Case Study: Analysis and findings of Qubee customer satisfaction in comparison to their service

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Methodology:

Data Collection: Questionnaire & Survey

Nowadays, internet turned out as a basic need for survival where Qubee has created its own appreciative brand value. Based on a semi-structured

questionnaire, the perceptions and relevant data have been collected from the 50 respondents which ultimately considered as the sample of this

research.

Beneath the particular questionnaire the number of queries, like- frequency of using service, customer service, problem solving etc. have been

covered. In fact, the recommendations are also taken from the respondents for the purpose of improving present lacking or existing features.

Data Input: SPSS (Variable)

After completing the survey all the variables are given input in the SPSS beneath the “Variable View” option. However the values, patterns, measures

of those variables have also been set. For example for the variable “Service”- the data pattern was numeric, measure was in scale.

Value Setting: SPSS (Data View)

As the variables are set the values of the variables are given input under the “Data View” section of SPSS, alike: for the variable “Service” (duration

of using Qubee), in a particular questionnaire the value was “3” (3 years to less than 5 years). As like as the variable “Service” all the values of other

variables have been located.

Analysis:

Afterwards, based on the data input the analysis is done by calculating the following-

1. Descriptive Statistics

2. Correlation &

3. Regression

Interpretations:

At the end of getting the output or results of the calculation shown above the proper interpretations of those is prepared. However, depending on the

separate statistical calculation separate interpretation has been stated that reflects how effectively Qubee is operating its operations. Moreover, from

the regression analysis Qubee may came to know how long its customers will show their loyalty towards their service.

Beneath, the separate calculation separate interpretations have been stated below:

Research Methodology in Business Analysis and Findings

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1. Interpretation of Descriptive Statistics-

Descriptive Statistics

N Range MinimumMaximu

mMean

Std. Deviation

Variance Skewness Kurtosis

Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic Statistic Std. Error StatisticStd.

Error

Service 50 3.00 .00 3.00 1.5400 .15175 1.07305 1.151 -.005 .337 -1.233 .662

Frequency 50 4.00 .00 4.00 1.7000 .16963 1.19949 1.439 .392 .337 -.890 .662

Professionalism (1) 50 3.00 .00 3.00 .8200 .12352 .87342 .763 .940 .337 .331 .662

Quality_ product 50 3.00 .00 3.00 .6600 .11990 .84781 .719 1.357 .337 1.486 .662

Customer needs 50 4.00 .00 4.00 1.0600 .14402 1.01840 1.037 .842 .337 .235 .662

Sales_ staff 50 3.00 .00 3.00 1.2200 .13489 .95383 .910 .418 .337 -.659 .662

Price 50 4.00 .00 4.00 1.4600 .14058 .99406 .988 .634 .337 -.326 .662

Resolved_ problem 50 3.00 .00 3.00 1.3400 .13282 .93917 .882 .332 .337 -.680 .662

Follow_ up_ call 50 1.00 .00 1.00 .3400 .06767 .47852 .229 .697 .337 -1.580 .662

Professionalism (2) 50 3.00 .00 3.00 .9200 .13352 .94415 .891 .771 .337 -.287 .662

Responsiveness 50 3.00 .00 3.00 .8400 .11198 .79179 .627 .812 .337 .527 .662

Politeness 50 3.00 .00 3.00 .7600 .11270 .79693 .635 1.222 .337 1.783 .662

Knowledge_ of _ Problem

50 3.00 .00 3.00 1.1200 .10552 .74615 .557 .414 .337 .212 .662

Efficiency_ in _solving_ problem

50 3.00 .00 3.00 1.2400 .11270 .79693 .635 .542 .337 .164 .662

Manner_ of_ handling_ follow-up_ questions

50 4.00 .00 4.00 1.1800 .13906 .98333 .967 .830 .337 .385 .662

Satisfaction_ with_ Customer_ support

50 3.00 .00 3.00 1.0200 .12286 .86873 .755 .739 .337 .172 .662

Satisfaction_ overall 50 3.00 .00 3.00 1.0600 .12259 .86685 .751 .665 .337 .048 .662

