Media iQ’s Cross Device Capability: Creating a 360-degree view of a Consumer’s Journey
Today’s shoppers are device hoppers. With 90%1 of consumers moving between devices, be it on smartphones, PCs, tablets or TV, it’s becoming increasingly difficult for a consumer to involve just one device and one online touch point during the decision-making process.
Consumers are interacting with brands in a number of ways, interchanging between multiple devices and channels, which means that brands need to learn to connect with consumers on a smarter and more targeted level.
So how can you be sure that you’re targeting the right messages to the right audiences at the right time? Or be sure that you are using what you’ve learned about the user journey from previous campaigns?
To adapt to this new reality, businesses need to implement cross-device strategies. Rupali Patel, Data Scientist at Media iQ tells you all that you need to know about cross-device tracking.
What is cross device tracking?
Cross device is the new opportunity in the digital ad world which provides a single view of a user who spends his/her time on the Internet throughout the day across different devices. The single view is a result of linking different IDs associated with the digital properties on which a user is observed. In case of mobile apps these IDs are Device IDs, while in case of desktops and mobile web they are Cookie IDs. Simply put, it’s the linking of multiple IDs of a single user.
While methodologies may vary for each provider, most of the cross device solutions follow two primary approaches: deterministic matching and probabilistic matching. The deterministic approach enables a user’s login details to connect to multiple devices. Though it is the most accurate way to track user existence across multiple devices, the only problem lies in scalability. For example, for users where login data is not available it’s not possible to track their existence across multiple devices. i.e. the number of data points is limited. For smaller players who don’t have login data, and want to compete with major portals or social networks, probabilistic matching is the only way to bridge the gap.
The probabilistic approach, on the other hand, is not as accurate as deterministic matching but is a highly adaptive approach because of the scale it handles. The probabilistic method analyses various attributes related to device usage. This method uses large data sets and machine learning to make educated guesses, suggesting that a mobile device owner and a desktop owner may be the same user. This prediction is based on what websites/applications the user visits, time of day, location, IP and network used along with the device, and cookie specific features such as browser version, OS version, device model etc.
Why is it important?
Recent studies have revealed that consumer behavior has become increasingly cross-device. In order to target/retarget audiences across multiple devices, ad networks must first be able to track users, which can be tricky in apps, due to the inability of cookie usage. Also traditional cookie based consumer behavior tracking may paint an incomplete picture of the consumer who switches between different web browsers at home and at work. Furthermore, a cookie stored on a consumer’s browser cannot provide insight into a consumer’s activities or preferences within the “sandboxed” apps on the consumer’s phone.
The lack of a universal, cross-device identifier means that brands are limited to siloed data gathering, ad delivery, and measurement. This leaves brands with an unclear understanding of frequency across screens, and the incapability to tell sequential brand stories. This limitation also means that rich third party data ecosystems that have been established across the desktop web over the last decade can’t be leveraged across mobile devices.
Figuring out if a smartphone user and desktop user are the same person makes it easier to not only target them with ads, but also determines whether a given ad seen on a desktop resulted in purchase on a mobile device, or vice versa.
Cross-device functionality is critical for successful content marketing too. Marketers are making sure that their content can be consumed on virtually any screen, and making content available to as many people as possible means that the content needs to be flexible enough to be present across multiple devices. It is therefore, important to track customers across multiple devices and convert them by sending the perfect discount at the right time. Though this technology is still in its early phase, by using cross device, advertisers can save wastage on ads by serving the same ad on multiple devices to the same prospect.
How MiQ’s cross device solutions work?
MiQ’s cross device technology is based on the probabilistic approach. We try to analyze a user’s behavior across multiple screens using features like IP, site/app content, daypart and geo location. We calculate the confidence score for a given pair of cookie and device by assigning different weightage to various probabilities derived from multiple features. For example, for a given cookie and device pair, the higher the confidence score, the higher is the chance that they both belong to the same user. Understanding the importance of how well a cross device solution can work to target the right audience on various channels, we at MIQ, are continuously innovating to get better accuracy to cater to the current market needs efficiently.
How will Media iQ’s cross device solution optimize campaigns?
Media iQ’s cross device solution enables a 360-degree view of a consumer’s journey without using any personal identifiable information (PII), thereby enabling the delivery of the right message to the right user, in just the right time.
It also makes it possible to not only devise new strategies to target users based on their interaction across multiple devices, but also helps identify the most potential prospect, thereby increasing the ROI.
We believe that cross device targeting will lead the advanced programmatic advertising space and enable digital marketing efforts with reporting, attribution, and path-to-purchase visibility and accuracy like never before.
1. Google Study