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Customer satisfaction, loyalty behaviors, and firm financial performance: what 40 years of research tells us

• Open access
• Published: 03 March 2023
• Volume 34 , pages 171–187, ( 2023 )

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• Vikas Mittal 1 ,
• Kyuhong Han 2 ,
• Carly Frennea 3 ,
• Markus Blut 4 ,
• Muzeeb Shaik 5 ,
• Narendra Bosukonda 6 &
• Shrihari Sridhar 6  

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The authors synthesize research on the relationship of customer satisfaction with customer- and firm-level outcomes using a meta-analysis based on 535 correlations from 245 articles representing a combined sample size of 1,160,982. The results show a positive association of customer satisfaction with customer-level outcomes (retention, WOM, spending, and price) and firm-level outcomes (product-market, accounting, and financial-market performance). A moderator analysis shows the association varies due to many contextual factors and measurement characteristics. The results have important theoretical and managerial implications.

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Customer satisfaction and firm performance: insights from over a quarter century of empirical research

Ashley S. Otto, David M. Szymanski & Rajan Varadarajan

Measuring Customer Satisfaction and Customer Loyalty

Avoid common mistakes on your manuscript.

1 Introduction

Oliver ( 2014 , p. 8) defines customer satisfaction (CS) as “a judgment that a product/service feature or the product or service itself provided (or is providing) a pleasurable level of consumption-related fulfillment, including levels of under- or over-fulfillment.” Similarly, Anderson and Sullivan ( 1993 , p. 126) characterize CS as a “post-purchase evaluation of product quality given repurchase expectations.” Thus, CS is a customer’s evaluative summary judgment of consumption experiences that is associated with customer- and firm-level outcomes.

Although we may theoretically know and expect that CS will have a positive association with many outcomes such as retention, WOM, and sales, a systematic and large-scale meta-analysis can provide important insights. First, it is important to compare differences in the strength of relationship across different customer- and firm-level outcomes (e.g., CS-retention vs. CS-sales). Second, it is important to examine the considerable variation in the magnitude of these relationships across studies. For example, some studies find the CS-retention correlation to be nonsignificant (e.g., van Birgelen, de Jong, and de Ruyter 2006 ) while others find a strong positive association (e.g., Anderson and Sullivan 1993 ).

Understanding the reasons behind these systematic differences can yield new and important research questions and insights. For example, is the association between CS and customer-level consequences stronger (or weaker) for business-to-consumer (B2C) markets relative to business-to-business (B2B) markets? What is the theoretical reason behind this difference, and what are its practical implications? Answering these questions can suggest more nuanced testable hypotheses and guide practitioners as well.

This study investigates the association of CS with 14 outcomes in a meta-analytic framework (see Fig. 1 , panel A). These outcomes include customer outcomes, product-market performance, accounting performance, and financial-market performance. These outcomes are of great importance to a firm’s chief marketing officer (CMO), chief sales officer (CSO), chief financial officer (CFO), and chief executive officer (CEO) (see Fig. 1 , panel B).

Customer satisfaction and its outcomes

As shown in Table 1 , there have been three meta-analyses of CS published in marketing journals. Szymanski and Henard ( 2001 ) conducted the first meta-analysis including 50 studies. Among them, 15 studies examined three CS outcomes (complaining, negative WOM, and repurchase) while 35 examined antecedents of CS. No studies investigated CS and firm-level outcomes.

Curtis et al. ( 2011 ) focused on CS and three customer-level outcomes, retention behavior, retention intention, and loyalty, with no firm-level outcomes. They showed that the positive association of CS with retention and loyalty varies across exchanges (goods vs. services), markets (B2C vs. B2B), and locations of study (North America vs. Europe vs. others).

The most recent meta-analysis by Otto, Szymanski, and Varadarajan ( 2020 ) did not examine any customer-level outcomes and included only five out of ten firm-level outcomes examined in the current study. While they included moderators such as goods vs. services and ACSI vs. non-ACSI metrics, factors such as location of study and scale points were not included.

This meta-analysis uses 535 effect sizes from 245 articles representing a combined sample size of 1,160,982 units, examines 14 effects, and includes nine moderators. It is the most comprehensive meta-analysis to date with a much larger number of articles, customer- and firm-level outcomes, and moderators (see Table 1 ).

2 Theoretical framework

Within the attitude-intentions-behavior framework (Fishbein and Ajzen 1975 ), satisfaction judgments are a function of expectations, disconfirmation, and performance (see, for a review, Oliver 2014 ). Satisfaction judgments drive customers’ behavioral intentions, which in turn guide subsequent actions such as WOM, repurchase, and spending. As customers repeatedly engage in these behaviors, their satisfaction judgments, intentions, and action are reinforced. The result of this process is a cumulative satisfaction judgment (Anderson, Fornell, and Lehmann 1994 ) and associated outcomes. This general process undergirds the framework in Fig. 1 , panel A. Note the current meta-analysis examines CS and its outcomes (and not antecedents).

2.1 Customer- and firm-level outcomes of customer satisfaction

Extant research has linked CS to four customer-level outcomes (retention, WOM, price outcomes, and spending outcomes) and ten firm-level outcomes (e.g., sales, cash flow, stock returns, and Tobin’s q ). Their definition, measures, and respective calculations are shown in Table 2 , panel A.

2.2 Moderators of the CS-outcomes relationship

Table 2 , panel B reports the nine moderators examined in this meta-analysis. These include (1) contextual factors such as type of exchange and location of study and (2) measurement characteristics including the number of items and the number of scale points in the CS measure, the source of CS measure (e.g., ACSI), the calculation of CS score (e.g., top-box score), and the measurement of outcome (e.g., behavior). Footnote 1

3 Methodology

3.1 literature search.

We identified studies using computerized searches of Web of Knowledge, ScienceDirect, and EBSCO with the keywords “customer satisfaction” and “consumer satisfaction.” We examined each issue of the major marketing journals in the USA and Europe starting from 1980. Footnote 2 Prior to 1980, CS research focused on its antecedents. We also reviewed and included pertinent articles from the three meta-analyses in Table 1 .

3.2 Criteria for inclusion/exclusion

A study was excluded if it: (1) measured satisfaction with specific attributes but not overall satisfaction, (2) used a composite measure of multiple outcomes (e.g., latent construct of repurchase and recommendation), and (3) did not report correlations or information that could be converted to correlations. Footnote 3 When a study provided multiple effect sizes, either for separate samples or relationships, we treated effects as independent. When a study provided multiple effect sizes for the same relationship (e.g., for subsets of the same sample), we calculated the average effect size. The final analyses use 535 correlations from 245 articles ( N = 1,160,982).

3.3 Approach to analysis

We calculate inverse-variance-weighted reliability-adjusted correlations between CS and each outcome (Hunter and Schmidt 2004 ). To adjust for reliability, we use Cronbach’s alpha (Nunally 1978 ) as a reliability measure and divide the raw correlations by the square root of the product of reliabilities of CS and the outcome. We are unable to correct for reliability for firm-level outcomes because they use a single metric based on archival financial data. We then transform the reliability-adjusted correlations to Fisher’s z coefficients and weight them by the inverse variance (i.e., 1/[ N  – 3]). Finally, we transform the Fisher’s z coefficients back to correlations to arrive at the weighted reliability-adjusted correlations. Footnote 4 The analyses use a random effects approach for effect size integration.

3.3.1 Publication bias

To address the file-drawer problem, we report the fail-safe N (FSN). This calculates the number of studies that would have to be missing from the analysis to nullify an effect or reduce it to a level that is not theoretically or practically significant (Orwin 1983 ). A funnel plot shows minimal publication bias (Fig. A 1 in Web Appendix A).

3.3.2 Homogeneity and moderator analysis

The Q test assesses between-study variability in the population effect size estimated by the individual studies. Footnote 5 In Table 3 , a statistically significant Q statistic suggests the need for subgroup analysis (e.g., Pick and Eisend 2014 ). Thus, we compare effect sizes across different levels of each moderator.

4.1 CS and customer-level outcomes

Table 3 , panel A reports that CS has a strong association with retention ( r = 0.60, p < 0.01) and WOM ( r = 0.68, p < 0.01) and is moderately correlated with spending ( r = 0.28, p < 0.01) and price outcomes ( r = 0.39, p < 0.01). Footnote 6 The statistically significant Q tests ( p s < 0.01) for all four outcomes indicate that effect sizes may vary based on exchange type, market type, location of study, measurement of outcome, scale items, and scale points. Disaggregated results are shown in panel A of Table A 2 in Web Appendix A and discussed next.

4.2 Moderator analysis for customer-level outcomes

4.2.1 exchange.

For retention, the association with CS is stronger for mixed exchanges ( r MIXED = 0.69) than for services ( r SERVICES = 0.56) but not for goods ( r GOODS = 0.57); the association does not differ between goods and services. The association between CS and WOM is statistically not different among goods ( r GOODS = 0.66), services ( r SERVICES = 0.64), and mixed exchanges ( r MIXED = 0.74). For spending outcomes, the association with CS is statistically similar for goods ( r GOODS = 0.38), services ( r SERVICES = 0.22), and mixed exchanges ( r MIXED = 0.27). Finally, the association of CS and price outcomes is also not statistically different across goods ( r GOODS = 0.08), services ( r SERVICES = 0.41), and mixed exchanges ( r MIXED = 0.34). Footnote 7

4.2.2 Market

The CS-retention association is statistically stronger in B2B ( r B2B = 0.66) than in B2C ( r B2C = 0.55) but not in mixed markets ( r MIXED = 0.63). The CS-WOM relationship is stronger in B2B markets than in others ( r B2C = 0.61 vs. r B2B = 0.74 vs. vs. r MIXED = 0.42). The CS-spending outcomes relationship is not statistically different across B2C ( r B2C = 0.33), B2B ( r B2B = 0.16), and mixed markets ( r MIXED = 0.23). Finally, the CS-price outcomes association is statistically similar in B2C and B2B markets ( r B2C = 0.41 vs. r B2B = 0.18).

