Ad Delivery Engine Personalization via Consumer Behavior Segmentation
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Solution Overview
Problem
Existing advertisement delivery systems fail to provide personalized and relevant advertisements to consumers based on their viewing habits, purchase history, and internet browsing behavior, leading to a suboptimal customer experience and inefficient ad campaigns.
Innovation Solution
A network system that includes an ad delivery engine, data management platform, analytics platform, and user interface, which collects user data and preferences to tailor advertisements in real-time, inserting relevant ads into media streams and tracking their effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional advertisement delivery systems are used, then advertisements can be delivered to consumers, but the advertisements are not personalized or relevant to consumers based on their viewing habits, purchase history, and internet browsing behavior
Solution Approach 1:
The system performs preliminary data collection and analysis of consumer viewing habits, purchase history, and browsing behavior before ad delivery. The ad delivery engine proactively segments consumers into audiences and pre-determines optimal ad placements based on analyzed behavior patterns, enabling personalized ads without requiring real-time data processing during ad delivery.
Solution Approach 2:
The patent introduces an intermediary ad delivery engine between the media stream and the consumer. This engine acts as a mediator that collects consumer behavior data, segments audiences, and selectively inserts personalized advertisements into media streams. The intermediary layer processes and enriches consumer information to enable relevant ad targeting without disrupting the original media content.
2Adaptability or versatility
If more consumer data is collected and analyzed for personalized ads, then ad relevance improves, but system complexity increases
Solution Approach 1:
The system segments the complex data processing function into distinct modular components: data collection modules for viewing habits, purchase history, and browsing behavior; consumer segmentation modules that group consumers into audiences based on shared characteristics; and ad delivery modules that insert targeted ads. This segmentation reduces overall system complexity by making each component independent and manageable.
Solution Approach 2:
The ad delivery engine is designed as a multi-functional universal system that handles data collection, consumer segmentation, ad selection, and media stream integration within a single platform. This universal approach consolidates multiple separate systems into one cohesive engine, reducing infrastructure complexity while maintaining high ad relevance through comprehensive data processing capabilities.
3Productivity
If advertisements are inserted into media streams in real-time, then ad delivery efficiency improves, but measurement and tracking of ad effectiveness becomes more difficult
Solution Approach 1:
The system implements feedback mechanisms that track ad effectiveness by monitoring consumer interactions with inserted advertisements. The ad delivery engine collects data on ad views, engagements, and conversions, then feeds this information back to optimize future ad selections and placements. This feedback loop enables continuous improvement of ad effectiveness measurement while maintaining real-time delivery capabilities.
Data Source
AI summary
A method includes receiving first information identifying profile information associated with a customer, habit information associated with the customer's television viewing habits, or Internet usage information associated with the customer. The method also includes receiving preference information from the customer, wherein the preference information identifies advertisements or types of advertisements that the customer would like to view or would not like to view. The method further includes identifying advertisements based on the received first information and the received preference information, inserting, by a service provider, the identified advertisements in a television programming data stream and providing the identified advertisements to the customer.