, as you talked about like an ebook or a webinar or whatever, that time spent, if that’s six months for your company, then they typically spend six months as anonymous on your website before doing any conversion. And that’s where we can see social channels and Slack channels, et cetera, are representing a lot of research arriving at your website, but they’re not reaching out to talk to you because timing is wrong and they don’t have the budget, et cetera.

Barry: So, let me get that straight. If a potential customer is anonymous for six months, and they actually buy your product, they’ll probably take them another six months when they’re not anonymous? Is that correct?

Steffen: Yeah. Yeah.

Barry: And that’s aggregated data or Dreamdata for your company?

Steffen: No, no, no. This is across all our accounts, which also, I think as a marketer, it’s extremely interesting because if you want to contribute to hitting the budget in 2022, you most likely have to start now because the journey is going to be super long. It also matters a lot when you are judging your user experience. Because if the journey is twice as long as you expect it to be, then you might shut down stuff that is actually working, just because you don’t have transparency into how long it actually takes to become a sales qualified lead in your company.

Understanding LinkedIn Attribution

Barry: So, this is a very specific revenue attribution question. But let’s go into the weeds. So LinkedIn is a popular B2B way to attract leads. People swear by LinkedIn ads. Some people really don’t like them, but a lot of people do like them in B2B. And LinkedIn definitely promotes this, that even if someone doesn’t click on your LinkedIn ad, they’ll still go to your website because of the brand impression or if someone clicked on it once, they’ll go to your website, maybe two months later.

Is there anything that as marketers, we can do about that, in regards to understanding the LinkedIn attribution a bit better?

Steffen: Yeah. It’s really good. So first of all, at Dreamdata we do a ton of social selling on LinkedIn. And we actually also run quite a bit of LinkedIn ads, as well. And so I’ll tell you about a recent experiment I did, where I created an audience of 5,000 matched accounts on LinkedIn that fits pretty much our ideal customer profile. And what we can see there is that, and these guys get a video, where I just sent it to the optimization to reach as many of these people as possible. I don’t care about any conversion. And then these guys are also getting a conversation that is more intended to collect their email and I’m also just exposing them to a lot of images, like my brand images.

And I can’t prove that we’ve seen a really big uplift in organic and a really big uplift in direct, but we can also see that the ad group itself is actually, close to break-even also, but it’s been the only major difference I’ve done in our inbound setup and we’ve still seen an uplift in direct and organic. And I think that’s where, as an experienced marketer, you need to use both the data, but also your gut feeling.

Does it make sense that I’ve picked 5,000 accounts, where I run ads towards the markets, of these 5,000 accounts? Yes, it does make sense, that I continue to feed them this message and I should trust that it’s the right thing. You know, as the Chris Walkers of this world would say, “You need to generate demand.” So I’ve picked a group of the right people who I’m exposing our message to, and I have no need, except from at least having some proof that it breaks even, then I know the rest of it is still valuable. Does that make sense?

Barry: Yeah. It sounds like it is hard to track some of that but because it’s hard to track, if you break even, then the rest is gravy. You know that as a marketer, there’s intuition that’s working. It might be harder to show that, to prove that to the CEO and that’s why you try to make the break-even point on some of those points. Is that correct?

Steffen: Yeah, exactly. And then it’s also qualitatively, just sharing inside of it, seeing how often you hear people mentioning that you are doing social selling on LinkedIn. And it’s so much for us nowadays that people say, “Oh, I’ve been following your post for six months before I booked the demo.” And from an attribution point of view, you don’t get any proof that that is true, but you listen to the customer who says it. And so the way to think about the attribution tool, is that it’s complimentary to your gut feeling, et cetera.

Barry: So, I love to hear that people in marketing still have to go with their gut. You can’t track everything. And even if you could track everything that’s trackable 100%, there’s still a lot missing there.

Steffen: Yeah. Yeah.

Barry: And that’s where you have to also do your own sales to the executive team and things like that.

Steffen: But just to stay on that point a little bit because no attribution tool measures everything, but there is stuff that is repeatable, trustworthy things that an attribute tool can tell you. For example, the Google search ads, every click on Google arrives at your website with a click ID. And if this click ID is consistently present in journeys you win, well, then it’s probably reasonable to scale the spend on that campaign that generates these click IDs. No, it’s not catching a conversation at a conference or in a very niche community, but this thing you can put more money into and you can expect to profit from it.

