srikanth banerjee ha2 imt-2013
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Marketing AnalyticsAssignment - 2
Group 13
M V S Srikanth (1301-108)Rupayan Banerjee (1301-186)
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Developing composites By applying causal logic, Bencare managers identified custom
satisfaction, trust and value as key causal predictors
Customer satisfaction - SAT1(inter1), SAT2(inter2), SAT3(inter3) SAT = (SAT1 + SAT2 + SAT3)/3 TrustIt has 2 categories
Representatives- Rep17(Trust-Agent1), Rep18(Trust-Agent2),Rep19(Trust-Agent3), Rep20(Trust-Agent4)
REP = (Rep17 + Rep18 + Rep19 + Rep20)/4
Management practices - Trust-Comp1, Trust-Comp2, Trust-Com
Comp4 MGT = (prac17 + prac 18 + prac19 + prac20)/4
ValueIt has 2 categories
Short Term Value - ST-VALUE1, ST-VALUE2, ST-VALUE3
STV = (val1 + val2 + val3)/3
Long Term Value - LT-VALUE1, LT-VALUE2, LT-VALUE3
LTV = (val4 + val5 + val6)/3
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Contd. Dependent Variable: Loyalty
Beh-Loyalty1, Beh-Loyalty2, Beh-Loyalty3, Beh-Loyalty4, Cog-LoyaLoyalty2 (Ignored Cog-Loyalty3, Cog-Loyalty4 as per our results froanalysis)
LOY = (loy1 + loy2 + loy3 + loy4 + loy5 + loy6)/6
Independent variables Dependent variables
SAT
LOYREP
MGT
STV
LTV
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Test of AssumptionsCountry: Germany*
Test of NormalitySkewness/Std Error < 3 i.e. Data is normal
* Data set has been divided into 3 sub sets based on the countries
Test of HomoscedastiNo pattern is observed.
homoscedastic
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Contd.
The partia
have beenand regres
difference
than 0.02.
considere
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Test of AssumptionsCountry: USA*
Test of NormalitySkewness/Std Error < 3 i.e. Data is normal
* Data set has been divided into 3 sub sets based on the countries
Test of HomoscedastiNo pattern is observed.
homoscedastic
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Contd.
The partial corhave been fit w
and regression
difference in R
than 0.02. The
considered to
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Test of AssumptionsCountry: Holland*
Test of NormalitySkewness/Std Error < 3 i.e. Data is normal
* Data set has been divided into 3 sub sets based on the countries
Test of HomoscedastiNo pattern is observed. S
homoscedastic
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Contd.
The partial chave been fi
and regressi
difference in
than 0.02. Th
considered t
B li i d l
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Baseline regression modelCountry: Holland
Loyalty = 5.122 + 0.336 *
TRUSTAGENT + 0.550 * LTVALUE
Country: USA
Country: Germany
Loyalty = 5.235 + 0.156 * SAT + 0.250
* TRUSTAGENT + 0.308 *
TRUSTCOMPY + 0.374 * LTVALUE
Loyalty = 5.026 + 0.438 * SAT + 0.233
* TRUSTCOMPY + 0.338 * STVALUE +0.215 * LTVALUE
Id tif i i fl i th d t b C
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Identifying influencers in the data by CodistancesCountry: Holland Country: USA Count
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C i b li i d l
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Comparing baseline regression modelsbefore and after removing influencers
Since, there is an significant increase in Adjusted R-square value after removing influencers, t
considered after removing the influencers for further analysis. Also, the models were changed aft
from the data set (Significant independent variables are changed)
Country: Holland Country: USA Countr
Before
After
Loyalty = 5.122 + 0.336 *
TRUSTAGENT + 0.550 *LTVALUE;
R-Square: 0.351
Loyalty = 5.157 + 0.462 *
LTVALUE;
R-Square: 0.408
Loyalty = 5.235 + 0.156 * SAT +
0.250 * TRUSTAGENT + 0.308 *TRUSTCOMPY + 0.374 * LTVALUE
R-Square: 0.721
Loyalty = 5.235 + 0.156 * SAT +
0.250 * TRUSTAGENT + 0.308 *
TRUSTCOMPY + 0.374 * LTVALUE
R-Square: 0.721
Loyalty = 5.038
* TRUSTAGENT
TRUSTCOMPY +
R-Square: 0.670
Loyalty = 5.026
* TRUSTCOMPY0.215 * LTVALU
R-Square:0.624
LTVALUE coefficient decreased
by 0.088
The effect of SA
LTVALUE has be
significant IV, TDifference No change
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Test of nonlinear relationships
Since, we already proved that the data for all the 3 couhaving linear relationships, our baseline regression modechange because of nonlinear relationships
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Moderator regression modelCountry: HollandModerating variable: GenderIt has 2 categories:
1Male ; 2FemaleCreated a dummy variable Dgender:
1Male, 0FemaleThe R-
signific
interac
DTAge
signific
significmodel
Since,
is not s
interac
be con
has no
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Moderator regression modelCountry: GermanyModerating variable: GenderIt has 2 categories:
1Male ; 2FemaleCreated a dummy variable Dgender:
1Male, 0Female
The
sign
Only
term
Contd
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Contd.
