Tag Archives: Customer Satisfaction

What You Need to Know about the Impact of Service Crises

Service crises and their impact on companies

Extreme and massive service failures. They are probably the worst nightmare for any service provider. Such crises have a profound impact on customers as the service they seek becomes simply unavailable. The scope of the problems – thousands, to even hundreds of thousands, of customers being hit at the same time – assures wide-scale media attention, damaging the reputation of the company even more. Vivid examples of such failures include JetBlue’s Valentine’s Day crisis in 2007, when over 130,000 customers got stranded; or the BlackBerry service failures in 2011 and 2012, when Blackberry owners around the globe could not access the Internet or their emails for several days in a row.

Whereas a traditional product-harm crisis still offers the possibility to trace the batches of defective goods and engage in recall actions, a mass service crisis does not have this option. All users are experiencing service failure at the same time. This drop in objective service performance (OSP) has an immediate and strong negative impact on the perceived service quality (PSQ). However, it may take much more time for companies to restore their customers’ satisfaction.

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Losses loom larger and last longer than gains

We studied the impact of mass service crises on the perceived service quality for a major European public transport provider. During the observation period, the company experienced several service crises caused by extreme winter weather, which was unprecedented in the recent past.

Consistent with the argument of the Nobel Prize winners Daniel Kahneman and Amos Tversky, we find that negative experiences – drops in service performance – have a much stronger negative impact on perceived service quality compared to improvements in service performance. However, our results also show that the detrimental impact of such crises goes beyond a stronger immediate negative impact. What is even worse for companies is the fact that this negative impact also lasts longer. Losses not only loom larger than gains, but they also linger. Any improvements in objective service performance will only have a short-lived effect on perceived service performance, whereas deteriorations in objective service performance will have a lasting negative effect on perceived service performance.

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The role of history

The ultimate impact of a mass service crisis may depend on the history of a company’s service performance. Customers can be more forgiving if the company has a good track record. On the other hand, an unexpected crisis may be of such an extreme disconfirmation of their (high) expectations that it can result in extreme anger. On the other hand, when the company has a history of bad service, customers may already have become cynical. A new crisis would not add any new information: the company is simply living up to the (low) expectations. But it may also become the final drop for these customers, infuriating them even more.

Our results show that, in case of a “business-as-usual” scenario with a relatively stable service performance, the following picture will emerge: improvements will have short-lived positive effects, and deteriorations will have lasting negative effects. In case of “sustained gains”, an upward spiral of ever better service, a new improvement will result in lasting positive effects (customer delight: the customer gets an even better performance than expected), but a sudden deterioration will have a strong lasting negative effect (extreme negative disconfirmation). When the company is already in a downward spiral (“sustained losses”), an additional deterioration will not have much effect anymore; the damage has already been done. A sudden improvement, on the other hand, will not add much either.

So, what to do?

Even though service companies would love to avoid such mass service crises, they often have little real power to do so, no matter how well prepared the companies are. Restoring the customers’ appreciation of the service quality to the pre-crisis level can only be attained by a continued service performance at a higher than pre-crisis level. A crisis will raise the bar for the future, and improving once is not enough. The customer needs to see that the company succeeds consistently in providing a better service. This is all the more important for companies with a good track record who suddenly face a crisis. Such crisis is an extreme deviation of what customers are used to, and has a strong detrimental effect. Getting back to the old pattern of good and significantly better service is crucial. A silver lining is that when one is already in a downward spiral, an additional negative experience will not further decrease the customers’ judgements in the long term.

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In sum, companies should focus on a stable (and good) service performance level. Such performance level has the best outcomes for customers’ service assessment, and takes much less effort compared to constant adjustments needed in response to peaks and troughs in service performance. A good and stable performance, in turn, is a strong argument for companies in their communication to customers, as it may engender favorable perceptions of the service quality.

This post is based on the article “Losses Loom Longer Than Gains. Modeling the Impact of Service Crises on Perceived Service Quality over Time” which is co-authored by Maarten Gijsenberg (University of Groningen, The Netherlands), Harald van Heerde (Massey University, New Zealand) and Peter Verhoef (University of Groningen, The Netherlands). It is published in the Journal of Marketing Research (Volume 52, Issue 5, October 2015; ).

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MJ Gijsenberg PictureMaarten J. Gijsenberg is Associate Professor of Marketing at the University of Groningen, the Netherlands. He holds an MSc in business engineering, and a PhD in marketing (both from the University of Leuven, Belgium).

