New Update
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LinkedIn has announced updates to its advertising attribution models, aiming to provide more accurate insights into ad performance by moving away from traditional, assumption-based methodologies.
The professional networking platform’s new approach integrates Multi-Touch Attribution (MTA) and Marketing Mix Modelling (MMM), offering a more comprehensive understanding of the customer journey. Unlike last-click attribution, which disproportionately credits final interactions, these methodologies consider multiple touchpoints, from initial awareness to final conversion, to better represent actual user responses.
“Methodologies such as MTA and MMM consider a broader range of factors and offer a more balanced view of the customer journey,” LinkedIn explained. “We have successfully deployed the system for our internal marketing and will leverage this methodology for advertisers on the LinkedIn Marketing Solutions platform.”
The system employs advanced neural network modelling to process sequential touchpoint data and user interactions.The platform claims that this approach allows for a more detailed analysis of campaign performance, particularly for upper and mid-funnel channels, which are often undervalued in traditional models.
In initial testing, the platform observed significant improvements. For example, it reported a 150x increase in credit attributed to campaigns using the new model compared to rule-based attribution (RBA). This outcome highlights the system’s ability to link impressions across user journeys, enabling advertisers to optimise spending decisions more effectively.
The platforms' attribution updates are now being rolled out to all advertisers, marking a significant shift towards a data-driven approach that better reflects modern user behaviours and engagement patterns.