Cross-device targeting has been a hot topic in advertising for the past few years. Marketers embrace a more holistic audience view and want to target, measure or engage a single person across all their digital devices. The reason for this attention is the increase in the number of consumers who use multiple devices at the same time.
According to a March 2016 survey by Econsultancy, 75% of marketers in North America mentioned “matching customers across multiple devices” as digital priority. Only 14% of the marketers in the same survey said that their company can handle such matching, indicating a gap of 60 pts between digital priority vs capability.
In a world of rapidly increasing multi-screen usage, 360-degree view of the consumer has become crucial for effective marketing. Marketers want to be in touch at key points on the customer journey. In order to do this, they must be able to map people across their devices. For example, a consumer can make product search from his/her tablet, then makes the purchase from his/her laptop and makes no action for that product from his/her smartphone. If there is no cross-device targeting identification capability; these three devices will be treated as three different users and digital marketers will target three different users with different digital marketing strategies. They may recommend the same product that the consumer already purchased from laptop which will lead to inefficient campaign management.
So, cross-device identity management is at the core of data-driven advertising. Most digital marketing companies take the data they have and use an algorithm to map users. The advertising companies that are strong in data-driven advertising will have a competitive advantage in this process.
There are different methods of tracking users across devices which can be summarized as follows:
- Footprint / Fingerprint method: Cross-device mapping is done according to statistical results from data such as browser resolution, fonts that are used in writing, browser add-ons etc.
- Deterministic method: Cross-device targeting relies on logged-in user data to identify people across devices. Logging into social media, search engine or email account sends an accurate signal on all of the devices that you are the same person.
- Probabilistic method: Uses an algorithm to analyze thousands of different anonymous data points and creates statistical matches by machine learning.
Deterministic and probabilistic methods are better for identity recognition and segmentation. Although deterministic tracking seems to be the most accurate cross-device tracking method, the companies with high number of users such as Google, Facebook and Verizon are being criticised for the privacy issues. Probabilistic method on the other hand uses anonymous data and users can opt-out anytime. Therefore the latter seems to be more appropriate in terms of privacy. Furthermore, probabilistic method does not require logged-in user data which is the walled gardens of giants such as Google and Facebook.
Cross-platform approach to digital advertising has two basic goals. Firstly, to recognize the same people as they engage at different screens and secondly building efficient campaign with a holistic view on audience targeting. There are 4 basic steps to build a successful cross-device targeting strategy:
- Aggregate audience data
- Consolidate the data into individual personas
- Activate real-time campaigns
- Enhance the CRM with the DMP
Aggregate audience data
Marketers can collect anonymous and legal audience data in real-time and create segments. The following data can be collected by using a DMP:
- First-party website data
- Third-party behavioral and demographic data
- Second-party data obtained with data partnership agreements
The larger the scale of the data, the better the profile matching and segmentation.
Consolidate the audience into individual personas
Cookies are the main tracking tools to serve the targeted ads in digital advertising industry. But cookies can not fulfill the needs of cross-device targeting and there are other unique identifiers in to track users’ activities on smartphones and tablets such as Google’s advertising ID (Advertising ID) and Apple’s Identifier for Advertisers (IDFA).
Each person’s different anonymous identifiers should be linked to one anonymous identifier: persona ID. In other words, every person in the target audience should be represented by a persona ID in DMP.
Probabilistic matching uses a collection of signals to approximate a match of multiple unique identifiers to the same persona ID. This matching is done by data mining and machine learning and its consistency depends on both the methods and quality source data.
Activate real-time campaigns
Once a DMP can recognize the same individuals on multiple devices, a DMP’s segments become far more useful and powerful. The segments that represent actual people are more descriptive since they consider data from all consumer touchpoints. For example, persona-based segments take geo-location information from the mobile app environment and make it actionable on the Web cookie environment.
This will help marketers to plan and buy media that targets the actual people they want to reach. However, to take full advantage of audience segments in a DMP, marketers need the DMP to connect in real-time with a Demand-Side Platform (DSP). To capture the most opportunities from a DMP and DSP, marketers should look for an integrated solution where the DMP and DSP work together in real-time.
Enhance the CRM with the DMP
Marketers commonly enhance their DMP with CRM data. However, marketers can also expect the reverse, to enhance their CRM with DMP data. This helps marketers personalize the customer experience and get deeper customer insights.
When marketers use their DMP to enhance CRM, they enhance all the channels that CRM influences. This includes email, website, social and call center activities. For example, DMP data can enrich the decisions made to customize content on websites, email newsletters or even on call center interactions.
Once the basics of cross-device targeting are built among smart phones, tablets and desktops; the next step would be connecting smart TVs, wearables and IoT ( Internet of Things). This will be another important milestone towards fully personalized advertising.