4.2.3 Location of study

Relative to Europe, North American samples exhibit a stronger association of CS with retention ( r NORTH.AMERICA = 0.63 vs. r EUROPE = 0.51 vs. r ASIA = 0.64 vs. r AFRICA = 0.82), WOM ( r NORTH.AMERICA = 0.71 vs. r EUROPE = 0.57 vs. r ASIA = 0.65 vs. r AFRICA = 0.41), and price outcomes ( r NORTH.AMERICA = 0.75 vs. r EUROPE = 0.35). For spending outcomes, the association with CS does not statistically differ among samples from North America ( r NORTH.AMERICA = 0.25), Europe ( r EUROPE = 0.30), and Asia ( r ASIA = 0.50).

4.2.4 Measurement of outcome

The association with CS is stronger when the outcome is measured as intentions than as behaviors for retention ( r BEHAVIOR = 0.21 vs. r INTENTION = 0.65) and WOM ( r BEHAVIOR = 0.50 vs. r INTENTION = 0.71) but not for spending outcomes ( r BEHAVIOR = 0.24 vs. r INTENTION = 0.41).

4.2.5 Scale items

The association with CS is stronger when a single- vs. a multiple-item CS scale is used for retention ( r SINGLE = 0.66 vs. r MULTI = 0.55) and WOM ( r SINGLE = 0.73 vs. r MULTI = 0.59) but statistically not different for spending outcomes ( r SINGLE = 0.22 vs. r MULTI = 0.31).

4.2.6 Scale points

The association of CS with outcomes is statistically similar for 5-, 7-, 10-, and 100-point scales ( r 5-POINT = 0.62 vs. r 7-POINT = 0.60 vs. r 10-POINT = 0.50 vs. r 100-POINT = 0.54 for retention; r 5-POINT = 0.65 vs. r 7-POINT = 0.71 vs. r 10-POINT = 0.50 vs. r 100-POINT = 0.65 for WOM; r 5-POINT = 0.28 vs. r 7-POINT = 0.33 vs. r 10-POINT = 0.21 vs. r 100-POINT = 0.23 for spending outcomes; and r 5-POINT = 0.24 vs. r 7-POINT = 0.41 for price outcomes).

4.3 CS and firm-level outcomes

The CS-outcomes correlation is smaller at the firm level than at the customer level (see Table 3 , panel B) potentially because firm-level outcomes are more distal than customer-level outcomes. Different than the association of CS with customer-level outcomes, the magnitude of the association of CS with firm-level outcomes can be classified as small to moderate. Footnote 8

Specifically, CS has a positive and statistically significant association with sales ( r = 0.15, p < 0.01), profit ( r = 0.10, p < 0.01), ROA ( r = 0.22, p < 0.01), Tobin’s q ( r = 0.29, p < 0.01), and stock returns ( r = 0.08, p < 0.05); a negative and statistically significant association with cash flow variability ( r = –0.10, p < 0.01), risk ( r = –0.23, p < 0.01), and cost of debt financing ( r = –0.14, p < 0.01). CS has a nonsignificant association with market share ( r = 0.05, p > 0.10) and a weak positive association with cash flow ( r = 0.09, p < 0.10), which may occur because they likely represent multiple subgroups with large between-group variability in the association (Whitener 1990 ). Footnote 9

The Q statistics for all outcomes, except for cost of debt financing, indicate a statistically significant heterogeneity among studies (see Table 3 , panel B). Yet, with a small number of exceptions, the association between CS and firm-level outcomes is not statistically different across subgroups based on different levels of moderators (see panel B of Table A 2 in Web Appendix A). There are several potential reasons for the statistically nonsignificant results. First, for several moderator levels, each outcome has been investigated by a small number of studies (i.e., k in panel B of Table A 2 in Web Appendix A). Second, most of the firm-level studies include samples from multiple industries and preclude us from isolating correlations based on specific industry settings. Finally, published studies typically do not report correlations disaggregated by firm-level moderators such as firm size, advertising and R&D intensity, and industry concentration. Therefore, we report means by subgroups for firm-level outcomes but do not discuss them further.

5 Implications

5.1 research implications.

First, the moderator analysis shows that there is substantial and systematic heterogeneity in the positive association between CS and customer-level outcomes. Yet, we do not understand the different patterns of variability and their implications. As an example, the association of CS with price outcomes is more heterogeneous than its association with spending outcomes across markets, exchange types, and locations of study. Is it because firms have more control on price outcomes but not on spending outcomes? These issues need further research.

Second, studies that simultaneously examine and compare the association of CS with multiple customer-level outcomes under different contexts are needed. Specifically, attention to differences in effect sizes among subgroups as well as their causes and implications is a key research direction.

Third, the association of CS is strongest for WOM, followed by retention, and is the weakest for spending and price outcomes. Future research should develop a conceptual and theoretical framework to understand these relative differences. Thus, is it the case that higher CS is more beneficial for growing new customers than retaining current customers? To the extent that WOM affects the cost of attracting new customers, customer equity research can be expanded by including CS as a contributing factor for retaining current customers and attracting new customers. Third, a wider set of potential moderators including psychological constructs such as trust and commitment as well as structural factors such as company size, industry growth, and competitive intensity should be investigated.

Fourth, these results make a very strong case that consumer behavior scholars should use CS as a consequential dependent variable in their studies. CS has a clear association with actual consumer behaviors and firm-level financial outcomes. Thus, consumer behavior scholars can be reasonably assured that differences in CS are consequential, i.e., predictive of actual consumer behaviors and firm financial outcomes.

Fifth, these results call into question the long-standing insistence on using multi-item scales for measuring CS. The CS-outcomes linkage is impervious to single- vs. multiple-item scales or number of scale points (i.e., 5- vs. 7- vs. 10- vs. 100-point scale). Simple and single item scales suffice; this is an important insight for practitioners who value simplicity to reduce the cost of customer surveys.

Sixth, at the firm level, the mean association of CS with market share ( p > 0.10) and cash flow ( p < 0.10) is nonsignificant to weak (Table 3 , panel B). This may be the case if the association of CS with these outcomes is nonlinear and/or contingent on factors such as firms’ ability to standardize or customize their offerings, the heterogeneity in consumer preferences, and the nature of the offering (e.g., goods vs. services; Anderson, Fornell, and Rust 1997 ). In the same vein, CS has a stronger association with ROA than with cash flow. While we can speculate on the potential reasons for this, more studies are needed to better estimate the effects and explain the differences. Finally, the small number of studies for subgroups within different levels of moderators precluded specific conclusions; clearly, more studies on CS-firm outcomes are needed.

5.2 Implications for firm strategy and senior executives

Figure 1 , panel B organizes the outcomes of CS based on their relevance to CMOs, CSOs, CFOs, and CEOs and board members. CMOs who organize their efforts around CS and make CS as their key metric should be able to make a case for their relevance and contribution to customer retention, WOM, spending, and price outcomes. While CMOs are free to focus on other constructs such as net promoter, this research provides clear, strong, and convincing evidence for using CS as a metric to measure marketing and sales performance and relate it to firm performance. Specifically, CS can provide the basis for CMOs and CSOs to collaboratively grow the current customer base organically as well as expand it through additional sales. The positive association of CS with ROA and cash flow and its negative association with cash flow variability speak to CFOs.

Finally, our work makes a clear case for CEOs and board members to utilize CS as an organizing framework for strategy planning and execution. By making customer value, as measured through CS, the central mechanism for creating and implementing strategy, CEOs can reliably increase Tobin’s q and stock returns while decreasing risk, outcomes for which CEOs are most responsible.

In summary, a focus on CS can align C-suite members (CEO, CFO, CMO, and CSO) using a theoretically sound, conceptually consistent, and empirically validated approach. We hope that senior leaders in firms embrace a satisfaction-based approach to strategy planning and execution based on these results.

6 Concluding comments

CS is a core construct for guiding strategy research and a consequential outcome for consumer-behavior research. This meta-analysis of 535 effect sizes from 245 articles shows that the positive outcomes of CS at the customer- and firm-level vary across different outcomes and across different study characteristics. The results provide guidance for research scholars and show how senior executives can adopt a CS-based framework to develop, guide, and implement firm strategy.

The current research has limitations. First, the results are limited by data availability, which precluded a larger number of outcomes or additional moderators. Second, variation in effect sizes remained even after accounting for contextual and measurement factors, suggesting that sources of variation still exist. Finally, our analysis was based on traditional meta-analytic framework and could not capture nonlinearity in the relationship between two constructs. Studies reporting correlations at different levels of moderators and boundary conditions in the association of CS with its consequences can be helpful in this regard.

Data Availability

Please contact authors for data availability.

We calculated the proportion of studies for each combination of levels in different moderators. Table A1 in Web Appendix A reports the proportions showing adequate variation in study settings.

Journals include Journal of Marketing , Journal of Marketing Research , Marketing Science , Journal of Consumer Research , Journal of Service Research , Journal of Retailing , Journal of the Academy of Marketing Science , Journal of Services Marketing , Journal of Service Industry Management , Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior , Journal of Business Research , and International Journal of Service Industry Management. The list of papers included in the meta-analysis is provided in Web Appendix B.

We contacted 44 authors to request missing correlations for studies, and 17% of them provided the correlations.

We use the Fisher’s z transformation due to potential issues associated with using raw correlations. Specifically, different than Fisher’s z scores, raw correlations may be highly skewed and have a problematic standard error formulation: the standard error is used to compute the inverse variance weight in the meta-analysis (Lipsey and Wilson 2001 ). Still, we computed results using raw correlations. Reassuringly, most of the results remained unchanged when using Fisher’s z or correlations.

