Scalable Online Survey Framework: from Sampling to Analysis
Weitao Duan, Qian Wang, Rogier Verhulst, Ya Xu

TL;DR
This paper presents a scalable online survey framework that addresses sampling and analysis challenges in large-scale digital environments, with applications at LinkedIn for product insights and user experience monitoring.
Contribution
It introduces a comprehensive approach for scalable survey sampling and analysis, integrating in-product surveys with monitoring and A/B testing at LinkedIn.
Findings
Effective sampling strategies for multiple email surveys.
In-product surveys can serve as real-time monitoring tools.
Integration of surveys with A/B testing enhances decision-making.
Abstract
With the advancement in technology, raw event data generated by the digital world have grown tremendously. However, such data tend to be insufficient and noisy when it comes to measuring user intention or satisfaction. One effective way to measure user experience directly is through surveys. In particular, with the popularity of online surveys, extensive work has been put in to study this field. Surveys at LinkedIn play a major role in influencing product and marketing decisions and supporting our sales efforts. We run an increasing number of surveys that help us understand shifts in awareness and perceptions with regards to our own products and also to peer companies. As the need to survey grows, both sampling and analysis of surveys have become more challenging. Instead of simply multiplying the number of surveys each user takes, we need a scalable approach to collect enough and…
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Taxonomy
TopicsSurvey Methodology and Nonresponse · Mobile Crowdsensing and Crowdsourcing · Privacy, Security, and Data Protection
