1. PROPENSITY SCORES
Learn how to efficiently identify customers most likely to respond to marketing campaigns
Webinar
Dec 1, 2016

2. PRESENTERS David Royal, Keaton Baughan, Client Success Manager
Product Manager

3. Anyone know who this is? Let’s test the chat functionality
We have a sweet prize for the first person to chat the correct answer!
Hint 1: He’s the greatest prospector in the North
Hint 2: He’s obsessed with silver and gold
Hint 3: Friends with Hermey and Rudolph

4. WHAT ARE PROPENSITY SCORES?
AGENDA 1
WHAT ARE PROPENSITY SCORES? 2
HOW TO CREATE PROPENSITY SCORES 3
ANALYTICS IN ACTION

5. WHAT ARE PROPENSITY SCORES?
AGENDA 1
WHAT ARE PROPENSITY SCORES? 2
HOW TO CREATE PROPENSITY SCORES 3
ANALYTICS IN ACTION

6. PROPENSITY SCORES Visible Equity Range: 0 - 100
A number that indicates how likely a customer is to purchase a product
Visible Equity Range:

7. PROPENSITY SCORE EXAMPLE
PRODUCT: Auto loan purchase in the next six months
Propensity Score:
John - 82
Steve - 12

8. WHY PROPENSITY SCORES? Targeting Save Resources Simple
- Identify the best customer/product combinations
Filter out customers less likely to purchase -
Save Resources
- Time
- Money
- Labor
Simple
Easy to understand
Interpretability

9. WHAT ARE PROPENSITY SCORES?
AGENDA 1
WHAT ARE PROPENSITY SCORES? 2
HOW TO CREATE PROPENSITY SCORES 3
ANALYTICS IN ACTION

10. PROPENSITY SCORE DATA

11. RESPONSE VARIABLE The response variable is the product of interest
Use predictors from previous period to predict who purchased product of interest within the period
Ex. PRODUCT: Auto loan purchases in the next six months

12. PROPENSITY SCORE EXAMPLE
PRODUCT: Auto loan purchase in the next six months
Propensity Score:
John - 82
Steve - 12

13. MODEL SELECTION This is a classic case of classification modeling
Generalized Linear Model
- Logit
- Probit
- Cloglog
- Loglog
Create training and test data sets with random assignment
Visible Equity used 70% of data for training and 30% for testing

14. HOW TO CHOOSE PREDICTORS
Dealing with many possible predictors
One way is to choose which variables make sense to you
LASSO – known as a shrinkage method, in that it takes many variables and narrows the list down to the most predictive ones

15. PRODUCTS AND PREDICTORS
PRODUCTS (6 Months)
Auto Loans
Bank Cards
First Mortgages
HELOCS
EXAMPLES OF PREDICTORS
FICO Score
Number of credit inquiries made in the past six months
Estimated Income
Number of loans with >0% utilization change in past six months
% of loans never delinquent in past six months

16. MODEL SELECTION
Using each model, create propensity scores for each person in test set
Separate propensity scores into quintiles (sort from lowest to highest scores and group into five equal size sets)
Within each quintile determine the proportion who actually purchased the product
Repeat this process many times
VE performed this using an expanded set of possible predictors and using a standard data pull

17. MODEL PERFORMANCE PRODUCT DATA 0% - 20% 20% - 40% 40% - 60% 60% - 80%
80% - 100%
QUINTILE
AUTO
STANDARD 4% 7%
10%
14%
23%
EXPANDED 8%
24%
BANK CARD 6% 9%
11%
15%
22%
13%
27%
FIRST MORTGAGE 1% 3% 5%
HELOC 2% 0%

18. OTHER POSSIBLE MODELS SUPPORT VECTOR MACHINES RANDOM FORESTS
LINEAR DISCRIMINANT ANALYSIS

19. WHAT ARE PROPENSITY SCORES?
AGENDA 1
WHAT ARE PROPENSITY SCORES? 2
HOW TO CREATE PROPENSITY SCORES 3
ANALYTICS IN ACTION

20. PROPENSITY SCORES IN CUSTOMER ANALYTICS

21. FUTURE IMPROVEMENTS Obtain more representative sample of population
Use Equifax and TransUnion data
Explore more predictors
Include more products
Incorporate time and changes in behaviors

22. SUMMARY Q&A What are propensity scores Use for marketing tool
Credit Bureau data
Model development
Coming soon

23. THANK YOU FOR MORE INFORMATION PLEASE CONTACT: