Customer satisfaction is a term frequently used in marketing. It is a measure of how products and services supplied by a company meet or surpass customer expectation. Customer satisfaction is defined as "the number of customers, or percentage of total customers, whose reported experience with a firm, its products, or its services (ratings) exceeds specified satisfaction goals." In a survey of nearly 200 senior marketing managers, 71 percent responded that they found a customer satisfaction metric very useful in managing and monitoring their businesses.
It is seen as a key performance indicator within business and is often part of a Balanced Scorecard. In a competitive marketplace where businesses compete for customers, customer satisfaction is seen as a key differentiator and increasingly has become a key element of business strategy.
"Within organizations, customer satisfaction ratings can have powerful effects. They focus employees on the importance of fulfilling customers' expectations. Furthermore, when these ratings dip, they warn of problems that can affect sales and profitability.... These metrics quantify an important dynamic. When a brand has loyal customers, it gains positive word-of-mouth marketing, which is both free and highly effective."
Therefore, it is essential for businesses to effectively manage customer satisfaction. To be able do this, firms need reliable and representative measures of satisfaction.
"In researching satisfaction, firms generally ask customers whether their product or service has met or exceeded expectations. Thus, expectations are a key factor behind satisfaction. When customers have high expectations and the reality falls short, they will be disappointed and will likely rate their experience as less than satisfying. For this reason, a luxury resort, for example, might receive a lower satisfaction rating than a budget motel—even though its facilities and service would be deemed superior in 'absolute' terms."
The importance of customer satisfaction diminishes when a firm has increased bargaining power. For example, cell phone plan providers, such as AT&T and Verizon, participate in an industry that is an oligopoly, where only a few suppliers of a certain product or service exist. As such, many cell phone plan contracts have a lot of fine print with provisions that they would never get away if there were, say, 100 cell phone plan providers, because customer satisfaction would be far too low, and customers would easily have the option of leaving for a better contract offer.
There is a substantial body of empirical literature that establishes the benefits of customer satisfaction for firms. This literature is summarized by Mittal and Frennea (2010). They summarize the outcomes in terms of customer behaviors, immediate financial outcomes such as sales and revenues, and long-term outcomes based on the stock market.
"Customer satisfaction provides a leading indicator of consumer purchase intentions and loyalty."  "Customer satisfaction data are among the most frequently collected indicators of market perceptions. Their principal use is twofold:" 
- "Within organizations, the collection, analysis and dissemination of these data send a message about the importance of tending to customers and ensuring that they have a positive experience with the company's goods and services."
- "Although sales or market share can indicate how well a firm is performing currently, satisfaction is perhaps the best indicator of how likely it is that the firm’s customers will make further purchases in the future. Much research has focused on the relationship between customer satisfaction and retention. Studies indicate that the ramifications of satisfaction are most strongly realized at the extremes."
Research also shows that a majority of the firms invest in measuring, monitoring, and disseminating customer satisfaction information; in fact, these authors found that customer satisfaction research is one of the most widely conducted marketing research activities in the firms.
On a five-point scale, "individuals who rate their satisfaction level as '5' are likely to become return customers and might even evangelize for the firm. (A second important metric related to satisfaction is willingness to recommend. This metric is defined as "The percentage of surveyed customers who indicate that they would recommend a brand to friends." When a customer is satisfied with a product, he or she might recommend it to friends, relatives and colleagues. This can be a powerful marketing advantage.) "Individuals who rate their satisfaction level as '1,' by contrast, are unlikely to return. Further, they can hurt the firm by making negative comments about it to prospective customers. Willingness to recommend is a key metric relating to customer satisfaction."
"In literature antecedents of satisfaction are studied from different aspects. The considerations extend from psychological to physical and from normative to positive aspects. However, in most of the cases the consideration is focused on two basic constructs as customers expectations prior to purchase or use of a product and his relative perception of the performance of that product after using it.
Expectations of a customer on a product tell us his anticipated performance for that product. As it is suggested in the literature, consumers may have various "types" of expectations when forming opinions about a product's anticipated performance. For example, four types of expectations are identified by Miller (1977): ideal, expected, minimum tolerable, and desirable. While, Day (1977) indicated among expectations, the ones that are about the costs, the product nature, the efforts in obtaining benefits and lastly expectations of social values. Perceived product performance is considered as an important construct due to its ability to allow making comparisons with the expectations.
It is considered that customers judge products on a limited set of norms and attributes. Olshavsky and Miller (1972) and Olson and Dover (1976) designed their researches as to manipulate actual product performance, and their aim was to find out how perceived performance ratings were influenced by expectations. These studies took out the discussions about explaining the differences between expectations and perceived performance." 
