The Value of Different Customer Satisfaction

The Value of Different Customer Satisfaction

Paper instructions:
The small business owner can live or die by word of mouth. In the article, The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business

Performance, Morgan discusses the issues with employee satisfaction analysis. What are the implications for small business owners? How can a small business owner

collect data on customer satisfaction that can be used to improve operations without the enormous costs of the traditional systems we see in place today?

Vol. 25, No. 5, September-October 2006, pp. 426-439
ISSN 0732-2399 I EISSN 152&548X ] 06 I 2505 [ 0426 Do1 10.1287/mi<SC-1050-0180
The Value of Different Customer Satisfaction and
Loyalty Metrics in Predicting Business Performance
Neil A. Morgan
Kelley School of Business, Indiana University, 1309 East Tenth Street, Bloomington, Indiana 47405-1701,
Lopo Leotte Rego
Tippie College of Business, University of Iowa, 108 PBB 5320, Iowa City, Iowa 52242-1994,
Managers commonly use customer feedback data to set goals and monitor performance on metrics such as
Top 2 Box customer satisfaction scores and intention-to-repurchase loyalty scores. However, analysts
have advocated a number of different customer feedback metrics including average customer satisfaction scores
and the number of net promoters among a firm’s customers. We empirically examine which commonly used
and widely advocated customer feedback metrics are most valuable in predicting future business performance.
Using American Customer Satisfaction Index data, we assess the linkages between six different satisfaction and
loyalty metrics and COMPUSTAT and CRSP data-based measures of different dimensions of firms’ business
performance over the period 1994-2000. Our results indicate that average satisfaction scores have the greatest
value in predicting future business performance and that Top 2 Box satisfaction scores also have good predic-
tive value. We also find that while repurchase likelihood and proportion of customers complaining have some
predictive value depending on the specific dimension of business performance, metrics based on recommenda-
tion intentions (net promoters) and behavior (average number of recommendations) have little or no predictive
value. Our results clearly indicate that recent prescriptions to focus customer feedback systems and metrics
solely on customers’ recommendation intentions and behaviors are misguided.
Key words: customer satisfaction; marketing metrics; marketing strategy
History: This paper was received November 10, 2004, and was with the authors 5 months for 2 revisions;
processed by Roland T. Rust.
Introduction used metric is a firm’s Top 2 Box satisfaction score
Managers often use customer feedback data to set (e-8-I Merge et 31- 2905: M_Ye1’5 1999): and f1rm5 that
goals and monitor performance on metrics that they else meeltefi leyelfyt mfthe eustemfir feedbtiel eye
believe to be leading indicators of future business per- ems typlca Y use In en [on *9 repurc ase as at pn-
mary customer loyalty metric (e.g., Kamakura et al.
formance (e.g., Hauser et al. 1994, Ittner and Larcker 2002 Mittal et al 1998)
1998)- Firms tYPieanY Collect feedbeel data Via W5 However, there currently exists no empirical knowl-
temer 5rVeY5 usmg measures of attribute 1eVe1 and edge concerning which of the metrics available from
overall satisfaction, behavioral loyalty intentions such standard customer feedback systems are of most
as repurchase likelihood and likelihood to recom- value in predicting future business performance (e.g.,
mend, and actual loyalty behaviors such as making Ambler 29033 Griffin and Hauser 1993: Rust ei 31-
recommendations (e.g., Griffin et al. 1995, Morgan 2004b)f’ This 15 a Important gap m_ma’keh_g kn°fl’
et a1_ 2005). For goal_setfing and PerfOrmanCe_moni_ edge or a number of reasons. First, while the lit-
erature advocates customer feedback systems as a
toring purposes, managers value customer feedback

mechanism for developing and protecting customer
memes that are easy to eemprehend and e°mm’ relationships (e.g. Day 1994 Griffin et al. 1995 West-
ieate and that have Simple and direct Predictive brook 2000), little attention has been paid to which
relationships With future business Performance (e’g’I metrics direct the firm’s attention to the aspects of
Ittner and Larcker 2003, Reichheld 2003). From this customer relationships that deliver the greatest future
perspective, academic researchers have advocated reW3rdS (Griffin and Hauser 1993, Shugan 2005)~
average customer satisfaction and repurchase inten- Seeolldr idefifYi_8 eustemel feedbaek memes that
tions (e.g., Anderson et al. 1994, 1997; Fornell 1992), predlet future busmees perfermenee 15 key to deVe1€²
while consultants have advocated loyalty metrics I

l_k uh d t d R , hh Id The proportion of customers rating their overall satisfaction on the
Such as 1 e Oo 0 reeommen (e’g’I ele e two highest-scoring points on the most commonly used five-point
1996, 2003). Meanwhile, in practice, the most widely scale.

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