The Q statistic is computed by summing the squared deviations of each study’s effect estimate from the overall estimate, weighting each study by the inverse of its variance, and has a chi-square distribution with k – 1 degrees of freedom ( k = number of effect sizes). A statistically significant Q statistic indicates the effect size varies across studies. The Q statistic has low power to detect heterogeneity when the number of studies is small or sample size within studies is low. Thus, it should be interpreted cautiously.

Following Cohen ( 1992 ), we deem a correlation of 0.10 as small, 0.30 as medium, and 0.50 as large. Notably, the magnitude and the statistical significance of the correlations of CS with retention and WOM are similar to those reported in Szymanski and Henard ( 2001 ) and Curtis et al. ( 2011 ).

The very small sample size for goods and mixed exchanges precludes meaningful statistical comparisons.

We use Cohen’s ( 1992 ) standards for effect sizes in our interpretation. It may be the case that higher/more conservative standards are required because lower-level variability influences higher-level effects (e.g., individual-level variability is ignored in the estimate of firm-level effects).

Notably, the magnitude and the statistical significance of the correlations of CS with market share, sales, profit, Tobin’s q , and stock returns are similar to those reported in Otto, Szymanski, and Varadarajan ( 2020 ).

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Vikas Mittal

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Mittal, V., Han, K., Frennea, C. et al. Customer satisfaction, loyalty behaviors, and firm financial performance: what 40 years of research tells us. Mark Lett 34 , 171–187 (2023). https://doi.org/10.1007/s11002-023-09671-w

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An empirical research on customer satisfaction study: a consideration of different levels of performance

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Customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Customers should be managed as assets, and that customers vary in their needs, preferences, and buying behavior. This study applied the Taiwan Customer Satisfaction Index model to a tourism factory to analyze customer satisfaction and loyalty. We surveyed 242 customers served by one tourism factory organizations in Taiwan. A partial least squares was performed to analyze and test the theoretical model. The results show that perceived quality had the greatest influence on the customer satisfaction for satisfied and dissatisfied customers. In addition, in terms of customer loyalty, the customer satisfaction is more important than image for satisfied and dissatisfied customers. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Traditional manufacturing factories converted for tourism purposes, have become a popular leisure industry in Taiwan. The tourism factories has experienced significant growth in recent years, and more and more tourism factories emphasized service quality improvement, and customized service that contributes to a tourism factory’s image and competitiveness in Taiwan (Wu and Zheng 2014 ). Therefore, tourism factories has become of greater economic importance in Taiwan. By becoming a tourism factory, companies can establish a connection between consumers and the brand, generate additional income from entrance tickets and on-site sales, and eventually add value to service innovations (Tsai et al. 2012 ). Because of these incentives, the Taiwanese tourism factory industry has become highly competitive. Customer satisfaction is seen as very important in this case.

Numerous empirical studies have indicated that service quality and customer satisfaction lead to the profitability of a firm (Anderson et al. 1994 ; Eklof et al. 1999 ; Ittner and Larcker 1996 ; Fornell 1992 ; Anderson and Sullivan 1993 ; Zeithaml 2000 ). Anderson and Sullivan ( 1993 ) stated that a firm’s future profitability depends on satisfying current customers. Anderson et al. ( 1994 ) found a significant relationship between customer satisfaction and return on assets. High quality leads to high levels of customer retention, increase loyalty, and positive word of mouth, which in turn are strongly related to profitability (Reichheld and Sasser 1990 ). In a tourism factory setting, customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Kutner and Cripps ( 1997 ) indicated that customers should be managed as assets, and that customers vary in their needs, preferences, buying behavior, and price sensitivity. A tourism factory remains competitive by increasing its service quality relative to that of competitors. Delivering superior customer value and satisfaction is crucial to firm competitiveness (Kotler and Armstrong 1997 ; Weitz and Jap 1995 ; Deng et al. 2013 ). It is crucial to know what customers value most and helps firms allocating resource utilization for continuously improvement based on their needs and wants. The findings of Customer Satisfaction Index (CSI) studies can serve as predictors of a company’s profitability and market value (Anderson et al. 1994 ; Eklof et al. 1999 ; Chiu et al. 2011 ). Such findings provide useful information regarding customer behavior based on a uniform method of customer satisfaction, and offer a unique opportunity to test hypotheses (Anderson et al. 1997 ).

The basic structure of the CSI model has been developed over a number of years and is based on well-established theories and approaches to consumer behavior, customer satisfaction, and product and service quality in the fields of brands, trade, industry, and business (Fornell 1992 ; Fornell et al. 1996 ). In addition, the CSI model leads to superior reliability and validity for interpreting repurchase behavior according to customer satisfaction changes (Fornell 1992 ). These CSIs are fundamentally similar in measurement model (i.e. causal model), they have some obvious distinctions in model’s structure and variable’s selection. Take full advantages of other nations’ experiences can establish the Taiwan CSI Model which is suited for Taiwan’s characters. Thus, the ACSI and ECSI have been used as a foundation for developing the Taiwan Customer Satisfaction Index (TCSI). The TCSI was developed by Chung Hua University and the Chinese Society for Quality in Taiwan. The TCSI provides Taiwan with a fair and objective index for producing vital information that can help the country, industries, and companies improve competitiveness. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs (Fig.  1 ). The relationships among the different aspects of the TCSI are different from those of the ACSI, but are the same as those of the ECSI (Lee et al. 2005 , 2006 ).

The Taiwan Customer Satisfaction Index model

The traditional CSI model for measuring customer satisfaction and loyalty is restricted and does not consider the performance of firms. Moreover, as theoretical and empirical research has shown, the relationship between attribute-level performance and overall satisfaction is asymmetric. If the asymmetries are not considered, the impact of the different attributes on overall satisfaction is not correctly evaluated (Anderson and Mittal 2000 ; Matzler and Sauerwein 2002 ; Mittal et al. 1998 ; Matzler et al. 2003 , 2004 ). Few studies have investigated CSI models that contain different levels of performance (satisfaction), especially in relation to satisfaction levels of a tourism factory. To evaluate overall satisfaction accurately, the impact of the different levels of performance should be considered (Matzler et al. 2004 ). The purpose of this study is to apply the TCSI model that contains different levels of performance to improve and ensure the understanding of firm operational efficiency by managers in the tourism factory. A partial least squares (PLS) was performed to test the theoretical model due to having been successfully applied to customer satisfaction analysis. The PLS is well suited for predictive applications (Barclay et al. 1995 ) and using path coefficients that regard the reasons for customer satisfaction or dissatisfaction and providing latent variable scores that could be used to report customer satisfaction scores. Our findings provide support for the application of TCSI model to derive tourist satisfaction information.

Literature review

National customer satisfaction index (csi).

The CSI model includes a structural equation with estimated parameters of hidden categories and category relationships. The CSI can clearly define the relationships between different categories and provide predictions. The basic CSI model is a structural equation model with latent variables which are calculated as weighted averages of their measurement variables, and the PLS estimation method calculates the weights and provide maximum predictive power of the ultimate dependent variable (Kristensen et al. 2001 ). Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ).

Although the core of the models are in most respects standard, they have some obvious distinctions in model’s structure and variable’s selection so that their results cannot be compared with each other and some variations between the SCSB (Swedish), the ACSI (American), the ECSI (European), the NCSB (Norwegian) and other indices. For example, the image factor is not employed in the ACSI model (Johnson et al. 2001 ); the NCSB eliminated customer expectation and replaced with corporate image; the ECSI model does not include the customer complaint as a consequence of satisfaction. Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ). The ECSI model distinguishes service quality from product quality (Kristensen et al. 2001 ) and the NCSB model applies SERVQUAL instrument to evaluate service quality (Johnson et al. 2001 ). A quality measure of a single customer satisfaction index is typically developed according to a certain type of culture or the culture of a certain country. When developing a system for measuring or evaluating a certain country or district’s customer satisfaction level, a specialized customer satisfaction index should be developed.

As such, the ACSI and ECSI were used as a foundation to develop the TCSI. The TCSI was developed by Chung Hua University and the Chinese Society for Quality. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs. The TCSI assumes that currently: (1) Taiwan corporations have ability of dealing with customer complaints; customer complaints have already changed from a factor that influences customer satisfaction results to a factor that affects quality perception; (2) The expectations, satisfaction and loyalty of customers are affected by the image of the corporation. The concept that customer complaints are not calculated into the TCSI model is that they were removed based on the ECSI model (Lee et al. 2005 , 2006 , 2014a , b ; Guo and Tsai 2015 ; Tsai et al. 2015a , b ; 2016a ).

TCSI model and service quality

Service quality is frequently used by both researchers and practitioners to evaluate customer satisfaction. It is generally accepted that customer satisfaction depends on the quality of the product or service offered (Anderson and Sullivan 1993 ). Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the NCSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). Ryzin et al. ( 2004 ) applied the ACSI to U.S. local government services and indicated that the perceived quality of public schools, police, road conditions, and subway service were the most salient drivers of satisfaction, but that the significance of each service varied among income, race, and geography. Hsu ( 2008 ) proposed an index for online customer satisfaction based on the ACSI and found that e-service quality was more determinative than other factors (e.g., trust and perceived value) for customer satisfaction. To deliver superior service quality, an online business must first understand how customers perceive and evaluate its service quality. This study developed a basic model for using the TCSI to analyze Taiwan’s tourism factory services. The theoretical model comprised 14 observation variables and the following six constructs: image, customer expectations, perceived quality, perceived value, customer satisfaction, and loyalty.

