CUSTOMER SATISFACTION-PSYCHOLOGY , SURVEY & ANALYSIS

1. CUSTOMER SATISFACTION-PSYCHOLOGY , SURVEY & ANALYSIS PRESENTATION BY: SAKSHI BAJAJ (29-MBA-2008) SHIVANI RANA (32-MBA-2008)

2. CONTENTS • The Psychology Of Customer Satisfaction • Designing Customer Satisfaction Survey • Analysing Customer Satisfaction Survey

3. THE PSYCHOLOGY OF CUSTOMER SATISFACTION

4. Overview • A customer has all the votes and hence, its extremely important to understand the psychology of a customer and why they choose to do what they do. • In the services context we are primarily interested in what makes a customer satisfied enough to come back. • Thus, we aim at understanding how the customer “perceives”the “reality”, and how he “feels” about it. • The depth of feeling can range form mild (satisfaction) to extreme (delight), resulting from the degree to which the customer’s perception of the service meets or exceeds his expectation.

5. Perception and reality • Due to the basic characteristics of services, the perceived quality matters far more than the “objective”quality of the service. For e.g- if a restaurant customer perceives the restaurant is too cold, it does little good for the matter to argue that the thermometer says 74ºF. • However the perceived quality of customer cannot be taken as the true indicator of its quality.

6. Time element • When viewed in long term, the perceived quality does not tend to converge on objective quality, when objective aspects are more important and if they become known. • Time element is important when there are repeated transactions over time, the customer is able to evaluate the service not based on single encounter but on multiple experiences.

7. Satisfaction – an emotional response ~ Contentment (the phone works) ~ Surprise (i won the lottery!) ~ Pleasure (the wine is good) ~ Relief (the dentist has finished drilling) • And perceived quality does influence satisfaction. • However merely saisfying the customer is not enough to produce customer loyalty. Businesses need to move beyond mere satisfaction, to “customer delight“ As perceived quality is a rational perception, satisfaction is an emotional reaction. Satisfation states may include:

8. Customer delight • Delight refers to going beyond the expected and generating better outcomes to result in highest level of satisfaction. • Delight is possible only if the customer is satisfied to begin with. • Delight leads to behavioural outcomes that are substantially better than mere satisfaction can provide. These include: ~ Repurchase ~ Positive word-of-mouth ~ Customer loyalty and devotion

9. Expectations • Satisfaction and delight are both strongly influenced by customer expectations. • Expectations – array of possible outcomes that reflect what might, could, will, should, or had better not happen. • Expectations are strongly influenced by : ~ Experience ~ Advertising ~ Word-of-mouth ~ Personal limitations

10. Expectation hierarchy

11. Expectancy Disconfirmation (GAPS) • The importance of disconfirmation in explaining satisfaction has been demonstrated in many contexts, including sales force interaction, restaurant services, security transactions, telephone service and the like.

12. Here perceived quality is higher than expected. This situation will usually result in satisfaction and will almost always result in expectation being raised. Perceived quality Level of quality Positive disconfirmation Will expectation

13. Perceived quality is not as good as expected. This situation will result probably in dissatisfaction, and will very likely result in lowered expectations of service Will expectation Level of quality Negative disconfirmation Perceived quality

14. The satisfaction process “Objective” Quality Expectations Perceived Quality Disconfirmation Future Expectations Satisfaction

15. Value Quality + Value - Price • Perceived value drives purchase and repurchase. Value is formed by the relation ship between quality and price . • Higher the quality, higher the value. • Higher the price lower the value. • .

16. Utility and choice • Econimic utility is a useful way to visualise the relationship between quality, value and choice. • Utility function varies with the individual and this helps explain why people differ in their decisions. • The concept of value has been used to represent this tradeoff between quality and price. Viewed from an economic utility point of view: value = utility of quality – disutility of price • Choice is then based primarily on getting the best value.

17. The utility of quality Utility Quality

18. The disutility of price Disutility Price

19. The disutility of price- individual differences (there exist market segments in terms of quality) Poor Person Disutility Rich Person A Price

20. Uncertainity in expectations Person 1 : Experienced Expected Likelihood Person 2 : Inexperienced Distribution Of Outcome

21. Downside risk • People generally find the potential losses from worse-than-expected outcomes to outweigh the potential gains from better-than-expected outcomes i.e a worse-than-expected outcome hurts more than a better-than-expected helps. • In the graph, its clear that the person#1 is more positive because of the less downside risk because of his experience. • Hence,under some circumstances its perfectly rational for a person to choose an option that is actually expected to be worse (on avg.), if the downside risk for that option is less

22. Thus, as experience increases, knowledge about the service increases, and the distribution of expected outcomes tightens up . • Downside risk is reduced, and probablity of repurchase increases, even if the perceived quality is only what was expected. • In other words, customers often appear loyal when they are being rational and avoiding risk.

23. Thus , when managing customer satisfaction and putting together customer satisfaction surveys, its useful to remember that: • The customer’s perception is what counts. Measure that. • Satisfaction is not the same as perceived quality. • Quality perceptions are rational. Satisfaction is emotional. • Delight is more than just complete satisfaction. • Expectations vary across individuals and change over time. • Disconfirmation is most important driver of satisfaction. • Value = utility of quality – disutility of price. • Expectations have distributions, which change shape with experience. • Customers avoid downside risk by displaying loyalty.

24. DESIGNING CUSTOMER SATISFACTION SURVEY

25. Overview • Surveys are used to determine the extent to which customers are satisfied and delighted, and the extent to which this influences customer retention. • These are also useful in pin-pointing the processes and subprocesses within the company where resources should be targeted. • The findings are used to improve the business processes and to monitor progress and identify areas of further opportunity.

