srikanth banerjee ha2 imt-2013

8/13/2019 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 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 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




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




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



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



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.


EXECUTE.



EXECUTE.


COMPUTE DSATEDU1=ZSAT * Dedu1.

EXECUTE.

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