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A random sample of 10,000 customers was selected over a six month period. The data provides information on their current status with the bank as well as 12 additional attributes describing their demographic and banking information.  what are the key predictors the

A random sample of 10,000 customers was selected over a six month period. The data provides information on their current status with the bank as well as 12 additional attributes describing their demographic and banking information.  what are the key predictors the

Principles Of Marketing
Principles Of Marketing
17th Edition
ISBN:9780134492513
Author:Kotler, Philip, Armstrong, Gary (gary M.)
Publisher:Kotler, Philip, Armstrong, Gary (gary M.)
Chapter1: Marketing: Creating Customer Value And Engagement
Section: Chapter Questions
Problem 1.1DQ
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A random sample of 10,000 customers was
selected over a six month period. The data provides information on their current status with the bank
as well as 12 additional attributes describing their demographic and banking information. 

what are the key predictors the
bank should be aware of with their customers? Examples are included, but not limited to:
Are female customers leaving more than males?
Is there indication of customer departures associated with specific countries?
Is there evidence of salary associated departures?

what is an appropiate marketing strategy to attract and retain long term customers

for feature, ax in zip(cat_features, axes.flat): plot sns.countplot(x=feature, hue=target, data=churn, ax=ax) Exited Exited 4000 0 1 4000 1 3500 3000- 3000 2500 LLL 2000 2000 1500 1000 1000 500 0 0 France Germany Female Male Spain Geography Gender Exited Exited 1 LL LL 0 0 IsActiveMember HasCrCard count count 4000 3000 2000 1000 count 5000 4000 3000 2000 1000
Transcribed Image Text:for feature, ax in zip(cat_features, axes.flat): plot sns.countplot(x=feature, hue=target, data=churn, ax=ax) Exited Exited 4000 0 1 4000 1 3500 3000- 3000 2500 LLL 2000 2000 1500 1000 1000 500 0 0 France Germany Female Male Spain Geography Gender Exited Exited 1 LL LL 0 0 IsActiveMember HasCrCard count count 4000 3000 2000 1000 count 5000 4000 3000 2000 1000
corr = churn.drop (columns=useless_features).corr() corr CreditScore Age 1.000000 -0.003965 0.000842 0.006268 CreditScore Age -0.003965 1.000000 -0.009997 0.028308 Tenure 0.000842 -0.009997 1.000000 -0.012254 Balance NumOfProducts 0.006268 0.028308 -0.012254 1.000000 0.012238 -0.030680 0.013444 -0.304180 HasCrCard -0.005458 -0.011721 0.022583 -0.014858 IsActiveMember 0.025651 0.085472 -0.028362 -0.010084 EstimatedSalary -0.001384 -0.007201 0.007784 0.012797 Exited -0.027094 0.285323 -0.014001 0.118533 Tenure Balance NumOfProducts HasCrCard IsActive Member EstimatedSalary Exited 0.012238 -0.005458 0.025651 -0.001384 -0.027094 -0.030680 -0.011721 0.085472 -0.007201 0.285323 0.013444 0.022583 0.007784 -0.014001 -0.028362 -0.010084 0.012797 0.118533 -0.304180 -0.014858 1.000000 0.003183 0.014204 -0.047820 0.009612 -0.011866 1.000000 -0.009933 -0.007138 0.003183 0.009612 -0.011866 -0.011421 -0.156128 1.000000 -0.011421 0.014204 -0.009933 1.000000 0.012097 -0.047820 -0.007138 -0.156128 0.012097 1.000000
Transcribed Image Text:corr = churn.drop (columns=useless_features).corr() corr CreditScore Age 1.000000 -0.003965 0.000842 0.006268 CreditScore Age -0.003965 1.000000 -0.009997 0.028308 Tenure 0.000842 -0.009997 1.000000 -0.012254 Balance NumOfProducts 0.006268 0.028308 -0.012254 1.000000 0.012238 -0.030680 0.013444 -0.304180 HasCrCard -0.005458 -0.011721 0.022583 -0.014858 IsActiveMember 0.025651 0.085472 -0.028362 -0.010084 EstimatedSalary -0.001384 -0.007201 0.007784 0.012797 Exited -0.027094 0.285323 -0.014001 0.118533 Tenure Balance NumOfProducts HasCrCard IsActive Member EstimatedSalary Exited 0.012238 -0.005458 0.025651 -0.001384 -0.027094 -0.030680 -0.011721 0.085472 -0.007201 0.285323 0.013444 0.022583 0.007784 -0.014001 -0.028362 -0.010084 0.012797 0.118533 -0.304180 -0.014858 1.000000 0.003183 0.014204 -0.047820 0.009612 -0.011866 1.000000 -0.009933 -0.007138 0.003183 0.009612 -0.011866 -0.011421 -0.156128 1.000000 -0.011421 0.014204 -0.009933 1.000000 0.012097 -0.047820 -0.007138 -0.156128 0.012097 1.000000
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