Podcast 🎧: Solving the marketing attribution puzzle 🧩

As the digital landscape grows more complex, businesses find it increasingly difficult to make sense of their activities. Attribution has emerged as the solution to this challenge, specifically marketing attribution.

Yet, paradoxically, the more complex customer journeys become, the harder it is to actually do attribution.

For B2Bs especially, the multiplicity of digital touchpoints and devices, not to mention the length of the sales cycle, makes attribution through analytics tools (like Google Analytics) a non-starter.

Enter Dreamdata, the B2B revenue attribution platform helping businesses repeat success and stop waste.

solving the marketing attribution puzzle

Dreamdata’s CRO, Steffen Hedebrandt recently sat down with Kathleen Booth from the Inbound Success Podcast, to discuss all things attribution.

In this blog post I’ll be dissecting the conversation and delving into some of key attribution takeaways.

I’ll cover:

  • The difference between B2C and B2B attribution 

  • How attribution is transforming binary thinking on revenue generation

  • Where Dreamdata pulls data from and where we sit on the techstack

  • How Dreamdata’s customers are using the product

  • Google Analytics’s limitations

  • Attributing content to find its real value

 
 

Want to dig deeper? We cover some of these topics in greater detail here:





The difference between B2C and B2B marketing attribution

One of the first points discussed on the podcast was the distinction between B2C and B2B attribution. It’s a question we at Dreamdata encounter time and again from prospects, blog readers, and curious users on our chatbot.

And we don’t tire highlighting the distinction between them. After all, a B2C attribution approach to B2B is, to put it mildly, inadequate.

So, in a nutshell, the difference between B2C and B2B attribution is the added complexity and length of the B2B customer journey. There are way more touchpoints and people involved in the B2B buying process, which also means it takes a lot longer to close a deal. A deal, as Steffen highlights in the episode, takes three, six, 12 months to close!

Let’s unpack this a little through a comparison.

B2C marketing Attribution

A customer journey for, say, buying a new jacket, will look something along the lines of:

  • Clicks ad/ goes to site directly/ organically and goes through with purchase immediately, or

  • Clicks ad/ goes to site directly/ organically but does not go through purchase, so is retargeted and then moves to purchase.

Granted, the user might be using two (or more) devices which complicates matters a little. Getting to the purchase might even take a few weeks, but unlikely much more than that.

B2B marketing Attribution

If we look at B2B however, it’s even difficult to create any sort of linearity.

Three reasons behind this: 

  • The multiplicity of touchpoints, which looks something like this 👇

B2B customer touch points

In B2B the customer is not a single individual but a company where a host of employees are typically involved in the buying journey. We’re very much in account territory here.

As Steffen puts it in the podcast, the individual starting the journey is very unlikely to be the one with the credit card.

  • The deal is closed by a team effort spanning marketing, sales, and even customer success teams.


    This is why it is crucial for B2Bs not to rely on stop-gap B2C attribution solutions. Only a B2B-specific attribution tool can decloak the complex B2B sales cycle. 

How attribution is transforming binary thinking on revenue generation

The B2B deal is closed by a team effort involving marketing, sales and customer success. Obvious, right? Then why does this notion not get reflected in practice? Too many organisations remain entrenched in a “binary” way of thinking, where only one team gets credit for closing a deal.

As Kathleen put it, “it's like marketing saying, okay, this lead, I get a hundred percent credit for, because I sourced it. And sales saying, well, I get a hundred percent credit for this one because I sourced it.

This binary thinking emerges from siloed perspectives (and measurement) of the pipeline. In fact, the respective techstacks are a good representative of this siloed approach: where CRM systems, CS tools and marketing analytics are used by the respective teams independently of each other.

I think what most B2B companies are struggling with is that the CRM system is dictating what's going on or what is perceived as the truth.


But how can a business realistically allocate credit to deals when there isn’t a complete, connected and transparent overview of customer journeys?

They can’t. The solution is offered by attribution, specifically, revenue attribution.

Dreamdata’s platform helps B2Bs move away from these silos (and thus binary thinking) by offering a holistic view of the customer journey - with all the touches. 

that's why we call ourselves a revenue attribution platform, because we heavily believe that all touches matter ... we want to give the holistic picture, not just the contribution of marketing to it.”

To achieve this, Dreamdata’s out-of-the-box platform joins, cleans and sorts all revenue related data, to provide unprecedented clarity of the customer journey. 

Once this clarity exists, the binary decision making becomes redundant. With every touchpoint revealed, proper attribution can be applied across the entire pipeline - without prejudice!

Where data is pulled and where Dreamdata sits on the techstack

Speaking of data, Kathleen was particularly curious about where Dreamdata draws its data from, and by extension, where we fall on the techstack. Again, one of the most common questions we face.

