Attribution Insights for Superior B2B Content Marketing

Attribution Insights for Superior B2B Content Marketing

Content marketing has transformed B2B marketing over the last few years. According to the Content Marketing Institute, 80% of B2B brands have a content marketing strategy. While content marketing can be a valuable way for brands to engage with new clients, it must be based off strong data and a detailed understanding of client behavior.  Brands must also understand the role marketing channels play in the effectiveness of their content. Using attribution insights on Google Analytics can help brands see how potential clients are interacting with their websites. They can optimize their content marketing strategies around their conversion data, which helps them improve their marketing funnel and boost sales.

Role Interactions Play in Your B2B Content Marketing Strategy

All B2B marketers recognize that the way customers are referred to your website affects their interaction with it. If customers engage with your site through one channel, they may be more likely to convert than others. For example, some brands find that website referrals tend to be more effective than traffic from organic search, while others find the opposite. However, if they took a more granular look at their data, they would see that conversions are usually the result of a long series of events. According to Adam Johnson, a marketing automation expert and Shopify merchant, attribution insights has helped many of his clients boost the ROI of their content marketing campaigns.
“Previous Google analytics channel data was far too primitive,” he says. “In 2017, brands will need to rely much more heavily on attribution insights.”
Brands often focus too heavily on the last interaction a client had with their site. While the last interaction is important, previous interactions may also play a role in shaping conversions. Let’s take a look at two series of interactions with two potential clients could have with your site:
  • A member of your sales team meets a potential client at a B2B networking event. You give them your business card and they visit your website. A few weeks later, you reach them again through a retargeting ad on Adwords. He reaches out for a quote.
  • A customer visits your site after finding your LinkedIn profile. A few weeks later, you reach them again through a retargeting ad on Adwords. This customer clicks the back button and doesn’t come back.
Under the default settings in Google Analytics, the report would show that the last two customers reached your site through paid advertising. However, their state of mind was very different, because their previous interaction with your brand. The first customer may have converted, because he already had a strong interest in your brand during the first interaction. After all, he actively sought your website out after speaking with your sales team. If this becomes a pattern over time, you would decide that it’s best to encourage customers to visit your website directly for the first interaction.

What Are Attribution Insights?

There are several ways users can reach your website:
  • Direct referral traffic
  • Paid advertising
  • Organic search
  • Referrals from other websites
The Google Adwords development team recognizes the impact different types of interactions have on conversions. Under the default setting, they credit conversions entirely to the last interaction a customer had with your website. However, you can change the setting to credit part of your conversions to previous interactions. You can choose either the “Linear” or “Position Based” options under the attribution settings, which are listed under the “Conversions” tab on your Analytics dashboard.


Many factors play a role in B2B content marketing. Choosing the right marketing channels is just as important as the content itself. Brands have struggled to choose the right distribution channels, because they made their decisions off of limited data. Google’s new attribution insights data will help them understand the role different interactions influence client behavior.

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