Snapchat to use machine learning to enhance ad targeting
Snapchat is extending its optimisation of campaign performance by using Goal-Based Bidding (GBB), a system that uses machine learning to determine which users will swipe what types of ads. Advertisers can use GBB to increase swipe-ups, rather than focusing only on impressions, by providing the platform with their predicted value of a swipe for auto-optimised bidding and delivery. Around 20% of advertisers are using GBB, and have reported up to 40% improvement in efficiency for cost per swipe. Snapchat is also acquiring patents for AI-driven tech such as facial and object recognition.

Snapchat is adding ways to optimize campaign performance with the help of machine learning.

Earlier this month, Snap Inc. began rolling out what it calls Goal-Based Bidding (GBB). The option, available to marketers buying ads through Snapchat's API, uses machine learning to know which users are most likely to swipe a certain type of ad.

Here's how it works: With goal-based bidding, advertisers can inform Snapchat of when their main goal is increasing swipe-ups—perhaps for app installs, web views or movie trailers—instead of focusing solely on impressions.