view with images and charts A Research Project on Customer Satisfaction of “Agora” The Chain Shop Marketing 1. Executive Summery Agora. is a super chain shop of Bangladesh which has several stores all over Bangladesh. Recently it has established a shop at Dhanmondi . But after establishing the shop it is found that this branch is not so profitable as other branches of “Agora”. It is due to low sales. The customers are not satisfied with Agora. Now we want to know which the factors that are affecting customer satisfaction are. And to what extent they are affecting. 2. Problem identification After analyzing some secondary data we have collected some factors that may affect customer satisfaction. 1. Dependent variables: The customer satisfaction of “Agora. ” . 1
Independent variables: a) b) c) d) e)
Convenience to buy product. Attitude toward frozen foods. Product availability. Behavior of sales people. Price of product.
Broad Objective: . • To identify the way to make the investment feasible • To develop the marketing strategy Specific Objective • •
To identify whether there are any relation among customer attendance and income level, price, product availabity. Customer’s perception regarding super shop.
Methodology Discussion with the sales people and household purchaser help me to identify the dependent and independent variables. Sampling plan: Sample units will be households of the local area. Sample size will be 30. Regression and discriminate analysis will be conducted.
3. Approach to the problem Hypotheses The hypotheses used for this research areH0: Customer satisfaction of “Agora. ” is not affected by the factors H1: Customer satisfaction of “Agora. ” is affected by the factors The hypotheses test would be a one-tailed test. That is, if the null hypotheses is rejected then automatically the alternative hypotheses would be accepted. 4. Research Design Type of Research Design Descriptive result is used in this research to identify the factors’ level of influence over the satisfaction level. Data Collection Data would be collected from primary sources. Survey method would be used for this purpose. Respondents would be asked questions from a preplanned questionnaire. The questionnaire is given at the appendix. The response would be recorded in the response table which is also given in the appendix. Scaling techniques To measure the usage rate, an ordinal scale would be used. To measure the other variables, metric scales would be used. A nine point Likert scale is prepared to measure the metric variables. Respondents would be asked to express their state of agreement with different statements related to different independent variables by placing a mark on the respective box. The scale used for this research is given in the appendix. 5. Data Analysis 4.1 Methodology Data is analyzed using two methods- Multiple Regression Analysis and Discriminate analysis. 4.2 Multiple Regression Analysis Multiple Regression analysis is used to test the hypothesis and also to measure the effect of each of the independent variables on the dependent variable.For multiple regression analysis, the data sheet prepared from the responses of the respondents was put into SPSS and the result was generated from it. The result was shown as the following equationY= β0+ β1X1+ β2X2+ β3X3+……….. Where β0 represent a constant and X1, X2, X3 etc. represent the various independent variables. To test the hypotheses, the following result was searched β0= β1= β2= β3=………..etc. If the corresponding β values are same for all the independent variables, then the null hypotheses would be accepted. If the values are not same then the alternative hypotheses would be accepted. Also, the multiple regression analysis is used to analyze the independent effects of each independent variable over the dependent variable. The magnitude of the effect is equivalent to the magnitude of the corresponding β value. The higher the β value, the higher the effect of that particular independent variable on the dependent variable. The result from SPSS is analyzed in the result section.
4.3Discriminant Analysis The dependent variable selected for this research is measured in a metric scale. But for discriminate analysis, dependent variables should be categorical. So, the metric scaled dependent variable is converted into a categorical scale by using the following relationshipCategory High Medium Low
Response 9, 8 & 7 6,5 & 4 3,2 & 1
After, the data are put again in the SPSS and discriminate analysis is done. The result is analyzed in two methods- analyzing the Wilks’ λ, and unvaried F ratio For Wilks’ λ, higher the value, lower it’s ability to discriminate. Also, lower the value, higher the ability to discriminate. For F ratio, the significance is considered. If the value of F is significant, the corresponding variable is said to have discriminate capability. 6.Result Major findings 5.1Multiple Regression Analysis From the multiple regression analysis, the following relationship can be foundSac= -.971+ .303Con+ .004 A/F+ .450Av + .191 B + .230P + ………………………….(1) Where, S= Satisfaction level Con= Convenience to buy A/F= Attitude toward frozen food Av= Availability of product B= Behavir of sales people P= Price This equation shows the desired relationship among the dependent and the independent variables. And here R2=0.902 Fcal = 44.139 F 5,24,0.05= 4.53 This relationship expresses the significance of the multiple regression analysis. Here the calculated F value has a higher value then the F value found for degree of freedom 1 as 5 and degree of freedom 2 as 24. So, it can be said that, for a significance level of 0.05, this multiple regression analysis is significant. Hypotheses Test In this analysis, β1≠ β2 ≠ β3≠ β4 ≠ β5 So the Null hypothesis can be rejected and the alternative hypothesis can be accepted. Relationship Analysis Customer Satisfaction level highly depend on availability of product. The effect is 45%. Peoples’ attitude toward frozen food does not have significant effect . Conveniency to buy has moderate effect on customer satisfaction. The effect of this variable is 30.3%.