Research methods

The measurement scale items for this study were primarily designed using the questionnaire from the TCSI model. In designing the questionnaire, a 10-point Likert scale (with anchors ranging from strongly disagree to strongly agree) was used to reduce the statistical problem of extreme skewness (Fornell et al. 1996 ; Qu et al. 2015 ; Tsai 2016 ; Tsai et al. 2016b ; Zhou et al. 2016 ). A total of 14 items, organized into six constructs, were included in the questionnaire. The primary questionnaire was pretested on 30 customers who had visited a tourism factory. Because the TCSI model is preliminary research in the tourism factory, this study convened a focus group to decide final attributes of model. The focus group was composed of one manager of tourism factory, one professor in Hospitality Management, and two customers with experience of tourism factory.

We used the TCSI model (Fig.  1 ) to structure our research. From this structure and the basic theories of the ACSI and ECSI, we established the following hypotheses:

Image has a strong influence on tourist expectations.

Image has a strong influence on tourist satisfaction.

Image has a strong influence on tourist loyalty.

Tourist expectations have a strong influence on perceived quality.

Tourist expectations have a strong influence on perceived values.

Tourist expectations have a strong influence on tourist satisfaction.

Perceived quality has a strong influence on perceived value.

Perceived quality has a strong influence on tourist satisfaction.

Perceived value has a strong influence on tourist satisfaction.

Customer satisfaction has a strong influence on tourist loyalty.

The content of our surveys were separated into two parts; customer satisfaction and personal information. The definitions and processing of above categories are listed below:

Part 1 of the survey assessed customer satisfaction by measuring customer levels of tourism factory image, expectations, quality perceptions, value perceptions, satisfaction, and loyalty toward their experience, and used these constructs to indirectly survey the customer’s overall evaluation of the services provided by the tourism factory.

Part 2 of the survey collected personal information: gender, age, family situation, education, income, profession, and residence.

The six constructs are defined as follows:

Image reflects the levels of overall impression of the tourism factory as measured by two items: (1) word-of-mouth reputation, (2) responsibility toward concerned parties that the tourist had toward the tourism factory before traveling.

Customer expectations refer to the levels of overall expectations as measured by two items: (1) expectations regarding the service of employees, (2) expectations regarding reliability that the tourist had before the experience at the tourism factory.

Perceived quality was measured using three survey measures: (1) the overall evaluation, (2) perceptions of reliability, (3) perceptions of customization that the tourist had after the experience at the tourism factory.

Perceived value was measured using two items: (1) the cost in terms of money and time (2) a comparison with other tourism factories.

Customer satisfaction represents the levels of overall satisfaction was captured by two items: (1) meeting of expectations, (2) closeness to the ideal tourism factory.

Loyalty was measured using three survey measures: (1) the probabilities of visiting the tourism factory again (2) attending another activity held by the tourism factory, (3) recommending the tourism factory to others.

Data collection and analysis

The survey sites selected for this study was the parking lots of one food tourism factory in Taipei, Taiwan. A domestic group package and individual tourists were a major source of respondents who were willing to participate in the survey and completed the questionnaires themselves based on their perceptions of their factory tour experience. Four research assistants were trained to conduct the survey regarding to questionnaire distribution and sampling.

To minimize prospective biases of visiting patterns, the survey was conducted at different times of day and days of week—Tuesday, Thursday, Saturday for the first week; Monday, Wednesday, Friday and Sunday for the next week. The afternoon time period was used first then the morning time period in the following weeks. The data were collected over 1 month period.

Of 300 tourists invited to complete the questionnaire, 242 effective responses were obtained (usable response rate of 80.6 %). The sample of tourists contained more females (55.7 %) than males (44.35 %). More than half of the respondents had a college degree or higher, 28 % were students, and 36.8 % had an annual household income of US $10,000–$20,000. The majority of the respondents (63.7 %) were aged 20–40 years.

Comparison of the TCSI models for satisfied and dissatisfied customers

Researchers have claimed that satisfaction levels differ according to gender, age, socioeconomic status, and residence (Bryant and Cha 1996 ). Moreover, the needs, preferences, buying behavior, and price sensitivity of customers vary (Kutner and Cripps 1997 ). Previous studies have demonstrated that it is crucial to measure the relative impact of each attribute for high and low performance (satisfaction) (Matzler et al. 2003 , 2004 ). To determine the reasons for differences, a satisfaction scale was used to group the sample into satisfied (8–10) and dissatisfied (1–7) customers.

The research model was tested using SmartPLS 3.0 software, which is suited for highly complex predictive models (Wold 1985 ; Barclay et al. 1995 ). In particular, it has been successfully applied to customer satisfaction analysis. The PLS method is a useful tool for obtaining indicator weights and predicting latent variables and includes estimating path coefficients and R 2 values. The path coefficients indicate the strengths of the relationships between the dependent and independent variables, and the R 2 values represent the amount of variance explained by the independent variables. Using Smart PLS, we determined the path coefficients. Figures  2 and 3 show ten path estimates corresponding to the ten research hypothesis of TCSI model for satisfied and dissatisfied customers. Every path coefficient was obtained by bootstrapping the computation of R 2 and performing a t test for each hypothesis. Fornell et al. ( 1996 ) demonstrated that the ability to explain the influential latent variables in a model is an indicator of model performance, in particular the customer satisfaction and customer loyalty variables. From the results shown, the R 2 values for the customer satisfaction were 0.53 vs. 0.50, respectively; and the R 2 value for customer loyalty were 0.64 vs. 0.60, respectively. Thus, the TCSI model explained 53 vs. 50 % of the variance in customer satisfaction; 64 vs. 60 % of that in customer loyalty as well.

Path estimate of the TCSI model for satisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

Path estimate of the TCSI model for dissatisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

According to the path coefficients shown in Figs.  2 and 3 , image positively affected customer expectations (β = 0.58 vs. 0.37), the customer satisfaction (β = 0.16 vs. 0.11), and customer loyalty (β = 0.47 vs. 0.16). Therefore, H1–H3 were accepted. Customer expectations were significantly related to perceived quality (β = 0.94 vs. 0.83). However, customer expectations were not significantly related to perceived value shown as dotted line (β = −0.01 vs. −0.20) or the customer satisfaction, shown as dotted line (β = −0.21 vs. −0.32). Thus, H4 was accepted but H5 and H6 were not accepted. Perceived value positively affected the customer satisfaction (β = 0.27 vs. 0.14), supporting H7. Accordingly, the analysis showed that each of the antecedent constructs had a reasonable power to explain the overall customer satisfaction. Furthermore, perceived quality positively affected the customer satisfaction (β = 0.70 vs. 0.62), as did perceived value (β = 0.83 vs. 0.74). These results confirm H8 and H9. The path coefficient between the customer satisfaction and customer loyalty was positive and significant (β = 0.63 vs. 0.53). This study tested the suitability of two TCSI models by analyzing the tourism factories in Taiwan. The results showed that the TCSI models were all close fit for this type of research. This study provides empirical evidence of the causal relationships among perceived quality, image, perceived value, perceived expectations, customer satisfaction, and customer loyalty.

To observe the effects of antecedent constructs of perceived value (e.g., customer expectation and perceived quality), customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Furthermore, satisfied customers were affected more by perceived quality (β = 0.83 vs. 0.74), as shown in Table  1 . Regarding the effect of the antecedents of customer satisfaction (e.g., image, customer expectations, perceived value and perceived quality), the total effects of perceived quality on the customer satisfaction of satisfied and dissatisfied customers were 0.92 and 0.72. The total effects of image on the customer satisfaction of satisfied and dissatisfied customers were 0.45 and 0.19. Thus, the satisfaction level of satisfied customers was affected more by perceived quality. Consequently, regarding customer satisfaction, perceived quality is more important than image for satisfied and dissatisfied customers. Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the CSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). This is consistent with the results of previous research ( O’Loughlin and Coenders 2002 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Chin and Liu 2015 ; Chin et al. 2016 ).

With respect to the effect of the antecedents of customer loyalty (e.g., image and customer satisfaction), the total effects of image on customer loyalty for satisfied and dissatisfied customers were 0.57 and 0.21. In other words, the customer loyalty of satisfied customers was affected more by customer satisfaction. Customer satisfaction was significantly related to the customer loyalty of both satisfied and dissatisfied customers, and satisfied customers were affected more by customer satisfaction ( β  = 0.63 vs. 0.14). Consequently, regarding customer loyalty, customer satisfaction is more important than image for both satisfied and dissatisfied customers. Numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Grønholdt et al. 2000 ). This study empirically supports the notion that customer satisfaction is positively related to customer loyalty.

The TCSI model has a predictive capability that can help tourism factory managers improve customer satisfaction based on different performance levels. Our model enables managers to determine the specific factors that significantly affect overall customer satisfaction and loyalty within a tourism factory. This study also helps managers to address different customer segments (e.g., satisfied vs. dissatisfied); because the purchase behaviors of customers differ, they must be treated differently. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively.

Fornell et al. ( 1996 ) demonstrated that the ability to explain influential latent variables in a model, particularly customer satisfaction and customer loyalty variables, is an indicator of model performance. However, the results of this study indicate that customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Moreover, they were affected more by perceived quality of customer satisfaction. Numerous researchers have found that the construct of customer expectations used in the ACSI model does not significantly affect the level of customer satisfaction (Johnson et al. 1996 , 2001 ; Martensen et al. 2000 ; Anderson and Sullivan 1993 ).

Through the overall effects, this study derived several theoretical findings. First, the factors with the largest influence on customer satisfaction were perceived quality and perceived expectations, despite the results showing that customer expectations were not significantly related to perceived value or customer satisfaction. Hence, customer expectations indirectly affected customer satisfaction through perceived quality. Accordingly, perceived quality had the greatest influence on customer satisfaction. Likewise, our results also show that satisfied customers were affected more by perceived quality than dissatisfied customers. This study determined that perceived quality, whether directly or indirectly, positively influenced customer satisfaction. This result is consistent with those of Cronin and Taylor ( 1992 ), Cronin et al. ( 2000 ), Hsu ( 2008 ), Ladhari ( 2009 ), Terblanche and Boshoff ( 2010 ), Deng et al. ( 2013 ), and Yazdanpanah et al. ( 2013 ).

