Media Guidance Application Subscription Optimization
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Solution Overview
Problem
Users often subscribe to multiple media content services inefficiently, as current systems fail to interpret viewing patterns to predict future media consumption, leading to unnecessary subscriptions and inefficient resource use.
Innovation Solution
A media guidance application that analyzes viewing patterns to determine the most relevant media packages for a subscriber, predicts viewing impact for upcoming time periods, and generates recommendations to add or remove subscriptions based on available time and budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users maintain multiple media content subscriptions, then they have access to more media content, but they inefficiently subscribe to services for media content they cannot consume
Solution Approach 1:
The system performs preliminary analysis of viewing patterns and media release schedules to predict future media consumption before subscriptions renew. By anticipating what content users will watch based on historical data and upcoming releases, the system enables proactive subscription management that prevents wasting resources on content users won't consume.
Solution Approach 2:
The system continuously monitors and analyzes actual viewing patterns, comparing them against predicted consumption based on release schedules. This feedback loop allows the system to refine its predictions and provide increasingly accurate recommendations for subscription optimization, balancing content access with resource efficiency.
2Adaptability or versatility
If users subscribe to premium packages within television programming services, then they access exclusive content, but they may not realize they are oversubscribed to media content subscriptions
Solution Approach 1:
The system acts as an intermediary between users and their subscriptions, providing a layer of intelligence that translates raw viewing data and release schedules into actionable insights. This intermediary function makes oversubscription visible by comparing what users pay for against what they actually watch, highlighting redundant subscriptions without requiring users to manually audit their own viewing habits.
3Quantity of substance
If current systems track viewing patterns and media content releases, then they have data on media consumption, but they are not effective to manage several media content subscriptions based on efficiently subscribing a subscriber to several media content subscriptions
Solution Approach 1:
The system transforms raw viewing pattern data and release schedule information into predictive parameters about future consumption. By converting historical viewing data into forecasts of what content users will watch based on upcoming releases, the system creates actionable management parameters that enable automated subscription optimization decisions.
Data Source
AI summary
Systems and methods are provided for managing subscriptions. A media guidance application obtaining viewing patterns of a subscriber and determining a first and second media package of interest to the subscriber. A first and second release schedule are obtained related to media content in the first and second media packages. The media guidance application predicts a first viewing impact of the first subscription and a second viewing impact of the second subscription, for the upcoming time period, wherein the viewing impact indicates a total predicted amount of time spent viewing the media packages in the upcoming time period. An available amount of time for viewing media in the upcoming time period is also determined. The media guidance application generates a subscription recommendation for the upcoming time period based on the first viewing impact, second viewing impact, and available amount of time for viewing media in the upcoming time period.