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    Why cross device targeting is the new black

    Why cross device targeting is the new black

    599 400 Apurv Lungade

    Scenario – (as if it is a magic!)

    User was searching for a t-shirt on a shopping site on his desktop but didn’t buy it. Now he searches for more t-shirts on his mobile and gets to see the same t-shirt which he had searched on the desktop previously. An instant question that comes to your mind is – how does mobile get to know that the user on desktop and mobile is the same? Is it cookie or device identifier? But the cookie is present on the desktop. Is it something different which links these two devices with a single identifier? Was he logged in chrome/shopping site on both desktop and mobile? Yes, maybe! We don’t know…let’s explore!

    What was the scenario about?

    We all can see the shift in content consumption. More and more people are consuming content on the go i.e. on their mobiles or tablets. The above scenario considers the case of a typical cross-device targeting which many brands/advertisers are adopting in order to engage with their users/consumers hopping on multiple devices. Peechha naa chhodenge! (Wherever you go, our ads will follow you). The basic understanding about cross device targeting is “Identify a user or its behavior on more than one device and show him relevant ads accordingly”. It can be any two devices (see the image % share by eMarketer)

    Why cross device targeting is the new black

    A recent TNS study in Australia showed that for those owning at least three Internet devices, share of time was split as follows: PC/laptop (52 percent), smartphone (29 percent), and tablet (19 percent) (Source: TNS Mobile Life 2013).

    There are two approaches to cross-device targeting.

    Deterministic – This approach is executed using PII (Personal Identified information) of the user. This process is more accurate since the user is either logged in or he has shared his information with the publishers. The user data/behavior is tracked (stored) against this PII (email id, mobile no., physical address) instead of storing it in a cookie. The reason being cookies and virtual IDs do differ device to device but PII remains same. Now once a new user logs in, it checks if the PII is available – if it is, then stores the behavior info as well as based on previous data it shows ad; else creates a new entry in the database and starts studying the user’s behavior (fingerprinting). The above example is based on the deterministic approach.

    Benefits: Since user’s behavioral pattern is studied across devices in this model the ads served are more accurate and precise. Digital advertisers prefer this approach as here the user is already identified and segmented for personalized targeting.

    Challenges: In the deterministic model it is difficult to scale. We can say “Scale is inversely proportional to accuracy”. Privacy of the user is the biggest concern in this model. Another challenge is browser incapability when serving ads on different devices with different default browsers.

    Probabilistic – This approach involves using data points and attributes from different devices, operating systems, location data associated with bid requests, time of day and a host of other such details to predict statistical, aka likely, matches between devices. These probabilities take time to result in accurate predictions. The accuracy level in this model is 60 to 90%. Example 1- if a phone, a tablet, and a laptop connect to the same networks or Wi-Fi hotspots in the same places every weekday, it’s safe to summarize that all three devices belong to a specific user.

    Example 2- SilverPush did it for one of their clients. When a user is watching the brand’s ad, an ultrasonic sound (Beacons) is emitted from TV which triggers the user’s mobile to show the offer on it.

    Benefits: In this model user privacy is not at risk at all. Users can be paired across devices, operating systems, browsers which increase the scalability. Advertisers get to reach to their fragmented audiences in a better way.

    Challenges: This model is based on probability, and continuously recursive algorithms. Precise predictions take time to deliver desired results.

    Conclusion: The future lies in cross-device/screen targeting. Today’s marketers and advertisers should be ready for the big change. Both the approaches require efforts to overcome huge technical feats. They require huge infra, database (Big data), strong analytics tool (multidimensional reporting through slicing and dicing) and self-learning complex algorithms. Cross-device targeting plays a vital role in campaign performance which should be kept in mind before setting up a campaign.