The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share‐of‐wallet

The purpose of this research is to examine different customer satisfaction and loyalty metrics and test their relationship to customer retention, recommendation and share of wallet using micro (customer) level data.

The data for this study come from a two‐year longitudinal Internet panel of over 8,000 US customers of firms in one of three industries (retail banking, mass‐merchant retail, and Internet service providers (ISPs)). Correlation analysis, CHAID, and three types of regression analyses (best‐subsets, ordinal logistic, and latent class ordinal logistic regression) were used to test the hypotheses.

Contrary to Reichheld's assertions, the results indicate that recommend intention alone will not suffice as a single predictor of customers' future loyalty behavior. Use of a multiple indicator instead of a single predictor model performs better in predicting customer recommendations and retention.

The limitation of the paper is that it uses data from only three industries.

The presumption of managers when looking at recommend intention as the primary, even sole gauge of customer loyalty appears to be erroneous. The consequence is potential misallocations of resources due to myopic focus on customers' recommend intentions.

This is the first scientific study that examines recommend intentions and its impact on retention and recommendation on the micro (customer) level.