Cross-Device User Profile Integration for TV Recommendations
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Solution Overview
Problem
Traditional targeted advertising methods are inadequate as they primarily focus on online behaviors, neglecting offline activities and cross-device user interactions, leading to incomplete user profiles and inaccurate conversion rate measurements, especially for users who don't frequently use the internet and in the mobile environment where cookies are unreliable.
Innovation Solution
A system and method that integrates online and offline user activity data across multiple devices to create comprehensive user profiles, using unique identifiers to link advertisement exposure with conversion events, enabling accurate conversion rate estimation and personalized TV program recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional online behavior tracking methods are used, then user profiling can be implemented, but the user profile completeness and accuracy deteriorates due to neglecting offline activities and cross-device interactions
Solution Approach 1:
The patent combines online and offline user behavior data into a unified user profile system. It integrates data from multiple sources including online activities, offline store visits, and cross-device interactions to create a comprehensive view of user behavior, thereby improving profile accuracy while capturing previously lost offline information.
Solution Approach 2:
The system creates a universal user identification framework that works across multiple platforms and devices. It uses device identifiers, IP addresses, and other tracking mechanisms to recognize users consistently whether they are online or offline, on mobile or desktop, ensuring the user profile system functions universally across different contexts.
2Reliability
If cookie-based tracking is used in mobile environment, then user identification is possible, but the reliability deteriorates due to unreliable cookies on mobile devices
Solution Approach 1:
The patent introduces intermediary tracking mechanisms such as device identifiers, mobile advertising identifiers, and network-based identification methods that serve as alternatives to cookies in the mobile environment. These intermediaries enable reliable user identification without depending on cookie-based systems, which are unreliable on mobile devices.
Solution Approach 2:
The system changes the identification parameters from cookie-based to device-based identifiers. It utilizes mobile device unique identifiers, IP addresses, and other persistent parameters that remain stable across different mobile applications and sessions, thereby improving reliability while managing the complexity of tracking across diverse mobile devices.
3Measurement precision
If advertisement conversion is measured at each platform separately, then platform-specific metrics are obtained, but the overall conversion rate measurement accuracy deteriorates due to inability to link activities across platforms
Solution Approach 1:
The patent merges conversion measurement data from multiple platforms into a unified analysis framework. It correlates user activities across online and offline platforms, mobile and desktop devices, to calculate overall conversion rates that reflect the complete customer journey, rather than isolated platform metrics.
Solution Approach 2:
The system adds a cross-platform dimension to conversion measurement by tracking user journeys that span multiple devices and platforms. It uses time-based correlation, user identification across platforms, and attribution modeling to measure conversions in a multi-dimensional space that captures the complexity of modern media consumption patterns.
4Measurement precision
If time gap between advertisement viewing and transaction is considered, then realistic conversion measurement is possible, but the measurement complexity increases due to difficulty in linking activities across time
Solution Approach 1:
The patent implements preliminary tracking and data collection mechanisms that capture user behavior data continuously over time. It establishes baseline user profiles and behavior patterns before conversion events occur, enabling accurate attribution even when significant time gaps exist between advertisement exposure and transaction.
Solution Approach 2:
The system uses feedback loops to continuously refine conversion attribution by analyzing time-correlated user behaviors. It monitors user activities over extended periods, compares them against advertisement exposure data, and adjusts attribution models based on observed conversion patterns, thereby improving accuracy while managing the complexity of temporal correlations.
Data Source
AI summary
Methods, systems, and programming for recommending targeted television programs based on online behavior is provided. In one example, information related to one or more online activities of a user is received. An identifier associated with the user is determined. Information related to television consumption of the user is assessed based on the identifier. An index is generated based on the online activity information and the television consumption information. One or more recommendations are generated based on the index.