The Disconfirmation Model
"The Disconfirmation Model is based on the comparison of customers’ [expectations] and their [perceived performance] ratings. Specifically, an individual’s expectations are confirmed when a product performs as expected. It is negatively confirmed when a product performs more poorly than expected. The disconfirmation is positive when a product performs over the expectations(Churchill & Suprenant 1982). There are four constructs to describe the traditional disconfirmation paradigm mentioned as expectations, performance, disconfirmation and satisfaction."  "Satisfaction is considered as an outcome of purchase and use, resulting from the buyers’ comparison of expected rewards and incurred costs of the purchase in relation to the anticipated consequences. In operation, satisfaction is somehow similar to attitude as it can be evaluated as the sum of satisfactions with some features of product."  "In the literature, cognitive and affective models of satisfaction are also developed and considered as alternatives(Pfaff, 1977). Churchill and Suprenant in 1982, evaluated various studies in the literature and formed an overview of Disconfirmation process in the following figure:" 
Non-linear and Asymmetric Relationships in Satisfaction
Since the 1990's a rich body of research has shown that many of the relationships of customer satisfaction with its antecedents and consequences are asymmetric and non-linear. The basis for this research resides in the key idea that people are usually more sensitive to negative information than to positive information, and that losses loom larger than gains. Thus, negative events are not only more salient to customers, but they also have a disproportionately larger impact in the satisfaction judgment formation process, and the consequent consumer intentions and behaviors. Much of this research is summarized in an award winning paper by Professors Eugene Anderson and Vikas Mittal. The general findings, as summarized in their paper show:
- Negative performance on an attribute has a larger impact on overall satisfaction than positive performance. Thus, the deleterious impact of failing expectations is proportionately much stronger than the beneficial impact of exceeding expectations by the same amount. This finding has been widely confirmed for many different industries and customer types.
- The association between overall satisfaction and repurchase intentions as well as behaviors is also non linear.
Organizations need to retain existing customers while targeting non-customers. Measuring customer satisfaction provides an indication of how successful the organization is at providing products and/or services to the marketplace.
"Customer satisfaction is measured at the individual level, but it is almost always reported at an aggregate level. It can be, and often is, measured along various dimensions. A hotel, for example, might ask customers to rate their experience with its front desk and check-in service, with the room, with the amenities in the room, with the restaurants, and so on. Additionally, in a holistic sense, the hotel might ask about overall satisfaction 'with your stay.'"
As research on consumption experiences grows, evidence suggests that consumers purchase goods and services for a combination of two types of benefits: hedonic and utilitarian. Hedonic benefits are associated with the sensory and experiential attributes of the product. Utilitarian benefits of a product are associated with the more instrumental and functional attributes of the product (Batra and Athola 1990).
Customer satisfaction is an ambiguous and abstract concept and the actual manifestation of the state of satisfaction will vary from person to person and product/service to product/service. The state of satisfaction depends on a number of both psychological and physical variables which correlate with satisfaction behaviors such as return and recommend rate. The level of satisfaction can also vary depending on other options the customer may have and other products against which the customer can compare the organization's products.
Work done by Parasuraman, Zeithaml and Berry (Leonard L) between 1985 and 1988 provides the basis for the measurement of customer satisfaction with a service by using the gap between the customer's expectation of performance and their perceived experience of performance. This provides the measurer with a satisfaction "gap" which is objective and quantitative in nature. Work done by Cronin and Taylor propose the "confirmation/disconfirmation" theory of combining the "gap" described by Parasuraman, Zeithaml and Berry as two different measures (perception and expectation of performance) into a single measurement of performance according to expectation.
The usual measures of customer satisfaction involve a survey from software providers such as Confirmit, Medallia, Opinionmeter and Satmetrix with a set of statements using a Likert Technique or scale. The customer is asked to evaluate each statement in terms of their perceptions and expectations of performance of the organization being measured. Their satisfaction is generally measured on a five-point scale.
"Customer satisfaction data can also be collected on a 10-point scale."
"Regardless of the scale used, the objective is to measure customers’ perceived satisfaction with their experience of a firm’s offerings." It is essential for firms to effectively manage customer satisfaction. To be able do this, we need accurate measurement of satisfaction.
