Multi-Tenant Survey Quality Filters for Low-Response Noise
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Solution Overview
Problem
Market research platforms face challenges with low-quality responses, such as spam, bots, and biased responses, which consume system resources, reduce research value, and incur financial costs.
Innovation Solution
A multi-tenant market research survey platform generates template-specific quality filters using machine learning models trained on aggregated usage data from multiple tenants, identifying and restricting low-quality respondents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the market research platform processes all survey responses without filtering, then all responses are captured for analysis, but system resources are wasted on low-quality responses and response quality deteriorates
Solution Approach 1:
The system performs preliminary filtering of survey responses using quality filters before full processing. Machine learning models predict response quality in advance, and low-quality responses are filtered out before consuming extensive system resources for analysis, storage, and processing.
Solution Approach 2:
The system extracts and separates low-quality responses from the overall response stream using quality filters. These filtered responses are excluded from further processing, effectively removing the harmful element (low-quality data) from the system workflow.
2Measurement precision
If template-specific quality filters are generated using machine learning models trained on aggregated data from multiple tenants, then filtering accuracy improves, but system complexity increases
Solution Approach 1:
The system uses a universal machine learning training framework that processes aggregated usage data from multiple tenants to generate template-specific quality filters. The same training infrastructure serves all tenants and survey templates, achieving high accuracy without proportionally increasing complexity for each individual case.
Solution Approach 2:
While using a universal training approach, the system generates customized quality filters specific to each survey template and tenant needs. The filters are tailored to local characteristics of each survey template while benefiting from the global learning across all tenants.
Data Source
AI summary
Techniques for improving performance and quality in a market research platform include: obtaining first usage data of a first set of survey respondents to a first market research survey, the first market research survey being associated with a first tenant of a multi-tenant market research survey platform and conforming to a market research survey template provided by the platform; obtaining second usage data of a second set of survey respondents to a second market research survey, the second market research survey being associated with a second tenant of the platform and conforming to the market research survey template; generating a template-specific quality filter associated with the market research survey template, based at least on the first usage data and the second usage data; and based at least on the template-specific quality filter and third usage data of a particular survey respondent, identifying the particular survey respondent as a low-quality respondent.