What kind of data does LinkedIn use to score relevance for ads in the auction?

LinkedIn uses user engagement and bidding amounts to score the relevance of ads in the auction process. This focus on user engagement means that the platform evaluates how users interact with ads, such as clicks, likes, comments, and shares, to determine how well the ad resonates with the audience. A high level of engagement indicates that the ad is relevant and appealing, which can enhance its performance in the auction.

Additionally, the bidding amounts play a crucial role because they contribute to how competitive an ad is within the auction framework. Higher bids can elevate an ad's visibility, but if the content is not engaging, it may still struggle in delivering optimal results. Therefore, the interplay of user engagement and the financial commitment indicated by bidding amounts creates a comprehensive measure of relevance that ultimately influences the success of the advertisement on LinkedIn.

In contrast, other options may suggest the significance of certain aspects, but they do not encompass the full spectrum of criteria that LinkedIn emphasizes in its ad relevance scoring. For instance, member feedback ratings, previous clicks and impressions, and aggregate data from similar campaigns are valuable metrics, but they may not reflect the dynamic nature of real-time user interactions and the competitive bidding landscape as effectively as user engagement and bidding amounts.