His research focusses on the econometric modelling of marketing decisions (timing and size of investments, targeting of actions) and their effectiveness, with special attention to the over-time dynamics of the latter (due to e.g. the impact of both macro-economic and firm-specific crises on consumers’ behavior), and main focus on advertising. His work has been published in the Journal of Marketing Research and the International Journal of Research in Marketing.

He was second runner-up of the 2010 EMAC McKinsey Marketing Dissertation Award, and his research has also been awarded with a Marie Curie Fellowship of the European Commission. Recently, his paper on advertising effectiveness around major sports events was selected by the Marketing Science Institute as one of the “2014 Must-Read Articles for Marketers”.

How Valuable Are the Net Promoter Score and Other Customer Feedback Metrics?

customermetric_bigBy Evert de Haan and Peter Verhoef

A large and growing amount of firms rely on Customer Feedback Metrics (CFMs) to monitor the customer base and the performance of the marketing department. Examples of these metrics include Customer Satisfaction (CS) and the Net Promoter Score (NPS). Recently, new customer feedback metrics, such as the Customer Effort Score (CES), are gaining traction with a promise to outperform the existing CFMs.

While the positive relationship between customer satisfaction and firm performance, including revenue and profitability, is well documented in academic literature, most findings are mixed for the NPS. In regards to the new Customer Feedback Metrics, such as the Customer Effort Score, third party empirical proof is relatively nonexistent, keeping managers in the dark about the reliability of these metrics. Despite the lack of academic literature and empirical proof, many firms rely on a single metric, specifically the NPS, as their key performance indicator.

Our research team aimed to shed more light on popular Customer Feedback Metrics by investigating the following issues:

  1.  The extent in which different Customer Feedback Metrics are appropriate to monitor the customer base, and
  2. The effectiveness of using multiple metrics as opposed to using one single metric.

The research performed by our team proved that the NPS is as good as Customer Satisfaction in predicting customer retention. We also found labeling customers as Promoters, Passives, and Detractors works well for many firms. The NPS, combined with information regarding Customer Satisfaction, further improves the ability to monitor the customer base. Using multiple Customer Feedback Metrics is therefore highly recommended.

Table 1. Customer Feedback Metrics (CFM)

CFM Measurement
CS (Customer Satisfaction) “All in all, how satisfied or unsatisfied are you with [company X]?” (1 = very unsatisfied, 7 = very satisfied).
Top-2-Box CS The proportion of customers of the firm that gave a score of 6 or 7 on the CS question.
Official NPS (Net Promoter Score) “How likely is it that you would recommend [company X] to a friend or colleague?” (0 = very unlikely, 10 = very likely). Respondents who gave a score of 0–6 are “detractors,” those who gave a 7 or 8 are “passives,” and those who gave a 9 or 10 are “promoters.” Subtracting the proportion of promoters by the proportion of detractors provides the Official NPS.
NPS (Net Promoter Score) This is the average untransformed NPS score (0–10 range) provided by the customer.
CES (Customer Effort Score)  “How much effort did you personally have to put forth to handle your request?” (1 = very low effort, 5 = very high effort).

In our research, we surveyed an extended group of customers from 98 firms across 19 different industries. In this survey we measured three different Customer Feedback Metrics, including Customer Satisfaction, the NPS and the Customer Effort Score. Information regarding these three different Customer Feedback Metrics can be found in the above table.

For Customer Satisfaction and the NPS, we used the untransformed scores as well as two popular transformations. The first transformation, the Top-2-Box CS, indicates the proportion of customers providing one of the two highest scores on Customer Satisfaction at a firm level. In other words, the Top-2-Box CS is the proportion of customers who are (very) satisfied. The second transformation is the official transformation for the NPS; grouping customers into Promoters, Passives, and Detractors. Further detail regarding NPS can also be found in the table above.

Two years after the initial survey measuring the Customer Feedback Metrics, we asked the same customers if they were still customers at the surveyed firm. This allowed us to test how accurately different Customer Feedback Metrics can predict actual behavior of customers. Given the historical strong, positive correlation to overall firm performance and firm value, our team looked at customer retention.

The graph below shows the strength of the relationship between the different Customer Feedback Metrics and customer retention, while controlling for firm- and industry heterogeneity, customer demographics and relationship length. Our research found that all Customer Feedback Metrics are significant in predicting customer retention, since all Customer Feedback Metrics perform better than having no Customer Feedback Metric information (i.e. the bar most to the left in the graph).