Second, the factors with the most influence on customer loyalty were image and customer satisfaction. The results of this study demonstrate that the customer loyalty of satisfied customers was affected more by customer satisfaction. Consequently, regarding customer loyalty, customer satisfaction is more important than image for satisfied customers. Lee ( 2015 ) found that higher overall satisfaction increased the possibility that visitors will recommend and reattend tourism factory activities. Moreover, numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Su 2004; Deng et al. 2013 ). In initial experiments on ECSI, corporate image was assumed to have direct influences on customer expectation, satisfaction, and loyalty. Subsequent experiments in Denmark proved that image affected only expectation and satisfaction and had no relationship with loyalty (Martensen et al. 2000 ). In early attempts to build the ECSI model, image was defined as a variable involving not only a company’s overall image but products or brand awareness; thus image is readily connected with customer expectation and perception. Therefore, this study contributes to relevant research by providing empirical support for the notion that customer satisfaction is positively related to customer loyalty.

In addition to theoretical implications, this study has several managerial implications. First, the TCSI model has a satisfactory predictive capability that can help tourism factory managers to examine customer satisfaction more closely and to understand explicit influences on customer satisfaction for different customer segments by assessing the accurate causal relationships involved. In contrast to general customer satisfaction surveys, the TCSI model cannot obtain information on post-purchase customer behavior to improve customer satisfaction and achieve competitive advantage.

Second, this study not only indicated that each of the antecedent constructs had reasonable power to explain customer satisfaction and loyalty but also showed that perceived quality exerts the largest influence on the customer satisfaction of Taiwan’s tourism factory industry. Therefore, continually, Taiwan’s tourism factories must endeavor to enhance their customer satisfaction, ideally by improving service quality. Managers of Taiwan’s tourism factories must ensure that service providers deliver consistently high service quality.

Third, this research determined that the factors having the most influence on customer loyalty were image and customer satisfaction. Therefore, managers of Taiwan’s tourism factories should allow customer expectations to be fulfilled through experiences, thereby raising their overall level of satisfaction. Regarding image, which refers to a brand name and its related associations, when tourists regard a tourism factory as having a positive image, they tend to perceive higher value of its products and services. This leads to a higher level of customer satisfaction and increased chances of customers’ reattending tourism factory activities.

Different performance levels exist in how tourists express their opinions about various aspects of service quality and satisfaction with tourism factories. Customer segments can have different preferences depending on their needs and purchase behavior. Our findings indicate that tourists belonging to different customer segments (e.g., satisfied vs. dissatisfied) expressed differences toward service quality and customer satisfaction. Thus, the management of Taiwan’s tourism factories must notice the needs of different market segments to meet their individual expectations. This study proposes two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Limitations and suggestions for future research

This study has some limitations. First, the tourism factory surveyed in this study was a food tourism factory operating in Taipei, Taiwan, and the present findings cannot be generalized to the all tourism factory industries. Second, the sample size was quite small for tourists (N = 242). Future research should collect a greater number of samples and include a more diverse range of tourists. Third, this study was preliminary research on tourism factories, and domestic group package tourists were a major source of the respondents. Future studies should collect data from international tourists as well.

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Authors’ contributions

Writing: S-CL; providing case and idea: Y-CL, Y-CW, Y-FH, C-HC; providing revised advice: S-BT, WD. All authors read and approved the final manuscript.

Acknowledgements

Department of Technology Management, Chung-Hua University, Hsinchu, Taiwan. This work was supported by University of Electronic Science Technology of China, Zhongshan Institute (414YKQ01 and 415YKQ08).

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Yu-Cheng Lee

Department of Business Administration, Chung-Hua University, Hsinchu, 300, Taiwan

Yu-Che Wang

PhD Program of Technology Management, Chung-Hua University, Hsinchu, 300, Taiwan

Shu-Chiung Lu & Chih-Hung Chien

Department of Food and Beverage Management, Lee-Ming Institute of Technology, New Taipei City, 243, Taiwan

Shu-Chiung Lu

Department of Business Administration, Lee-Ming Institute of Technology, New Taipei City, 243, Taiwan

Chih-Hung Chien

Department of Food and Beverage Management, Taipei College of Maritime Technology, New Taipei City, 251, Taiwan

Yi-Fang Hsieh

Zhongshan Institute, University of Electronic Science and Technology of China, Dongguan, 528402, Guangdong, China

Sang-Bing Tsai

School of Economics and Management, Shanghai Maritime University, Shanghai, 201306, China

Law School, Nankai University, Tianjin, 300071, China

School of Business, Dalian University of Technology, Panjin, 124221, China

College of Business Administration, Dongguan University of Technology, Dongguan, 523808, Guangdong, China

Department of Psychology, Universidad Santo Tomas de Oriente y Medio Día, Granada, Nicaragua

School of Economics and Management, Shanghai Institute of Technology, Shanghai, 201418, China

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Lee, YC., Wang, YC., Lu, SC. et al. An empirical research on customer satisfaction study: a consideration of different levels of performance. SpringerPlus 5 , 1577 (2016). https://doi.org/10.1186/s40064-016-3208-z

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An empirical research on customer satisfaction study: a consideration of different levels of performance

Yu-cheng lee.

1 Department of Technology Management, Chung-Hua University, Hsinchu, 300 Taiwan

Yu-Che Wang

2 Department of Business Administration, Chung-Hua University, Hsinchu, 300 Taiwan

Shu-Chiung Lu

3 PhD Program of Technology Management, Chung-Hua University, Hsinchu, 300 Taiwan

4 Department of Food and Beverage Management, Lee-Ming Institute of Technology, New Taipei City, 243 Taiwan

Yi-Fang Hsieh

6 Department of Food and Beverage Management, Taipei College of Maritime Technology, New Taipei City, 251 Taiwan

Chih-Hung Chien

5 Department of Business Administration, Lee-Ming Institute of Technology, New Taipei City, 243 Taiwan

Sang-Bing Tsai

7 Zhongshan Institute, University of Electronic Science and Technology of China, Dongguan, 528402 Guangdong China

8 School of Economics and Management, Shanghai Maritime University, Shanghai, 201306 China

9 Law School, Nankai University, Tianjin, 300071 China

10 School of Business, Dalian University of Technology, Panjin, 124221 China

11 College of Business Administration, Dongguan University of Technology, Dongguan, 523808 Guangdong China

12 Department of Psychology, Universidad Santo Tomas de Oriente y Medio Día, Granada, Nicaragua

Weiwei Dong

13 School of Economics and Management, Shanghai Institute of Technology, Shanghai, 201418 China

Customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Customers should be managed as assets, and that customers vary in their needs, preferences, and buying behavior. This study applied the Taiwan Customer Satisfaction Index model to a tourism factory to analyze customer satisfaction and loyalty. We surveyed 242 customers served by one tourism factory organizations in Taiwan. A partial least squares was performed to analyze and test the theoretical model. The results show that perceived quality had the greatest influence on the customer satisfaction for satisfied and dissatisfied customers. In addition, in terms of customer loyalty, the customer satisfaction is more important than image for satisfied and dissatisfied customers. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Traditional manufacturing factories converted for tourism purposes, have become a popular leisure industry in Taiwan. The tourism factories has experienced significant growth in recent years, and more and more tourism factories emphasized service quality improvement, and customized service that contributes to a tourism factory’s image and competitiveness in Taiwan (Wu and Zheng 2014 ). Therefore, tourism factories has become of greater economic importance in Taiwan. By becoming a tourism factory, companies can establish a connection between consumers and the brand, generate additional income from entrance tickets and on-site sales, and eventually add value to service innovations (Tsai et al. 2012 ). Because of these incentives, the Taiwanese tourism factory industry has become highly competitive. Customer satisfaction is seen as very important in this case.

Numerous empirical studies have indicated that service quality and customer satisfaction lead to the profitability of a firm (Anderson et al. 1994 ; Eklof et al. 1999 ; Ittner and Larcker 1996 ; Fornell 1992 ; Anderson and Sullivan 1993 ; Zeithaml 2000 ). Anderson and Sullivan ( 1993 ) stated that a firm’s future profitability depends on satisfying current customers. Anderson et al. ( 1994 ) found a significant relationship between customer satisfaction and return on assets. High quality leads to high levels of customer retention, increase loyalty, and positive word of mouth, which in turn are strongly related to profitability (Reichheld and Sasser 1990 ). In a tourism factory setting, customer satisfaction is the key factor for successful and depends highly on the behaviors of frontline service providers. Kutner and Cripps ( 1997 ) indicated that customers should be managed as assets, and that customers vary in their needs, preferences, buying behavior, and price sensitivity. A tourism factory remains competitive by increasing its service quality relative to that of competitors. Delivering superior customer value and satisfaction is crucial to firm competitiveness (Kotler and Armstrong 1997 ; Weitz and Jap 1995 ; Deng et al. 2013 ). It is crucial to know what customers value most and helps firms allocating resource utilization for continuously improvement based on their needs and wants. The findings of Customer Satisfaction Index (CSI) studies can serve as predictors of a company’s profitability and market value (Anderson et al. 1994 ; Eklof et al. 1999 ; Chiu et al. 2011 ). Such findings provide useful information regarding customer behavior based on a uniform method of customer satisfaction, and offer a unique opportunity to test hypotheses (Anderson et al. 1997 ).

