The group presented models to predict customer subscription to term deposits for a Portuguese bank. They described the customer data, built decision tree, regression, and neural network models with and without customer profiles. The decision tree model performed best with a profit of $15,768 and savings of $10,356. Rules from the decision tree could save $1200 by not contacting some customers or generate $10,000 revenue from others. The project aimed to boost sales, loyalty, and recapture customers through targeted marketing.

1. Bank Marketing Project
Group 7:
Zhaodi Liu
Preete Dixit
Nandini Naik
Rashmi Nadubeedi Ramesh
Pravin Kumar Prem Kumar

2. Agenda
• Project Motivation
• Data Description
• Our BI Models
• Experimental Results
• Association Rule mining
• Managerial Implications
• Challenges
• Conclusion

3. Project Motivation
• Direct marketing targets customers directly
with a personalized message as opposed to
Mass marketing
• The primary benefit to businesses:
– Increased lead generation
– Increase sales volume
– Increased customer base
– Minimize losses
• Focus on generating more "qualified" leads

4. Impact of Data Mining
• Can be very effective for direct marketing
• Use of sophisticated algorithms generate
rules, determine the most useful attributes
and predict future outcome
• Our goal is to predict the probability of a
client subscribing to the term deposit
• In the interest:
– To boost sales to existing customers
– Increase customer loyalty
– Recapture old customers and generate new
business

5. Data Description
• The data is related with direct marketing
campaigns (phone calls) of a Portuguese
banking institution
Data Set
Characteristics:
Multivariate Number of Instances: 45211 Area: Business
Attribute
Characteristics:
Real Number of Attributes: 17 Date Donated 2012-02-14
BANK CLIENT DATA
Serial No Name Description Data Type
1. age Client’s age numeric
2. job type of job categorical
3. marital marital status categorical
4. education level of education categorical
5. default has credit in default? binary
6. balance average yearly balance in euros numeric
7. housing has housing loan? binary
8. loan has personal loan? binary

6. DATA RELATED WITH THE LAST CONTACT OF THE CURRENT CAMPAIGN
Serial No Name Description Data Type
9. contact contact communication type categorical
10. day last contact day of the month numeric
11. month last contact month of year categorical
12. duration last contact duration, in seconds numeric
13. campaign number of contacts performed
during this campaign and for this
client
numeric
14. pdays Number of days that passed by
after the client was last contacted
from a previous campaign
numeric
15. previous number of contacts performed
before this campaign and for this
client
numeric
16. poutcome outcome of the previous
marketing campaign
categorical
OUTPUT VARIABLE (DESIRED TARGET)
Serial No Name Description Data Type
17. y has the client subscribed a term
deposit
binary

7. BI Model - With Target Profile

8. Profit/Loss Matrix

9. Decision Tree

12. Regression

13. Regression Node:
To convert categorical values to interval value using dummy
variables concept.
To group logically related categories in order to reduce the number of
independent variables in regression equation.

15. Neural Network

16.  The model converged after
70 iterations and contains
124 weights.

17. Neural Network with Input Selection

18. • Reducing the number of modelling inputs reduces the number of modelling
weights as well as computational costs and possibly improves the model
performance.
• The useful inputs are selected by connecting the Neural Network Node to
the Regression Node.

19.  The model converged after 42 iterations and contains 46 weights.

20. BI Model - Without Target Profile

21. Decision Tree

22. Regression

23. Neural Network
 The model converged after 70 iterations and contains 124 weights.

24. Neural Network with Input Selection
 The model converged after 76 iterations and contains 85 weights.

25. Model Assessment and Scoring results
with Target Profile Model
– The performance of the four models are compared based on the average
profit using the model comparison node.

26. Fit Statistics

27. ROC Plots

28. Confusion Matrix

29. Scoring
– Scoring is used to implement the model deemed best by the model
comparison node for predicted the outcome for a new case/observation
for which the outcome is unknown.
Replaced Variables:
– Job – management
– Education – Secondary
– Contact – Cellular
Rejected Variables:
– Poutcome
– Target y

30. Scoring

31. Scoring
Actual Data Scores:
– Percentage No = 88.476%
– Percentage Yes = 11.524%
There is a slight difference of 1.725% in the prediction model
outcome and the actual outcome.

32. Model Assessment and Scoring results
without Target Profile Model
– The performance of the four models are compared based on the
misclassification rate using the model comparison node.

33. Fit Statistics

34. ROC Plots

35. Confusion Matrix

36. Scoring

37. Scoring

38. Scoring
Actual Data Scores:
– Percentage No = 88.476%
– Percentage Yes = 11.524%
There is a slight difference of 1.725% in the prediction model
outcome and the actual outcome.

39. Association Rule Mining

41. Data Pre-processing
• Default : D(Yes), D(No)
• Housing : H(Yes) , H(No)
• Personnel Loan : PL(Yes),PL(No)
• Age : 20- 40, 40-60, 60-90, and 90-100

42. Results & Interpretation

43. Managerial Implications
if
pdays < 19.5 or MISSING
AND month IS ONE OF: MAY, JUN, JUL, AUG, NOV, JAN or MISSING
AND duration < 348.5 or MISSING
AND age < 60.5 or MISSING
then
Predicted: y=YES = 0.02
Predicted: y=NO = 0.98
A total of 100 customers who and the cost of calling a customer is $12 then there
will be a saving $1200 just by not contacting these set of customers.
if
pdays < 19.5 or MISSING
AND month IS ONE OF: FEB
AND housing IS ONE OF: NO
AND duration < 466.5 or MISSING
AND day < 20.5 AND day >= 9.5
AND age < 60.5 or MISSING
then
Predicted: y=YES = 0.75
Predicted: y=NO= 0.25
A total of 100 customers and cost of calling a customer is $12 and if the profit is $100 then the Ban
could generate revenue of $10,000.

44. Decision Tree Model – Our best-fit model
for maximizing profits
Decision Tree
Predicted
Positive Negative
Actual
Positive 1314 1330
Negative 863 19098
Predicted
Positive Negative
Actual
Positive $15,768 $0
Negative -$10,356 $0

45. Regression Model
• If the pdays increase by 1-unit then it has
absolutely no impact on the odds of not
subscribing to the term deposit.
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46. Challenges
• To implement the Profit/Loss matrix
• Absence of ROC plot in the result of
Model Assessment
• Non-convergence of Neural Network
• Scoring

47. Conclusion
• Successfully implemented 2 predictive analysis
models to predict the outcome of term deposit
subscription
• Decision Tree best fit-model based on Profit/Loss
• Decision Tree best-fit model based on
Misclassification Rate
• Using the Decision rules
– Results in a saving of $1200
– Generates a revenue of $10,000
• Using Profit/Loss Matrix
– Profit of $15,768
– Savings of $10,356

48. Q&A