The Evolution of B2B Marketing Attribution: Where to Now?

The evolution of B2B marketing attribution: where to now?

Attribution has established itself as the go-to solution for putting a dollar figure on marketing; a saving grace for many B2B marketers, who struggle to make sense of the long and complex customer journey.

Yet, Google’s recent decision to reduce the number of attribution models available in Google Ads and Analytics has brought the future of attribution into focus.

Are traditional models redundant? Has attribution climaxed?

In this post, I’m exploring what we can expect from the attribution space over the coming years by zeroing in on three predicted trends: data-driven attribution modeling, the shift towards niche tools tailored specifically for B2Bs, and revenue automation which encompasses attribution tools funneling data back to advertising platforms.

Contents:

• Why marketing attribution matters to the B2B digital marketer
• Marketing attribution: a turning point?
• Data-driven attribution: a taste of the future
• The need for B2B-specific attribution solutions
• Activation and automation of revenue data: closing the loop

Why Marketing Attribution Matters to the B2B Digital Marketer

In the dynamic B2B marketing landscape, the idea that a simple click or conversion can provide comprehensive insight into a customer's journey is at best deluded, at worst, dangerous.

The path towards purchase in the B2B world is a convoluted web of touchpoints that take place across multi-stakeholder accounts. 

This complexity makes understanding and analyzing journeys a significant challenge.

And without a clear picture of what channels and campaigns are attracting accounts and helping them down the funnel, knowing where to invest marketing dollars is little more than a guessing game.

Enter attribution.

Tying marketing to revenue

Marketing attribution aims to make sense of this convoluted web of touchpoints to provide B2B marketers with a clear picture of their customer's journey. From start to finish.

Attribution does this by bringing together, transforming and modeling copious amounts of data from across the B2B tech stack, to identify exactly what’s happening and when. 

With this clarity, marketers can then track the paths to success and pinpoint their contribution(s) to closed deals. Which ultimately means being able to make data-driven decisions when optimizing their marketing budgets to focus on the channels that yield the highest return. So that every dollar spent is contributing to the bottom line.

Marketing attribution: A Turning Point?

Despite these now well-established benefits, Google’s recent decision to drop attribution models has thrown the whole concept back into focus. 

First-touch, Linear, Time decay, and Position-based attribution models have now disappeared across Google Ads and Google Analytics 4. The official line is that “these models don’t provide the flexibility needed to adapt to evolving consumer journeys.” 

Instead, Google will offer only last-touch and data-driven models.

But what does this mean for the B2B attribution space as a whole? 

Well, Google’s decision gives us three clues about where the future may lie:

1. The pivot towards data-driven modeling.
2. The rising need for B2B-focused attribution solutions.
3. The untapped opportunity for activating data through revenue automation.

Data-driven Attribution: A taste of the Future

Data-driven attribution models have emerged as the most obvious innovation in the space. In fact, Google has now placed their ‘data-driven’ model as default for all users. There’s little surprise here as data-driven attribution models remove both the bias and arbitrariness of traditional multi-touch models. 

Where positioned-based models would give undue credit to touches based on where in the journey they happen to take place, data-driven modeling uses dynamic data from your own customer journeys to assign credit on the touches that matter most.

Thanks to this, marketers no longer have to pick and choose between multi-touch models and bear the compromises that this involves.

So what is data-driven attribution?

Data-driven attribution is a multi-touch model that uses machine learning to determine which touchpoints are the most influential across your historic customer journeys and assigns more credit to these touches accordingly. 

Note that different attribution providers might introduce different machine learning modeling to better cater for their primary user.

But in general, data-driven attribution works as follows:

First, all known account-based customer journeys across all channels are mapped, and the frequency of touchpoints computed. This data then paints what’s called the typical journey map.

Once the typical journey map is created, the attribution weight of each channel is calculated by removing individual channels from the typical journey map and comparing the relative effects on the overall journey with each other. 