The basic structure of the CSI model has been developed over a number of years and is based on well-established theories and approaches to consumer behavior, customer satisfaction, and product and service quality in the fields of brands, trade, industry, and business (Fornell 1992 ; Fornell et al. 1996 ). In addition, the CSI model leads to superior reliability and validity for interpreting repurchase behavior according to customer satisfaction changes (Fornell 1992 ). These CSIs are fundamentally similar in measurement model (i.e. causal model), they have some obvious distinctions in model’s structure and variable’s selection. Take full advantages of other nations’ experiences can establish the Taiwan CSI Model which is suited for Taiwan’s characters. Thus, the ACSI and ECSI have been used as a foundation for developing the Taiwan Customer Satisfaction Index (TCSI). The TCSI was developed by Chung Hua University and the Chinese Society for Quality in Taiwan. The TCSI provides Taiwan with a fair and objective index for producing vital information that can help the country, industries, and companies improve competitiveness. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs (Fig.  1 ). The relationships among the different aspects of the TCSI are different from those of the ACSI, but are the same as those of the ECSI (Lee et al. 2005 , 2006 ).

The Taiwan Customer Satisfaction Index model

The traditional CSI model for measuring customer satisfaction and loyalty is restricted and does not consider the performance of firms. Moreover, as theoretical and empirical research has shown, the relationship between attribute-level performance and overall satisfaction is asymmetric. If the asymmetries are not considered, the impact of the different attributes on overall satisfaction is not correctly evaluated (Anderson and Mittal 2000 ; Matzler and Sauerwein 2002 ; Mittal et al. 1998 ; Matzler et al. 2003 , 2004 ). Few studies have investigated CSI models that contain different levels of performance (satisfaction), especially in relation to satisfaction levels of a tourism factory. To evaluate overall satisfaction accurately, the impact of the different levels of performance should be considered (Matzler et al. 2004 ). The purpose of this study is to apply the TCSI model that contains different levels of performance to improve and ensure the understanding of firm operational efficiency by managers in the tourism factory. A partial least squares (PLS) was performed to test the theoretical model due to having been successfully applied to customer satisfaction analysis. The PLS is well suited for predictive applications (Barclay et al. 1995 ) and using path coefficients that regard the reasons for customer satisfaction or dissatisfaction and providing latent variable scores that could be used to report customer satisfaction scores. Our findings provide support for the application of TCSI model to derive tourist satisfaction information.

Literature review

National customer satisfaction index (csi).

The CSI model includes a structural equation with estimated parameters of hidden categories and category relationships. The CSI can clearly define the relationships between different categories and provide predictions. The basic CSI model is a structural equation model with latent variables which are calculated as weighted averages of their measurement variables, and the PLS estimation method calculates the weights and provide maximum predictive power of the ultimate dependent variable (Kristensen et al. 2001 ). Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ).

Although the core of the models are in most respects standard, they have some obvious distinctions in model’s structure and variable’s selection so that their results cannot be compared with each other and some variations between the SCSB (Swedish), the ACSI (American), the ECSI (European), the NCSB (Norwegian) and other indices. For example, the image factor is not employed in the ACSI model (Johnson et al. 2001 ); the NCSB eliminated customer expectation and replaced with corporate image; the ECSI model does not include the customer complaint as a consequence of satisfaction. Many scholars have identified the characteristics of the CSI (Karatepe et al. 2005 ; Malhotra et al. 1994 ). The ECSI model distinguishes service quality from product quality (Kristensen et al. 2001 ) and the NCSB model applies SERVQUAL instrument to evaluate service quality (Johnson et al. 2001 ). A quality measure of a single customer satisfaction index is typically developed according to a certain type of culture or the culture of a certain country. When developing a system for measuring or evaluating a certain country or district’s customer satisfaction level, a specialized customer satisfaction index should be developed.

As such, the ACSI and ECSI were used as a foundation to develop the TCSI. The TCSI was developed by Chung Hua University and the Chinese Society for Quality. Every aspect of the TCSI that influences overall customer satisfaction can be measured through surveys, and every construct has a cause–effect relationship with the other five constructs. The TCSI assumes that currently: (1) Taiwan corporations have ability of dealing with customer complaints; customer complaints have already changed from a factor that influences customer satisfaction results to a factor that affects quality perception; (2) The expectations, satisfaction and loyalty of customers are affected by the image of the corporation. The concept that customer complaints are not calculated into the TCSI model is that they were removed based on the ECSI model (Lee et al. 2005 , 2006 , 2014a , b ; Guo and Tsai 2015 ; Tsai et al. 2015a , b ; 2016a ).

TCSI model and service quality

Service quality is frequently used by both researchers and practitioners to evaluate customer satisfaction. It is generally accepted that customer satisfaction depends on the quality of the product or service offered (Anderson and Sullivan 1993 ). Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the NCSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). Ryzin et al. ( 2004 ) applied the ACSI to U.S. local government services and indicated that the perceived quality of public schools, police, road conditions, and subway service were the most salient drivers of satisfaction, but that the significance of each service varied among income, race, and geography. Hsu ( 2008 ) proposed an index for online customer satisfaction based on the ACSI and found that e-service quality was more determinative than other factors (e.g., trust and perceived value) for customer satisfaction. To deliver superior service quality, an online business must first understand how customers perceive and evaluate its service quality. This study developed a basic model for using the TCSI to analyze Taiwan’s tourism factory services. The theoretical model comprised 14 observation variables and the following six constructs: image, customer expectations, perceived quality, perceived value, customer satisfaction, and loyalty.

Research methods

The measurement scale items for this study were primarily designed using the questionnaire from the TCSI model. In designing the questionnaire, a 10-point Likert scale (with anchors ranging from strongly disagree to strongly agree) was used to reduce the statistical problem of extreme skewness (Fornell et al. 1996 ; Qu et al. 2015 ; Tsai 2016 ; Tsai et al. 2016b ; Zhou et al. 2016 ). A total of 14 items, organized into six constructs, were included in the questionnaire. The primary questionnaire was pretested on 30 customers who had visited a tourism factory. Because the TCSI model is preliminary research in the tourism factory, this study convened a focus group to decide final attributes of model. The focus group was composed of one manager of tourism factory, one professor in Hospitality Management, and two customers with experience of tourism factory.

We used the TCSI model (Fig.  1 ) to structure our research. From this structure and the basic theories of the ACSI and ECSI, we established the following hypotheses:

Image has a strong influence on tourist expectations.

Image has a strong influence on tourist satisfaction.

Image has a strong influence on tourist loyalty.

Tourist expectations have a strong influence on perceived quality.

Tourist expectations have a strong influence on perceived values.

Tourist expectations have a strong influence on tourist satisfaction.

Perceived quality has a strong influence on perceived value.

Perceived quality has a strong influence on tourist satisfaction.

Perceived value has a strong influence on tourist satisfaction.

Customer satisfaction has a strong influence on tourist loyalty.

The content of our surveys were separated into two parts; customer satisfaction and personal information. The definitions and processing of above categories are listed below:

• Part 1 of the survey assessed customer satisfaction by measuring customer levels of tourism factory image, expectations, quality perceptions, value perceptions, satisfaction, and loyalty toward their experience, and used these constructs to indirectly survey the customer’s overall evaluation of the services provided by the tourism factory.
• Part 2 of the survey collected personal information: gender, age, family situation, education, income, profession, and residence.

The six constructs are defined as follows:

• Image reflects the levels of overall impression of the tourism factory as measured by two items: (1) word-of-mouth reputation, (2) responsibility toward concerned parties that the tourist had toward the tourism factory before traveling.
• Customer expectations refer to the levels of overall expectations as measured by two items: (1) expectations regarding the service of employees, (2) expectations regarding reliability that the tourist had before the experience at the tourism factory.
• Perceived quality was measured using three survey measures: (1) the overall evaluation, (2) perceptions of reliability, (3) perceptions of customization that the tourist had after the experience at the tourism factory.
• Perceived value was measured using two items: (1) the cost in terms of money and time (2) a comparison with other tourism factories.
• Customer satisfaction represents the levels of overall satisfaction was captured by two items: (1) meeting of expectations, (2) closeness to the ideal tourism factory.
• Loyalty was measured using three survey measures: (1) the probabilities of visiting the tourism factory again (2) attending another activity held by the tourism factory, (3) recommending the tourism factory to others.

Data collection and analysis

The survey sites selected for this study was the parking lots of one food tourism factory in Taipei, Taiwan. A domestic group package and individual tourists were a major source of respondents who were willing to participate in the survey and completed the questionnaires themselves based on their perceptions of their factory tour experience. Four research assistants were trained to conduct the survey regarding to questionnaire distribution and sampling.

To minimize prospective biases of visiting patterns, the survey was conducted at different times of day and days of week—Tuesday, Thursday, Saturday for the first week; Monday, Wednesday, Friday and Sunday for the next week. The afternoon time period was used first then the morning time period in the following weeks. The data were collected over 1 month period.

Of 300 tourists invited to complete the questionnaire, 242 effective responses were obtained (usable response rate of 80.6 %). The sample of tourists contained more females (55.7 %) than males (44.35 %). More than half of the respondents had a college degree or higher, 28 % were students, and 36.8 % had an annual household income of US $10,000–$20,000. The majority of the respondents (63.7 %) were aged 20–40 years.

Comparison of the TCSI models for satisfied and dissatisfied customers

Researchers have claimed that satisfaction levels differ according to gender, age, socioeconomic status, and residence (Bryant and Cha 1996 ). Moreover, the needs, preferences, buying behavior, and price sensitivity of customers vary (Kutner and Cripps 1997 ). Previous studies have demonstrated that it is crucial to measure the relative impact of each attribute for high and low performance (satisfaction) (Matzler et al. 2003 , 2004 ). To determine the reasons for differences, a satisfaction scale was used to group the sample into satisfied (8–10) and dissatisfied (1–7) customers.

