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RELEVANT INFORMATION 
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 (or not) subscribed.
DATA ATTRIBUTES 
Number of Instances: 45211 
Number of Attributes: 16 + output attribute. 
Attribute information: 
Input variables: 
# bank client data: 
1 - age (numeric) 
2 - job : type of job (categorical: admin.", "unknown", "unemployed", "management", 
"housemaid", “entrepreneur", "student", "blue-collar", "self-employed",“ retired", "technician", 
"services") 
3 - marital : marital status (categorical: "married",“ divorced", "single"; note: "divorced" means 
divorced or widowed) 
4 - education (categorical: "unknown", "secondary", "primary", "tertiary") 
5 - default: has credit in default? (binary: "yes", "no") 
6 - balance: average yearly balance, in euros (numeric) 
7 - housing: has housing loan? (binary: "yes", "no") 
8 - loan: has personal loan? (binary: "yes","no")
DATA ATTRIBUTES… 
# related with the last contact of the current campaign: 
9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 
10 - day: last contact day of the month (numeric) 
11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 
12 - duration: last contact duration, in seconds (numeric) 
# other attributes: 
13 - campaign: number of contacts performed during this campaign and for this client 
(numeric, includes last contact) 
14 - pdays: number of days that passed by after the client was last contacted from a previous 
campaign (numeric, -1 means client was not previously contacted) 
15 - previous: number of contacts performed before this campaign and for this client 
(numeric) 
16 - poutcome: outcome of the previous marketing campaign (categorical: 
"unknown","other","failure","success")
EXPECTED OUTPUT 
Output variable (desired target): 
17 - y - has the client subscribed a term deposit? (binary: "yes", "no")
INITIAL ANALYSIS
CUSTOMER PROFILE
DESCRIPTIVE STATISTICS 
Subscribed for Term Deposit 
Not Subscribed for Term Deposit
MARKETING CAMPAIGN
MARKETING CAMPAIGN…
CLUSTER ANALYSIS
CLUSTER ANALYSIS
CLUSTER ANALYSIS – PREDICTOR IMPORTANCE
CLUSTER ANALYSIS – CLUSTER PROFILING
CLASSIFICATION
FEATURE SELECTION – BEST PREDICTORS 
Using Feature Selection and 
Variable Screening - Statistica
Tree 1 graph for y 
Num. of non-terminal nodes: 9, Num. of terminal nodes: 10 
ID=1 N=45211 
no 
ID=2 N=40238 
no 
ID=4 N=37936 
no 
ID=7 N=36800 
no 
ID=9 N=12160 
no 
ID=13N=10417 
no 
ID=14 N=7410 
no 
ID=16 N=6073 
no 
ID=3 N=4973 
no 
ID=6 N=1136 
no 
ID=8 N=24640 
no 
ID=12 N=1743 
no 
ID=18 N=6071 
no 
ID=19 N=2 
yes 
ID=17 N=1337 
no 
ID=15 N=3007 
no 
ID=5 N=2302 
no 
ID=24 N=3191 
no 
ID=25 N=1782 
yes 
duration 
<= 521.500000 > 521.500000 
Year 
<= 2009.500000 > 2009.500000 
month 
= oct , ... = Other(s) 
Year 
<= 2008.500000 > 2008.500000 
month 
= jun , ... = Other(s) 
housing 
= yes ... = Other(s) 
duration 
<= 306.500000 > 306.500000 
age 
<= 68.500000 > 68.500000 
duration 
<= 827.500000 > 827.500000 
no 
yes
ACCURACY 
Observed Predicted No Predicted Yes Row Total 
Number no 39176 746 39922 
Column % 90.21% 41.82% 
Row % 98.13% 1.87% 
Total % 86.65% 1.65% 88.30% 
Number yes 4251 1038 5289 
Column % 9.79% 58.18% 
Row % 80.37% 19.63% 
Total % 9.40% 2.30% 11.70% 
Count All Groups 43427 1784 45211 
Total % 96.05% 3.95% 
Accuracy = (39176+1038)/45211) = 0.8894
RECOMMENDATION 
The business need to optimize the Marketing Campaign 
With the classification model – the conditions to predict the client subscribing a 
term loan 
• Last contact duration, in sec should be more than 521.5 sec and Year 
should be 2009 /2010 
• Contact Type should be Cellular 
• Contact month to be Oct, Dec, Mar, Sep 
• Age less than 60.5 Years 
• Conversion rate with Marital Status – Single (17.5%), Divorced (13.5%) and 
Married (11.2%)
THANK YOU
APPENDIX
CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED WITH THE 
BANK CLIENT DATA VARIABLES
IMPORTANCE PLOT – BANK CLIENT DATA
CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED WITH THE LAST 
CONTACT OF THE CURRENT CAMPAIGN VARAIBLES (DURATION IS THE SIGNIFICANT VARIABLE)
CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED 
WITH THE OTHER ATTRIBUTES
CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED 
WITH THE OTHER ATTRIBUTES (EXCLUDING POUTCOME)
Based on the Previous campaign; the response effectiveness
PARALLEL COORDINATE PLOT IN NODE 1
WITHOUT – PREDICTOR VARIABLE SELECTED

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Portuguese Bank - Direct Marketing Campaign

  • 1.