Who’s Manages Revenue Attribution Data

Barry: Absolutely. That makes a lot of sense. So let’s bring this back to more of the revenue operations professionals that are listening. So definitely, revenue attribution is interesting for marketing. I’m sure there’s a way to make it very relevant for sales teams and SDR teams. Sure, companies can increase the value of their sale by knowing which decisions make different patterns. So what’s the rev ops role in this, I guess it’d be the marketing ops person, would be in charge of this data or is it ever a rev ops person who’s in charge of this data? Someone that is looking at things more holistically? Who usually is involved in this?

Steffen: Good question. So first of all, our own salespeople use the product every day and the way that they use it is, that whenever they go into a meeting, they would check the customer journey and see what is in the content that this account has consumed? So they’re not sitting blindfolded in a demo call. It could also be that they hadn’t heard from an account lately and suddenly they see that that account had an activity somewhere, either on the website or downloaded a new book or something, so they would know what to reach out with again, when they wanted to activate the account.

Or quite typically, a buying triangle or something like that, there’s the market to market that’s going to use the product every day. They have some kind of VP-ish person who will sign off on the budget and wants to know where to put it in the budget. And then there are ops and data people who validate that. We’re not just bullshitting our way in our demos, but actually can validate all the data is getting pulled from all the right sources and it seems to be met correctly, so you can actually trust this data is true.

I think where the ops people get a big relief, is that they don’t have to answer these 100 questions that marketers have every day, through all parts of the customer journey and campaigns, et cetera. They have a platform where they can point people to, where they know that they can trust that data gets in there, it gets sorted correctly. And then they can use the green application, just do all the queries that they might have. So, the ops people are typically relieved from a lot of work that can be very time-consuming to do.

Barry: Yeah, absolutely. And then do you see more marketing ops people or revenue ops people?

Steffen: For us, I think it’s more marketing ops. I don’t know if it’s just me, but rev ops seems very geared towards the salespeople and they tend to be less interested in the earlier customer journey touchpoints. Whereas, the more we win these deals, the reps did this, they had this many calls, et cetera. So, I would say, ops people who are interested in the full journey and not just the last mile.

Barry: Yeah, absolutely. Every few months something’s changing. So it’s pretty interesting.

Steffen: I mean, overall a company can only spend its money one time. And I think the whole purpose is, that we are trying to show them where you can use the money most effectively, whether that is into a more sales rep or if it’s customer success people, or if it’s ads, we just want to help you allocate the money you have most effectively.

Learning From Revenue Attribution Data

Barry: So before we go, I want to take advantage of this time. You have a lot of data on your platform. Is there any data that you can share with us, like buying patterns of the average customer and the average sales cycle? I’m trying to think of some cool things: the average number of touchpoints for a typical B2B company selling, let’s say, an average contract value around 25 grand?

Steffen: So it’s funny to say, Barry, because we’ve actually been debating on, should we start to do some quality reports on these things?

Barry: You have to.

Steffen: Yeah. It’s true because we know from every company size, to every deal size, the length of the journey, what’s the best, first touch channel, et cetera. There’s not a lot of things I can show, but I’ll take it as a reminder to keep pushing on this, releasing the information. But the one thing I did say, which people can take with them, is that the known sales cycle is typically 1 x t anonymous time or research time or what you want to say, which matters immensely if you want to actually drive revenue.

The organic and the paid channels are typically contributing anywhere between three and seven times in a first touch model than they are in the last touch model, which means that the marketers are probably spending a lot of time, spending less money than they should. If you could spend seven X amount of money on ads, your company would be growing extremely faster than what it is. And these are some of the fast aspects that people typically find because the journey is now account-based, that means that one person can start the journey, a Q&A can take over and then they have a boss who signs the contracts. When we put all of that into the same timeline, then the research gets connected with the revenue you produce, and then you can see which activities you should do more of.

Barry: I definitely get it from a marketer’s point of view. And from a revenue operations perspective, just from A to Z, is nice to see. So Steffen, thank you so much for joining our podcast and giving valuable insight into the customer journey and revenue attribution. I gained a deeper understanding and I’m sure our listeners did also. If someone has any questions or wants the follow-up, where’s the best place to do that?

Steffen: My cup says, ‘Add me on LinkedIn’.

Barry: Perfect.