Good quality measures need to have high satisfaction loadings, good reliability, and low error variances. In an empirical study comparing commonly used satisfaction measures it was found that two multi-item semantic differential scales performed best across both hedonic and utilitarian service consumption contexts. A study by Wirtz & Lee (2003), found that a six-item 7-point semantic differential scale (for example, Oliver and Swan 1983), which is a six-item 7-point bipolar scale, consistently performed best across both hedonic and utilitarian services. It loaded most highly on satisfaction, had the highest item reliability, and had by far the lowest error variance across both studies. In the study, the six items asked respondents’ evaluation of their most recent experience with ATM services and ice cream restaurant, along seven points within these six items: “pleased me to displeased me”, “contented with to disgusted with”, “very satisfied with to very dissatisfied with”, “did a good job for me to did a poor job for me”, “wise choice to poor choice” and “happy with to unhappy with”.
A semantic differential (4 items) scale (e.g., Eroglu and Machleit 1990), which is a four-item 7-point bipolar scale, was the second best performing measure, which was again consistent across both contexts. In the study, respondents were asked to evaluate their experience with both products, along seven points within these four items: “satisfied to dissatisfied”, “favorable to unfavorable”, “pleasant to unpleasant” and “I like it very much to I didn’t like it at all”.
The third best scale was single-item percentage measure, a one-item 7-point bipolar scale (e.g., Westbrook 1980). Again, the respondents were asked to evaluate their experience on both ATM services and ice cream restaurants, along seven points within “delighted to terrible”.
It seems that dependent on a trade-off between length of the questionnaire and quality of satisfaction measure, these scales seem to be good options for measuring customer satisfaction in academic and applied studies research alike. All other measures tested consistently performed worse than the top three measures, and/or their performance varied significantly across the two service contexts in their study. These results suggest that more careful pretesting would be prudent should these measures be used.
Finally, all measures captured both affective and cognitive aspects of satisfaction, independent of their scale anchors. Affective measures capture a consumer’s attitude (liking/disliking) towards a product, which can result from any product information or experience. On the other hand, cognitive element is defined as an appraisal or conclusion on how the product’s performance compared against expectations (or exceeded or fell short of expectations), was useful (or not useful), fit the situation (or did not fit), exceeded the requirements of the situation (or did not exceed).
Recent research shows that in most commercial applications, such as firms conducting customer surveys, a single-item overall satisfaction scale performs just as well as a multi-item scale. Especially in larger scale studies where a researcher needs to gather data from a large number of customers, a single-item scale may be preferred because it can reduce total survey error. Thus, there is an increasing trend to use a single items for measuring overall satisfaction.
American Customer Satisfaction Index (ACSI) is a scientific standard of customer satisfaction. Academic research has shown that the national ACSI score is a strong predictor of Gross Domestic Product (GDP) growth, and an even stronger predictor of Personal Consumption Expenditure (PCE) growth. On the microeconomic level, academic studies have shown that ACSI data is related to a firm's financial performance in terms of return on investment (ROI), sales, long-term firm value (Tobin's q), cash flow, cash flow volatility, human capital performance, portfolio returns, debt financing, risk, and consumer spending. Increasing ACSI scores has been shown to predict loyalty, word-of-mouth recommendations, and purchase behavior. The ACSI measures customer satisfaction annually for more than 200 companies in 43 industries and 10 economic sectors. In addition to quarterly reports, the ACSI methodology can be applied to private sector companies and government agencies in order to improve loyalty and purchase intent. ASCI scores have also been calculated by independent researchers, for example, for the mobile phones sector, higher education, and electronic mail.
The Kano model is a theory of product development and customer satisfaction developed in the 1980s by Professor Noriaki Kano that classifies customer preferences into five categories: Attractive, One-Dimensional, Must-Be, Indifferent, Reverse. The Kano model offers some insight into the product attributes which are perceived to be important to customers.
SERVQUAL or RATER is a service-quality framework that has been incorporated into customer-satisfaction surveys (e.g., the revised Norwegian Customer Satisfaction Barometer) to indicate the gap between customer expectations and experience.
J.D. Power and Associates provides another measure of customer satisfaction, known for its top-box approach and automotive industry rankings. J.D. Power and Associates' marketing research consists primarily of consumer surveys and is publicly known for the value of its product awards.
Other research and consulting firms have customer satisfaction solutions as well. These include A.T. Kearney's Customer Satisfaction Audit process, which incorporates the Stages of Excellence framework and which helps define a company’s status against eight critically identified dimensions.
For B2B customer satisfaction surveys, where there is a small customer base, a high response rate to the survey is desirable. The American Customer Satisfaction Index (2012) found that response rates for paper-based surveys were around 10% and the response rates for e-surveys (web, wap and e-mail) were averaging between 5% and 15% - which can only provide a straw poll of the customers' opinions.
