marketing campaign to sell long term deposits
TRANSCRIPT
Effectiveness of a Marketing Campaign to Sell Long Term Bank Deposits
Group 1Aditya Bahl, Prabhu Bijja, Pratibha Kumar, Sohini Sarkar
Background
● Term deposits held at a financial institution for fixed term.
● Higher interest compared to traditional savings accounts.
● Maturities range from a month to a few years.
● Safe investment.
● Appealing to conservative, low-risk investors.
● Allows banks to invest in higher gain financial products.
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Problem Statement
Which customers should be targeted for long-term bank deposits?
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Project Outline•Gather and explore data
•Data preprocessing
•Model building
•Model comparison
•Conclusion
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Gather and Explore Data• Number of instances – 41,188
• Number of attributes – 20o Attributes related to bank client datao Attributes related with the last contact of the current
campaigno Attributes related to social and economic contexto Other attributes related to previous campaigns
• Target variable (Y) – Has the client subscribed a term deposit?
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Data Exploration
Categoric data converted to numeric6
Data Preprocessing• Target leakage possible with Duration: Excluded
• Highly skewed #Contacts: Excluded
• Important variables from histograms• Education, job, personal loan, marital status, default,
market characteristics
• Missing values reported as “unknown” or “999”• > 5% - Excluded • < 5% - Imputed by Predictive Mean Matching
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Data Preprocessing• Class imbalance problem• SMOTE used to generate balanced data
Original Dataset Balanced Dataset8
Model Building• Models assessed
– Decision tree – Random Forest– Naïve Bayes
– Adaptive Boosting
– Clustering• Nominal and numeric data – Kmeans not a good option• Simple Matching Coefficient prohibitive for large dataset
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Train with balanced dataTest with original data
Train and Test with original data
Model Comparisons
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Modeling Method
Accuracy Overall Error ROC
Decision Tree 0.87 0.13 0.73
Random Forest 0.88 0.12 0.78
Naïve Bayes 0.72 0.28 0.76
Adaptive Boosting
0.90 0.1 0.8
Selected Model - Adaptive Boosting
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Important Variables
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*** Campaign specific
**** Customer specific
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* Market specific
** Bank specific
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Customer Specific Characteristics
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Campaign Specific Characteristics
Distribution by Campaign
# of times contacts performed for the client during this campaign
Distribution by Previous (contacts) Distribution by Month
# of times contacts performed for the client before this campaign
Conclusion• Customers to be Targeted:
• Age: 30-50• Education: University, High School, Professional Courses• Job: Admin, Blue-collar, technician
• Campaign Targets:• Customers who were not contacted before• Plan Campaigns from May through Aug.• Success rate for customer enrollment is more for 1-5 contacts
during present campaign.
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Questions?
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