Assignement2_Group4

Assignment 2_ Group 4

Souhardya BanerjeeSaiyam Arora

1• Managerial Problem is defined

2• Computation of aggregate factors and factor Analysis

3• Regression Analysis with impact of linear/non-linear relationships on Key Loyalty

predictors

4• Take assumptions and validate for regression

5• “Best” predictive model for aggregate data is interpretted

6• Moderating affects of variables on best aggregate model

7• customer segments to loyalty predcitors implicaions

8• Recommendations for “Driving Loyalty Up”

FLOW OF PRESENTATION

Problem Statement

• An insurance company facing stiff global competition chipping its loyal customer base

• Tough business environment • Price competitiveness need to match customer

needs for trusting relationships• To enhance the company’s competitive position in

the customer’s mind• Company Seeking for “Driving Loyalty Up” Strategy

Factor Analysis

Satisfaction

Trust Company

ShortTerm value

LongTerm Value

Loyalty

Trust Agent

Composites for Regression Analysis

• Beh_loyalty: (loy1+loy2+loy3+loy4)/4• Cog_loyalty : (loy5+loy6+loy7+loy8)/4• Satisfaction = (inter1 + inter2 + inter3)/3• Trust on Agent = (rep17+rep18+rep19+rep20)/4.• Trust on company

=(prac17+prac18+prac19+prac20)/4.• Value_Shortterm = (val1+val2+val3)/3.• Value_Longterm= (val4+val5+val6)/3.

• Hypothesis : – H0 : KLP’s (trust on agent, trust on company, customer satisfaction, long

term and short term value ) do not have a influence in driving up the loyalty loyalty.

– H1: KLP’s (trust on agent, trust on company, customer satisfaction, long term and short term value ) have a influence in driving up the loyalty loyalty.

• If p value > 0.05 , we accept the null hypothesis and reject otherwise

Variable Setup in SPSS Statistics

Outcome Variable : •Cog_loyalty ( for cognitive Loyalty)•Beh_Loyaltty(For behavioral Loyalty)

Predictor Variable : Satisfaction Trust_agent Trust_Comp Value_ST, Value_LT

KLP’s explain 55.2% of the variablity on Beh_Loyalty

The table shows that the independent variables statistically significantly predict the dependent variable, F(5,773) = 1095, p < .0005 (i.e., the regression model is a good fit of the data

Regression on Behavior Loyalty(Beh_Loyalty)

From the "Sig." column that all independent variable coefficients are statistically significantly different from 0 (zero)

Statistical significance of the independent variables

Results• Adjusted R square change is 0.552 means 55.2% of main factors are

influencing model.• It is significant as for p < 0.05 and hypothesis is rejected• Behavioural Loyalty is dependent on all the independent variables• The dependency is higher in satisfaction and long term value variable• VIF< 10 that satisfies that they are not multi-collinear.

Normality

Since median comes user +-2sigma mean. Also std. residual is zero at mean. Thus it is normal

Linear

Trends show that it is following a nearly a linear trial.

Independent of Errors

The linear curve shows that it is Independent of errors..

Regression on Cognitive Loyalty(Cog_Loyalty)

KLP’s explain 52.3% of the variablity on Beh_Loyalty

The table shows that the independent variables statistically significantly predict the dependent variable, F(5,762) = .912, p < .0005 (i.e., the regression model is a good fit of the data


influencing• It is significant as for p < 0.05 and null hypothesis is rejected• Cognitive Loyalty is dependent on all the independent variables• The dependency is higher in short term value variable.• VIF< 10 that satisfies that they are not multi-collinear.

Normality


Linear

Trends shows that it is following a nearly a linear trial.



KLP’s explain 60% of the variablity on Beh_Loyalty

The table shows that the independent variables statistically significantly predict the dependent variable, F(5,773) = .902, p < .0005 (i.e., the regression model is a good fit of the data

Regression on Total Loyalty


influencing model.• It is significant as for p < 0.05 and hypothesis is rejected• Behavioural Loyalty is dependent on all the independent variables• The dependency is higher in satisfaction and long term value variable• VIF< 10 that satisfies that they are not multi-collinear.

Normality


Linear

Trends show that it is following a nearly a linear trial.



Removing Outliers

Outliers present in the model as COO Distance > 0.04 for some data points

MulticollinearityWe have considered 0.9 as the cut-off value. Since no correlation value is above or equal to 0.9, it can be concluded that significant multicollinearity is not present between any of the independent variables.

Therefore, it is appropriate to proceed with multiple regression.

Multiple regression

The Adjusted R square value is 0.600.This indicates that 60% variation in Loyalty which is the dependant variable is explained by the independent variables which we have considered.The model is converging well since significance is less than 0.5

Multiple regression

All the Independent Variables have significance values <0.05. Hence all of them are going to be part of the Multiple Regression Model.Based on the unstandardized coefficients of the IVs, following model is made –

Loyalty = 0.004 + 0.127*Agent + 0.123*Trust + 0.135*Value_Shortterm+ 0.160*Value_Longterm + 0.133*Satisfaction

Analysis – Aggregate Data

• Based on the outcome of multiple regression, we can conclude that all independent variables has to be considered.

• Further, observing the coefficients tell us that a unit increase in Value_Longterm will contribute most to Dependent Variable Loyalty.

