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Does Cross-device Tracking Present New Issues for Privacy Minded Consumers?

A topic on the tips of advertisers’ and marketers’ tongues these days is “cross-device tracking,” a unique digital advertising method viewed within the data, analytics, and marketing spaces as a game-changer.

What is cross-device tracking, and what are the implications for organizations that must comply with data privacy laws?

What Is Cross-Device Tracking?

Cross-device tracking is the umbrella term for different techniques used to serve target ads to an individual user on a user’s multiple devices so that messages can be better tailored to the right individual at the right moment.

The ads and promotions served to the user across devices, channels, and platforms are more effective (more likely to lead to conversions) because they are informed by that user’s previous interactions on all of the devices, not merely the device or browser currently in use.

Cross-device tracking allows for better attribution or understanding of consumer purchases, behavior, and intent.

How does this work in practice?

As one oversimplified example, a unique user browses for a book on her mobile phone during breakfast. Hours later she puts a copy of the book into her shopping cart on her work laptop browser but doesn’t purchase it.

Later that evening, she may receive an advertisement on her personal desktop computer’s browser for other books by that author or even a discount promo code at the retailer’s site where she almost made the afternoon purchase.

This type of connecting the dots to identify and reach a single user across devices is accomplished through two primary methods.

Deterministic and Probabilistic Linking

The first method is deterministic linking (DL), whereby a user self-identifies to a service, such as by logging in, which directly confirms that multiple devices in use belong to the same user.

Accordingly, if a user logs onto a social media app from a smart watch, tablet, mobile app or computer web browser, then any user data collected becomes part of that user’s broader “profile,” and can be used to target ads to that user on any device or platform.

This data includes clicks, purchases, logins, website page visits and more.

The second method is probabilistic linking (PL), whereby statistical modeling, algorithms and/or predictive pattern recognition is applied to a variety of digital technical parameters to infer links between devices.

Firms in the PL space often partner with online publishers or ad exchanges and monitor ad request traits such as IP address, device type, geolocation, time of day usage patterns, and installed browser fonts.

This information is then correlated with other data sources and use proprietary processing to build device graphs that, over time and in the aggregate, can link multiple devices, cookies, and mobile IDs to a common user, who is assigned an anonymous identifier.

Privacy Considerations with Cross-Device Tracking

The use of cross-device tracking is a response to consumers’ plethora of options for accessing the internet compared with two decades ago. As well as the inherent limitations of delicate, mobile-deficient and browser cookies traditionally utilized in online behavioral advertising.

But does this new means of crossing data streams to gain a holistic view of a consumer along the entire path to purchase give rise to issues for privacy-conscious consumers and businesses?

For instance, can these techniques lead to the collection of unnecessary or superfluous data, at odds with the generally recognized privacy principle of data minimization?

  • Can they lead to the possible triggering of unintended legal regimes, or erroneous inferences that lead to bad ad spend?
  • Should different privacy approaches be utilized for DL versus PL?
  • Is it technically feasible for the industries involved to build an omnibus opt-out mechanism that can be honored across all devices and platforms?

In Andy Dale’s words, “Cross-device technology is really about understanding the customer journey and this technology is powerful but needs to be harnessed and utilized within a privacy framework which allows users an ability to understand the practice and make meaningful, choices.

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