Price has also moderately significant value. The effect is 23%. 5.2Discriminant Analysis After putting the data and analyzing in SPSS, the following table was found for discriminant analysis. Tests of Equality of Group Means Wilks' Lambda .317
F 29.089
df1 2
df2 27
Sig. .000
Conveniency Attitude toward .990 .141 2 27 .870 frozenfood Availability of .261 38.200 2 27 .000 product Behavior of .707 5.585 2 27 .009 salespeople High price .388 21.330 2 27 .000 The discriminate analysis is done for wilks’ Lambda and F ration. Both of them are described in the following sections. Wilks' Lambda analysis The value of Wilks’ Lambda is not same for all the groups, that is, the group mean of all the groups are not same. As the group means are not same, the decision can be obtained that, the null hypotheses is rejected. So, we can accept the alternative hypotheses. The rest of the analysis is done on the basis of the acceptance of the alternative hypotheses. The value of Wilks' Lambda is least for availability of product. As we know, the lower the value, the higher the ability to discriminate. So, availability of product has the highest discriminating power among all the independent variables. Value of Wilks' Lambda is also relatively low for Conveniences and price. So Conveniences and price have also relatively high discriminating capability. Attitude toward frozen food, behavior of sales people has a moderate value of Wilks' Lambda. So these independent variables can be described as medium discriminating variables. F ratio analysis The value of F has high significance for availability of product, conveniences to buy, and price. So they all affect customer satisfaction. As, all the independent variables are significant, so they cannot be discriminated well only from F ratio. However, the F calculated for each of these variables and their corresponding F for the given degree of freedoms can be compared. From this comparison, it is seen that, availability of product has the highest F value among the independent variables. So availability of product has the highest discriminating power among the categories. 8.Conclusion
The research is not free from flaws. As the sample size was small the final result may not be conclusive. But it may surely be used for further large scale research. It can also be used as a source of secondary data. 9.Recommendation Our sample size is small so we can not take final decision based on the result. We can give some suggestion like.. Availability of product and conveniences to buy product is very important to the customers. So the authority should take care of this. Again price has also significant effect so price level should be reduced. Behavior of sales people has proved to be less important. But it may be error due to the small sample size. So large scale conclusive research should be conducted for this. Attitude toward frozen food also proved to be less important. So it is not a matter of concern. 10.Exhibits Exhibits 1 Questionnaire This study is being conducted by me of Dhaka University student as the part of my project in "project work". The main objective of the survey is to find out what are the major causes for the lower customer attendance at super shop “Agora.”. It will help to develop a comprehensive marketing strategy to increase the customer attendance rates as well as profitability. We are appreciating your contribution for share your valuable views for our survey. Thank you very much for your extended help. Put tick mark on following option as per your perception. 1. I am satisfied with “Agora. ”. The super shop. Extremely Strongly Agree Somewhat Neither Disagree Somewhat Strongly Extremely agree agree agree agree disagree disagree disagree nor disagree 9 8 7 6 5 4 3 2 1
