Diginuvo’s Personalized Services platform in the mobile sector uses a combination of user/group profiling, collaborative filtering, data analytics, and location/presence information to facilitate the delivery of tailor-made enhanced content, such as interactive wallpapers, True Tones, WeeMees, and widgets that change with location and/or presence. The service is motivated by the recognition that a user has needs, and meeting them successfully is likely to lead to a satisfying long-term relationship.

• User/Group Profiling:Profiles are built through active involvement of the users,either through fill-in forms on a Web portal or through an interactive voice response system or an USSD session through their phone. Users can specify the type of content desired, as well as a general area of interest (e.g. genre of television programs, or news for a particular football team), and may even select a preferred delivery method (e.g. SMS, MMS, WAP Push) or specify the make and model of their device so that the content is optimized for that platform .Frequently accessed content (e.g.specific company stocks or weather in a given area) is tracked and presented before other information in a given category in subsequent transactions.

• Collaborative Filtering:This technique compares a user’s tastes with those of other users in order to build up a picture of like-minded people. The choice of content is then based on the assumption that this particular user will value that which the like-minded people also enjoyed. The user’s tastes are either inferred from their previous actions (for example buying a ringtone, music, or viewing an ad is assumed to show an interest (or taste) for that product) or else measured directly by asking the user to rate products.

• Click-stream Analysis and Data Mining:This technique collects data about user movements on the service provider’s website and personalization portal by recording a track of the links visited, including where a user came from, their route through the website and their destination on exiting the site. Link analysis also includes observations of the links clicked and their associated position on the screen, time spent within a page and making connections between links visited and consequences (e.g. purchase made). The information gathered is intensively processed, giving insight into the makeup of visitors using the site, which can be used for characterizing users and segmenting customers. Data collected by this technique and used for group profiling is anonymized before analysis to protect user's privacy, and then used to build rule-based systems to determine what content to offer.

• Location and Presence:Diginuvo enables the delivery of a number of personalized services that are tied to user’s location and/or presence, which can be collected through our platform or by integrating with third-party products.

• Open Data:This method involves making use of structured open data available on the web via APIs, web services, and open data standards, which can be inter-connected and re-used by third parties. Diginuvo supports a whole host of open data standards, such as, OpenID, DataPortability, OpenSocial and APML. The information collected in this manner is then used to construct a social graph of the user, which is available to be used for promotions and campaigns, and to better target the content and/or service.

• Cookies:Last but not the least, Diginuvo uses cookies on service provider’s portal to enable personalized services. The data about the user stored in cookies is updated on repeat visits and linked to profile-specific information stored with the service provider.