Cross-device measurement

Understand how customers move across devices, channels and touchpoints to build a more complete picture of marketing performance.

What it is

Cross-device measurement connects customer interactions across devices and channels to better understand how people discover, engage with and convert with a business.

Where customers log in or provide consented first-party identifiers, interactions across websites, apps and other environments can be connected with greater confidence.

Where direct identifiers aren’t available, analytics and advertising platforms may use modelling to estimate cross-device behaviour.

Combining these approaches can provide a more complete view of attribution, reach, frequency, audiences and customer acquisition. The important part is understanding which customer journeys are directly observed and which rely on modelling.

Why it matters

Customer journeys rarely happen on a single device.

Someone might discover a brand on their phone, research on a laptop and eventually purchase through an app or in store. Looking at each interaction separately can make one customer appear to be several different people.

Privacy changes have also reduced access to some traditional identifiers, making first-party data and consented identity increasingly important to cross-device measurement.

A stronger cross-device measurement approach helps organisations understand customer journeys more accurately, manage media reach and frequency, improve audience suppression and make better decisions about marketing performance.

The goal isn’t perfect visibility. It’s creating the most reliable view possible from the data available.

What Louder does

User ID architecture - where a login exists, getting it into Google Analytics, the warehouse and the ad platforms consistently, rather than in one of the three.
Identity resolution in the warehouse - with coverage and confidence stated rather than implied.
Measurement design around the known gap - reporting that accounts for unresolved traffic instead of quietly assuming it away.
Cross-device suppression and frequency - so purchase on one device stops pursuit on another.
Offline and in-store linkage - connecting the store transaction back to the digital journey where the data allows it.
Aggregate testing - geo and incrementality experiments that answer channel-level questions without needing the device graph at all.

Common challenges

User ID implemented on some properties and not others. Resolution works on web and not in the app, and the resulting reports describe a population that does not exist.
Login behaviour generalised to everyone. Logged-in customers are more loyal and more frequent by definition, so their cross-device patterns do not extrapolate to the unresolved majority.
Suppression that only works on one device. The customer who bought on desktop keeps seeing acquisition advertising on their phone, which reads to them as the brand not knowing who they are.
Household treated as person. CTV and retail media frequently resolve to a household, and personalising at that level goes wrong in ways other people in the room can see.