Continue_ doing_ business

50 3.00 .00 3.00 1.0400 .13389 .94675 .896 .670 .337 -.342 .662

Recommendation 50 3.00 .00 3.00 1.3000 .14070 .99488 .990 .000 .337 -1.158 .662

Gender 50 1.00 .00 1.00 .4800 .07137 .50467 .255 .083 .337 -2.078 .662

Age 50 7.00 .00 7.00 2.5400 .23072 1.63145 2.662 .822 .337 .890 .662

Income 50 7.00 .00 7.00 3.1200 .35873 2.53659 6.434 .376 .337 -1.250 .662


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Valid N (list-wise) 50

Number of Observations (N)-

Number of observation=50

Range-

Range indicates to the inconsistency of the data set.

For the variable “Service”, data set variability is up to 3.

For the variable “Frequency” data set variability is up to 4.

However, for the variable “Follow up call” variability is only up to 1.

For the “Age” and “Income” the variability is up to 7.

All in all, the average variability is around 3.5.

Minimum-

Data set of all the variables has minimum value of 0.

Maximum-

For the variable “Service” & “Quality_ Service”, maximum data set value is 3.

For the variable “Frequency” & “Customer Needs”, maximum data set value is 4.

However, for the variable “Age” & “Income”, maximum data set value is 7

And, for the variable “Follow up call” & “Gender”, maximum data set value is 1.

Moreover, the average maximum data set value is approximately 3.6.

Mean-

Mean signifies as the mathematics standard value of the data set which is also called central tendency.

For the variable “Service”, mean is “1.54”. That means, all the respondents use Qubee’s service around 3-4 years.

For the variable “Price”, mean is “1.46”. That means, all the respondents are somewhat satisfied and to some extent neutral about the price of service

of Qubee

In addition, for the variable “Frequency”, mean is 1.7 which means, the respondents use the service of Qubee habitually.


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For the variable “Age” and “Income” the mean is 2.54 and 3.12, that indicates the entire respondent’s buying or use of the service of Qubee depends

or varies much on the basis of age and income.

However, “Satisfaction with Customer Support” and “Gender”, both the variables has the lowest mean among all the other variables that are 1.02

and .48. It designates that the factor gender doesn’t create any differences in the uses or purchase of Qubee’s service where the satisfaction with the

Qubee’s customer support seems low.

Standard Error-

Standard error is simply the sampling error that measures how much the sample is statistically viable.

For the variables “Follow up call” and “Gender” the data set range is 1 for both where the standard error of “.06767” (Follow up call) and “.07137”

(Gender) seems very low.

For the variable “Frequency” of purchase, data set range is 3 where the standard error of “.16963” is comparatively high.

Conversely, the data set range of the variable “Age” are 7 in which standard error of “.23072” is moderately low from the variable “Frequency”.

But the data range of “Income” was also 7 where the standard error is “.35873” which is somewhat high in respect of data range.

Standard Deviation-

Standard deviation; also designated as mean deviation, evaluates how much individual values fluctuates from the mean.

However, all the Standard deviations significantly differ from the mean.

For the variable “Age” the mean was “2.5400” but the standard deviation of this is “1.63145”. The deviation seem high and thus it’s can’t be an

appropriate representative of the distribution.

For the variable “Frequency”, Standard deviation is “1.19949” which is relatively high. Consequently, the mean value is not a good representative of

the distribution.

For the variable “Follow up call”, standard deviation is “.47852” which is relatively low from the previous one. Hence, it can be said that the mean

value is a good representative of the distribution.

Variance

For almost all the variables “variance” seems near to the mean of “mean”, likewise-

For the variable “Service” the variance is “1.151” which differs from the mean only by “.389”. It means it’s a good representative of mean.

