Apple is at it again. They've apparently acquired yet another target marketing patent and now that they're rolling iAd out aggressively, there's no turning back. Some believe that iAd could eat up half the mobile ad market in the future while most think that Apple is really out to challenge Google. More importantly perhaps is that actual paying customers of iAd's services, like Nissan, have seen their ad time-traffic soar ten-fold. All the more reason why Apple is seeking to further fine-tune their target marketing capabilities - and in today's patent report we'll take you on a short trek through the mindset of the ad industry as it schemes to further burrow their tracking technology into Apple's iOS devices like only Mad Men could.
The use of mobile phones in the United States and around the world has increased dramatically. It is projected that soon the number of mobile phone users will exceed the number of fixed telephone subscribers. The proliferation of mobile phone usage has engendered corresponding advances in mobile phone technology. Mobile phones can now handle many types of multimedia content. Consumers can navigate the "Web" from their mobile phones to much the same degree as from their home computers. Often, users switch between access devices, using a wireless device, a personal computer interchangeably throughout the day to access the same content.
The proliferation of these new multimedia mobile phone devices and the implementation of wireless application protocols (WAP) have created a ripe market for the presentation of mobile-enabled content and advertising, which provides significant revenue opportunities for both third-party advertisers and wireless carrier companies. Some of the key challenges in providing targeted content and advertising to users are (i) to be able to accurately identify the user as often as possible, and (ii) to maintain comprehensive usage histories for individual users. The combination of these two capabilities leads to improved ad targeting and an enhanced user experience as users are provided with the content they want, when, where and on what device they want it.
While there have been many solutions with respect to traditional web usage (i.e., users browsing websites from a personal computer), no such solution provides the same depth and breadth of user profiling for mobile web usage, much less for identifying the same users as they alternate among delivery channels.
While there are many differences, one key distinction is the added complexity of supporting multiple phone types, network types and carriers. For example, carrier-specific mobile identifiers such as x-up-subno and msisdn identify each subscriber with a unique user ID and pass it through request headers. However, the request header names vary from carrier to carrier, as some carriers may use phone numbers as the unique user ID, whereas others may use a randomly generated ID. Moreover, content providers (e.g., content distribution networks, advertising networks, etc.) cannot access a carrier's identifiers or headers when they are removed or scrambled from network data flows. This lack of information limits third-party mobile networks from providing a consistent experience to their users across different devices, carriers, and content formats.
In cases in which the mobile device supports the use of device-resident cookies, a network cookie identifier can be set. When a page request is serviced, a cookie is placed in the mobile browser. Such an approach does not work, however, for devices that do not accept cookies, or in cases in which the user disables such functionality. In implementations in which WAP gateways honor non-persistent cookies, session identifiers may be used to track session-specific usage, but such an approach does not provide a complete or persistent picture of a user's behavior over time.
What is needed, therefore, are techniques and supporting systems to track users and site visitors across multiple networks of mobile media properties as the users interact with the properties via mobile web, SMS, within mobile-device resident applications and conventional "wired" content sites.
The network cookie enables advertiser targeting across multiple user sessions, content distribution channels, devices, and across different media interactions. Furthermore, data gathered over time enables the clustering of users having similar behavior patterns. Each of the mobile channels supplies unique data points to enable richer targeting and clustering based attributes of the content requests. As examples, mobile web sites identify content interests (based on the sites visited by a user); SMS supplies a phone number (from which we may derive a location); application supplies precise location (on platforms such as the iPhone). The user cookie/record is then augmented with these data points and uses the data for site personalization and targeting purposes.
To associate specific content requests to particular users and/or sessions, a hash_id is created as a unique user ID (UUID) for users in a locality-sensitive manner which optimizes the group containment logic of mobile users based on different attributes. The constituent attributes are spread across different dimensions (device type, model type, carrier, behavioral patterns, etc.) and thus captures various data points for each interaction. A probabilistic matching technique is then used to identify content requests that emanate from the same user (or device), to facilitate targeted advertising and content delivery.
Therefore, in a first aspect, a computer-implemented method for identifying and/or associating requests for WAP-enabled content with mobile subscribers includes receiving mobile content requests that include various request attributes and assigning each request attribute to attribute groups. A hash function is applied to each attribute group, and multiple mobile content requests are then assigned to a mobile subscriber based on a degree of match (which may be less than 100% in some cases) among corresponding hashed attribute groups.
