AI Membership Recommendations for Channel Owner Conversion

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Solution Overview

Problem

Content sharing platforms face inefficiencies in generating membership recommendations for channel owners, leading to wasted computing resources and missed revenue opportunities due to non-targeted and ineffective recommendations, resulting in channel owners failing to see the value in enabling membership tiers.

Innovation Solution

A system utilizing AI models to predict the number of new members subscribing to membership tiers and generate personalized incentives for channel owners, based on channel and media item features, to encourage the adoption and promotion of membership tiers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional non-targeted membership recommendations are provided to channel owners, then implementation cost is reduced, but conversion rate and revenue are worsened due to ineffective recommendations

Engineering Contradiction:

Improveconversion rateVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system changes parameters by using AI models to predict membership conversion rates and revenue potential for each channel, transforming static recommendations into dynamic, data-driven recommendations that adapt to individual channel characteristics and performance metrics

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system performs preliminary actions by pre-calculating predicted membership values and revenue projections for each channel before generating recommendations, allowing the system to identify high-potential channels in advance and prioritize recommendation delivery to those most likely to convert

Inventive Principle:

Principle #10Preliminary action

2Productivity

If AI models are used to generate personalized membership recommendations, then revenue and conversion rates are improved, but computing resource consumption increases

Engineering Contradiction:

ImproverevenueVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing AI model computations only on channels most likely to convert based on predicted membership values and revenue potential, rather than processing all channels uniformly, thus reducing overall computing resource consumption while maintaining high revenue generation

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system implements local quality by providing customized recommendation strategies to different channel owners based on their specific channel characteristics, content categories, and predicted performance metrics, rather than applying a uniform recommendation approach across all channels

Inventive Principle:

Principle #3Local quality

3Ease of operation

If membership tiers are enabled without targeted incentives, then implementation simplicity is maintained, but channel owner engagement and adoption are worsened

Engineering Contradiction:

Improveimplementation simplicityVSAvoidadoption rate

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system implements feedback by providing channel owners with personalized incentive recommendations based on their predicted membership performance and revenue potential, creating a closed-loop system where recommendation effectiveness is continuously measured and used to improve future recommendations

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system applies segmentation by dividing channel owners into different groups based on their predicted conversion rates, content categories, and engagement patterns, allowing the system to tailor recommendation strategies to specific segments rather than treating all channels uniformly

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250350802A1Systems and methods for generating memberships recommendations using machine learning

Publication Date: 2025.11.13 GOOGLE LLC

AI summary

A method includes identifying a plurality of channel owners, each channel owner associated with a respective channel of a set of channels of a content sharing platform. For each respective channel, using one or more artificial intelligence (AI) models, a first and second value is determined, each indicating a respective number of projected members subscribing to a respective channel. For each respective channel, based on the respective first and second value, a set of actions to be performed by the respective channel owner for enabling the set of membership tiers is determined. For each channel owner, one or more rewards for performing at least a subset of the set of actions is determined. A recommendation is generated that reflects the one or more rewards and the subset of the actions. A respective indicator referencing the recommendation is provided for presentation to each of the plurality of channel owners.