The other night, I took my two young boys to the local barber to get haircuts. They turned off the iPad they were watching a video on, and off we went. While we were waiting for our turn, they asked me if they could use my phone. I handed it over and watched them access the video they were watching, picking up right where they left off. After we got home, they jumped on the bed in my room and turned on the video again, this time from an app on the TV to finish it before bedtime. I marveled at the matter-of-fact expectations these boys had of their video experience.
Every day, we switch devices without thinking, letting the experiences move us through content consumption, purchases, and digital or physical interactions. But most customer-facing teams — and the technologies they use — are designed and built around specific channels and devices. This poses a significant problem for businesses that want to influence customer engagement over time and conversion in the moments that matter.
Nonlinear engagement by customers across multiple devices is a reality. Knowing how your customers behave on one device isn’t enough to optimize the customer experience or their ongoing loyalty to your brand and recurring revenue to your business. Old analytics paradigms and proxies to measure how people engage with your brand — like unique visitors — need to change.
A turnkey solution for cross-device analytics
It’s time to transform how you see your customers and go from a device-centric view to a person-centric view, so you can see actual customer journeys. Last month, we announced cross-device analytics in Adobe Analytics, a turnkey solution that empowers brands to understand customers as people, not just devices.
Utilizing a private device graph, cross-device analytics can stitch together multichannel data from different devices into a single journey stream, so that you can better understand how people move from touchpoint to touchpoint across devices. Besides gaining more accurate metrics to inform resource investment, you can also minimize negative and redundant experiences for customers. For instance, advertisers can dramatically improve the efficiencies from paid media investments by understanding where, when, or even if they should be delivering ads. If you know a customer is further in a journey, your ad content could revolve more around evaluation instead of new customer acquisition and be much more personalized.
Attribution is also a great use case where cross-device analytics provides real insight and value by helping you understand which channels and campaigns drove conversion and revenue. For example, consider the following scenario:
Let’s imagine I had five digital brand engagements across three different devices. First, I searched for a product on my phone at one of my favorite retailers. Later, while on my desktop, I see a display ad, so I click through to a landing page. Not seeing anything all that interesting, I leave. The retailer sends me a follow-up email with an offer that I open on my work PC. I click through, get interested, and decide to come back later because I’ve got work to do. Later that day I directly access the retailer’s website to do some research. Then, finally, I make a grand purchase totaling $5.
You can see that in a single device or “unique visitor” world, last-touch attribution credit would solely go to search because the purchase happened on the mobile device, essentially ignoring all the devices and interaction in the middle. But when using cross-device analytics, the other devices I used to engage with the brand are taken into consideration, so the data can see that email was the channel that led to the product view and the closed revenue.
