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
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
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
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
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)