Executive Summary
As a manufacturer and retailer of desktops, laptops, notebooks, and tablets, CRU experiences the challenge of satisfying its customers. To improve customer satisfaction, CRU undertook a study to determine factors that influence customer satisfaction so that it can leverage them. Therefore, this report analyses the current degree of customer satisfaction and recommends the most effective strategies that would increase customer satisfaction. The methods employed in data analysis are a one-sample t-test, two-sample t-test, analysis of variance, paired two-sample t-test, and correlation tests. From the data analysis, the report derives several important information about the nature and extent of customer satisfaction. The findings indicate that the current level of overall customer satisfaction is below the expectation of the management, female customers have a lower satisfaction level than male customers, and customer satisfaction increases with age.
Moreover, the findings show that there is no significant difference in satisfaction responses to initiatives. Response time satisfaction, advice satisfaction, and communication satisfaction have positive correlations with overall customer satisfaction while loyalty rewards satisfaction has a negative correlation. In this view, the report recommends that CRU should improve the overall satisfaction of customers, enhance the satisfaction of female customers, and boost the satisfaction level of the young customers. Furthermore, the report recommends that CRU needs to improve response time satisfaction, advice satisfaction, and communication satisfaction because they correlate positively with overall customer satisfaction and eliminate loyalty rewards program because it has a negative correlation with the overall satisfaction.
Introduction
Computers R Us (CRU) is a notable computer company that manufactures and retails computers such as desktops, laptops, notebooks, and tablets. Given that CRU has experienced massive growth in the recent past and has increased its market share considerably, it decided to create a new division, CompleteCare, to deal with technical inquiries and repairs. CRU delegated the duties of customer care to CompleteCare so that customers can easily make their technical inquiries and request for warranty repairs. However, CompleteCare has not performed its duties as envisioned because it is experiencing issues related to communication, distribution of computers, and availability of accessories. Preliminary findings indicate that customer satisfaction is the primary issue that CompleteCare is experiencing. To improve customer satisfaction, CRU undertook a study to establish aspects of customer satisfaction that customers consider requiring improvement. The study collected information related to demographic attributes of customers, customer care response time, provision of advice on product and services, loyalty reward program, communication, and overall satisfaction level. Therefore, the objective of the report is to analyze the current degree of customer satisfaction and recommend the most effective strategies that would increase customer satisfaction.
Research Design
The research design that the study employed in survey design. This research design is appropriate because it effectively assesses diverse opinions, attitudes, feelings, beliefs, and thoughts of customers regarding their satisfaction with products and services that CRU offers. Gideon (2012) states that survey design is advantageous because it is easy to administer, cost-effective to implement, and can collect data remotely from a large number of participants. Owing to its advantages, the study designed surveys and administered it to 500 customers. In sampling the participants, the study used a convenience method of sampling. Using records of customers as sampling a frame, the study selected 500 customers. Out of 500 targeted customers, 420 customers responded to the surveys and provided complete information.
Basing on the above information, the response rate of the survey is 84%, which is significantly high. According to Rubin and Babbie (2010), a response rate of more than 70% is very good because it has minimal response bias. In this case, the response rate indicates that the data is valid and reliable because of the insignificant response bias. In the aspect of ethics, the study assured the participants that they would be anonymous and their data would be treated confidentially. Love (2012) states researchers should guarantee anonymity and confidentiality to protect participants from ensuing victimization. Usually, people avoid participating in research because they harbor fears that researchers could reveal their information to other people leading to undue victimization. In this case, the assurance encouraged participants to take part in the study without having some reservations about their opinions and thoughts.
Data Analysis
The two key issues that the analysis report derives from the analysis of data are the current level of customer satisfaction and strategies aimed at increasing customer satisfaction. From Table 1, it is apparent that the mean customer satisfaction is 5.37, which is less than the hypothesized mean of 7. Given that the hypothesis is directional, a one-tail t-test provides a robust test. Babbie (2009) states that the directional hypothesis requires a one-tailed test because it indicates whether the hypothesized value is greater or less than the calculated value. The conclusion is that the customer satisfaction level is lower than the expected level. Regarding gender differences in the level of satisfaction, the conclusion is that female customer (M = 5.14) have a lower level of satisfaction than male customers (M = 6.96). Hence, dissatisfaction among female customers is the major issue that CRU needs to examine.
