Offer Engine Personalization via Decision Tree User Segmentation
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Solution Overview
Problem
Conventional systems for managing user interactions in online resources fail to consider predicted user behaviors, leading to ineffective trade-offs between converting users into paying subscribers and requesting content uploads in lieu of subscription fee revenue.
Innovation Solution
The Offer Engine extracts user features and feeds them into a decision tree with multiple levels of machine learning models to determine whether to offer a subscription fee or non-subscription fee options, balancing user traffic and revenue generation by calculating user subscription and content upload probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional systems focus on converting users into paying subscribers, then subscription fee revenue is improved, but user traffic and content upload participation deteriorate
Solution Approach 1:
The system dynamically changes the parameter of offer type (subscription vs. content upload) based on user behavior probabilities. By calculating subscription probability and content upload probability for each user, the system selects the offer type that optimizes the trade-off between revenue and user engagement, rather than using a fixed conversion approach
Solution Approach 2:
The system uses machine learning models to continuously learn from user interactions and update probability predictions. This feedback mechanism allows the system to adapt to changing user behaviors and optimize offer decisions over time, balancing revenue generation with user traffic maintenance
2Quantity of substance
If conventional systems request content uploads from users, then content corpus diversity is improved, but subscription fee revenue deteriorates
Solution Approach 1:
The system dynamically switches between subscription offers and content upload offers based on calculated probabilities. When content upload probability is high for a user, the system offers content upload opportunities instead of subscription, thereby acquiring diverse content while forgoing some potential subscription revenue in a controlled manner
3Productivity
If the system personalizes offers using machine learning models, then user conversion effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments users into different groups based on their behavior probabilities and characteristics. By using decision trees and machine learning models to identify distinct user segments with different preferences (subscription-oriented vs. content upload-oriented), the system can apply personalized strategies to each segment, improving conversion effectiveness while managing complexity through structured segmentation
Data Source
AI summary
Various embodiments of an apparatus, method(s), system(s) and a computer program product(s) described herein are directed to a Offer Engine. The Offer Engine extracts one or more features from data associated with a first user requesting access to a portion of content of a content corpus. The Offer Engine feeds at least one of the features of the first user into a decision tree. The decision tree has multiple levels, wherein at least one level comprises a plurality of leaves and each respective leaf implements at least one machine learning model. The Offer Engine determines whether to provide the first user with a subscription fee offer first option or a non-subscription fee offer second option based at least in part on output of the decision tree.