• This is followed by Value_Shortterm and Satisfaction, which are closely followed by Agent and Trust

ANOVAa

ModelSum of

Squares df Mean Square F Sig.1 Regression 500.027 5 100.005 111.642 .000b

Residual 293.813 328 .896 Total 793.840 333

a. Dependent Variable: loyaltyb. Predictors: (Constant), Value_Shortterm, Satisfaction, Trust, Agent, Value_Longterm

Multiple Regression Model significance

ANOVAa

Model Sum of Squares df Mean Square F Sig.1 Regression 423.537 5 84.707 125.127 .000b

Residual 159.088 235 .677 Total 582.625 240

a. Dependent Variable: loyaltyb. Predictors: (Constant), Value_Shortterm, Satisfaction, Trust, Agent, Value_Longterm

The US-specific data

ANOVAa

Model Sum of Squares df Mean Square F Sig.1 Regression 133.154 5 26.631 23.006 .000b

Residual 229.196 198 1.158

Total 362.350 203

a. Dependent Variable: loyalty

b. Predictors: (Constant), Value_Shortterm, Satisfaction, Trust, Agent, Value_Longterm

Germany-specific data

Holland-specific data

It is observed that the models are significant (<0.05) in case of the data specific to all three countries.

Model Summaryb

Model R R SquareAdjusted R

Square

Std. Error of the

Estimate

Change StatisticsDurbin-Watson

R Square Change F Change df1 df2

Sig. F Change

1 .853a .727 .721 .82278 .727 125.127 5 235 .000 2.170

a. Predictors: (Constant), Value_Shortterm, Satisfaction, Trust, Agent, Value_Longterm

b. Dependent Variable: loyalty

Model Summaryb


Square

Std. Error of the

Estimate



Sig. F Change

1 .794a .630 .624 .94645 .630 111.642 5 328 .000 1.836

a. Predictors: (Constant), Value_Shortterm, Satisfaction, Trust, Agent, Value_Longterm

b. Dependent Variable: loyalty

Model Summaryb


Square

Std. Error of the

Estimate



Sig. F Change

1 .606a .367 .351 1.07590 .367 23.006 5 198 .000 2.102a Predictors: (Constant), Value_Shortterm, Satisfaction, Trust, Agent, Value_Longtermb. Dependent Variable: loyalty

The US

Germany

Holland

Adjusted R Square

The highest value of Adj. R sq. is in case of the US, which is 0.721. i.e. 72.1% variation in Loyalty is explained by the IVs considered. That value is 62.4% in case of Germany.Holland, however, has the adj. R sq. value just 0.351, which means there exist other factorsapart from the IVs considered who drive the customer loyalty.

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

95.0% Confidence Interval for B Collinearity Statistics

B Std. Error BetaLower Bound

Upper Bound Tolerance VIF

1 (Constant) .312 .220 1.421 .157 -.121 .745

Satisfaction .081 .037 .115 2.172 .031 .007 .154 .414 2.418

Agent .128 .044 .185 2.884 .004 .041 .216 .282 3.543

Trust .158 .050 .231 3.165 .002 .060 .256 .219 4.569

Value_Shortterm

.096 .053 .131 1.838 .067 -.007 .200 .228 4.394

Value_longterm

.190 .057 .278 3.341 .001 .078 .303 .168 5.958


Multiple Regression Model

All the Independent Variables have significance values <0.05. Therefore all of them need to be considered for the Multiple Regression Model.Based on the unstandardized coefficients of the IVs, following model is made –

Loyalty = 0.312 + 0.081*Satisfaction + 0.128*Agent + 0.158*Trust+ 0.096*Value_Shortterm + 0.190*Value_Longterm

The US

Coefficientsa

Model


Standardized

Coefficients

t Sig.




1 (Constant) -.255 .237 -1.077 .282 -.722 .211 Satisfaction .226 .054 .292 4.185 .000 .120 .332 .232 4.308Agent .064 .053 .083 1.207 .228 -.040 .168 .239 4.191

Trust .120 .060 .151 1.980 .049 .001 .238 .194 5.145

Value_Shortterm

.184 .051 .218 3.626 .000 .084 .283 .313 3.195

Value_longterm

.110 .053 .137 2.079 .038 .006 .213 .261 3.825



All the Independent Variabless have significance values <0.05 except for the Agent. Rest of them are going to be part of the Multiple Regression Model.Based on the unstandardized coefficients of the IVs, following model is made –

Loyalty = -0.255 + 0.226*Satisfaction + 0.120*Trust+ 0.184*Value_Shortterm + 0.110*Value_Longterm

Germany

Coefficientsa

Model


Standardized

Coefficients

t Sig.




1 (Constant) .236 .459 .515 .607 -.669 1.142

Satisfaction

.096 .100 .098 .956 .340 -.101 .293 .302 3.308

Agent .172 .082 .176 2.086 .038 .009 .335 .447 2.238

Trust .091 .089 .097 1.023 .308 -.084 .266 .353 2.831

Value_Shortterm

.010 .112 .011 .088 .930 -.211 .230 .221 4.517

Value_longterm

.280 .093 .316 3.025 .003 .098 .463 .293 3.415



Only Agent and Value_Longterm are significant variables.Therefore only these two are going to be part of the Multiple Regression Model.Based on the unstandardized coefficients of the IVs, following model is made –

Loyalty = 0.236 + 0.172*Agent + 0.280*Value_Longterm

Holland

Analysis – Country-specific Data

•Based on the coefficients obtained from Multiple Regression, it is observed that a unit increase in Value_LongTerm contributes most to the dependent variable Loyalty in case of US and Holland.•For Germany, Satisfaction has the most bearing on the Loyalty for every unit increase.•The results of multiple regression on the US-specific data indicates that customer loyalty is significantly dependent on all the Independent Variables under consideration.•For Germany, Agent is not significant. •For Holland, only Agent and Value_Longterm are significant.•Thus, factors influencing customer loyalty varies from country to country

Recommendations

Company should implement good CRM programs to keep the customer base satisfiedbecause Satisfaction and long term value are the major factor significant in driving up the Loyalty

More long term insurance policies to generate high Long term value from Customers should be included

Assignement2_Group4

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