The research model was tested using SmartPLS 3.0 software, which is suited for highly complex predictive models (Wold 1985 ; Barclay et al. 1995 ). In particular, it has been successfully applied to customer satisfaction analysis. The PLS method is a useful tool for obtaining indicator weights and predicting latent variables and includes estimating path coefficients and R 2 values. The path coefficients indicate the strengths of the relationships between the dependent and independent variables, and the R 2 values represent the amount of variance explained by the independent variables. Using Smart PLS, we determined the path coefficients. Figures  2 and ​ and3 3 show ten path estimates corresponding to the ten research hypothesis of TCSI model for satisfied and dissatisfied customers. Every path coefficient was obtained by bootstrapping the computation of R 2 and performing a t test for each hypothesis. Fornell et al. ( 1996 ) demonstrated that the ability to explain the influential latent variables in a model is an indicator of model performance, in particular the customer satisfaction and customer loyalty variables. From the results shown, the R 2 values for the customer satisfaction were 0.53 vs. 0.50, respectively; and the R 2 value for customer loyalty were 0.64 vs. 0.60, respectively. Thus, the TCSI model explained 53 vs. 50 % of the variance in customer satisfaction; 64 vs. 60 % of that in customer loyalty as well.

Path estimate of the TCSI model for satisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

Path estimate of the TCSI model for dissatisfied customers. *p < 0.05; **p < 0.01; ***p < 0.001

According to the path coefficients shown in Figs.  2 and ​ and3, 3 , image positively affected customer expectations (β = 0.58 vs. 0.37), the customer satisfaction (β = 0.16 vs. 0.11), and customer loyalty (β = 0.47 vs. 0.16). Therefore, H1–H3 were accepted. Customer expectations were significantly related to perceived quality (β = 0.94 vs. 0.83). However, customer expectations were not significantly related to perceived value shown as dotted line (β = −0.01 vs. −0.20) or the customer satisfaction, shown as dotted line (β = −0.21 vs. −0.32). Thus, H4 was accepted but H5 and H6 were not accepted. Perceived value positively affected the customer satisfaction (β = 0.27 vs. 0.14), supporting H7. Accordingly, the analysis showed that each of the antecedent constructs had a reasonable power to explain the overall customer satisfaction. Furthermore, perceived quality positively affected the customer satisfaction (β = 0.70 vs. 0.62), as did perceived value (β = 0.83 vs. 0.74). These results confirm H8 and H9. The path coefficient between the customer satisfaction and customer loyalty was positive and significant (β = 0.63 vs. 0.53). This study tested the suitability of two TCSI models by analyzing the tourism factories in Taiwan. The results showed that the TCSI models were all close fit for this type of research. This study provides empirical evidence of the causal relationships among perceived quality, image, perceived value, perceived expectations, customer satisfaction, and customer loyalty.

To observe the effects of antecedent constructs of perceived value (e.g., customer expectation and perceived quality), customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Furthermore, satisfied customers were affected more by perceived quality (β = 0.83 vs. 0.74), as shown in Table  1 . Regarding the effect of the antecedents of customer satisfaction (e.g., image, customer expectations, perceived value and perceived quality), the total effects of perceived quality on the customer satisfaction of satisfied and dissatisfied customers were 0.92 and 0.72. The total effects of image on the customer satisfaction of satisfied and dissatisfied customers were 0.45 and 0.19. Thus, the satisfaction level of satisfied customers was affected more by perceived quality. Consequently, regarding customer satisfaction, perceived quality is more important than image for satisfied and dissatisfied customers. Numerous researchers have emphasized the importance of service quality perceptions and their relationship with customer satisfaction by applying the CSI model (e.g., Ryzin et al. 2004 ; Hsu 2008 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Temizer and Turkyilmaz 2012 ; Mutua et al. 2012 ; Dutta and Singh 2014 ). This is consistent with the results of previous research ( O’Loughlin and Coenders 2002 ; Yazdanpanah et al. 2013 ; Chiu et al. 2011 ; Chin and Liu 2015 ; Chin et al. 2016 ).

Table 1

Path estimates of the satisfied and dissatisfied customer CSI model

CS customer satisfaction

* p < 0.05; ** p < 0.01; *** p < 0.001

With respect to the effect of the antecedents of customer loyalty (e.g., image and customer satisfaction), the total effects of image on customer loyalty for satisfied and dissatisfied customers were 0.57 and 0.21. In other words, the customer loyalty of satisfied customers was affected more by customer satisfaction. Customer satisfaction was significantly related to the customer loyalty of both satisfied and dissatisfied customers, and satisfied customers were affected more by customer satisfaction ( β  = 0.63 vs. 0.14). Consequently, regarding customer loyalty, customer satisfaction is more important than image for both satisfied and dissatisfied customers. Numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Grønholdt et al. 2000 ). This study empirically supports the notion that customer satisfaction is positively related to customer loyalty.

The TCSI model has a predictive capability that can help tourism factory managers improve customer satisfaction based on different performance levels. Our model enables managers to determine the specific factors that significantly affect overall customer satisfaction and loyalty within a tourism factory. This study also helps managers to address different customer segments (e.g., satisfied vs. dissatisfied); because the purchase behaviors of customers differ, they must be treated differently. The contribution of this paper is to propose two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively.

Fornell et al. ( 1996 ) demonstrated that the ability to explain influential latent variables in a model, particularly customer satisfaction and customer loyalty variables, is an indicator of model performance. However, the results of this study indicate that customer expectations were not significantly related to perceived value for either satisfied or dissatisfied customers. Moreover, they were affected more by perceived quality of customer satisfaction. Numerous researchers have found that the construct of customer expectations used in the ACSI model does not significantly affect the level of customer satisfaction (Johnson et al. 1996 , 2001 ; Martensen et al. 2000 ; Anderson and Sullivan 1993 ).

Through the overall effects, this study derived several theoretical findings. First, the factors with the largest influence on customer satisfaction were perceived quality and perceived expectations, despite the results showing that customer expectations were not significantly related to perceived value or customer satisfaction. Hence, customer expectations indirectly affected customer satisfaction through perceived quality. Accordingly, perceived quality had the greatest influence on customer satisfaction. Likewise, our results also show that satisfied customers were affected more by perceived quality than dissatisfied customers. This study determined that perceived quality, whether directly or indirectly, positively influenced customer satisfaction. This result is consistent with those of Cronin and Taylor ( 1992 ), Cronin et al. ( 2000 ), Hsu ( 2008 ), Ladhari ( 2009 ), Terblanche and Boshoff ( 2010 ), Deng et al. ( 2013 ), and Yazdanpanah et al. ( 2013 ).

Second, the factors with the most influence on customer loyalty were image and customer satisfaction. The results of this study demonstrate that the customer loyalty of satisfied customers was affected more by customer satisfaction. Consequently, regarding customer loyalty, customer satisfaction is more important than image for satisfied customers. Lee ( 2015 ) found that higher overall satisfaction increased the possibility that visitors will recommend and reattend tourism factory activities. Moreover, numerous studies have shown that customer satisfaction is a crucial factor for ensuring customer loyalty (Barsky 1992 ; Smith and Bolton 1998 ; Hallowell 1996 ; Su 2004; Deng et al. 2013 ). In initial experiments on ECSI, corporate image was assumed to have direct influences on customer expectation, satisfaction, and loyalty. Subsequent experiments in Denmark proved that image affected only expectation and satisfaction and had no relationship with loyalty (Martensen et al. 2000 ). In early attempts to build the ECSI model, image was defined as a variable involving not only a company’s overall image but products or brand awareness; thus image is readily connected with customer expectation and perception. Therefore, this study contributes to relevant research by providing empirical support for the notion that customer satisfaction is positively related to customer loyalty.

In addition to theoretical implications, this study has several managerial implications. First, the TCSI model has a satisfactory predictive capability that can help tourism factory managers to examine customer satisfaction more closely and to understand explicit influences on customer satisfaction for different customer segments by assessing the accurate causal relationships involved. In contrast to general customer satisfaction surveys, the TCSI model cannot obtain information on post-purchase customer behavior to improve customer satisfaction and achieve competitive advantage.

Second, this study not only indicated that each of the antecedent constructs had reasonable power to explain customer satisfaction and loyalty but also showed that perceived quality exerts the largest influence on the customer satisfaction of Taiwan’s tourism factory industry. Therefore, continually, Taiwan’s tourism factories must endeavor to enhance their customer satisfaction, ideally by improving service quality. Managers of Taiwan’s tourism factories must ensure that service providers deliver consistently high service quality.

Third, this research determined that the factors having the most influence on customer loyalty were image and customer satisfaction. Therefore, managers of Taiwan’s tourism factories should allow customer expectations to be fulfilled through experiences, thereby raising their overall level of satisfaction. Regarding image, which refers to a brand name and its related associations, when tourists regard a tourism factory as having a positive image, they tend to perceive higher value of its products and services. This leads to a higher level of customer satisfaction and increased chances of customers’ reattending tourism factory activities.

Different performance levels exist in how tourists express their opinions about various aspects of service quality and satisfaction with tourism factories. Customer segments can have different preferences depending on their needs and purchase behavior. Our findings indicate that tourists belonging to different customer segments (e.g., satisfied vs. dissatisfied) expressed differences toward service quality and customer satisfaction. Thus, the management of Taiwan’s tourism factories must notice the needs of different market segments to meet their individual expectations. This study proposes two satisfaction levels of CSI models for analyzing customer satisfaction and loyalty, thereby helping tourism factory managers improve customer satisfaction effectively. Compared with traditional techniques, we believe that our method is more appropriate for making decisions about allocating resources and for assisting managers in establishing appropriate priorities in customer satisfaction management.

Limitations and suggestions for future research

This study has some limitations. First, the tourism factory surveyed in this study was a food tourism factory operating in Taipei, Taiwan, and the present findings cannot be generalized to the all tourism factory industries. Second, the sample size was quite small for tourists (N = 242). Future research should collect a greater number of samples and include a more diverse range of tourists. Third, this study was preliminary research on tourism factories, and domestic group package tourists were a major source of the respondents. Future studies should collect data from international tourists as well.

