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Presentations by Prof. Galit Shmuéli, SRITNE Chaired Professor of Data Analytics, ISB at NASSCOM Big Data and Analytics Summit 2014.
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Galit ShmuéliSRITNE Chaired
Professor of Data Analytics
Predicting, Explaining and the Business Analytics Toolkit
Business Intelligence
Traditional: Describe the past
State-of-the-Art: Describe the present
Business Analytics
Predictive Analytics: Predict future of individual records
Explanatory Analytics: Explain cause-effect of “average record”
(overall effect)
Today’s Talk
1. Predictive Analytics: The process & applications
2. Prediction is not explanation
3. The Explanatory Analytics toolkit
Will the customer pay?
What causes non-payment?
Past Present Future
Case Studies
Overall Behaviour
“Presonalized” Behaviour
The Predictive Analytics Process
Determine Outcome and Predictors
MeasurementDraw sample,Split into training/holdout
DataData Mining algorithms& Evaluation
Models
Predict New Records;Get More Data;Re-Evaluate
Actions
What to Predict? Why? Implications?
Problem Identification:
5 Examples of Predictive Analytics
Applications
Problem Identification
Outcome: redemptionPredictors: customer, shop & product info
Measurement
From similar past campaign (redeemers and non-redeemers)
Data
Predictive AlgorithmsExpected gain per offer sent
Models & Evaluation
Example 1:Personalized
Offer
Who to target?
Which coupon?
What medium?
Send Offers (or not!) More Data & Re-Evaluation
Actions
Problem Identification
Outcome: performance Predictors: employee & training info
MeasurementFrom past training efforts (successes and failures)
Data
Which employees to train?
Example 2: Employee Training
Send employees for training (or not!) More Data & Re-Evaluation
Actions
Predictive AlgorithmsExpected gain per employee
Models & Evaluation
Problem Identification
MeasurementOutcome: renewal Predictors: customer & membership info
DataPast renewal campaigns (successes and failures)
Which members most likely not to renew?
Example 3: Customer Churn
Send renewal incentive (or not!) More Data & Re-Evaluation
Actions
Predictive AlgorithmsExpected gain per person
Models & Evaluation
Example 4: Product-level demand forecastingProblem Identification
ActionsUpdate Orders, Pricing, PromoGet More Data, Re-Evaluate
Historic infoData
Forecasting;Expected gain
Models & Eval
MeasurementOutcome: month-ahead weekly forecasts of #units purchased, per itemPredictors: past demand for this & related items, special events, economic outlook, social media
Item-level weekly demand forecasts
Problem Identification
Outcome: pay/not Predictors: customer, product, transaction info
MeasurementPast deliveries (payments and non-payments)
Data
Predict payment probability
Example 5: COD Prediction
Reconfirm with suspect deliveriesMore Data & Update Model
Actions
Predictive AlgorithmsExpected gain per delivery
Models & Evaluation
Predictive Analytics: It’s all about correlation, not causation
Algorithms search for correlation between the outcome and inputs
Different algorithms search for different types of structure – lots of predictive algorithms!
Every time they turn on the seatbelt sign it gets bumpy!
Causality?
www.tylervigen.com
The Causal Explanation Process
Determine Outcome and Causes
MeasurementAssign records to treatment(s)Collect data on inputs+output
DataStatistical models& Evaluation of uncertainty
Models & Eval
Make Decisions; Implement Changes Get More Data and Re-Evaluate
Actions
Which Inputs Cause the Output? How? Implications?Inputs under our control, inputs uncontrollable
Problem Identification:
What causes average customer to redeem?
Example 1:Personalized Offer
Change coupon design/typeCollect new data (gender)
Actions
Problem Identification:
Tailor trainingPrepare employeesIncentivize learning
Actions
Example 2: Employee Training
What causes average employee to succeed?
Problem Identification:
Improve serviceChange target market
Actions
What causes average member not to renew?
Example 3:Customer Churn
Problem Identification:
Create flexible designsOpen new locations
Actions
Example 4: Demand
Forecasting
What causes high/low demand?
Problem Identification:
Modify payment policyChange website designTrain delivery staff
Actions
What causes average transaction to result in non-payment?
Example 5: Cash-On-Delivery Prediction
Problem Identification:
Toolkit for Determining Causality
Gold Standard: Controlled, Randomized Experiment
Beyond A/B Testing:Multiple factors andInteractions between factors
Causal Explanation withObservational Data
(not a controlled experiment)
Self Selection
Current PracticeCompare online/offline performance stats
Turns out: online and offline users differ on “awareness”
Awareness of electronic services provided by Government of India
Performance Evaluation:% Using Agent
Naïve Comparison:Online system →Less agents
After correcting for self-selection:Online system → More agents for “unaware” users!
Aware Unaware
Asia Analytics Lab @ ISBfacebook.com/groups/asiaanalytics