A detailed marketing study using the help of SPSS on Warid a telcomm operator based in Pakistan.

1. Case Processing Summary
N %
Cases Valid 50 100.0
Excluded(
a)
0 .0
Total 50 100.0
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
Cronbach's
Alpha N of Items
.905 21
The Cronbach Alpha value of our model is 0.905 which tells us that our questionnaire is reliable.
Model Summary(b)
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate Durbin-Watson
1 .843(a) .711 .685 .59797 2.271
a Predictors: (Constant), AA4, AA2, AA1, AA3 Table 1
b Dependent Variable: DV
Table 1 is the model summary table providing us the summary of regression statistics the value of R2. This
shows the magnitude of relationship between the independent and the dependent variables. The above
value of R2 i.e. 0.711 shows that 71.1% variation in dependent variable can be ascribed to the set of
independent variables. The value of R2 lies between 0-1. Higher values of R2 shows stronger impact of IVs
on DV and vice versa.
ANOVA(b)
Model
Sum of
Squares Df Mean Square F Sig.
1 Regression 39.589 4 9.897 27.679 .000(a)
Residual 16.091 45 .358
Total 55.680 49
a Predictors: (Constant), AA4, AA2, AA1, AA3 Table 2
b Dependent Variable: DV
Table 2 the ANOVA table provides us with the F statistics. The significance value 0.000 tells us that we can
apply regression on our model. If this value were to be more than 0.05 to it would have shown that
regression could not be applied to our model

2. Coefficients(a)
Model
Unstandardized
Coefficients
Standardized
Coefficients
B Std. Error Beta T Sig.
1 (Constant) .080 .568 .141 .888
AA1 .504 .168 .347 3.000 .004
AA2 -.301 .107 -.227 -2.818 .007
AA3 .197 .173 .148 1.136 .262
AA4 .529 .131 .456 4.051 .000
a Dependent Variable: DV Table 3
Table 3 is the coefficient table providing us the values for beta coefficients and t-statistics. The
Beta coefficients of constant is .080 revealing the value of dependant variable keeping all the IVs
are equal to 0 but the t-statistics for this value is .888(p > 0.05) which means that the constant
value is insignificant.
The quality of advertisements (AA1) has a positive relationship with effectiveness of ad
campaigns (DV) as shown by the positive beta sign. The beta value shows the variation in
effectiveness of ad campaigns (DV) caused by quality of advertisements (AA1). The beta value of
quality advertisements is 0.504that tells us that a 1 degree change in quality of advertisements
(AA1) will bring a 50.4% change in the effectiveness of ad campaigns (DV). The t value of this
beta coefficient is 3.000 (p < 0.05) which means that the relationship between quality of
advertisements and the effectiveness of ad campaigns is significant.
The competitors (AA2) have an inverse relationship with effectiveness of ad campaigns (DV) as
shown by the negative beta sign. The beta value of competitors is -0.301 that tells us that a 1
degree change in quality of advertisements (AA1) will bring a 30.1% negative change in the
effectiveness of ad campaigns (DV). The t value of this beta coefficient is -2.818 (p < 0.05) which
means that the relationship between quality of advertisements and the effectiveness of ad
campaigns is significant. This negative value represents that as the competition increases the
effectiveness of ad campaigns diminishes.

3. The pricing and promotion (AA3) has a positive relationship with effectiveness of ad campaigns
(DV) as shown by the positive beta sign. The beta value of pricing and promotion (AA3) is 0.197
that tells us that a 1 degree change in quality of advertisements (AA1) will bring a 19.7% change
in the effectiveness of ad campaigns (DV). The t value of this beta coefficient is 4.051 (p < 0.05)
which means that the relationship between Research and the effectiveness of ad campaigns is
significant.
The Research (AA4) has a positive relationship with effectiveness of ad campaigns (DV) as shown
by the positive beta sign. The beta value of Research (AA4) is 0.529 that tells us that a 1 degree
change in quality of advertisements (AA1) will bring a 52.9% change in the effectiveness of ad
campaigns (DV). The t value of this beta coefficient is 1.136 (p < 0.05) which means that the
relationship between pricing and promotion and the effectiveness of ad campaigns is
insignificant.

