In this example, Emma moves smoothly from connected car to mobile app, back to connected car, and on to an in-store visit. She switches devices without thinking, letting the experience move her through the original purchase — and an upsell.
On the back end, there’s a lot more going on. Marketers and analysts have to juggle several moving parts to make that experience possible. They push and combine behavioral data with their customer relationship management, loyalty, and point-of-sale data. They use AI and machine learning to identify how likely Emma is to purchase at a given time so they can push special offers to the appropriate device.
In short, they’re piecing together the entire journey that Emma takes with their brand — both online and offline — so they can identify ways to make her experience as easy, satisfying, and valuable as possible. And they’re using analytics to make it happen.
Only it’s not the same analytics you’re used to. In the past, analytics focused on what happens with “visitors.” In that world of analytics, “visitors” meant devices, not people, because brands didn’t have the strategies and tools to get to know a person at the individual level or get a complete view of their behavior and preferences. They could only see what happened on disconnected devices.
In a world with customer journey analytics, it all changes. Like the oxygen we breathe, analytics brings life to the customer behind those devices, over time getting to know their favorite pastry choice, when they’re most likely to buy gas, and how long they stay online while in the station’s café. And brands can adjust in real time to make all of those interactions even better.