Transforming Customer Satisfaction and the NPS do significantly improve the predictions. This is indicated by the higher bars of these two Customer Feedback Metrics compared to their untransformed counterparts. The difference between the Top-2-Box CS and the Official NPS is not significant, so these two Customer Feedback Metrics work equally well in predicting customer retention. When looking at the three bars on the right you can see that combing the Top-2-Box CS with one of the other Customer Feedback Metrics leads to even better predictions. The combination of Top-2-Box CS and the Official NPS leads to the best predictions.

Predictive Strength of Customer Feedback Metrics

Predictive Strength of Customer Feedback Metrics

The Customer Effort Score, although statistically significant, is the least predictive Customer Feedback Metric compared to the other predictive measures. This finding contradicts the promises made by the developers of the Customer Effort Score who stated that it would outperform both Customer Satisfaction and NPS. Although this may be the case in some conditions, on a broader level this Customer Feedback Metric performs quite poorly. Therefore, we highly recommend firms and managers not rush to adopt Customer Effort Score, especially as a single metric, until it has been objectively shown that it is a good indicator of future customer behavior and/or firm performance. Customer Effort Score, as an indicator of future customer behavior and/or firm performance, can be proven by independent (scientific) research, or tested by the firm.

In conclusion, we recommend firms to continue using the NPS to track customers and performance, but also include the Top-2-Box CS in the dashboard of metrics. This dashboard enhancement will enable firms to better monitor and predict customer behavior and firm performance. Furthermore, we recommend firms to not only measure these Customer Feedback Metrics, but also link these metrics to customer behavior and firm performance. Doing so will result in a better understanding of the consequences of changes in the Customer Feedback Metrics, and help to make a more educated decision about which Customer Feedback Metrics to include, or exclude, in the dashboard. This approach can better enable firms to financially quantify the impact of marketing initiatives, which ultimately can help improve the position of marketing departments within firms.

The article The Predictive Ability of Different Customer Feedback Metrics for Retention featured in the post was co-authored by Evert de Haan (University of Groningen, The Netherlands), Peter Verhoef (University of Groningen, The Netherlands), and Thorsten Wiesel (Westfälische Wilhelms-Universität Münster, Germany). It is published in the International Journal of Research in Marketing, Volume 32, Issue 2, Pages 195-206.

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foto evert_smallEvert de Haan is a PhD candidate at the Department of Marketing of the University of Groningen, The Netherlands. In September 2015 he will start as a Junior Professor in Marketing at the Department of Marketing of the Goethe University in Frankfurt, Germany. His research interests concern customer feedback metrics, marketing accountability, the effectiveness of (on- and offline) advertising, the customer’s online journey and the role of mobile devices play in this. He has published in the International Journal of Research in Marketing.

Peter VerhoefPeter C. Verhoef is Professor of Marketing at the Department of Marketing, Faculty of Economics and Business, University of Groningen, The Netherlands. He also holds a visiting position as professor at BI Oslo Norwegian Business School. He obtained his Ph.D. in 2001 at the School of Economics, Erasmus University Rotterdam, The Netherlands. His research interests concern customer management, customer loyalty, multi-channel issues, category management, and buying behavior of organic products. He has extensively published on these topics. His publications have appeared in journals, such as Journal of Marketing, Journal of Marketing Research, Marketing Science, International Journal of Research in Marketing, Harvard Business Review, Marketing Letters, Journal of Consumer Psychology, Journal of the Academy of Marketing Science, and Journal of Retailing. His work has been awarded with the Donald R. Lehmann award for the best dissertation based article in the Journal of Marketing and Journal of Marketing Research in 2003, the Harald M. Maynard Award for the best paper published inJournal of Marketing, and the Sheth Award for long-term impact of the Journal of Marketing in 2013. He is currently an editorial board member of the Journal of Marketing, Journal of Marketing Research, Marketing Science,  Journal of Retailing, Journal of Service Research, Journal of Interactive Marketing, and the International Commerce Review. He functions as an area editor forJournal of Marketing Research and he International Journal of Research in Marketing. He has extensive teaching experience for undergraduate, graduate and Ph.D. students. He is also involved in executive teaching on customer management and is the founder of the Customer Insights Center, University of Groningen. He is department chair of the marketing department.