Custom attribution for LinkedIn moves beyond standard models to provide a deeper understanding of touchpoint contributions to conversions. Recognizing non-linear customer journeys, it allows defining custom rules for credit assignment based on business goals and customer behavior. Analyzing data from LinkedIn and other channels reveals effective interaction combinations. For example, a display ad followed by a branded search and direct visit might be a key conversion path. Custom attribution assigns credit to each touchpoint, enabling informed budget allocation to high-performing channels. It also improves performance measurement by clarifying which campaigns influence customers at different stages.

Audience insights analysis in LinkedIn provides valuable information about users interaction with ads and websites. It reveals demographic data like age, gender, and location, as well as seniority level, company size, etc. This data helps to understand who the target audience is. By understanding these audience characteristics, advertisers can refine their targeting strategies to reach the most relevant users. Audience insights also help to create custom ad messaging and creative to resonate with specific audience segments, improving ad relevance and engagement.

Digitl makes use of data science models to score users based on engagement signals – such as scroll depth or video completion – enabling dynamic bidding strategies and higher ROI, prioritizing users likely to convert. For example, users exhibiting high attention signals receive higher bids, increasing the chance of showing them relevant ads. On the other side, low-engagement users receive lower bids or are excluded, optimizing budget allocation. This data-driven approach improves campaign performance by focusing on users most likely to engage, leading to higher conversion rates and a better ROI. It enables personalized advertising based on predicted user interest.