How to do the Coupons Analytics

1. Arun Shukla
Consultant
Data Science|Machine Learning
Coupons Analytics

2. Objective
• To explore opportunities of leveraging existing data for operational insights
• To derive optimal business scenarios based on operational insights
• To find the expected redemption based on historic behavior
Case in point
• Descriptive Analysis of Distribution
• Analysis of issues related to Distribution
• Cross tabulation Analysis across all consumable verticals
• Building predictive models around redemption in regards to loss of redemption value.

3. Data Leveraged
Coupons
Detail
Data
•Coupons Amount
•Coupons
Codes(1D/2D/Text)
•Online /Off Line
details(Digital Coupons)
•Offers segmentation
•Seasonal Trend
•Existence Time detail
Marketing
and sales
Data
Master
Data
•Industry coupons
•Company coupons
•Offer’s Coupons
•Product & service
•Demographic/
Geograpic Coupans
Vendor
Data
•Vendor Code
•Vendor Name
•Active coupons status
•Inactive coupons date
Coupons
Utility
Data
Utility Date
Utility Amount
•Various
Marketing detail
across the
Industry/Company
/Products
•Sales detail
across the
Industry/company
/Product

4. 4
DashBoards

5. Coupons
Industry
Company
Products
Offers 0
1
2
3
4
5 Active
Used
Loss
CouponsCount(K/M)
Active/Redemption/Loss Status
• Coupons Distribution Status Analysis
• Distribution of Coupons status across
geographical hierarchies
• Efficient Diagnosis of specific challenges
through drill down approach
Analytics Model
Descriptive/Diagnostic
Demo/
Geographic

6. Analytics Model
Descriptive/Diagnostic
• Cross Tabulation Analysis of redemption
of various Coupons
• Redemption vs Industry
• Redemption vs Product
• Redemption vs Company
• Redemption vs Offer
• Redemption vs Demography
• Redemption vs Geography
• Redemption vs Retailers
• Redemption of Digital Coupons
•Coupon Efficiency/Gain(%) across Industry/product category/Company/types of offer
= Consumed Coupons Value/Distributed Coupons value
•Redemption Value loss(%) across Industry/product category/Company/types of offer
= (Distributed value-Expired value)/Distributed value*100

7. Advanced Analytics Model:
Trend/Predictive Analytics
•Know how to find patterns in data to
avoid risks & spot opportunities
Priority areas
•Promotions- preferences- Basket sizes
•Shopper targeting- Location, Age
•Pricing- price sensitivity
•Inventory management
Monitor and Measure-Best ROI
•Select your target audience
•Put your coupons into action
•Use Multinomial Logistic Regression/Binary
Classification to predict the action on
coupons
Modelling Techniques:
•Coupons Consumed-Logistic regression
•Coupons “Used by Category” -
Classification-Decision tree
•Coupons “Customers Targets”-
recommendation engine

8. Thank
YOU