Authors’ contributions

Writing: S-CL; providing case and idea: Y-CL, Y-CW, Y-FH, C-HC; providing revised advice: S-BT, WD. All authors read and approved the final manuscript.

Acknowledgements

Department of Technology Management, Chung-Hua University, Hsinchu, Taiwan. This work was supported by University of Electronic Science Technology of China, Zhongshan Institute (414YKQ01 and 415YKQ08).

Competing interests

The authors declare that they have no competing interests.

Contributor Information

Yu-Cheng Lee, Email: moc.liamg@861eelrd .

Yu-Che Wang, Email: wt.ude.uhc@gnawyrrej .

Shu-Chiung Lu, Email: moc.liamg@56ulecarg .

Yi-Fang Hsieh, Email: moc.liamg@gnafiyheish .

Chih-Hung Chien, Email: moc.liamtoh@neihctsirhc .

Sang-Bing Tsai, Phone: +86-22-2350-8785, Email: moc.liamtoh@gnibgnas .

Weiwei Dong, Email: moc.361@4949gnodiewiew .

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• How To Generate a Customer Satisfaction Analysis Report

Emily Louise Spencer

When your customers are not satisfied with the services you provide, they tend to look elsewhere for their next set of purchases. In the age of eCommerce, where alternative providers are easy to find, over 80% of customers are ready to switch companies after merely a single bad experience. This is very bad news if you want to stay in business, since it’s generally much much easier and cheaper to retain existing customers than it is to attract new ones.

While it’s expected that there will be some people walking away unsatisfied, you need to keep track of the levels of customer satisfaction you provide and act on any deficiencies you notice. There are several metrics which you can use to measure customer satisfaction, with the choice of which to use being up to you depending on your specific needs.

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What Does a Customer Satisfaction Report Aim To Do?

The ultimate goal of a customer satisfaction data analysis report is to measure customer satisfaction levels. This might seem straightforward, but depending on what it is you’re looking at specifically you will want to use different metrics. 

There are a myriad of ways to get information, but all have a fatal flaw — you only get answers to the questions that you put on your survey! How specific are you supposed to be in your survey questions? The more specific your questions the more quantifiable your data will be, but overall less specific too as your customers are limited in expressing their views. Using several metrics will give you a good handle on different perspectives, but in turn will make your data harder to analyze.

Types of Customer Satisfaction Metrics

There are several different metrics you can use in order to measure your customer satisfaction score. Below we’ve listed a few of the most useful and commonly used ones, and a little bit of information about them.

Customer Satisfaction Score (CSAT)

The customer satisfaction score is a direct measure of the satisfaction customers had with a particular interaction or process they went through with your organization. It’s usually measured using a scale from one to five, with one being extremely dissatisfied and five being extremely satisfied. Those who rated the interaction a four or five out of five are counted as satisfied customers, with all others being dissatisfied. The percentage of customers who are satisfied with your service is your CSAT score.

The CSAT scale is good for fine details, as each interaction can be rated out of five to get a look at the overall quality of each step in an interaction. You should be aware, however, that there is a cognitive bias involved – people tend to fixate on a standout experience, whether good or bad – thus your responses are likely to be biased towards the extremes.

Net Promoter Score (NPS)

The net promoter score is used in cases where you want to look at the long-term customer satisfaction and/or loyalty to you and your brand. NPS looks at the overall experience a customer has had with you, rated as a percentage of those who would promote you vs those who would actively discourage others from interacting with you.

The NPS has been criticized as flawed by some due to its methodology, which actively ignores those who seem indifferent. The method assumes that, due to the human tendency to only create buzz after either a very good or very bad experience, others who those indifferent customers come into contact with will have no impact on your overall reputation.

Customer Effort Score (CES)

The customer effort score is different to the previous two metrics, as it measures not the experience but the amount of effort that a customer had to put in in order to get what they wanted out of an interaction with you. It’s usually measured in a percentage, similar to CSAT, with customers rating your interaction out of seven and those who score five or above being counted as satisfied.

CES is one of the strongest predictors of whether a customer will return to you, as many consumers seem to prefer settling for a lower quality product or service that is easier to obtain. Think about it – would you fly all the way to another country simply to obtain a slightly better quality product, or settle for one you can find in your local high street?

How To Collect Your Data

Once you’ve decided on a metric to measure, you need to decide on your method. The most common way of gaining data for customer satisfaction scores is surveys, both at the point of sale and after the fact. 

The layout and style of your survey is decided for the most part by the metric you’ve chosen, however you can add more questions if you feel like it. Remember though, while shorter surveys give you less information they are more likely to be completed!

The metrics described earlier generally have the following layouts:

• CSAT: A series of questions about satisfaction levels, with answers from 1-5 (very bad to very good).
• NPS: A single question – “How likely are you to recommend this product or service” – with a rating out of 10 (not likely at all to extremely likely).
• CES: A series of questions about how easy customers found it to interact with you, with answers from 1-7 (very difficult to very easy).

Additional questions should be added on after the questions about the main metric, and making the option for additional feedback optional will definitely help when you’re gathering data. Remember, most customers will want to just tick a box and be done with your survey, so forcing them to input detailed information will cause a lot of them to simply abandon their feedback – the last thing you want.

Exactly when and how the survey is distributed will also vary by metric:

• CES focused surveys should be issued at the point of sale or immediately afterwards such that the experience is fresh in the customer’s mind.
• NPS focused surveys should be issued after several interactions with a customer, and can be done at any point so long as the method of distribution is not intrusive.
• CSAT focused surveys can be issued at any point during the sales process, and in fact can be broken down into several questions that are asked at each step in the process so that the experience of each step is examined rather than the overall experience. This is much easier to do in online spaces, where you can have feedback popups appear without disrupting the overall experience too much.

You should try to keep such questions to a minimum however, as repeatedly asking a customer to leave you feedback can get irritating and may even cause them to leave.

Analyzing Your Data: Quantitative Results

Once you have your data, the next step is to analyze it. Using software you can easily filter through thousands of responses to give overall scores, but what that software spits out is decided by you. Computers are very good at working with numbers, so this step should be quick and easy to perform.

Common ways to break down survey responses are:

• By demographic
• By location
• By which product or service is being examined
• By the number of interactions a customer has had with you

These categories will all give you more detailed insight into how your customers think. Are there differences between new and existing customers? Is there a particular product that is causing problems? In theory you can assume that repeat purchases are a good sign of customer satisfaction, but are there alternatives available in that particular sector?

Analyzing Your Data: Qualitative Results

If you’ve added space for additional write-in feedback, you’re going to get text responses. This type of feedback isn’t something that can be reduced to a set of numbers, so a more detailed analysis is needed. Luckily, the number of people leaving write-in feedback is usually small, and limited to those who have had a particularly good or particularly bad experience with you. The more detailed information that such feedback provides is more valuable in uncovering a customer’s motivations and feelings than a single tickbox. 

Computers can help you with your qualitative feedback in some respects. Text mining and other tools can help separate out those pieces of feedback that are similar, adding some order to the madness that is raw text data. You can also use sentiment analysis to extract the intended meaning of the text rather than simply filtering by the words a review contains, though you will require specialized software to do so.

Overall, quantitative data tends to show you where you stand and what your customers think of you, while qualitative data tells you why that is the case. It’s not perfect by any means, but you can only work with the information customers are willing to give you.

Visualizing Your Results

There are plenty of ways to visualize results. Bar charts, pie charts, simple graphs – all of them have a place in presenting your customer satisfaction data and can be useful at times. Overall, your aim when presenting the data is simple – make sure that the general results can be understood at a glance, with the more specific results being available when you examine them more closely.

What you want to examine determines your presentation. Want to take a look at customer satisfaction before and after a new protocol or procedure is implemented? A score vs time graph is probably your best bet. Do you want to compare across demographics? Bar charts are your friend. Want to produce a simple chart that will give the current satisfaction rates at a glance? Pie charts are delicious.

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Additional: Churn Rate

Another potential source of information is your churn rate, which is the percentage of customers that cease interactions with you without leaving any kind of review or rating as feedback. There will always be those who simply don’t want to leave reviews – they see it as wasting their time and don’t want to provide feedback to an organization they’re dissatisfied with.

If you have a high churn rate, it’s safe to say that you have issues. By taking a look at when and where the customers leave you, you can hazard a guess as to what the underlying issues might be. You won’t be able to get as detailed information as if you had feedback on the topic, but it’s better than nothing at all. Your churn rate can also be used to verify the results of other forms of customer satisfaction analysis, making sure that their predictions match up with reality.

Of course, this isn’t applicable to every industry. When buying a car, for example, a high churn rate would be seen as successful as the customer has settled and is satisfied with the car that the dealership has provided them with. This is something that’s true for every metric we’ve described today, so keep in mind how the specifics of your industry might make things vary.

Additional: CSAT & DSAT

CSAT (Customer Satisfaction Score) has a counterpart in DSAT, or Customer Dissatisfaction Score. While you may think that is simply the inverse of the CSAT score, keep in mind that the CSAT score takes into account only those customers who are deemed “satisfied” and ignores those who are indifferent. 

The DSAT is the percentage of customers who are actively dissatisfied with their interactions with you.It’s taken from the same survey as the CSAT, and takes those who answer 1 or 2 out of 5 to be “dissatisfied”. In this way, it counts the truly dissatisfied customers rather than those who merely seem indifferent to their experiences.

The DSAT is important to keep an eye on, some might say even more so than CSAT. It actively identifies problem areas and reasons why you might be losing customers. From the data that the DSAT provides you should be able to perform a root cause analysis and improve the underlying issues rather than simply attempting to smooth over surface level problems.

Emily Louise Spencer is an in-house content writer at Revuze. She is a graduate of the University of York with a master's degree in Chemistry. A published scientific author, she now works as a content writer and copy editor.

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