Survey Targeting System for User Experience Optimization
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Solution Overview
Problem
Users of online services, such as social networks, are often overexposed to surveys, leading to a decline in user experience and the quality of information gathered, as continuous surveying can make users reluctant to engage with the platform.
Innovation Solution
A method is implemented to generate candidate lists of users based on specific characteristics, such as survey history and demographic data, and assign surveys based on priority values, ensuring users are not overexposed by distributing surveys over time and targeting active users effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users are continuously surveyed to maximize data collection, then the quantity of survey data increases, but user experience deteriorates and users become reluctant to engage
Solution Approach 1:
The system implements periodic survey distribution by tracking the last survey date for each user and only inviting users whose time since last survey exceeds a threshold. This creates regular intervals between surveys, preventing continuous exposure while maintaining data collection efficiency.
Solution Approach 2:
The system applies different survey invitation rules to different user segments based on their characteristics and survey history. Each user receives personalized treatment with customized time thresholds and survey selection, rather than a uniform approach, optimizing both user experience and data quality for each individual.
2Measurement precision
If multiple candidate lists are created for different survey characteristics, then survey targeting precision improves, but user overexposure increases
Solution Approach 1:
The system merges multiple candidate lists by identifying users who appear in more than one list and consolidating their survey invitations. When a user qualifies for multiple surveys based on different characteristics, the system applies a unified selection process that considers all candidate lists simultaneously, preventing duplicate invitations and overexposure while maintaining precise targeting.
Solution Approach 2:
The system uses feedback from user survey participation and candidate list matching to dynamically adjust survey selection. When users are identified in multiple candidate lists, the system evaluates priority values and makes informed decisions about which survey to assign, preventing overexposure while maintaining targeting precision through iterative optimization.
3Quantity of substance
If surveys are distributed widely to maximize participation, then the breadth of user base increases, but survey quality decreases due to overexposure
Solution Approach 1:
The system dynamically adjusts survey distribution strategies based on real-time user characteristics, survey history, and candidate list matching. Time thresholds and selection criteria are not fixed but adapt to individual user patterns, allowing the system to maximize user base participation while maintaining survey quality through flexible, context-aware decision-making.
Data Source
AI summary
Exemplary methods, apparatuses, and systems generate a first candidate list of users that meet a first set one or more characteristics and a second candidate list of users that meet a second set one or more characteristics. When a user appears in both of the first and second candidate lists, the user is selected to receive only the first survey or only the second survey based upon a first priority value for the first survey and a second priority value for the second survey.