The request attributes could be a device ID, a carrier ID, a telephone number, an account number, a MAC address, an IP address, a location, a SIM card ID, and/or a device model. The request attributes may be assigned to one group, no groups or in some cases more than one group. The groups may, in some cases, be unique across users. In some embodiments, a match confidence level is calculated that represents a degree of match between mobile content requests. In such cases, the match confidence level may be used to sort the mobile content requests, and some number (e.g., the top k requests where k is an integer) of the requests are assigned to a single mobile subscriber. The number of selected requests may be predetermined and/or based on a minimum confidence level threshold.
Once associated with a particular mobile subscriber, the content requests may be augmented with a user-specific identifier and stored in a database. The database of user-specific content requests may be used to identify subsequent requests as being associated with known subscribers. For example, subsequent mobile content requests may be received from an unidentified mobile subscriber. A query may be executed against the database to identify previously received mobile content requests that match, to some degree, the stored mobile content requests, such that the subsequent mobile content requests can be attributed to the same mobile subscriber as those in the query results. As such, the requested content may then be augmented with additional content (e.g., advertisements) consistent with the previously received mobile content requests, usage histories, and/or demographics.
In another aspect, a system for identifying and/or associating requests for WAP-enabled content with mobile subscribers includes a domain server and an ID processing module. The domain server is configured to receive mobile content requests that include various request attributes, such as a device ID, a carrier ID, a telephone number, an account number, a MAC address, an IP address, a location, a SIM card ID, and/or a device mode. The ID processing module is configured to assign each request attribute to an attribute group, apply a hash function to each attribute group, and assign mobile content requests to a mobile subscriber based on a degree of match among corresponding hashed attribute groups of the mobile content requests.
In some embodiments, the ID processing module assigns each of the request attributes to one group, whereas in other cases the attributes may be assigned to more than one group, or, in other cases, some attributes may be ignored and not used. The ID processing module may also calculate a match confidence level between mobile content requests based on the degree of match between corresponding hashed attribute groups. The resulting series of hashed groups may be sorted based on the match confidence level, and may be assigned a certain number of mobile content requests from the sorted mobile content requests to a single mobile subscriber. The number of assigned requests may be determined by confidence level threshold, for example.
The system may also include a database for storing the content requests, either as received, as processed by the ID processing module, and/or as augmented with a mobile subscriber ID. In such cases, the domain server may be further configured to receive subsequent mobile content requests from a mobile subscriber and query the database for previously received mobile content requests based on a degree of match among corresponding hashed attribute groups of the subsequent mobile content requests and the stored mobile content requests. The subsequent mobile content requests can then be assigned to the same mobile subscriber as those resulting from the query. Further content returned to the subscriber in response to the request may be augmented with advertisements consistent with the previously received mobile content requests, user preferences, and/or demographic information.
Apple credits Eswar Priyadarshan Jayasurya Vadrevu, Ravikiran Chittari and Dan Grigorovici as the inventors of patent application 20100228625, originally filed in Q4 2009.
What do you Think about Ads and Target Marketing Schemes?
Every one of Apple's Ad patents provides us with a piece of the larger picture. The depth of tracking is both intriguing and alarming. Many are personally uncomfortable with this level of tracking and yet ads are a necessary evil if you're interested in hearing about new and interesting products. The problem is that for every cool and informative advertisement that we see, there are 10 obnoxious or useless ads which turn us all off to ads in general. General ads on TV are like that but they're not tracking you. It's the latter phenomenon of tracking that has us wondering if there's an industry watchdog keeping an eye on this segment of the industry's activities.
The fact that our phone numbers are associated with tracking techniques to specifically locate us is disturbing if that information gets into the wrong hands. For every responsible company like Apple, there must be 100 if not 1000 or more unscrupulous companies that would sell your phone number and other collected information for a dime.
A cell phone is no longer just a cell phone. That's progress and well appreciated. I'm just not sure that we really understand where all of this tracking of our activities will or could lead. It'll take some kind of a horrific incident where this tracking technology has lead to a death, kidnapping or rape for the public to finally realize and wake up to the fact that phone-tracking technology could have consequences down the line.
Where do you stand on tracking technologies? If you have a point on this subject matter that you could articulate well, please send in your comment below. Rants will not be posted. Thank you.
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