4. Correlations
AA1 AA2 AA3 AA4 DV
AA1 Pearson Correlation 1
Sig. (1-tailed)
AA2 Pearson Correlation .059 1
Sig. (1-tailed) .342
AA3 Pearson Correlation .710(**) .086 1
Sig. (1-tailed) .000 .276
AA4 Pearson Correlation .578(**) .112 .688(**) 1
Sig. (1-tailed) .000 .218 .000
DV Pearson Correlation .702(**) -.143 .688(**) .732(**) 1
Sig. (1-tailed) .000 .161 .000 .000
** Correlation is significant at the 0.01 level (1-tailed).
 The relationship of Quality of Advertisement (AA1) and Competitors (AA2) is insignificant
as shown by the significance value .342.
 The relationship of Quality of Advertisement (AA1) and Pricing and Promotion (AA3) is
significant as shown by the significance value 0.000. Hence a 1% change in Quality of
Advertisement (AA1) will cause a 71% change in the variable Pricing and Promotion
(AA3).
 The relationship of Quality of Advertisement (AA1) and Research (AA4) is significant as
shown by the significance value 0.000. Hence a 1% change in Quality of Advertisement
(AA1) will cause a 57.8% change in the variable Research (AA4).
 The relationship of Quality of Advertisement (AA1) and Effectiveness of Ad Campaigns
(DV) is significant as shown by the significance value 0.000. Hence a 1% change in Quality
of Advertisement (AA1) will cause a 70.2% change in the variable Effectiveness of Ad
Campaigns (DV).
 The relationship of Competitors (AA2) and Pricing and Promotion (AA3) is insignificant as
is shown by the significance value .276.
 The relationship of Competitors (AA2) and Research (AA4) is insignificant as is shown by
the significance value .218.
 The relationship of Competitors (AA2) and Effectiveness of Ad Campaigns (DV) is
insignificant as is shown by the significance value .161.

5.  The relationship of Pricing and Promotion (AA3) and Research (AA4) is significant as
shown by the significance value 0.000. Hence a 1% change in Pricing and Promotion
(AA3) will cause a 68.8% change in the variable Research (AA4).
 The relationship of Pricing and Promotion (AA3) and Effectiveness of Ad Campaigns (DV)
is significant as shown by the significance value 0.000. Hence a 1% change in Pricing and
Promotion (AA3) will cause a 68.8% change in the variable Effectiveness of Ad Campaigns
(DV).
 The relationship of Research (AA4) and Effectiveness of Ad Campaigns (DV) is significant
as shown by the significance value 0.000. Hence a 1% change in Research (AA4) will cause
a 73.2% change in the variable Effectiveness of Ad Campaigns (DV).
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
AA1 50 1.00 4.75 3.3100 .73290
AA2 50 2.25 5.00 3.9750 .80377
AA3 50 1.00 4.75 3.2150 .79861
AA4 50 1.00 4.50 3.2050 .91848
DV 50 1.00 5.00 2.8800 1.06599
Valid N (listwise) 50
The descriptive stats reveal that the ad campaigns of Warid are ineffective. The mean value of
the dependent variable is less than 3 which signify that majority of the people in the survey
were in disagreement of the effectiveness of ad campaigns of Warid. If this value were to be
more than 3 then it would have meant that the ad campaigns of Warid are effective.

6. Conclusion
After analyzing all the results and findings I would like to conclude this chapter by saying that all
the independent variables except for pricing and promotion impact the dependent variable. The
hypothesis of pricing and promotion has been found to be null i.e. any change in pricing and
promotion will have no effect on the effectiveness of ad campaigns.
Also after analysis we also came to found out that competition has an inverse relationship with
the dependent variable i.e. an increase in competition will lead to reduced effectiveness of ad
campaigns and vice versa. The rest of the variables (quality of advertisement and research) have
a direct relationship with the dependent variable i.e. and increase in any of these will increase the
effectiveness of ad campaigns and vice versa.

7. Pie Charts of Items