But for the variables like “Sales staff”, “Price” and “Resolved problem” the variances are “.910”, ”.988” and “.882”, which is pretty much

deviated. Thus it’s not a good representative of the mean.


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Variance measures the same as the standard deviation. The only

difference between variance and standard deviation is that standard

deviation measures deviation from mean in the same unit as of the data

set while variance measures the deviation in the square unit of the data

set.

Skewness

o However, if the value is negative, skewness will be on left.

o If the value is positive skewness will be on right.

Skewness of normal distribution is 0. For the variable “Recommendation” shows “.000” which seems the distribution has very high middle

skewnwss that is normal distribution.

The skewness of the variable “Service” is “-.005” which means that the distribution

has very low left skewness and is close to normal distribution.

In comparison, the skewness of the variable “Politeness” is “1.222”, which means

that the distribution is highly right skewed.

Kurtosis

Kurtosis

designates to

whether the

distribution is

steep or flat than the normal distribution.

The kurtosis of the normal distribution is 3.

However, the values of kurtosis beneath all the variables are less than 3

Even some of them are negative which indicates to that all the distributions are

flatthan the normal distribution.


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2. Interpretation of Correlations-

Correlations

Variables ProductQuality _ product

Price Sales staffResolved_ Problem

Follow up call

IncomeContinue

doing Business

Service

Pearson Correlation 1 -.400** -.257 -.198 -.368** -.365** .081 -.564**

Sig. (2-tailed) .004 .072 .168 .009 .009 .578 .000

N 50 50 50 50 50 50 50 50

Quality_ Service

Pearson Correlation -.400** 1 .601** .549** .379** .291* .029 .704**

Sig. (2-tailed) .004 .000 .000 .007 .041 .842 .000

N 50 50 50 50 50 50 50 50

Price

Pearson Correlation -.257 .601** 1 .472** .244 .351* -.144 .587**

Sig. (2-tailed) .072 .000 .001 .087 .012 .319 .000

N 50 50 50 50 50 50 50 50

Sales staff

Pearson Correlation -.198 .549** .472** 1 .348* .190 .124 .442**

Sig. (2-tailed) .168 .000 .001 .013 .185 .392 .001

N 50 50 50 50 50 50 50 50

Resolved_ Problem

Pearson Correlation -.368** .379** .244 .348* 1 .419** .171 .329*

Sig. (2-tailed) .009 .007 .087 .013 .002 .235 .020

N 50 50 50 50 50 50 50 50

Follow up call

Pearson Correlation -.365** .291* .351* .190 .419** 1 .218 .375**

Sig. (2-tailed) .009 .041 .012 .185 .002 .128 .007

N 50 50 50 50 50 50 50 50

Income Pearson Correlation .081 .029 -.144 .124 .171 .218 1 .023


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Sig. (2-tailed) .578 .842 .319 .392 .235 .128 .872

N 50 50 50 50 50 50 50 50

Continue_ doing_ business

Pearson Correlation -.564** .704** .587** .442** .329* .375** .023 1

Sig. (2-tailed) .000 .000 .000 .001 .020 .007 .872

N 50 50 50 50 50 50 50 50

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Correlation

The correlation co-efficient of the variables “Quality _ Service” and “Continue_ doing_ business” is “.704” which signifies that there is a strong

and positive correlation between them and they move in the same direction.

The significant value reflects the risk of rejecting of null hypothesis. The significant value for the variables “Quality_ Service” and “Continue_

doing_ business” usage” is “.000”. It means that there is no risk of risk of rejecting the null hypothesis and the correlation between the variables is

highly statistically significant.

In other worlds, the correlation between the variables “Quality_ service” and “Continue_ doing_ business” is highly statistically significant.

On the other hand, the correlation co-efficient of the variables “Service” and “Sales staff” is “-.198” which means that there is a weak and negative

correlation between the variables and they move in the opposite direction.

However, the significant value for the variables is “.168” which is very high than the level of significance (α=5%). That is, the risk of rejecting null

hypothesis is 16.8% which is very high.