1) Shopping from super shop is really convenience. Extremely Strongly Agree Somewhat Neither Disagree Somewhat Strongly Extremely agree agree agree agree disagree disagree disagree nor disagree 9 8 7 6 5 4 3 2 1
2) (Attitude toward ) Frozen food is not problem for me
Extremely Strongly Agree Somewhat Neither Disagree Somewhat Strongly Extremely agree agree agree agree disagree disagree disagree nor disagree 9 8 7 6 5 4 3 2 1
3) Every product is available at super shop like “Agora. ” Extremely Strongly Agree Somewhat Neither Disagree Somewhat Strongly Extremely agree agree agree agree disagree disagree disagree nor disagree 9 8 7 6 5 4 3 2 1
4) The behavior of sales people of “Agora. ’ is good Extremely Strongly Agree Somewhat Neither Disagree Somewhat Strongly Extremely agree agree agree agree disagree disagree disagree nor disagree 9 8 7 6 5 4 3 2 1
5) Price of product is high Extremely Strongly Agree Somewhat Neither Disagree Somewhat Strongly Extremely agree agree agree agree disagree disagree disagree nor disagree 1 2 3 4 5 6 7 8 9
Thank you again for providing your valuable time Interviewer Name : Time of the Interview
:
Date of the Interview Exhibit 2 Sample response on Customers’ Satisfaction level of “Agora. ” Respondent Satisfactio Number n Level
Convenience Attitude To buy toward frozen food
Availability Behavior Price of product of sales people
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
4 2 2 4 7 2 3 3 5 1 4 7 6 2 3 6 2 2 3 6 3 3 7 1 3 7 4 2 4 1
4 2 4 3 6 2 4 3 6 2 3 7 7 3 2 6 2 2 3 6 4 3 7 2 3 8 3 3 4 2
6 4 5 6 5 4 4 5 5 4 5 5 7 5 3 4 4 5 3 4 4 4 6 4 4 6 3 4 5 4
Exhibit 3 SPSS Output of Regression Descriptive Statistics
Satisfac Convenienc e Att..frozen Availabilyt y Behavior Price
Mean 3.6333
Std. Deviation 1.92055
N 30
3.8667
1.85199
30
6.1333
8.71674
30
3.4000
1.94049
30
4.8667 4.1000
1.07425 1.42272
30 30
Variables Entered/Removed(b)
2 2 2 3 7 2 3 3 6 2 3 7 6 1 2 7 2 2 2 4 3 2 6 2 3 7 4 2 4 1
5 4 6 4 6 5 4 4 6 3 6 6 5 5 5 6 6 2 4 6 5 6 5 3 4 7 5 4 4 4
3 3 3 4 6 4 3 3 4 3 3 7 7 3 3 6 32 4 4 6 4 4 6 2 4 7 4 3 4 3
Mode Variables l Entered 1 Price, Att..froze n, Behavior, Availabil yty, Convenie nce(a)
Variables Removed
Method
.
Enter
a All requested variables entered. b Dependent Variable: Satisfac Model Summary Mode l R 1 .950(a)
Adjusted R Square R Square .902 .881
Std. Error of the Estimate .66117
a Predictors: (Constant), Price, Att..frozen, Behavior, Availabilyty, Convenience Coefficients(a)
Mode l 1 (Constant) Convenienc e Att..frozen Availabilyt y Behavior Price
Unstandardized Coefficients Std. B Error -.971 .676
Standardized Coefficients t Beta
B -1.435
Sig. Std. Error .164
.303
.185
.292
1.632
.116
.004
.015
.020
.301
.766
.450
.150
.455
2.994
.006
.191 .230
.153 .204
.107 .170
1.249 1.126
.224 .271
a Dependent Variable: Satisfac ANOVA(b) Mode l 1 Regressio n Residual Total
Sum of Squares df
Mean Square
F
Sig.