- Business case
- Computer user satisfaction
- Customer service
- Customer Loyalty
- The International Customer Service Institute
- Farris, Paul W.; Neil T. Bendle; Phillip E. Pfeifer; David J. Reibstein (2010). Marketing Metrics: The Definitive Guide to Measuring Marketing Performance. Upper Saddle River, New Jersey: Pearson Education, Inc. ISBN 0-13-705829-2. The Marketing Accountability Standards Board (MASB) endorses the definitions, purposes, and constructs of classes of measures that appear in Marketing Metrics as part of its ongoing Common Language in Marketing Project. Material used from this publication in this article has been licensed under Creative Commons Share Alike and Gnu Free Documentation License. See talk.
- Gitman, Lawrence J.; Carl D. McDaniel (2005). The Future of Business: The Essentials. Mason, Ohio: South-Western. ISBN 0-324-32028-0.
- Mittal, Vikas, and Carly Frennea. "Customer satisfaction: a strategic review and guidelines for managers." MSI Fast Forward Series, Marketing Science Institute, Cambridge, MA (2010). Available at SSRN: http://ssrn.com/abstract=2345469
- Neil A. Morgan, Eugene W. Anderson, & Vikas Mittal, (2005). "Understanding Firms' Customer Satisfaction Information Usage". Journal of Marketing, 69(3), July, 131-151.. Available at SSRN: http://ssrn.com/abstract=2343818
- Kucukosmanoglu, Ahmet Nuri; Sensoy Ertan (2010). "Customer Satisfaction: A Central Phenomenon in Marketing". 
- Eugene W. Anderson and Vikas Mittal (2000). "Strengthening the Satisfaction-Profit Chain", Journal of Service Research, 3(2), November, 107-120. Available at SSRN: http://ssrn.com/abstract=2345327
- Mittal, Vikas and Ross, William T. and Baldasare, Patrick M., The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions (January 1, 1998). Journal of Marketing, 62, January, 33-47, 1998. Available at SSRN: http://ssrn.com/abstract=2345370
- Mittal, Vikas & Carly Frennea (2012) "16- Managing Customer Satisfaction," Handbook of Marketing Strategy, Venky Shankar and Gregory Carpenter (Eds.), 261. Available at SSRN: http://ssrn.com/abstract=2345484
- Mittal, Vikas and Baldasare, Patrick M., Eliminate the Negative: Managers Should Optimize Rather than Maximize Performance to Enhance Patient Satisfaction (1996). Journal of Health Care Marketing, 16(3), 24-31, 1996. Available at SSRN: http://ssrn.com/abstract=2345382
- Vikas Mittal and Wagner Kamakura. (2001) "Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics". Journal of Marketing Research, 38(1): 131-142. Available at SSRN: http://ssrn.com/abstract=2344925
- John, Joby (2003). Fundamentals of Customer-Focused Management: Competing Through Service. Westport, Conn.: Praeger. ISBN 978-1-56720-564-0.
- Batra, Rajeev and Olli T. Athola (1990), “Measuring the Hedonic and Utilitarian Sources of Consumer Attitudes,” Marketing Letters, 2 (2), 159-70.
- Berry, Leonard L.; A. Parasuraman (1991). Marketing Services: Competing Through Quality. New York: Free Press. ISBN 978-0-02-903079-0.
- Kessler, Sheila (2003). Customer satisfaction toolkit for ISO 9001:2000. Milwaukee, Wis.: ASQ Quality Press. ISBN 0-87389-559-2.
- "Gleansight Benchmark Report: Customer Feedback Management". Gleanster Research. 2010-11-01. Retrieved 2013-09-17.
- Wirtz, Jochen and John E. G. Bateson (1995), “An Experimental Investigation of Halo Effects in Satisfaction Measures of Service Attributes,” International Journal of Service Industry Management, 6 (3), 84-102.
- Extracted from: How Do You Build Effective Customer Satisfaction Surveys?
- Wirtz, Jochen; Chung Lee, Meng (2003), “An Empirical Study on The Quality and Context-specific Applicability of Commonly Used Customer Satisfaction Measures,” Journal of Service Research, Vol. 5, No. 4, 345-355.
- Eroglu, Sergin A. and Karen A. Machleit (1990), “An Empirical Study of Retail Crowding: Antecedents and Consequences,” Journal of Retailing, 66 (Summer), 201-21.
- Westbrook, Robert A. (1980), “A Rating Scale for Measuring Product/Service Satisfaction,” Journal of Marketing, 44 (Fall), 68-72.
