Journal of Data Science

This paper introduces a label-efficient response modelling method useful when the target labels are unknown a priori. Unlike most response modelling methods that adopt a supervised or semi-supervised approach, we apply clustering to partition data into homogeneous segments, which are assumed to reflect the underlying response behaviours. We then take a random sample from each cluster. For each sampled record, the true target label is acquired. Through this cluster-based stratified sampling approach, we reduced the cost of label acquisition needed to estimate the cluster-specific and overall basic response rates. The goal is to identify a subset of the population more likely to respond (e.g., make a purchase) while controlling campaign costs. This idea of subsetting the population represents a departure from conventional classification tasks, which require full labeling of all observations. We regard clusters with response rates significantly higher than the estimated basic response rate as high-propensity clusters and proceed to acquire all their remaining labels. Our experimental results show that the response rates of high-propensity clusters are at least 1.7 times the basic response rate. This suggests that the proposed approach significantly reduces costs by targeting only high-propensity groups and is useful in scenarios lacking historical ground truth.