  In online experimentation, appropriate metrics (e.g., purchase) provide
strong evidence to support hypotheses and enhance the decision-making process.
However, incomplete metrics are frequently occurred in the online
experimentation, making the available data to be much fewer than the planned
online experiments (e.g., A/B testing). In this work, we introduce the concept
of dropout buyers and categorize users with incomplete metric values into two
groups: visitors and dropout buyers. For the analysis of incomplete metrics, we
propose a clustering-based imputation method using $k$-nearest neighbors. Our
proposed imputation method considers both the experiment-specific features and
users' activities along their shopping paths, allowing different imputation
values for different users. To facilitate efficient imputation of large-scale
data sets in online experimentation, the proposed method uses a combination of
stratification and clustering. The performance of the proposed method is
compared to several conventional methods in both simulation studies and a real
online experiment at eBay.
