Churn Prediction Model Using Segmented User Data Filtering
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Solution Overview
Problem
Social networking systems face challenges in efficiently utilizing vast amounts of user data to predict user churn, making it difficult to determine relevant information and draw accurate conclusions about user tendencies and preferences.
Innovation Solution
A churn prediction model is trained using past user information and churn data, which includes user-connection entity affiliations and usage time, to calculate a churn propensity score indicative of a user's likelihood to churn, allowing for filtering and ranking of users based on this score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vast amounts of user data are collected for churn prediction, then the quantity of available information increases, but the difficulty of determining relevant information and drawing accurate conclusions increases
Solution Approach 1:
The patent segments user data into distinct categories including user attributes, connection entity affiliations, and usage information. This segmentation allows the system to organize vast amounts of data into manageable components, making it easier to identify relevant features for churn prediction while maintaining comprehensive data utilization.
Solution Approach 2:
The patent extracts specific relevant features from the vast user data through filtering criteria that identify meaningful patterns. The system extracts key user-connection entity affiliations and usage metrics that are most predictive of churn, separating signal from noise in the large dataset to improve prediction accuracy.
2Quantity of substance
If all user data is used for training the churn prediction model, then the completeness of training data increases, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by using filtering criteria to select only the most relevant subset of user data for model training. Rather than processing all available data equally, the system identifies and processes key features that have the highest predictive value for churn, reducing computational complexity while maintaining model effectiveness.
Solution Approach 2:
The patent changes parameters by applying usage time thresholds and filtering criteria that transform the raw data into a optimized training set. These parameter changes enable the system to work with a refined dataset that maintains completeness of important information while reducing overall data volume and computational requirements.
3Measurement precision
If user-specific usage time thresholds are applied to filter data, then the relevance of training data improves, but the loss of potentially useful information increases
Solution Approach 1:
The patent uses parameter changes by adjusting usage time thresholds to optimize the balance between data relevance and information retention. The filtering criteria are configured to remove only clearly irrelevant data while preserving borderline cases that may contain valuable predictive signals, thus maintaining measurement precision without excessive information loss.
Solution Approach 2:
The system applies partial filtering action by using multiple filtering criteria including both user-specific and population-specific thresholds. This layered approach allows the system to be selective about what data to exclude, keeping potentially useful information that doesn't meet strict thresholds while still improving overall data relevance through targeted filtering.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can collect past user information and churn data for a plurality of users. A churn prediction model is trained using the past user information and churn data. A churn propensity score is calculated for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.