Optimizing Census Bureau Survey Contact Strategies

Thomas M. Menino Convention & Exhibition Center 

The Census Bureau collects survey responses from millions of households, requiring efficient and effective contact strategies. Balancing operational cost, response rates, and respondent burden is a core challenge. To address this, we leverage contact history data to model the probability of a completed interview. Predictors include number of contacts, contact method, time-of-day, and day-of-week, with features engineered as binary indicators, aggregate counts, and temporal variables.

We explore a range of statistical and machine learning approaches, including regression models, tree-based methods, and unsupervised techniques such as clustering, to identify key patterns in contact behavior and predict completion likelihood. Models are compared using standard validation frameworks and evaluated with metrics such as ROC curves, classification accuracy, and calibration. By analyzing contact patterns across multiple large surveys, we leverage a high volume of cases and rich operational data to uncover strategies that maximize responses while managing costs. The findings provide actionable insights that may help the Census Bureau improve efficiency and increase response rates.

Survey operations

Survey contact strategies

Machine learning

Statistical models 

Government Statistics Section