
LinkedIn has recently enhanced its advertising attribution models to provide data that more accurately reflects real user responses to ads, moving beyond the limitations of traditional advertising methods. These improvements aim to offer marketers a clearer understanding of the effectiveness of their campaigns.
Marketers are well aware that most attribution models rely on presumptive frameworks, such as last-click attribution, which fail to grasp the intricate behaviors of modern web users and their interactions with related content. This outdated approach often overlooks vital user engagement metrics that can significantly impact campaign outcomes.
This challenge is particularly pronounced in B2B marketing campaigns, which are central to LinkedIn’s offerings. To tackle this issue effectively, LinkedIn has introduced a new attribution methodology that adopts a fresh perspective on measuring ad performance, ensuring a more comprehensive analysis of user interactions across the platform.
As detailed by LinkedIn:
“Methodologies such as Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM) consider a broader range of factors and offer a more balanced view of the customer journey, from initial awareness to final conversion. 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. We have successfully deployed the system for our internal marketing (i.e., marketing for LinkedIn’s products), and will leverage this methodology for advertisers on the LinkedIn Marketing Solutions platform.”
The new methodology integrates elements from both MTA and MMM, utilizing a broader array of data points in LinkedIn’s evaluation process. This advancement allows for a more precise measurement of user engagement across various factors, rather than relying on limited sampling techniques that may distort the true picture of ad effectiveness.
While the technical intricacies of the new system can be complex,
for instance:
“Positional representations are combined with the sequential touchpoint data generated by members. These sequences are fed through a self-attention module. We concatenate member and company representations and feed these through a dense layer to create a representation of the acting member. The member’s representation and the output of the attention layers are combined and fed through a classification head for the learning task.”
Although this may sound daunting, LinkedIn’s explanation reveals that they have built a sophisticated system that considers a multitude of factors, interconnected through a neural network. This innovative approach enables LinkedIn to track and measure audience responses to advertisements more effectively, leading to a more accurate attribution of campaign performance.
Preliminary tests conducted by LinkedIn indicate significant improvements in results, validating the efficacy of this new approach.
“As an example, the business [in testing] observed the performance of both models for Upper and Mid Funnel campaigns which are found in Video Ads, Digital Display and Social Media. When comparing both models for non-search channels, Modeled Attribution was able to recognize and deliver credit whereas Last Click remained flat. This is due to the model’s ability to stitch impressions from these campaigns in the user journey which RBA models are not able to do. Initial results show a 150x increase in credit found in Modeled Attribution which paces well with Marketing’s spending increase during this time frame.”
In essence, LinkedIn’s revamped tracking process has proven to offer a more nuanced understanding of user responses by expanding its tracking and attribution capabilities. This transition provides advertisers with deeper insights into how their ads influence actual user behavior, which is vital for making informed marketing decisions.
This level of insight is crucial for ensuring that advertising budgets are allocated effectively to maximize campaign success.
“LinkedIn marketing had historically relied on rule-based attribution (RBA) based on last-click, where full credit for a conversion point was given to the last-click event. This over-indexed the credit towards low funnel channels that convert demand, such as Search or Email. However, last-click attribution understates the value of upper and mid-funnel channels which devoids marketers with the ability to see their performance or how to optimize it.”
With this new methodology, marketers will gain enhanced visibility into their campaigns, enabling them to make smarter spending decisions without relying on potentially misleading data that may have previously guided their strategies.
Ultimately, this refined approach should lead to improved marketing outcomes and greater ROI.
LinkedIn has announced that it will be rolling out this innovative methodology to all advertisers, ensuring that everyone can benefit from these advancements in ad attribution.