Dreamdata collects data from two broad sources: integrated tools - CRM, Automations, and Customer Success tools - and behavioural on-site data tracked and gathered by Dreamdata’s script.


On the tool side of things, Dreamdata integrates with just about every commercial tool and traffic source out there. 

On the on-site data side, Dreamdata’s behavioural data tracking starts from the moment a user comes onto your site - whether through paid ads, direct, or organically. 

Our algorithms then collect, join and analyse the data from both sources, allowing Dreamdata’s users to see the multiple touches in account based format. Or to use Steffen’s example:

And then instead of Kathleen being five Kathleen's in each system, we would organize you to be one person that are present and have touched us in all the tools. And then we map you to the account.

Finally, in terms of techstack, because we are sourcing data directly and from tools, Dreamdata sits both within and above the teckstack.

How Dreamdata’s customers are using the product

The great thing about Dreamdata is that it offers a whole spectrum of metrics and analytics. 

This means that our customers are pretty much at liberty to use the platform in the way that best suits their particular needs. Although with that said, our best-in-class customer success will work with you to make sure you can exploit Dreamdata’s true potential.

It also means we have a wide set of ‘user types’ using our product. Amongst others, digital marketing specialists, demand gen, sales reps, marketing ops, and CMOs.

At the moment, our customers are using Dreamdata in three primary (and interlinked) ways:

  1. Insight into the complete buyer journey

    Dreamdata customers are deep diving into the details our platform offers on their buyer journeys. Through our ‘Deal Inspector’ and ‘Deal Analytics’ features, our customers unpack every aspect of their activities, from campaign and channel performance, to the LTV of ads, to ROAS.

  2. Attribution

    From these insights our customers are then applying attribution models across (and between) their deals. In fact, applying distinct attribution models to separate stages of the pipeline is one of Dreamdata’s most attractive features.

    As Steffen highlighted on the podcast: 

    Dreamdata even has some customers who trust our data so much that they're starting to assign compensation to the marketers based on the attribution models, like are marketing actually starting journeys that ended up converting like three months later. So that's radical, but they are judged on whether they actually just have these first touches on the deals that actually end up becoming like actual signed deals.

  3. Business Development

    Finally, our customers are making use of Dreamdata for business development. Through our platforms they are able to identify when and where accounts are making their touches. This means that they are then able to tweak their targeting, for instance, account X visited our website and browsed the blog after receiving our newsletter

Google Analytics’s limitations

During the conversation Kathleen revealed her frustration with not being able to see the value of her super niche content clusters in Google Analytics. Something which resonated very closely with Steffen’s own experience while heading marketing at his previous job.

But before looking at the content aspect more closely below 👇 it’s important to highlight why Google Analytics is so inept in the wider B2B context.

Indeed it’s something that overwhelmingly resonates with marketers and growth professionals across B2B. But it shouldn’t be a surprise. Using Google Analytics in the B2B context is like trying to force a square peg into a round hole.

It’s something that Google Analytics simply can’t do.

There are three main reasons for this:

  • Google Analytics tracks individual devices not accounts;

  • Google Analytics can’t handle multiple stakeholders and long user journeys; and,

  • Google Analytics can’t help track revenue outside the website, lifetime value of ads, nor apply attribution


To get the details on each of these (and more), read this fantastic post.

But specifically on the content issue Kathleen faced, we’re back to the length and complexity of the customer journey. It is unlikely that the individual carrying out the preliminary research (and so reading the content) is also the one holding the credit card and signing on the dotted line.

So, without account based tracking, without holding data for longer than 90 days, and therefore, without being able to attribute back to the content, Google Analytics will continue to frustrate on content.

Attributing content to find its real value

This ties neatly into the wider question of attributing content in order to find its real value. 

In the podcast Steffen used the direct experience of Dreamdata’s customers to highlight the most common pitfall when measuring contents’ success. 

When I look across all our B2B customers here at Dreamdata, all those articles that they've done, where they've done keyword research, and they have a lot of traffic on the keywords. So rarely that there's any revenue on those articles. But, very narrow articles with a lot of intent, like searching for an alternative to a competing product, or those sorts, they drive so much revenue out of very small chunks of traffic.

Paradoxically, the content with the highest traffic is often not the content that generates revenue, particularly in B2B. As Kathleen underlined, niche content topics, where the traffic that does come in shows high intent, produce the best results.

Yet, without accurate data on the entire customer journey, you are actually unable to identify content that is ultimately leading to revenue (as detailed in the section above 👆).

Which leads us to the one key conclusion of the podcast. You can only arrive at informed decisions on your activities when you are attributing revenue on all touchpoints across the whole customer journey.

Check this article on measuring ROI of B2B content.

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