This study explores several donor location strategies and discusses experiment results, which contributes to the saving of time and effort required in designing data fusion processes. In particular, three concepts are introduced. The Mahalanobis distance is applied to locate the nearest neighbors more effectively; which incorporates the covariance structure of attributes. The ideal point helps reduce the dimensionality problem that arises in conjoint-type experiments. The correspondence analysis is used to derive the coordinates from non-metric attributes. The Monte Carlo simulation results show that the proposed donor location strategies provide better fusion performance, compared to the currently-in-use methods.