Customer satisfaction (often abbreviated as CSAT, more correctly CSat) 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."
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. 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.
"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."
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." 
In some research studies, scholars have been able to establish that customer satisfaction has a strong emotional, i.e., affective, component. Still others show that the cognitive and affective components of customer satisfaction reciprocally influence each other over time to determine overall satisfaction.
Especially for durable goods that are consumed over time, there is value to taking a dynamic perspective on customer satisfaction. Within a dynamic perspective, customer satisfaction can evolve over time as customers repeatedly use a product or interact with a service. The satisfaction experienced with each interaction (transactional satisfaction) can influence the overall, cumulative satisfaction. Scholars showed that it is not just overall customer satisfaction, but also customer loyalty that evolves over time.
The Disconfirmation ModelEdit
"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 a 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:" 
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 using a Likert scale. The customer is asked to evaluate each statement in terms of their perceptions and expectations of performance of the organization being measured.
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”.
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.
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 have 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.
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.
Recently there has been a growing interest in predicting customer satisfaction using big data and machine learning methods (with behavioral and demographic features as predictors) to take targeted preventive actions aimed at avoiding churn, complaints and dissatisfaction.
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