Created personalized product recommendation engine predictive models and Market basket analysis for products to be purchased by existing and new customers. Created Multi output ensemble classifier model with MAP score of 0.39.

1. SANTANDER BANK PRODUCT
RECOMMENDATION
TEAM 2
CHRISTOPHER BRECHLIN
PRACHI KHAMBHALIA
SWARAJ MACHIRAJU
SREERAM KASARLA
AJAY YEDAMA

2. IMPORTANT TAKEAWAYS FROM
PREVIOUS WORK
• Data consists of more than 13 million records for 48 variables; 956,645
unique customers
• There are 24 products offered by Santander bank
• There are 3 major segments of customers – VIP, INDIVIDUAL,
UNIVERSITY GRADUATES
• Data has some seasonality component
• Majority of the customers are inactive
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3. CURRENT ANALYTICS APPROACH 3

4. DATA PREPROCESSING
• Removed Columns with 99% empty records
• Converted columns into relevant data types
• One Hot Encoding of Categorical variables
• Missing value imputation for Household Income
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5. EXPLORATORY DATA ANALYSIS 5

6. EXPLORATORY DATA ANALYSIS 6

7. EXPLORATORY DATA ANALYSIS 7

8. EXPLORATORY DATA ANALYSIS
Channels effective for Existing
Customers
• KHE (Most effective)
• KAT
• KFC
• KFA
• KHK
• KHQ
• RED
Channels effective for New
Customers
• KHK
• KHQ
• KHM
• KHN
• KHL
• KHO
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9. EXPLORATORY DATA ANALYSIS
A - Active
I- Inactive
P- Former
R- Potential
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Note: Averages of income tend to
be skewed, hence the high
values.

10. EXPLORATORY DATA ANALYSIS 10

11. EXPLORING THE SEASONALITY
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12. PRODUCT PURCHASE TRENDS OF NEW CUSTOMERS
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13. May 2015 May 2016
Key New products : Credit Card, Debit Card, Payroll, Pension, E-Account, Current Accounts
PRODUCT PURCHASE TRENDS OF EXISTING CUSTOMERS
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14. FEATURE IMPORTANCE 14

15. MODELING
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Let’s say, we recommended 7 products and 1st, 4th, 5th, 6th product was correct. so the
result would look like — 1, 0, 0, 1, 1, 1, 0.
In this case,
1. The precision at 1 will be: 1/1 = 1
2. The precision at 2 will be: 0
3. The precision at 3 will be: 0
4. The precision at 4 will be: 2/4 = 0.5
5. The precision at 5 will be: 3/5 = 0.6
6. The precision at 6 will be: 4/6 = 0.66
7. The precision at 7 will be: 0
Average Precision will be: 1 + 0 + 0 + 0.5 + 0.6 + 0.66 + 0 /4 = 0.69
MODEL METRIC - MAP@7

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CALCULATING - MAP@7 ON OUR FINAL MODEL

18. ASSOCIATIVE ANALYSIS 18

19. BUSINESS ADD-ONS FROM THE
ANALYSIS
● Personalized recommendations as a service
● Reduce Manual Workload
● Transform Prospects to Clients
● Customer Satisfaction
● Increased Revenue
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20. BUSINESS STRATEGIES
● Start with Lowest hanging fruit
● Understand the Channels customers willing to cross-sold through
● Make use of the data obtained from browsing patterns of the
customer on the website and use selective advertisement
● Offer more digital banking services and capitalize on Zero Moment of
Truth by introducing the sales team at the right time
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21. BUSINESS ADD-ONS
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Novantas 2013 Multi-Channel Sales Survey (Total US Respondents = 4,813)

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