So the null hypothesis cannot be rejected. It has to be accepted which signifies that there is no statistically significant relationship between the variables

“Service” & “Sales staff”


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3. Interpretation of Regression-

Coefficient

ModelUnstandardized

CoefficientsStandardized Coefficients

t Sig.B Std. Error Beta

1 (Constant) .164 .239 .688 .495

Quality_ service .582 .148 .521 3.924 .000

Price .252 .123 .265 2.051 .046

Resolved_ Problem

.061 .112 .061 .547 .587

Income .013 .039 .036 .345 .732

a. Dependent Variable: Continue doing business

From the above table-

α= .164

β1=.582

β2= .252

β3=.061 and

β4=.013

So, the regression equation is-

Continue doing business= .164+.582 β1+.252 β2+.061 β3+.013 β4

i.e. Continue doing business=.164+.582 Quality_ Service +.252Price +.061 Resolved Problem +.013Income

Thus, if “Quality_ Service” is increased or decreased by 1 unit, “Continue doing business” is increased or decreased respectively by “0.582” unit.


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t value

“t” value is the assessment statistics of hypothesis. It measures how much “B” is statistically significant and to verify whether B is

statistically significant or not, significant value is used.

The significant value for “Quality_ service” and “Price” are “.000” and “.046” respectively which are lower than α (5%), that is the risk of

rejecting null hypothesis in the former case is zero and in the latter case is very low. So, the null hypothesis (β1=0 & β2=0) can be rejected.

Therefore it can be said that “Continue doing business” has a high statistically significant relationship with both “Quality_ service” and

“Price”.

On the other hand, the significant value for “Income” is “.732” which is very high than α (5%), that is the risk of rejecting null hypothesis is

very high (73.2%). Therefore, the null hypothesis (β4=0) should be accepted.

Consequently, it can be said that B is not statistically significant and there is no relationship between “Continue doing Business” and

“Income”.

ANOVA

ModelSum of Squares

dfMean

SquareF Sig.

1

Regression 23.833 4 5.958 13.348 .000a

Residual 20.087 45 .446

Total 43.920 49

a. Predictors: (Constant), Income, Quality_ service, Resolved Problem, Price

b. Dependent Variable: Continuing usage

F Value

“F” is the test statistic which shows the effect of multiple variables at the same time; that is the joint significance of β1, β2, β3 and β4.

In this case-

o Hο: β1=β2=β3=β4=0

o H1: β1≠β2≠β3≠β4≠0

The significant value is “0.000” which means that there is no risk of rejecting the null hypothesis. The null hypothesis can be rejected which

ensures that jointly, dependent variable that is “Continue doing Business” has a high statistically significant relationship with independent

variables alike- “Income”, “Quality_ service”, “Resolved problem” and “Price”.


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Model Summary

Model R R SquareAdjusted R

SquareStd. Error of the Estimate

1 .737a .543 .502 .66811

a. Predictors: (Constant), Income, Quality_ service, Resolved Problem, Price

R Square

“R square” is the major decisive value of regression scrutiny which imitates the generally illustrative supremacy of the regression form and

signifies whether to accept the regression model or not. However, it also connotes to what extent deviation in dependent variable is explained

by independent variables.

Here the value of R Square is “0.543” which represents that only 54.3% variation is “Continue doing business” is explained by “Income”,

“Quality_ service”, “Resolved problem” and “Price” since “Continue doing business” is determined not only by these variables but by many

other variables.

Hence it can be said that the overall explanatory power of the regression model is not good.

Adjusted R Square

For multiple regression equation, there is a penalty for additional independent variable. Adjusted R Square is the adjusted penalty for additional

independent variable.

The more independent variables the greater the overall explanatory power of the regression model but lower the statistical significance of the

model.

Here, the value of Adjusted R Square is “0.502” which mean that the penalty for additional independent variables is very high.


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Thus, it can be said that the overall regression model is not good.