From Table 3, it is evident that the level of customer satisfaction increases with an increase in the ages of customers. Customers who fall in the age group of 20 years and below have the lowest level of customer satisfaction (M = 4.26) while customers who fall in the age groups of 41 to 50 years and 51 and above years have the highest level of satisfaction. In the age groups of 21 to 30 years and 31 to 40 years have mean satisfaction levels of 5.23 and 5.50 respectively. Table 4 indicates the significance of variation in the means of customer satisfaction across all ages.
The relevant correlation coefficients in Table 6 are the ones that indicate the relationships between the overall customer satisfaction and the four initiatives. Response time satisfaction (r = 0.98), advice satisfaction (r = 0.96), and communication satisfaction (r = 0.97) have very strong positive relationships with the overall customer satisfaction. The correlations imply that an increase in the satisfaction levels of the four initiatives results in a commensurate increase in the overall satisfaction level. In contrast, loyalty rewards satisfaction (r = -0.82) has a strong negative correlation with overall customer satisfaction.
Recommendations
- From the analysis, it is evident that male customers (M = 6.96) have a higher level of customer satisfaction than female customers (M =5.14). The difference is statistically significant, and thus, it indicates that male and female customers receive different treatment. Although female customers constitute 56% (237) of CRU’s customers, they do not receive satisfying services. Therefore, the report recommends that CRU needs to focus on improving the services it provides to female customers.
- The analysis of the level of customer satisfaction indicates that it is lower than the management’s goal of 7/10. Specifically, the mean customer satisfaction is 5.7, which is considerably lower than expected. In this view, the report recommends that CRU should improve customer care services it provides through CompleteCare.
- The analysis shows that there is a significant difference in the overall customer satisfaction across the five age groups, namely, 20 years and below, 21 to 30 years, 31 to 40 years, 41 to 50 years, and above 51 years. The satisfaction trend suggests that the old customers are more satisfied than the young customers. In this view, the report recommends CRU to concentrate on improving satisfaction among young customers.
- Regarding the satisfaction responses to the initiatives of ‘decreasing response time’ and ‘new loyalty rewards program’, the analysis shows that they do not differ significantly. Given that these initiatives have negative relationships, CRU should increase response time satisfaction and decrease the loyalty rewards program.
- The correlation analysis indicates that initiatives proposed by management relate to the overall satisfaction of CRU customers. Specifically, the correlation coefficients of response time satisfaction, advice satisfaction, and communication satisfaction are 0.98, 0.96, and 0.97 respectively, which indicate that they have very strong positive relationships with the overall customer satisfaction. Hence, CRU should improve satisfaction in these three initiatives.
- Correlation analysis also indicates that loyalty rewards satisfaction (r = -0.82) has a significant negative correlation with overall customer satisfaction. In this view, to improve overall customer satisfaction, CRU should terminate the initiative of a loyalty rewards program.
References
Babbie, E. (2009). The Basics of Social Research. New York: Cengage Learning.
Field, A. (2013). Discovering statistics using SPSS (4th ed.). London: SAGE Publisher.
Gideon, L. (2012). Handbook of survey methodology for the social sciences. New York: Springer.
Love, K. (2012). Ethics in social research. Bingley: Emerald.
Rubin, A., & Babbie, R. (2010). Essential research methods for social work. Belmont: Cengage Learning.
Zikmund, W., Babin, B., Carr, J. & Griffin, M. (2012). Business Research Methods (9th ed.). New York: Cengage Learning.
Appendices
Appendix 1: Managerial goal
The First Question
Does the current level of customer satisfaction differ from the management’s goal of 7 out of 10?
Hypotheses
H0: The current level of customer satisfaction is equal to or greater than the management’s goal of 7/10.
HA: The current level of customer satisfaction is less than the management’s goal of 7/10.
Hypothesis Testing
The appropriate statistical test for hypothesis testing is the one-sample t-test. According to Babbie (2009), a one-sample t-test applies when one wants to determine if a population mean differs from a hypothesized mean. In this case, the management wanted to compare the mean of customer satisfaction and the hypothesized mean that is equal to or greater than 7/10. Table 1 below displays the results of the one-sample t-test generated by Excel.
Table 1
The test rejects the null hypothesis that the current level of customer satisfaction is equal to or greater than the management’s goal of 7, t(419) = 1.65, p = 3.32E-42. Hence, the alternative hypothesis holds that the level of customer satisfaction is lower than expected by the management.
