“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Robert Webster, chief product officer at Crimtan.
Recently the ad tech world has been getting very excited about cross-device tracking. What has been a theoretical holy grail for a long time is now being touted as ready for prime time. But as much as I am personally excited about cross-device campaigns, much of the recent hype is misleading. It is not ready for prime time because much more cross-device activity goes untracked than tracked.
The big Internet giants like Google, Facebook, Apple, Twitter and Amazon have the potential to deliver on this. But until they join the party, or we see the creation of a network of publisher log-in matched data, no one will be able to link the majority of journeys that users make.
As an industry, we need to be cautious. Marketers must understand the limitations of current cross-device methodologies, what is required to expand reach and the implications this entails.
There are currently four common methods to achieve cross-device targeting:
1. Linking log-in data: When a user logs in on more than one device, this data can then be linked.
2. Householding: Where different devices can be seen on one IP range and are combined with home data, behavior and more, they can be inferred as the same user.
3. Probabilistic methods:Combining device data (particularly with tablets), location, time and more with TV data to provide a multiscreen advertising and targeting approach.
4. Data links: Apps that can hear TV sounds, QR codes, NFC and more data links can join up devices to TV, print and outdoor for a cross-channel approach (more than cross-device).
Now all of these methods are very valuable for companies with the right technology. Many of the stats touted in the media are based around the householding and probabilistic methods. The problem is that the stats you hear in this space focus (obviously) on the number of devices you can join, not on the number you cannot.
In this simple example, I have a work PC, a home PC, a smart device and a TV. My home PC and smart device are regularly connected to my home network so I can be successfully targeted through the householding method. When you add TV, there’s also a good chance of targeting through probabilistic means.
However, my work PC is never on this network and none of those devices are ever connected to my work network, so this device can’t be added into the mix. Additionally, my smart device will only periodically be connected to my home network. As a result, using the householding and probabilistic approaches only work on around half the journeys. This percentage can drop quickly as users can have very complex digital lives using many different devices and networks. We then have to factor in an error factor when identifying audiences if there are many users in each household.
The Missing Piece
Log-in data linking is the theoretical way to solve some of these problems. If we can see the user logging into the same service at work, at home and on a smart device then these journeys are all joined up. I called it a theoretical solution because this is not happening at mass reach today.
The challenge here is that there are few, if any, services outside the giants, including Google, Apple, Facebook, Twitter and Amazon, which have enough reach of users logging in on multiple devices consistently for this data to be useful outside of their own user ecosystem. Therefore, if accurate cross-device tracking is not to be the sole preserve of the usual suspects, it requires publishers and data providers of log-in data to pool their resources together, with appropriate controls and consent.
Many of the larger players are also planning their own cross-device IDs based on log-in data. It will be fascinating to see whether these players keep this information to themselves, or release it to the wider ecosystem, with appropriate user control.
So where does this leave us? It means that we have a number of providers offering a very useful cross-device service, but still no one who is yet able to join up the majority of journeys that users make. This will not change until either the giants aggressively enter the space, or until a network of publisher log-in matched data becomes available.
Marketers must understand that in many cases, their cross-device results only measure a percentage of the true impact, and that cross-device campaigns will only be able to effectively target a percentage of possible journeys. It is important that those who offer cross-device targeting are open and honest about these limitations and their implications.
Understanding such dynamics will become the key to running effective cross-device campaigns and analyzing performance. Only then will cross-device opportunities live up to the hype.