Regression model:
Loyalty = 4.887 + 0.18 * Dgender + 0.184 * ZSTVAL + 0.148 * Dgender * ZSTVAL
For Male (Dgender = 1)Loyalty = 5.067 + 0.332 * ZSTVAL
For Female (Dgender = 0)
Loyalty = 4.887 + 0.184 * ZSTVAL
Effect of short term value on loyalty in male category
female category. (0.332/0.184 = 2 (approx.))
So, Short term value is an important factor in case of
category
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Moderator regression modelCountry: HollandModerating variable: AgeIt has 5 categories:
118-24 yrs ; 225-34 yrs;335-44 yrs; 445-54 yrs; 555+ yrs
Created 4 dummy variables: Dage1, Dage2, Dage3and Dage4
Dage1: 1 Category 2; 0 Else
Dage2: 1 Category 3; 0 Else
Dage3: 1 Category 4; 0 Else
Dage4: 1 Category 5; 0 Else
The R-
change
signific
has no
effect.
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Moderator regression modelCountry: GermanyModerating variable: AgeIt has 4 categories:
118-24 yrsNo cases in this age category;225-34 yrs; 335-44 yrs; 445-54 yrs; 555+ yrs
So, number of valid categories: 4
Created 3 dummy variables: Dage1, Dage2, Dage3and Dage4
Dage2: 1 Category 3; 0 Else
Dage3: 1 Category 4; 0 Else
Dage4: 1 Category 5; 0 Else
The R-
change
signific
has no
effect.
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Moderator regression modelCountry: HollandModerating variable: EducationIt has 4 categories:
1High school, 2Some college, 3College,4Graduate school
Created 4 dummy variables Dedu1, Dedu2, and Dedu3
Dedu1: 1 Category 2; 0 Else
Dage2: 1 Category 3; 0 Else
Dage3: 1 Category 4; 0 Else
The R-
change
signific
educat
interac
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Moderator regression modelCountry: GermanyModerating variable: EducationIt has 4 categories:
1High school, 2Some college, 3College,4Graduate school
Created 4 dummy variables Dedu1, Dedu2, and Dedu3
Dedu1: 1 Category 2; 0 Else
Dage2: 1 Category 3; 0 Else
Dage3: 1 Category 4; 0 Else
The R-
change
signific
educat
interac
Final regression model for Bencare and
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Final regression model for Bencare andinsights
Country: Germany
Country: USA
Country: Holland
Loyalty = 5.157 + 0.462 * LTVALUE;
R-Square: 0.408
People in Holland are loyal to the company, when they see long term va
the model built is having a low R-square value, it is advised to conduct regres
much bigger sample
Loyalty = 5.235 + 0.156 * SAT + 0.250 * TRUSTAGENT + 0.308 * TRUSTCOMPY + 0.374 *
R-Square: 0.721
Companys focus on Short term value can be reduced, as it is not affecting its
Loyalty = 5.038 + 0.365 * SAT + 0.192 * TRUSTAGENT + 0.343 * TRUSTCOMPY + 0.289 * STVALU
R-Square: 0.670Companys focus on long term value is not needed as it does not affect its customer
Interaction of gender is significant and should be kept in mind
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Other key insightsIn all the three countries, Age and Education has no interaction effects.
Consumers cannot be segmented as per age and Education for m
loyalty
Company can go for generic advertising i.e. not based on the ag
In Germany, gender factor is significant in loyalty. The company
male with high short term value to build their loyalty
Consumer loyalty is independent of education. It means that co
introduce special insurance plans for consumers with higher edu
increase their loyalty
This can lead to consumer thinking of Bencare when ever he
insurance
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Thank you
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AppendixSynta
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Removing missing data:
DATASET COPY BencareNoMissing.
DATASET ACTIVATE BencareNoMissing.
FILTER OFF.
USE ALL. SELECT IF (rep17 * rep18 * rep19 * rep20 * inter1 * inter2 * inter3 * prac
prac18 * prac19 *
prac20 * val1 * val2 * val3 * val4 * val5 * val6 * loy1 * loy2 * loy3 * loy* loy6 * age *
sex * educ * loc ~= 0).
EXECUTE. DATASET ACTIVATE DataSet1.
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Filtering by Country
DATASET ACTIVATE BencareNoMissing. DATASET COPY Germany.
DATASET ACTIVATE Germany.
FILTER OFF.
USE ALL.
SELECT IF (loc = 3).
EXECUTE.
DATASET ACTIVATE BencareNoMissing.
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Calculating Dummy variables DATASET ACTIVATE Germanycook.
COMPUTE DREPEDU1=ZREP * Dedu1.
EXECUTE.
DATASET ACTIVATE Germanycook. COMPUTE DREPEDU2=ZREP * Dedu2.
EXECUTE.
DATASET ACTIVATE Germanycook.
COMPUTE DREPEDU3=ZREP * Dedu3.
EXECUTE.
DATASET ACTIVATE Germanycook.
COMPUTE DREPEDU4=ZREP * Dedu4.
EXECUTE.
DATASET ACTIVATE Germanycook.
COMPUTE DSATEDU1=ZSAT * Dedu1.
EXECUTE.