- Retrieved from: “Customer Satisfaction Measurement.”
- Drolet, Aimee L., and Donald G. Morrison. "Do we really need multiple-item measures in service research?." Journal of service research 3, no. 3 (2001): 196-204.
- Salant, Priscilla, and Don A. Dillman. "How to Conduct your own Survey: Leading professional give you proven techniques for getting reliable results." (1995)
- Mittal, Vikas and Frennea, Carly, Customer Satisfaction: A Strategic Review and Guidelines for Managers (2010). MSI Fast Forward Series, Marketing Science Institute, Cambridge, MA, 2010. Available at SSRN: http://ssrn.com/abstract=2345469
- Mittal, Vikas and Frennea, Carly and Westbrook, Robert A., Managing Customer Value in Business-to-Business Markets (March 2014). Marketing Review St. Gallen 3/2014. Available at SSRN: http://ssrn.com/abstract=2430196
- Evanschitzky, Heiner and Groening, Christopher and Mittal, Vikas and Wunderlich, Maren, How Employer and Employee Satisfaction Affect Customer Satisfaction: An Application to Franchise Services (December 17, 2011). Journal of Service Research, 14(2), May 2011, 136-148. Available at SSRN: http://ssrn.com/abstract=2339332
- Mittal, Vikas and Kamakura, Wagner A. and Govind, Rahul, Geographic Patterns in Customer Service and Satisfaction: An Empirical Investigation (July 1, 2004). Journal of Marketing, 68 (July 2004), 48-62. Available at SSRN: http://ssrn.com/abstract=2343896
- Vikas Mittal, Jerome Katrichis, & Pankaj Kumar. (2001). "Attribute Performance and Customer Satisfaction Over Time: Evidence from Two Field Studies." The Journal of Services Marketing, 2001, 15(4/5): 343-356.. Available at SSRN: http://ssrn.com/abstract=2344878
- Fornell, C., R.T. Rust and M.G. Dekimpe (2010). "The Effect of Customer Satisfaction on Consumer Spending Growth," Journal of Marketing Research, 47(1), 28-35.
- Vikas Mittal; Carly Frennea (2010). "Customer Satisfaction: A Strategic Review and Guidelines for Managers." Marketing Science Institute: MSI Fast Forward (10-701).
- Anderson, E.W., C. Fornell & S.K. Mazvancheryl (2004). "Customer Satisfaction and Shareholder Value." Journal of Marketing, Vol. 68, October, 172-185.
- Fornell, C., S. Mithas, F.V. Morgeson III, and M.S. Krishnan (2006). "Customer Satisfaction and Stock Prices: High Returns, Low Risk," Journal of Marketing, 70(1), 3−14.
- Morgeson, F. V., & Petrescu, C. (2011). "Do They All Perform Alike? An Examination of Perceived Performance, Citizen Satisfaction and Trust with US Federal Agencies." International Review of Administrative Sciences, 77(3), 451-479.
- Turel, Ofir; Alexander Serenko (2006). "Satisfaction with mobile services in Canada: An empirical investigation" (PDF). Telecommunications Policy 30 (5-6): 314–331. doi:10.1016/j.telpol.2005.10.003.
- Serenko, Alexander (2010). "Student satisfaction with Canadian music programs: The application of the American Customer Satisfaction Model in higher education" (PDF). Assessment and Evaluation in Higher Education 35 (4).
- Dow, Kevin; Alexander Serenko; Ofir Turel; Jeff Wong (2006). "Antecedents and consequences of user satisfaction with e-mail systems" (PDF). International Journal of e-Collaboration 2 (2): 46–64. doi:10.4018/jec.2006040103.
- Johnson, Michael D.; Anders Gustafssonb, Tor Wallin Andreassenc, Line Lervikc and Jaesung Cha (2001). "The evolution and future of national customer satisfaction index models". Journal of Economic Psychology 22 (2): 217–245. doi:10.1016/S0167-4870(01)00030-7. ISSN 0167-4870.
- Bluestein, Abram; Michael Moriarty; Ronald J Sanderson (2003). The Customer Satisfaction Audit. Axminster: Cambridge Strategy Publications. ISBN 978-1-902433-98-1.
- Customer Relationship Management, Emerging Concepts, Tools and Application, Edited by Jagsish N Sheth, Atul Parvatiyar and G Shainesh, published by Tata McGraw-Hill Education - see Chapter 21, pages 193 to 199
- European Commission: eGovMoNet: eGovernment Monitor Network.