We call this the removal effect. 

The higher the removal effect is, the higher the weight assigned to the particular channel is.

This process is constantly recalibrated as more deals go through the pipeline, meaning that the weighting will always reflect the latest data.

As a result, uninfluential touches will no longer be given undue credit, as in the linear model, but neither will the position in the journey a touch happens to land. For example, a sales call to edit a contract shouldn’t be credited as much as a demo which secured the purchase.

You can dig deeper into the differences between data-driven and traditional multi-touch models in this article.

The Need for B2B-specific Attribution Solutions

Important though they are, models are only half the attribution story. 

The data sitting behind the models, how it’s collected and how it’s transformed (prior to modeling) has a fundamental impact on the analyses you can make.

After all, the model is only as good as the data you feed into it.

This opens the next trend in the attribution space: user / need-focused solutions.

General purpose, point solutions like Google Analytics, which have dominated the space for so long, principally cater for B2C customer journeys.

And while there’s no arguing that the B2C customer journey has become more complex as devices and channels have multiplied, the B2B customer journey, as I’ve already set out, has considerably more layers of complexity. 

Whether it’s the number of touchpoints, the number of buyers (from the same accounts) or the sheer length of the journey, B2B marketers need to contend with tons more data points than their B2C counterparts.

This has seen a flourishing of more specialized tools that better cater for the B2B marketer’s needs.

Dedicated B2B attribution solutions

B2B attribution solutions operate an account-based data model that integrates with the wide spectrum of B2B data points. Including, for instance, LinkedIn engagement data. 

In fact, Dreamdata ran a study that found there are 4.3x more (pre-conversion) business interactions with LinkedIn ads, which when run through our attribution modeling, saw a 7.7x increase in the accuracy of measured ROI on LinkedIn ads.  

These tailored insights enable B2B marketers to make sense of the customer journey and discover which efforts are delivering business value. 

Although B2B attribution tools are already seeing increasing adoption, we can expect this trend to grow over the coming years. Especially as these tools continue to innovate for the space.

Which leads us to a final trajectory B2B marketing attribution is looking to take moving forward.

Activation and Automation of Revenue data: Closing the Loop

As I’ve already covered, attribution is as much about the models as the data that’s fed into them. 

Getting the data ready, however, requires a phenomenal amount of transformation: deduplication, mapping, enriching, standardizing, and of course, modeling. 

The end result is a treasure trove of accurate data that is ready to be used beyond just reporting and analytics. The high-quality data can be pushed back into the tools which then go on to optimize campaigns automatically.

Two great examples are LinkedIn offline conversions and Google offline conversions, which allow their users to feed pipeline data directly to their Ads AI - resulting in better performance (LinkedIn’s offline conversions reduces CPA by an average of 7%).

Reverse ETL tools, like Hightouch and GetCensus, have carved out their niche for this data feedback function - though, this remains their singular role.

Ultimately they rely on other tools to provide quality data in the first place.

The unique positioning of attribution tools presents an opportunity to provide a complete, closed-loop solution. One that doesn't only automate the collection, transformation, and modeling of B2B go-to-market data, but also pushes this data back into the respective tools. 

Follow the B2B Marketing Attribution Trends

In exploring the future of B2B marketing attribution, we can see several key trends come into focus. The shift towards data-driven, B2B-specific attribution solutions indicates a move away from generic models, acknowledging the unique complexities of B2B marketing.

Beyond this, attribution tools are likely set to morph into a comprehensive, closed-loop solution. Where the tools will collect, transform, model, and feed data back into the marketing ecosystem, to offer B2B marketers an automated ecosystem for driving revenue.

So, as we anticipate the next phase in the evolution of B2B marketing attribution, we see a future of enhanced capability, addressing the unique needs of the B2B landscape. A future where attribution solutions will move from simply a measurement tool but one that forms a critical part of driving business growth.