Data Mining Solutions For Direct Market

Running Head: DATA MINING SOLUTIONS FOR DIRECT MARKETTING CAMPAIGNS
DATA MINING SOLUTIONS FOR DIRECT MARKETTING CAMPAIGNS
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DATA MINING SOLUTIONS FOR DIRECT MARKETTING CAMPAIGNS1
Executive Summary
Banks always adopt different strategies to enhance their business. Marketing campaign is
one of the typical strategies. In this case, a bank has decided to conduct an over-phone marketing
campaign. For this, they require a cost efficient solution based on the client profile to see
whether a customer will accept their offer or not. Here a Portuguese bank dataset has been given
based on 45211 customers. The main goal is to find whether a user takes the subscription or not.
To find out this, it is necessary to identify those factors responsible for the success of a
campaign. For this, some descriptive studies have been done at first. The graphical representation
of the variables show that people having low balance, good educational qualification are much
more interested in term loan. If the number of contacts for a client exceeds 10, then it would lead
to certain rejection. In the data analysis part, machine learning tools such as Decision tree,
logistic regression, SVM are applied to the data (Lantz 2013). The accuracy of the models are
very close to each other. However, logistic has shown the best fit to the data with 90% accuracy.
It also shows that job profile, qualification, marital status, duration of contact, month of contact,
result of previous campaign have significant role in the decision of a customer regarding the
bank offer. Therefore, it can be suggested that if bank considers these facts, then their strategies
will become more successful.

DATA MINING SOLUTIONS FOR DIRECT MARKETTING CAMPAIGNS2
Table of Contents
Introduction......................................................................................................................................3
Discussion........................................................................................................................................4
Data Description..........................................................................................................................4
Data Validation............................................................................................................................5
Descriptive Study.........................................................................................................................5
Data Visualization.......................................................................................................................8
Data Analysis:............................................................................................................................14
 Decision Tree:.....................................................................................................................14
 Logistic Regression:...........................................................................................................16
 Support Vector Machine(SVM):........................................................................................19
Comparison of the models:........................................................................................................21
Conclusion.....................................................................................................................................22
References......................................................................................................................................23
Appendix........................................................................................................................................25

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Introduction
A bank has decided to conduct a marketing campaign based on phone calls. For this,
previous records of a Portuguese bank have been provided (Moro 2014). The main objective is to
find out a cost efficient solution to support the campaign based on a customer’s profile. In other
words, it is required to find whether a client will accept the offer for term deposit given by the
bank or not.
To achieve this goal, a thorough analysis have been done here. First of all, some
descriptive studies have been performed to observe the nature of the variables. Then some
graphical representation has been made to see the trends, patterns of the data. In the analysis part,
three machine learning techniques have been performed- Decision Tree, Logistic Regression and
Support Vector Machine (SVM). The best model for the data is chosen by comparing these three
models. The results show the significant factors that were responsible for the outcome of the
campaign. Hence, if those factors are considered by bank, then it would lead to higher success
rate of the proposed programme. In other way, it can be said that if bank takes necessary steps
for those factors, then more customer will be convinced to accept the proposal.
The necessary calculations are performed using R software (Cirillo 2016). The relevant R
codes are given in the Appendix.

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Discussion
Data Description
The data is based on marketing campaigns of a Portuguese banking institution. The
campaigning was conducted over phone-calls. Further, it should be noted that often more than
one time a same client was contacted to make sure whether he/shell will open a term deposit or
not. There are 45211 observations with 17 variables. The variables are:
Nominal Variables:
 Job: Occupation of the client
 Marital: Marital Status of the client (single, married,divorced)
 Education: Education level of the client
 Default: Does the person have credit in default? (Yes or No)
 Housing: Does he have any housing loan? (Yes or No)
 Loan: Does the client have any loan?(Yes or No)
 Contact: contact communication type( “cellular”, ”telephone”, ”unknown”)
 Month: last contact month of the year
 poutcome: outcome of the previous campaign( “failure”, “non-existent”, “success”)
 y: has the client subscribed a term deposit(“yes”, “no”)
Numerical Variables:
 Age: Age of the client
 Balance: balance of the client
 Day: last contact day in a week

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 Duration: duration of contact in last time(seconds)
 Campaign: number of contacts performed for a client during the campaign
 pdays: number of days after the client was contacted last time
 previous: number of contacts performed before this campaign for a client
The relevant R codes are shown in the Appendix.
Data Validation
At first, it is required to check whether the data has any missing values or duplicate rows.
Here the data has no missing value. Hence, data cleaning tools were not required for this study.
Descriptive Study
An overall descriptive analysis is performed on the bank data (Plonsky 2015). The results
are given below.

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