First, the findings are not generalizable to smaller firms or B2B businesses since these weren’t included in the study.
Second, it's difficult to control for differences between industries, and since there may be differences in the preconditions leading to loyalty in each industry, this limitation may affect the utility of satisfaction and loyalty metrics.
Third, all customers were treated equally in the analysis. There's no data on which consumers are most relevant to a firm's success (for example, where should a firm spend the majority of its resources, on the best customers, on the most number of customers, those who complain the most, etc.)?
Fourth, the study was limited to customer feedback mechanisms that were easy for employees and managers to comprehend. Further, the relationship between these and a firm's performance was linear; non-linear relationships and interaction effects among the customer feedback variables might be more useful. NOTE: To understand the importance of how non-linear and asymmetrical relationships might be relevant read this article by Anderson & Mittal (2000) [opens into PDF].
ALSO NOTE: As mentioned before the way the net promoter score was calculated in this study is different than the Bain/Satmetrix Net Promoter Score. Stay tuned, however, from these authors for using the same language and method in future studies.
Avenues for Future Research
First, maybe the reason that satisfaction is more related to firm performance is becuase it costs more for the firm to generate positive WOM recommendations than for customers to simply be satisfied.
Second, promoters don't seem to buy more nor does their influence on potential new prospects seem to be as strong as people believe. Why? Maybe the process of getting customers to engage in positive WOM paradoxically increases their involvement in the category and their desire to seek out the variety offered by other brands for future purchases. Or maybe people who engage in positive WOM are more likely to be opinion leaders who find utility in seeking out variety in brands and companies. Thus, more research needs to be done about the impact of recommendation behaviors on not only the behaviors of others, but also the behaviors of the person doing the recommending. Also, the authors wonder if more active repurchase behaviors that indicate loyalty, like share of wallet, are better predictors of financial performance than the self-reported attitudinal indicators of loyalty which tend to be more passive.
Third, some of the correlational findings between recommendation and satisfaction measures seem to contradict service-profit chain logic (opens into PDF). There was a significant positive correlation between number of recommendations and the proportion of customers complaining. The authors wonder to what extent are WOM behaviors, both positive and negative, driven by consumer characteristics versus the firm’s marketing actions?
So, in conclusion, then, the authors maintain that their results show the value of customer feedback metrics and their ability to predict business performance of the firm. Further, they argue that the best feedback metrics are average customer satisfaction, Top 2 Box customer satisfaction scores, proportion of customers complaining, and the repurchase likelihood loyalty metric. The authors failed to find support for the predictive value of loyalty metrics based on data from recommendation behaviors, net promoters, and the number of recommendations.
This is the end of the Morgan & Rego article summary series. Here are some additional thoughts on the implications of this study.
Tags: Net Promoter Score word of mouth Word-of-Mouth Marketing WOM Loyalty marketing communication
Friday, November 03, 2006