User Segmentation via Posterior Probability Prediction
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Solution Overview
Problem
Conventional user segmentation methods are inefficient and inaccurate due to dynamic user needs, incomplete data, and time-consuming processes, failing to effectively segment users into optimal groups for IT resource allocation.
Innovation Solution
A method and system that predict attribute values for user segmentation by segregating users with incomplete attributes, identifying suggestive values based on complete attribute groups, and computing prior and posterior probabilities to determine optimal segment assignment, allowing for dynamic re-alignment and new segment generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional user segmentation methods are used, then users can be grouped into segments, but the segmentation is inaccurate and time-consuming due to incomplete data and dynamic user needs
Solution Approach 1:
The system enables users to self-segment into appropriate segments by automatically computing segment assignments based on their attribute values and the posterior probabilities of segment membership. This eliminates the need for manual segmentation processes and reduces time loss while maintaining accuracy through probabilistic modeling.
Solution Approach 2:
The system handles dynamic user needs by allowing attribute values to change over time and automatically re-computing segment assignments when attributes are updated. This enables the segmentation to adapt to parameter changes without requiring complete re-segmentation processes.
2Measurement precision
If complete attribute data is collected for all users, then segmentation accuracy improves, but data collection time and resources increase significantly
Solution Approach 1:
The system segments users into groups based on their attribute values and computes segment assignments independently for each user or group. This allows partial data to be processed efficiently without requiring complete data collection from all users simultaneously, reducing data collection time while maintaining segmentation accuracy through probabilistic inference.
Solution Approach 2:
The system pre-computes segment assignments and stores them for later retrieval. When segmenting users, it uses these pre-computed assignments rather than performing complete data collection and analysis each time, significantly reducing the time and resources required for segmentation operations.
3Adaptability or versatility
If users are re-segmented frequently to accommodate changing needs, then segment alignment with user needs improves, but computational resources and time are wasted on unnecessary re-segmentation
Solution Approach 1:
The system dynamically adjusts segment assignments based on changes in user attribute values. When an user's attributes change, the system automatically re-computes the segment assignment using the updated attributes and posterior probabilities, allowing the segmentation to adapt to changing user needs without requiring complete re-segmentation of all users.
Solution Approach 2:
The system monitors attribute changes and triggers re-segmentation only when necessary based on detected changes in user attributes. This feedback mechanism prevents unnecessary re-segmentation operations while ensuring that segment alignment with user needs is maintained when actual changes occur, thereby preserving productivity.
Data Source
AI summary
This disclosure relates generally to performing user segmentation, and more particularly to predicting attribute values for user segmentation. In one embodiment, the method includes segregating a user with an incomplete attribute value and a user with complete attribute values for an attribute into a first group and a second group respectively, computing prior probability for each suggestive attribute value, identified for the incomplete attribute value, based on number of users in second group having the suggestive attribute value as attribute value for the attribute. Computing likelihood for each suggestive attribute value based on similarity of the attribute values of the user of the first group with users of the second group, computing a posterior probability for each suggestive attribute value based on the prior probability and the likelihood, selecting a suggestive attribute value with the highest posterior probability as the attribute value for the incomplete attribute value of the user.