96.475
5
19.295
44.139
.000(a)
10.491 106.967
24 29
.437
a Predictors: (Constant), Price, Att..frozen, Behavior, Availabilyty, Convenience b Dependent Variable: Satisfac Exhibit 4 SPSS Output for Discriminant Analysis Analysis Case Processing Summary Unweighted Cases Valid Excluded Missing or out-of-range group codes At least one missing discriminating variable Both missing or out-of-range group codes and at least one missing discriminating variable Total Total
N 30
Percent 100.0
0
.0
0
.0
0
.0
0 30
.0 100.0
Discriminant Analysis Case Processing Summary Unweighted Cases Valid Exclude Missing or out-ofd range group codes At least one missing discriminating variable Both missing or outof-range group codes and at least one missing discriminating variable Total Total
N 30
Percent 100.0
0
.0
0
.0
0
.0
0 30
.0 100.0
Wilks' Lambda .317
F 29.089
df1 2
df2 27
Sig. .000
.990
.141
2
27
.870
.261
38.200
2
27
.000
.707
5.585
2
27
.009
.388
21.330
2
27
.000
Tests of Equality of Group Means
Conveniency Attitude frozenfood Availability product Behavior salespeople High price
toward of of
Group Statistics
Satisfactio n Low Conveniency Attitude frozenfood Availability product Behavior salespeople High price Medium Conveniency Attitude frozenfood Availability product Behavior salespeople High price High Conveniency Attitude frozenfood Availability product Behavior salespeople High price Total Conveniency Attitude frozenfood Availability product Behavior salespeople High price
Valid N (listwise) Unweighte Weighte d d 17 17.000 toward of of
toward of of
toward of of
toward of of
17
17.000
17
17.000
17
17.000
17 9
17.000 9.000
9
9.000
9
9.000
9
9.000
9 4
9.000 4.000
4
4.000
4
4.000
4
4.000
4 30
4.000 30.000
30
30.000
30
30.000
30
30.000
30
30.000
Pooled Within-Groups Matrices
Correlatio Conveniency n Attitude frozenfood
Convenien cy 1.000 toward -.105
Attitude toward frozenfoo d -.105 1.000
Availabilit y of product .653 -.033
Behavior of salespeople .356 -.304
High price .715 -.060
Availability product Behavior salespeople High price
of of
.653
-.033
1.000
.174
.599
.356
-.304
.174
1.000
.337
.715
-.060
.599
.337
1.000
Analysis 1 Box's Test of Equality of Covariance Matrices Log Determinants Satisfaction Low Medium High Pooled withingroups
Rank 5 5 .(a)
Log Determinant 1.167 -.350 .(b)
5
2.696
The ranks and natural logarithms of determinants printed are those of the group covariance matrices. a Rank < 4 b Too few cases to be non-singular Test Results(a) Box's M F Appro x. df1 df2 Sig.
62.970 3.051 15 1074.004 .000
Tests null hypothesis of equal population covariance matrices. a Some covariance matrices are singular and the usual procedure will not work. The nonsingular groups will be tested against their own pooled within-groups covariance matrix. The log of its determinant is 3.285. Summary of Canonical Discriminant Functions Eigenvalues Functio Eigenvalu % of Cumulative Canonical n e Variance % Correlation 1 3.119(a) 98.8 98.8 .870 2 .036(a) 1.2 100.0 .187 a First 2 canonical discriminant functions were used in the analysis. Wilks' Lambda
Test Function(s) 1 through 2 2
of Wilks' Lambda .234 .965
Chisquare 36.282 .892
df 10 4
Sig. .000 .926
Standardized Canonical Discriminant Function Coefficients
Conveniency Attitude frozenfood Availability product Behavior salespeople High price
Function 1 2 .304 -.248 toward of of
.054
.257
.723
-.636
.142
-.325
.014
1.330
Structure Matrix Function 1 2 Availability product Conveniency High price Behavior salespeople Attitude frozenfood
of
of toward
.952(*)
-.067
.831(*) .708(*)
.144 .647
.364(*)
-.153
-.046
.323(*)
Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function. * Largest absolute correlation between each variable and any discriminant function Functions at Group Centroids Satisfactio n Low Medium High
Function 1 -1.305 .935 3.442
2 .072 -.257 .273
Unstandardized canonical discriminant functions evaluated at group means Classification Statistics
Classification Processing Summary Processed Excluded
30 Missing or out-of0 range group codes At least one missing discriminating 0 variable Used in Output 30 Prior Probabilities for Groups Cases Used in Prior Analysis Satisfactio Unweighte Weighte Unweighte n d d d Low .333 17 17.000 Medium .333 9 9.000 High .333 4 4.000 Total 1.000 30 30.000 Classification Results(a)
Origina Coun l t %
Satisfactio n Low Medium High Low Medium High
Predicted Group Membership Low Medium High 17 0 0 2 5 2 0 0 4 100.0 .0 .0 22.2 55.6 22.2 .0 .0 100.0
a 86.7% of original grouped cases correctly classified.
Total Low 17 9 4 100.0 100.0 100.0