Lecture on "A Practical Exposition of Data Science in the Retail Marketing and Financial Services" delivered by Delali Agbenyegah, Director of Data Science and Analytics, Express, Columbus OH, USA.

1. A Practical Exposition of Data
Science in the Retail Marketing
and Financial Services
Delali Agbenyegah, Director of Data Science and Analytics, Express,
Columbus , Ohio
Co-founder,Wave-2 Analytics, Accra, Ghana

2. ABOUT ME

3. Outline
• Why Data Science in Retail and Financial services
• The end to end Data Science process
• Major Data Science Applications in Retail
• Major Data Science Applications in Financial Sector
• Retail Case Study

4. The Data Challenge and Opportunity in Retail
Retail Data Examples
• Customer information and
demographic
• POS/Customer purchase data
• Product interaction data
• Marketing promotion data
• Website interaction data
• Shopping channel and location data
• Customer survey and feedback data
• Product pricing data
• Customer contact data
• Social media interaction data
• Call center data
Retail-selling consumer goods and
services through multiple channels

5. The financial Services-Data everywhere
Businesses that manage money…banks, credit
unions, insurance, credit card, stock
brokerages, investment funds, consumer
finance, etc.
Financial Services Data Examples
• Customer identification/information
• Credit card interaction
• Insurance premium/claims data
• Deposits/withdrawals
• Stock price data
• Customer feedback
• Financial products purchase interaction
• Fraud/money laundering interactions
• Marketing promotions
• Service notifications/response
• Image/ID card data
• ATM location/behavior data
• Call center data

6. How do retail & financial services get value out of
the massive Data? Data Science is the answer!

7. Data Science ≠ Business Intelligence
Concepts
Business
Intelligence/Reporting Data Science
Questions What happened? What will happened? What if?
Actions Slice and Dice Interact
Data Warehouse, siloed Distributed, real time
Perspective Looking backwards Understanding past but looking forwards
Scope Unlimited, general Specific business question(s)
Output Reports,Tables Model scores, integrated applications
Tools SAP,Cognos,Microstrategy,etc.
Python,R,H20,SAS Enterprise
Miner,Viya,etc.
Applicability Historic,possible confoundings Future,correcting for influences
Sexy? I don't know Very Sexy

8. Data Science Process in Practice

9. Popular Retail Data Science Applications

10. Popular Financial Sector Data Science Applications
Data Science
in the
Financial
Sector
Fraud
Detection/
Cyber
Security Algorithmic
Trading
Underwriting
and credit
scoring
Automated
Risk
management
Insurance/
Product
PricingClaim
Modeling
Deposit/
Withdrawal
Forecasting
Natural
language
processing
Customer
Relationship
Management
Identity
verification

11. Retail Case study-Customer Life Stage Prediction
• Large Apparel Retailer with strong
online presence
• Over 1,800 stores world wide
• Acquires about 12M customers a
year, with $250 spend per buyer
• Have about 31M active buyers
• 77% one time buyer rate
• 35% retention rate
• 46% purchase in only one
department
• 22% rewards redemption rate
• 9% email open rate
(1)Business Question: How can I reduce my one time buyer rate and increase
overall customer retention?

12. Retail Case study-Customer Life Stage Prediction
Customer
Purchase
Behavior
Rewards
Earning and
redemption
Contact
history
Demography
Browsing
Behavior
Purchase
tender
Call center
and chat
bots
(2) Acquire data (3)Reformat and clean

13. Retail Case study-Customer Life Stage Prediction
(4) Hypothesize (5) Transform and Visualize

14. Retail Case study-Customer Life Stage Prediction
(4) Hypothesize (5) Transform and Visualize
• What attributes relate to time
between purchases?
• Are there certain product
purchases/departments that make
customers to not return?
• Are there size fit problems?
• Is retention rate different for different
purchase channels?
• Are certain messaging channels driving
more retention?

15. Retail Case study-Customer Life Stage Prediction
What to Model Methodologies Tested
Life stage: Is customer active, at risk
or completely lost
Decision Trees, Random Forest,
XGboost
Severity of life stage: How long
inactive or lost
Survival Analysis, Hazard
Modeling
Content to drive Action: What
messaging/offering do you give to
each life stage?
A/B design testing, simulation and
optimization
(6) Model and Validate [Iterate (4) to (5) until…..]

16. Retail Case study-Customer Life Stage Prediction
What to Model Methodologies Tested
Life stage: Is customer active, at risk
or completely lost
Logistic regression,Decision Trees,
Random Forest, XGboost
Severity of life stage: How long
inactive or lost
Survival Analysis, Hazard
Modeling
Content to drive Action: What
messaging/offering do you give to
each life stage?
A/B design testing, simulation and
optimization
(6) Model and Validate [Iterate (4) to (5) until…..]

17. Understanding the trade-offs in Machine
Learning

18. Ensemble Learning-Bagging
f

19. Ensemble Learning-Boosting

20. Retail Case study-Customer Life Stage Prediction
Transactional Profile: January 2018 -December 2018
Active and Buying At Risk
Rank Customers
Retention
rate Avg Trip Avg SpendCustomers
Retention
rate Avg Trip Avg Spend
1 365K 88% 5.22 $1,085 40K 77% 3.36 $695
2 316K 67% 2.01 $428 89K 57% 1.45 $299
3 264K 53% 1.35 $280 141K 47% 1.06 $212
4 274K 36% 0.79 $160 131K 39% 0.81 $161
5 267K 26% 0.5 $98 138K 29% 0.57 $113
6 252K 17% 0.29 $55 153K 24% 0.45 $85
7 233K 13% 0.2 $38 172K 19% 0.33 $64
8 114K 8% 0.1 $18 291K 15% 0.26 $51
9 405K 9% 0.14 $25
10 405K 5% 0.07 $12
Overall 2.08M 44% 1.62 $335 1.97M 21% 0.45 $90
(7) Report results

21. Retail Case study-Customer Life Stage Prediction
(8) Deployment
• Model Scoring (serving) automation
• User interface development
• Content/Messaging testing and
optimization
• Actual results validation and model
monitoring

22. Retail Case study-Customer Life Stage
Prediction
Pre-Data Science Project
(12 months metrics)
Data Science
Product build
and
Implementation
Post-Data Science
Project(12 months
metrics)
73% one time buyer rate 68% one time buyer rate
35% retention rate 40% retention rate
46% purchase in only one
department
30% purchase in only one
department
22% rewards redemption 34% rewards redemption
rate
9% email open rate 15% email open rate
(9) Showcasing results

23. Questions