Track Mobiles Without Cookies

Why Cookies Don’t Work Well on Mobile Platforms

Where most consumers use the same computer consistently, each of us may have several mobile devices. A single person may have a work cell phone, a home cell phone, a tablet, an Internet-connected game console, a car-based Internet-connected device, and more. How can ad servers and other players identify that person as the same person when she surfs the Web on different devices? Worse yet, since mobile applications (apps) use a different “sandbox” from each other and the mobile browser uses its own sandbox, even though they’re all on the same device, sites have a hard time identifying a visitor as the same person when she uses one app, a different app, and her mobile browser.

The upshot is that on mobile platforms, cookies are much less effective than on traditional computers. Another rising issue is that as Microsoft and others try to unify the user experience between mobile and laptops (think Windows 8), this challenge is creeping into the laptop arena too. Tracking individual users across multiple apps and browsers, on multiple devices, running on different networks has become a nearly insuperable challenge.

Types of Tracking Solutions on Mobile Platforms

Given the challenges discussed above, the industry is in the process of developing alternate technical options. Most of these solutions fall into one of the following classes: client/device-generated identifiers, statistical IDs, and universal logins.

1. Client- or Device-specific IDs: These include Apple’s UDID and its replacement ID For Advertisers (IDFA), Google’s Android ID, MAC address, etc. Users cannot alter or opt out of tracking with most of these solutions, raising the privacy concerns discussed above. The IDFA does allow such alterations and opt-outs, making it nearly ideal from the user’s perspective. However, the IDFA and other dynamic device identifiers make it hard to attribute ad performance across channels and devices. They also fail to tie together different devices when used by the same consumer.

2. Statistical IDs: Algorithms operating off the user’s device, but using information provided by it, and/or by the gateway it uses to access the Internet. This class includes services such as those provided by TapAd, DrawBridge, and AdTruth (see below). These statistical solutions are probability-based, and thus suffer from a certain lack of certainty and stability, especially where an employer may offer a large number of employees the same types of device, with centralized software control and updating.

3. Universal login tracking: This solution, which does not yet exist, would offer users the option of setting up a login, where they can specify their preferences. This solution, most likely synchronized “in the cloud,” would require the agreement of all parties to collaborate. If consumers agree, this solution would allow them to register their devices and applications through a single dashboard, where they could indicate their privacy preferences. Those willing to participate would gain the benefit of increased personalization, and potentially free access to ad-supported services and content that those opting out would be required to pay for.

Another direction that may yet develop and gain traction, but has not yet done so, is so-called “network-inserted management,” implementing state management through intermediaries such as Wi-Fi networks, Internet Service Providers (ISPs), and other third party servers. Such a solution allows unified identification and preference management for all devices in the same household or office. Partnerships between the relevant third parties could potentially allow the solution to persist when mobile devices travel to a new network.

http://www.allaboutcookies.org/mobile/mobile-tracking.html