  • 2. RELEVANT INFORMATION 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 (or not) subscribed.
  • 3. DATA ATTRIBUTES Number of Instances: 45211 Number of Attributes: 16 + output attribute. Attribute information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: admin.", "unknown", "unemployed", "management", "housemaid", “entrepreneur", "student", "blue-collar", "self-employed",“ retired", "technician", "services") 3 - marital : marital status (categorical: "married",“ divorced", "single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown", "secondary", "primary", "tertiary") 5 - default: has credit in default? (binary: "yes", "no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes", "no") 8 - loan: has personal loan? (binary: "yes","no")
  • 4. DATA ATTRIBUTES… # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")
  • 5. EXPECTED OUTPUT Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes", "no")
  • 8. DESCRIPTIVE STATISTICS Subscribed for Term Deposit Not Subscribed for Term Deposit
  • 13. CLUSTER ANALYSIS – PREDICTOR IMPORTANCE
  • 14. CLUSTER ANALYSIS – CLUSTER PROFILING
  • 16. FEATURE SELECTION – BEST PREDICTORS Using Feature Selection and Variable Screening - Statistica
  • 17. Tree 1 graph for y Num. of non-terminal nodes: 9, Num. of terminal nodes: 10 ID=1 N=45211 no ID=2 N=40238 no ID=4 N=37936 no ID=7 N=36800 no ID=9 N=12160 no ID=13N=10417 no ID=14 N=7410 no ID=16 N=6073 no ID=3 N=4973 no ID=6 N=1136 no ID=8 N=24640 no ID=12 N=1743 no ID=18 N=6071 no ID=19 N=2 yes ID=17 N=1337 no ID=15 N=3007 no ID=5 N=2302 no ID=24 N=3191 no ID=25 N=1782 yes duration <= 521.500000 > 521.500000 Year <= 2009.500000 > 2009.500000 month = oct , ... = Other(s) Year <= 2008.500000 > 2008.500000 month = jun , ... = Other(s) housing = yes ... = Other(s) duration <= 306.500000 > 306.500000 age <= 68.500000 > 68.500000 duration <= 827.500000 > 827.500000 no yes
  • 18. ACCURACY Observed Predicted No Predicted Yes Row Total Number no 39176 746 39922 Column % 90.21% 41.82% Row % 98.13% 1.87% Total % 86.65% 1.65% 88.30% Number yes 4251 1038 5289 Column % 9.79% 58.18% Row % 80.37% 19.63% Total % 9.40% 2.30% 11.70% Count All Groups 43427 1784 45211 Total % 96.05% 3.95% Accuracy = (39176+1038)/45211) = 0.8894
  • 19. RECOMMENDATION The business need to optimize the Marketing Campaign With the classification model – the conditions to predict the client subscribing a term loan • Last contact duration, in sec should be more than 521.5 sec and Year should be 2009 /2010 • Contact Type should be Cellular • Contact month to be Oct, Dec, Mar, Sep • Age less than 60.5 Years • Conversion rate with Marital Status – Single (17.5%), Divorced (13.5%) and Married (11.2%)
  • 22.
  • 23.
  • 24. CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED WITH THE BANK CLIENT DATA VARIABLES
  • 25. IMPORTANCE PLOT – BANK CLIENT DATA
  • 26. CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED WITH THE LAST CONTACT OF THE CURRENT CAMPAIGN VARAIBLES (DURATION IS THE SIGNIFICANT VARIABLE)
  • 27. CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED WITH THE OTHER ATTRIBUTES
  • 28. CLASSIFICATION DONE TO CHECK THE DEPENDENCY AND IMPORTANCE OF VARIABLES RELATED WITH THE OTHER ATTRIBUTES (EXCLUDING POUTCOME)
  • 29.
  • 30. Based on the Previous campaign; the response effectiveness
  • 32. WITHOUT – PREDICTOR VARIABLE SELECTED