Bank Marketing Campaign Predictive Analysis
Rudra Teja Namala
February 2nd 2019
I. Definition
Project Overview
In banks, huge data records information about their customers. This data can be used to create and keep clear relationship and connection with the customers in order to target them individually for definite products or banking offers. Usually, the selected customers are contacted directly through: personal contact, telephone cellular, mail, and email or any other contacts to advertise the new product/service or give an offer, this kind of marketing is called direct marketing. In fact, direct marketing is in the main a strategy of many of the banks and insurance companies for interacting with their customers [1].
Historically, the name and identification of the term direct marketing suggested first time in 1967 by Lester Wunderman, which he is considered to be the father of direct marketing [2]. In addition, some of the banks and financial-services companies may depend only on strategy of mass marketing for promoting a new service or product to their customers. In this strategy, a single communication message is broadcasted to all customers through media such as television, radio or advertising firm, etc. [3]. In this approach, companies do not set up a direct relationship to their customers for new-product offers. In fact, many of the customers are not interesting or respond to this kind of sales promotion [4].
Accordingly, banks, financial-services companies and other companies are shifting away from mass marketing strategy because its ineffectiveness, and they are now targeting most of their customers by direct marketing for specific product and service offers [1, 4]. Due to the positive results clearly measured; many marketers attractive to the direct marketing. For example, if a marketer sends out 1,000 offers by mail and 100 respond to the promotion, the marketer can say with confidence that the campaign led immediately to 10% direct responses. This metric is known as the 'Response Rate', and it is one of many clear quantifiable success metrics employed by direct marketers.
From the literature, the direct marketing is becoming a very important application in data mining these days. The data mining has been used widely in direct marketing to identify prospective customers for new products, by using purchasing data, a predictive model to measure that a customer is going to respond to the promotion or an offer [5]. Data mining has gained popularity for illustrative and predictive applications in banking processes.
Problem Statement
All bank marketing campaigns are dependent on customers‟ huge electronic data. The size of these data sources is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in the performance of these campaigns.
The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success (the deposit subscribed by the client) based on a handful of algorithms that we will test (e.g. Logistic Regression, Gaussian Naive Bayes, Decision Trees and others). We the experimental results we will demonstrate the performance of the models by statistical metrics like accuracy, sensitivity, precision, recall, etc. We the higher scoring of these metrics, we will be able to judge the success of these models in predicting the best campaign contact with the clients for subscribing deposit.
Metrics
The evaluation metrics proposed are appropriate given the context of the data, the problem statement, and the intended solution. The performance of each classification model is evaluated using three statistical measures; classification accuracy, sensitivity and specificity. It is using true positive (TP), true negative (TN), false positive (FP) and false negative (FN). The percentage of Correct/Incorrect classification is the difference between the actual and predicted values of variables. True Positive (TP) is the number of correct predictions that an instance is true, or in other words; it is occurring when the positive prediction of the classifier coincided with a positive prediction of target attribute. True Negative (TN) is presenting a number of correct predictions that an instance is false, (i.e.) it occurs when both the classifier, and the target attribute suggests the absence of a positive prediction. The False Positive (FP) is the number of incorrect predictions that an instance is true. Finally, False Negative (FN) is the number of incorrect predictions that an instance is false. Table below shows the confusion matrix for a two-class classifier.
II. Analysis
Data Exploration
The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).Input variables:
1. age (numeric)
2. job : type of job (categorical:
'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unkn own')
3. marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4. education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
5. default: has credit in default? (categorical: 'no','yes','unknown')
6. housing: has housing loan? (categorical: 'no','yes','unknown')
7. loan: has personal loan? (categorical: 'no','yes','unknown')
8. contact: contact communication type (categorical: 'cellular','telephone')
9. month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
10. day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
11. duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
12. campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
13. pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
14. previous: number of contacts performed before this campaign and for this client (numeric)
15. poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
16. emp.var.rate: employment variation rate - quarterly indicator (numeric)
17. cons.price.idx: consumer price index - monthly indicator (numeric)
18. cons.conf.idx: consumer confidence index - monthly indicator (numeric)
19. euribor3m: euribor 3 month rate - daily indicator (numeric)
20. nr.employed: number of employees - quarterly indicator (numeric)
Output variable (desired target):
1. y - has the client subscribed a term deposit? (binary: 'yes','no')
Exploratory Visualization
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