Keynote address at 2012 ReTechCon.com (annual conference of the Retailers Association of India), Mumbai.

1. Galit Shmuéli
SRITNE Chaired Prof.
of Data Analytics
De-mystifying Predictive Analytics

4. Will the
customer pay?

5. Today’s Talk
1. How predictive analytics differ from Reporting and
other BI tools
2. The predictive analytics process
3. Examples of problems that can be tackled
4. Logic behind predictive analytics algorithms
5. Predictive Analytics for retail in India

7. Overall Behaviour
Case Studies
Past Present Future
“Presonalized” Behaviour

8. Today’s Talk
1. How predictive analytics differ from Reporting and
other BI tools
2. The predictive analytics process
3. Examples of problems that can be tackled
4. Logic behind predictive analytics algorithms
5. Predictive Analytics for retail in India

9. The Predictive Analytics Process
Problem
Identification
Measurement Data Models
Determine Draw sample, Data Mining
Outcome and Split into algorithms
Predictors training/holdout & Evaluation
Deployment
Re-evaluation
More data

10. Today’s Talk
1. How predictive analytics differ from Reporting and
other BI tools
2. The predictive analytics process
3. Examples of problems that can be tackled
4. Logic behind predictive analytics algorithms
5. Predictive Analytics for retail in India

11. Example 1:
Personalized
Offer
Problem Who to Which What
Identification target? coupon? medium?
Measurement Data Models
Outcome: redemption From similar past ?
Predictors: customer, campaign Expected
shop & product info (redeemers and gain per
non-redeemers) offer sent
Deployment (or not!)
Re-evaluation
More data

12. Example 2: Employee Training
Problem Which employees to train?
Identification
Measurement Data Models
Outcome: performance From past ?
Predictors: employee & training efforts Expected
training info (successes and gain per
failures) employee
Deployment (or not!)
Re-evaluation
More data

13. Example 3: Customer Churn Problem
Identification
Which members most
likely not to renew?
Membership renewal
Measurement Data Models
Outcome: renewal Past renewal ?
Predictors: customer & campaigns Expected
membership info (successes and gain per
Deployment (or not!) failures) customer
Re-evaluation
More data

14. Example 4: Product-level demand forecasting
Problem Weekly
Identification forecasts per
clothing item
Measurement
Outcome: month-ahead
weekly forecasts of #units
purchased per item
Predictors: past demand for
this & related items, special
events, economic outlook,
social media
Deployment (or not!) Data Models
Re-evaluation Historic info ?
More data Expected gain

15. Example 5: COD Prediction
Problem Predict payment
Identification probability
Measurement Data Models
Outcome: pay/not Past deliveries ?
Predictors: customer, (payments and Expected
product, transaction info non-payments) gain per
transaction
Deployment (or not!)
Re-evaluation
More data

16. Today’s Talk
1. How predictive analytics differ from Reporting and
other BI tools
2. The predictive analytics process
3. Examples of problems that can be tackled
4. Logic behind predictive analytics algorithms
5. Predictive Analytics for retail in India

17. Predictive Analytics:
It’s all about correlation, not causation
Every time they turn on the
seatbelt sign it gets bumpy!
Algorithms search for correlation between the
outcome and predictors
Different algorithms search for different types of
structure

18. Example: Direct Marketing
Maharaja Bank wants to run a
campaign for current customers
to purchase a loan
They want to identify the
customers most likely to accept
the offer
They use data from a previous
campaign on 5000 customers,
where 480 (9.6%) accepted

19. Data sample

20. Data Partitioning
Training
4,000 customers
Holdout
1,000 customers

21. Classification & Regression Trees
No
Yes No Yes
No Yes

22. Regression Models
Probability (Accept Offer) = function of
b0 + b1 Age + b2 Experience + b3 Income + b4 CCAvg +…
The Regression Model
Input variables Coefficient
Constant term -6.16805744
Age -0.0227915
Experience 0.03030424
Income 0.06047214
ZIP Code -0.00006691
Family 0.61913204
CCAvg 0.13191609
Mortgage 0.00016262
Securities Account -0.51986736
CD Account 4.10482931
Online -1.11415482
CreditCard -1.02319455
EducGrad 3.93598175
EducProf 4.01372194

23. K-Nearest Neighbours
Customer1 = [age=25, exper=1, income=49, family=4, CCAvg=1.6, education=UG,…]
Customer2 = [age=49, exper=19,income=34, family=3, CCAvg=1.5, education=UG,…]

24. Performance Evaluation: Holdout Data
Predict each Overall Missed Targeted
Error acceptors non-
customer’s action acceptors
Baseline: no offers 9.3% 9.3% 0.0%
Holdout
Tree 2.5% 12.9% 1.4%
1,000 customers Regression 4.3% 35.5% 1.1%
K-NN 4.3% 41.9% 0.4%
Different: Identify
20% of customers
most likely to
accept

25. More predictive analytics methods:
based on distance
Customer1 = [age=25, exper=1, income=49, family=4, CCAvg=1.6, education=UG,…]
Customer2 = [age=49, exper=19,income=34, family=3, CCAvg=1.5, education=UG,…]

26. Where do the buzzwords fit in?

27. Big Data Cloud Computing
Real-time data
Unstructured
Social Media data
Mobile Data

28. Today’s Talk
1. How predictive analytics differ from Reporting and
other BI tools
2. The predictive analytics process
3. Examples of problems that can be tackled
4. Logic behind predictive analytics algorithms
5. Predictive Analytics for retail in India

29. Step 1: Identify “classic” applications
used by other companies

30. Step 2: Get Creative In India:
Cash On Delivery
Counter service
Huge growth in ATMs
Multiple languages
Regional customer preferences
Informative names
Bargaining

31. What you’ll need
Top management commitment
Analytics team
with close ties to all departments (IT, Marketing,…)
understands the business and its goals
creative and fearless
is allowed to experiment (and fail)
Data in a reachable place
Software

32. Last Thought: Mindful Predictive Analytics
“VIP syndrome”
Predictive analytics for
scaling-up to public white-
glove treatment
Predictive analytics for
reducing the burden on
consumers, employees etc.
(less offers & overload)

33. Asia Analytics Lab @ ISB
facebook.com/groups/asiaanalytics