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Many business organizations measure customer loyalty by using a question suggested by Reichheld (2003) —“likelihood to recommend the company to friend or colleague (LTR, 0=extremely unlikely, 10=extremely likely)”. The LTR question can determine a customer’s status as a detractor (LTR=0-6), a passively satisfied customer (LTR=7-8), or a promoter (LTR=9-10). Although this measure of customer status has been widely used in industry, no quantitative method so far has been introduced to analyze the underlying predictors of customer status as detractors, passively satisfied customers, or promoters. This study bridges the research gap by advocating Generalized Ordinal Logistic Regression (GOLR) as a viable statistical approach for identifying predictors for transforming customer status into a higher level (i.e., pulling customers out of the pool of detractors and driving them into the pool of promoters). Using online shopping as a research context, we found that GOLR outperformed traditional linear regression in identifying important predictors of customer status and in testing whether predictors have increasing or decreasing marginal effects on improving customer status to a higher level. Based on the results of GLOR, companies can make full use of the LTR question and design appropriate strategies for improvement

More Promoters and Less Detractors: Using Generalized Ordinal Logistic Regression to Identify Drivers of Customer Loyalty