How B2B Attribution Unlocks the Power of GTM Automation

Over the last 5 years, attribution has taken a firm hold in B2B marketing. And we here at Dreamdata are pleased to have played our part. Revenue should be at the heart of B2B marketing. 

In pursuit of B2C-ing B2B go-to-market, marketers have jumped onto the attribution bandwagon to understand whether their efforts drive pipeline and revenue - not just clicks and leads.

However, the power of attribution goes above and beyond this. And not for the reasons you might think.

As a by-product of attribution, B2B marketing teams are able to solve the go-to-market data problem. And in doing so they’ve unleashed the power to activate this data in ways many can’t even imagine.

In this post, we’re taking inspiration from Lars Grønnegaard’s recent conversation and unpacking the hidden power of the attribution revolution and how this will shape B2B marketing in the coming years.

B2B Attribution: way more than you thought

By now the definition of B2B Attribution is well known. B2B attribution is a performance tracking method used by businesses to determine which go-to-market efforts contribute to pipeline and revenue.

This process involves tracking the interactions and touchpoints a potential customer has with a company before making a purchase and analyzing these using different models, e.g. first or last touch, linear, W-shaped, etc. The goal is to identify and measure the effectiveness of different channels, campaigns, and strategies in driving sales and revenue.

But this undersells attribution’s power. 

Attribution requires a solid data infrastructure to offer any benefit. That’s why attribution solutions are first and foremost data solutions that automatically join and clean go-to-market data (ie. track site user behavior and connect all the tools in the go-to-market tech stack).

Side note: the added benefit of collecting first-party data, means attribution helps overcome any issues resulting from the sunsetting of third-party cookies and other such data privacy concerns.

In fact, Dreamdata was conceived under the fundamental idea that “the problem wasn’t so much one of creating more data, but more one of making sense of the data that exists and integrating it”. For more information on the history of Dreamdata, click here. 

And here is where we find the future of the attribution space.

By solving the go-to-market data problem, B2B marketers can not only analyze the data but also activate it in more powerful ways than ever before.

Activation and Automation: the ultimate expression of attribution

Up until now the analysis of data through attribution models got you so far. But because of the available end-to-end data set, the future looks to go beyond this and towards increasingly sophisticated activation.

“[there is a] trend seeing attribution as sort of the link between what you do and the outcomes when you can measure them, and then turning that into something, I would say more intelligent and something that is more automated.”

Getting the most out of data with Activation 

Being data-driven shouldn’t stop at analyzing reports. Getting these insights into the hands of those who need it, where they need it, ensures that you’re maximizing data utility.

For instance, getting down-funnel data (pipeline/ revenue) on campaign performance back into the ad networks enables marketers to optimize directly on the campaign managers. Or further still, hands the ad platforms with key data points for their AI engines. This is made readily available by the ad networks themselves through Google Offline Conversions and LinkedIn CAPI.

“Feeding that data back enables an AI optimization of your ads on those platforms,” Lars explained. “Google doesn’t know who converted in your Salesforce instance and LinkedIn doesn’t know who became an MQL because it didn't happen somewhere they care about. So feeding that data back enables an AI optimization of your ads on those platforms.”

And here the quality of the data really comes to the fore.

Without accurate B2B customer journey data, activation isn’t possible. In other words, the data platform that supports attribution solutions also enables activation. This is why the future of attribution is increasingly focused on data activation. 

These processes are typically quicker, easier, and less prone to errors if they are automated.

Closing the revenue loop with Automation

Attribution is already operating an automated process. Data is collected, cleaned, and modeled automatically, with no need for data engineers to intervene at every corner. The future lies in replicating this process in reverse, i.e. automating data activation by automatically sending data back to the tools revenue teams work in.

As we look towards a future of signal-based go-to-market, where you target the right accounts at the right time (more on signal-based marketing here →), the utility of automation is hard to ignore.

When you integrate automation into the activation process, the hidden power of attribution is revealed: a “closed-loop” revenue solution that goes way beyond the narrow perception of attribution. 

By automating feedback loops, companies can significantly improve ad performance. Lars highlighted, “LinkedIn, they publish numbers on this. So they launched their conversion API. First version I guess, like I can’t remember maybe two years ago and then recent version 3 or 4 months ago. We were launch partners for that together with Zapier and Google Tag Manager...they say like their numbers is like 20% efficiency increase in your LinkedIn ads.”

Of course, you don’t necessarily need automation to activate your data in these platforms. You can activate manually by downloading and uploading CSVs. But when doing so you need to consider that inputting your data manually has the potential to generate issues like data loss, inefficiency, and a lack of fresh data.

How Dreamdata is moving towards automation and activation

Dreamdata has its focus honed in on enabling data activation through automation. 

A key aspect of this focus involves the use of conversion APIs that communicate with platforms like Google and LinkedIn. By automatically integrating a variety of data sources into the platform, Dreamdata provides users with activations based on the optimization of ad spend and targeting based on actual down-funnel performance metrics. 

Dreamdata has helped B2B marketers measure and optimize their LinkedIn Ads through revenue attribution- which has shown 7.7x more revenue attributed to LinkedIn Ads on average - and CAPI integration - which has already seen an enterprise customer decrease their lead cost per acquisition by 87%. 

Find out more about data activation with Dreamdata here.

Many marketers fail to realize that attribution goes a long way toward solving the go-to-market data problem and in doing so unleashes the power to activate the data in a single closed-loop automated process.

This is where the future of the space lies.

Using automation to activate your go-to-market data will allow revenue teams to go beyond reporting and analytics and enable them to work with the data directly in the tools they operate on their day-to-day.

The impact of this will be more efficient budget allocation, more timely and personalized outreach, and more use cases than we can even imagine at present.