This change is particularly relevant for B2B campaigns, LinkedIn’s core focus, where complex customer journeys often involve multiple touchpoints. To address these dynamics, LinkedIn has developed a hybrid attribution system that combines Multi-Touch Attribution (MTA) and Marketing Mix Modelling (MMM).
“Methodologies such as Multi-Touch Attribution (MTA) and Marketing Mix Modelling (MMM), consider a broader range of factors and offer a more balanced view of the customer journey, from initial awareness to final conversion,” LinkedIn explained. “At LinkedIn, we leverage the complementary value of both MMM and MTA approaches and have developed a unified system bridging the two methodologies in our attribution stack.”
The system utilises advanced neural networks and expanded data points to map user journeys more effectively. While the full technical explanation is dense, the simplified takeaway is that LinkedIn’s updated approach accounts for more variables and touchpoints, ensuring a clearer picture of how ads influence user behaviour.
Testing of the updated methodology has shown promising results. LinkedIn reported that for upper and mid-funnel campaigns, such as video ads and digital display, its modelled attribution system identified significantly more engagement compared to last-click models, delivering a 150x increase in credit for these campaigns. This broader view enables marketers to better allocate spending across channels.
Historically, LinkedIn’s reliance on rule-based attribution (RBA) overemphasised low-funnel channels like search or email. The new system corrects this imbalance, offering advertisers actionable insights to optimise performance across the full marketing funnel.
The updated attribution models are being rolled out to all advertisers on LinkedIn Marketing Solutions, promising enhanced clarity in campaign performance and better-informed ad spend decisions.
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