The news: Uber is offering customer data to marketers to enhance ad targeting and placement capabilities. The data is available through Uber Intelligence, its new insights platform powered by LiveRamp.

Advertisers can get information on how people “move, dine, travel, and order” to build a better understanding of customers’ behavior patterns, the company said in a blog post.

How it works: Uber feeds data on ride-hailing and delivery behavior into Uber Intelligence and, through its partnership with LiveRamp, uses clean-room technology to remove personally identifiable information (PII).

It then packages aggregated, “privacy-enhanced” audience segments that advertisers can merge with their own customer data sets to uncover new insights, such as mapping travel patterns against purchase histories.

The information is pseudonymized, not anonymized, meaning PII is replaced with artificial intelligence so PII can't be linked back to individuals without additional, corresponding information that advertisers may already have.

The opportunity: Uber’s vast trove of on-the-go activity data includes details like what kind of food users order at certain times of day, when and where they tend to take rides, how frequently they visit certain areas, and the types of trips they take around major events.

These context-rich insights could help Uber compete with companies like Amazon, Google, and Meta for retail-ready intelligence. They will also support Uber’s ad business, which accounted for 9% of its total revenues in Q3.

The risk: Offering this information could spark concerns over how much consumer behavior data is fair game to marketers.

Even though data is pseudonymized, it includes hyper-personal information on user routines and preferences. Sharing it may raise red flags with consumers, putting pressure on Uber to prove that its privacy safeguards are more than just promises.

What marketers should do: Experiment with Uber Intelligence’s insights to create bespoke ad strategies that segment audiences, identify relevant customers, and personalize targeting.

• Test Uber Intelligence alongside other data platforms to see whether movement-based insights improve return on ad spend (ROAS).
• Pilot small-scale campaigns with Uber-based data to gauge engagement impact and determine if incorporating user behavior information increases placement optimization.