Appendix 2: Overall satisfaction and gender
The Second Question
Does any difference exist between the overall satisfaction of male and female customers at CRU?
Hypotheses
H0: There is no significant difference in the overall satisfaction of male and female customers
HA: There is a significant difference in the overall satisfaction of male and female customers.
Hypothesis Testing
Two-sample t-test with the assumption of unequal variables is the appropriate form of statistical test for hypothesis testing. Zikmund, Babin, Carr, and Griffin (2012) explain that a two-sample t-test applies in the analysis of data obtained from two unrelated or independent groups. The two samples of data, in this case, are male and female customers. Table 2 below presents the results of the two-sample t-test, which are relevant to hypothesis testing.
Table 2
Given that the hypothesis is not directional, two-tailed values apply. The values suggest the rejection of the null hypothesis that there is no significant difference in the overall satisfaction of male and female customers, t(403) = 1.97, p = 4.21E-14. The p-value indicates that there is a statistically significant difference in the level of satisfaction among customers, and thus, support the alternative hypothesis.
Appendix 3: Overall satisfaction and age groups
The Third Question
Does any difference exist in the overall customer satisfaction across all age groups?
Hypotheses
H0: There is no significant difference in the overall customer satisfaction across all age groups.
HA: There is a significant difference in the overall customer satisfaction across all age groups.
Hypothesis Testing
ANOVA is the appropriate statistical test that can establish if customer satisfaction varies across all age groups. Specifically, single-factor ANOVA provides a robust statistical test that can compare means of different age groups, namely, 20 years and below, 21 to 30 years, 31 to 40 years, 41 to 50 years, and above 51 years. Table 3 and Table 4 display outcomes of ANOVA analysis, which are important in hypothesis testing.
Table 3
Table 4
From the ANOVA table, p-value supports the rejection of the null hypothesis that there is no significant difference in the overall customer satisfaction across all age groups, F(4) = 4.36, p = 0.002. In this view, the alternative hypothesis holds that there is a significant difference in the overall customer satisfaction across the five age groups.
Appendix 4: Response time and loyalty rewards program
The Fourth Question
Does any difference in customer satisfaction exist between responses to the initiatives of ‘decreasing response time’ and ‘new loyalty reward program’?
Hypotheses
H0: There is no significant difference between the satisfaction responses to the initiatives of ‘decreasing response time’ and ‘new loyalty reward program’.
HA: There is a significant difference between the satisfaction responses to the initiatives of ‘decreasing response time’ and ‘new loyalty reward program’.
Hypothesis Testing
The appropriate statistical test for the null hypothesis is the paired two-sample t-test. This test is appropriate because the hypothesis seeks to determine if responses of customer satisfaction to two initiatives, namely, ‘decreasing response time’ and ‘new loyalty reward program’, are different. Table 5 below displays the results of the paired two-sample t-test, which are essential in hypothesis testing.
Table 5: Paired Two Sample for Means
The results fail to reject the null hypothesis that there is no significant difference between the satisfaction responses to the initiatives of ‘decreasing response time’ and ‘new loyalty reward program’, t(419) = 1.97, p = 0.24. Therefore, the test holds that satisfaction responses to the initiatives of ‘decreasing response time’ and ‘new loyalty reward program’ do not differ significantly.
Appendix 5: Overall satisfaction and initiatives
The Fifth Question
Does any of the initiatives proposed by management related to the overall satisfaction of CRU customers?
Hypotheses
H0: The initiatives proposed by management do not relate to the overall satisfaction of CRU customers.
HA: The initiatives proposed by management relate to the overall satisfaction of CRU customers.
Hypothesis Testing
Given that the null hypothesis seeks to determine the relationship between the satisfaction of initiatives and the overall satisfaction, the appropriate statistical t-test is the correlation. Field (2013) asserts that researchers apply correlation in determining the relationship between two variables that are on a continuous scale. Table 6 shows the correlation coefficients of all possible relationships.
Table 6
The p-values of response time satisfaction, advice satisfaction, communication satisfaction, and loyalty rewards satisfaction are less than 0.05. Hence, the analysis rejects the null hypothesis that the initiatives proposed by management do not relate to the overall satisfaction of CRU customers.
Table 7