26. Customer satisfaction survey process

27. Preparing to survey • Purpose – first we need to be clear about why the survey is being conducted. Identifying and priotizing the goals helps in providing a framework. • Ensuring buy-in – top management must agree with the purpose of research, and share the understanding of what actions will result from the survey. • Exploratory research – this phase, prior to framing of questionnaire, ensures that the issues covered by the questionnaire are relevant to the customer.

28. Sampling • Choosing the sample population – current customers , prospective consumers along with the former customers that have left are a good source of obtaining useful information. They help in identifyin the previous mistakes they have committed and the future expectations of the customers. • Comparison with competitors – knowledge of the competition helps in identifying strengths and weaknesses of the service providers. • Drawing the sample – a probability sample needs to be drawn. Probability samples can be : ~ simple random sample ~ systematic random sample Techniques such as stratified sampling, cluster sampling and quota sampling are used.

29. Data collection • Mode of data collection ~ telephonic surveys ~ Mail surveys ~ Personal interviews ~ Interactive media (internet) ~ comment cards • To overcome the limitations of each of the above methods, avoiding personal biases and making the investment worthwhile , an outside researcher or company can be commissioned to administer the questionnaire.

30. Wording • Wording of the items is crucial to the questionnaire’s success. • The questionnaire must reflect the language of the customer & not the jargon of the company. • Decide what mix of closed -end & open-end questions to use.

31. Unnecessary Questions • These include: • Expectations questions: indicates performance versus expectations. • Ex.: Please rate the level of quality you expected • Importance questions: concentrate on issues important to customers. • Ex.: How important is airline safety to you? Very Somewhat Neutral Somewhat Very imp important UnimpUnimp 0 Very poor 10 Excellent

32. Questionnaire Structure

33. Other Issues • Questionnaire length: • As a rule of thumb, a questionnaire should not be longer than about two pages • How often to survey: • One rule of thumb is that the interval of measurement should be roughly equal to the time it takes to implement a quality improvement effort • Promise of confidentiality or anonymity should be maintained • Ethics should not be violated

34. ANALYZING CUSTOMER SATISFACTION SURVEYS

35. Overview • The primary purposes of analyzing customer satisfaction surveys are: • Give management a better idea of how satisfied customers are • Pinpoint areas in which customer satisfaction improvement is likely to generate desirable customer behaviors • Statistical methods can be used to find out whether solving problems or adding extras has the larger affect.

36. Predicting Repurchase Intention • It has a direct link to customer retention • If the information is collected as a percentage , we may see an immediate link to market share & profitability • Overall satisfaction & delight relate to repurchase intention • Ex.: Effect of overall satisfaction & delight Repurchase Intention Delighted 95.2% Merely Satisfied 84.7% Dissatisfied 31.3% Effect of delight = .952 - .847 = .105 Effect of satisfaction = .847 - .313 = .534

37. Predicting Overall Satisfaction • It involves analyzing satisfaction data from the business processes. • For separating the effects of satisfaction & delight, we create two new variables – a satisfaction variable & a delight variable • These variables are referred as ‘dummy variables’ & their value is either “0” for no or “1” for yes Converting satisfaction scores to dummy variables Score Satisfaction Dummy Delight Dummy 1 (dissatisfied) 0 0 2 (satisfied) 1 0 3 (delighted) 1 1

38. Predicting Overall Satisfaction Continued… • Generally, missing data exist in the data set. • Strategies for dealing with them are: • List-wise deletion • Mean substitution • Data imputation

39. Predicting Overall Satisfaction Continued… • Run regression analysis : • Bi-variate approach: Taking one predictor variable at a time. • Ex.: Relating Billing satisfaction with the overall satisfaction • Multi-variate approach: Uses Equity estimator that takes the multicollinearity of the predictors into account • Ex.: Relating Billing, Sales, Product & Repairs satisfaction with the overall satisfaction; all at once

40. Predicting Overall • The approach here is similar to predicting overall satisfaction, with one key exception. • It is possible to delight only a customer who is already satisfied. • Therefore, dissatisfied customers should be deleted from the analysis. • Delete all cases for which overall satisfaction = 0

41. Predicting Process Satisfaction • Here, the process dimensions satisfaction is related to the overall process satisfaction. • This can be done by using: • Bivariate regression: • Ex.: relating Billing accuracy with Billing satisfaction • Equity estimator regression: • Ex.: relating Billing accuracy & whether bill is easy to understand with Billing satisfaction

42. Predicting Process Delight • Again, we must take out the respondents who are not prospects for delight. • Delete all cases for which process satisfaction = 0 • Bivariate regressions can be used to determine relative impact of the predictors

43. Estimating Relative Importance • Measure of importance must reflect not only the size of the statistical link, but also how many customers will be affected. • Ex.: Complaint recovery process. Suppose regression analysis shows moving customers from dissatisfaction to satisfaction has large impact on customer retention. Suppose we also know that only one-tenth of one percent of customers ever had a complaint. • The results of bivariate or mulitvariate regression analysis is used for calculating relative importance.

44. Importance – Performance Mapping • This approach argues that we should be most concerned about those issues for which the importance is high and our performance is poor. • We measure performance as either percent satisfied, or percent of satisfied who are delighted. • Ex.:

45. Importance – Performance MappingContinued… • The processes are mapped in the quadrants as shown: • Importance-Performance in driving satisfaction: quadrant map

46. Importance – Performance MappingContinued… • Similarly, quadrant map can be drawn for importance- performance in driving delight

47. Importance – Performance MappingContinued… • Comparison shows that the satisfaction quadrant map may be very different from the delight quadrant map. • Apparently something right is done to delight repair customers, even though there is some trouble getting the customers satisfied in the first place.