This document discusses using predictive analytics and machine learning models to target customers for marketing promotions. Specifically: 1. It aims to build an accurate predictive model using customer purchase history and other data to identify those most likely to redeem a coupon for a specific product. 2. The model would then be deployed to select the top 15% of customers who are predicted to redeem the coupons in order to maximize return on investment. 3. Testing on sample data showed targeting the most likely 300 customers led to a profit of over €1,300,000, demonstrating the potential of using predictive analytics for targeted marketing.

1. Shona Dunleavy
Alex O Dwyer
Robert Sutcliffe-Booth
Aoife O'Gorman
Aditya Labhe
Group 8

2. Why?
Predictive analysis
Who?
are we
targeting
What?
Can we target them with
How?
are we targeting
them
Those with higher probable chances to act on our
desired outcome (in this case redeeming the coupon)
With upcoming promotions, promotions based on
their purchase history and so on.
Using various data science techniques, predictive analytics
models developed from the data we already have
A way to predict better marketing results for higher ROIs

3. The process | Building the model
Trying to build an accurate model
which can then be used for
deployment on the final data

4. The process |Deployment
Using the created model for
deploying it and coming with
probable customers who would
redeem the coupon

5. 300
The Chosen ones
The ones who we think
are most probably going
to redeem the coupons

6. Budget
Test Data Actual Data
Respondents targeted 2,000 15m
Top 15% most likely to buy
Special K
300 2.25m
Total cost of targeting (15c) €45 €337,500
Amount redeemed based on
our 82.4% redemption rate
247.2 1,854,000
Profit made from redeemed
coupons (90c)
€222.48 €1,668,600
Net profit (€177.48) €1,331,100
ROIs

7. Budget
Net profit based
different cut offs

8. The
Future
Of Retail

9. Future of retail
Using GPS in trolleys and
baskets and integrating their
club card system we are able
to gain a host of customer
insights and behaviours that
is tailored to each individual
store giving management the
most accurate and better
insight on personalising the
shopping experience

10. Customer scans his
loyalty card to get a
GPS enabled trolley
Tracking the
customer journey
Data extracted during
the check out
Tracking the customer’s
shopping
Data then used for further
recommendations and
personalised targeting
Data also used for developing
the product placement as per
the insights

11. Thank you Any
Questions
?