In this presentation we illustrate how latent class (LC) clustering should be performed on HB utilities to avoid strange results, and describe the situations where Latent GOLD’s scale-adjusted (SALC) model would be expected to result in more meaningful segments. Our MaxDiff example yields 88% agreement with the gold standard, vs. 60% when clustering is done incorrectly. Attendees will take away new insights into the segmentation process when latent class models are applied to HB utilities.
