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Keynote address at 2012 ReTechCon.com (annual conference of the Retailers Association of India), Mumbai.
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De-mystifying Predictive Analytics
Galit Shmuéli
SRITNE Chaired Prof. of Data Analytics
Will the customer pay?
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
Past Present Future
Case Studies
Overall Behaviour
“Presonalized” Behaviour
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
The Predictive Analytics Process
Problem Identification
Deployment Re-evaluation More data
Determine Outcome and Predictors
Measurement
Draw sample, Split into training/holdout
Data
Data Mining algorithms & Evaluation
Models
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
Problem Identification
Deployment (or not!) Re-evaluation More data
Outcome: redemption Predictors: customer, shop & product info
Measurement
From similar past campaign (redeemers and non-redeemers)
Data
? Expected gain per
offer sent
Models
Example 1: Personalized
Offer
Who to target?
Which coupon?
What medium?
Problem Identification
Deployment (or not!) Re-evaluation More data
Outcome: performance Predictors: employee & training info
Measurement
From past training efforts (successes and failures)
Data
? Expected gain per
employee
Models
Which employees to train?
Example 2: Employee Training
Problem Identification
Deployment (or not!) Re-evaluation More data
Measurement
Outcome: renewal Predictors: customer & membership info
Data
Past renewal campaigns (successes and failures)
? Expected gain per
customer
Models
Which members most likely not to renew?
Membership renewal
Example 3: Customer Churn
Example 4: Product-level demand forecasting
Problem Identification
Deployment (or not!) Re-evaluation More data
Historic info
Data
? Expected gain
Models
Measurement
Outcome: month-ahead weekly forecasts of #units purchased per item Predictors: past demand for this & related items, special events, economic outlook, social media
Weekly forecasts per clothing item
Problem Identification
Deployment (or not!) Re-evaluation More data
Outcome: pay/not Predictors: customer, product, transaction info
Measurement
Past deliveries (payments and non-payments)
Data
? Expected gain per
transaction
Models
Predict payment probability
Example 5: COD Prediction
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
Predictive Analytics: It’s all about correlation, not causation
Algorithms search for correlation between the outcome and predictors Different algorithms search for different types of structure
Every time they turn on the seatbelt sign it gets bumpy!
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
Data sample
Data Partitioning
4,000 customers
Training
1,000 customers
Holdout
Classification & Regression Trees
No Yes
Yes Yes No
No
Regression Models
Probability (Accept Offer) = function of b0 + b1 Age + b2 Experience + b3 Income + b4 CCAvg +…
The Regression Model
Coefficient
-6.16805744
-0.0227915
0.03030424
0.06047214
-0.00006691
0.61913204
0.13191609
0.00016262
-0.51986736
4.10482931
-1.11415482
-1.02319455
3.93598175
4.01372194
Online
CreditCard
EducGrad
EducProf
ZIP Code
Family
CCAvg
Mortgage
Securities Account
CD Account
Input variables
Constant term
Age
Experience
Income
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,…]
Performance Evaluation: Holdout Data
Predict each customer’s action
Overall Error
Missed acceptors
Targeted non-acceptors
Baseline: no offers 9.3% 9.3% 0.0%
Tree 2.5% 12.9% 1.4%
Regression 4.3% 35.5% 1.1%
K-NN 4.3% 41.9% 0.4%
Different: Identify 20% of customers most likely to accept
1,000 customers
Holdout
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,…]
Where do the buzzwords fit in?
Unstructured data
Mobile Data
Social Media
Real-time data
Cloud Computing Big Data
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
Step 1: Identify “classic” applications used by other companies
Step 2: Get Creative In India:
Cash On Delivery Counter service Huge growth in ATMs Multiple languages Regional customer preferences Informative names Bargaining
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
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)
Asia Analytics Lab @ ISB facebook.com/groups/asiaanalytics