Galit Shmuéli
SRITNE 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 ...
Today’s Talk
1. Predictive Analytics: The process & applications
2. Prediction is not explanation
3. The Explanatory Analy...
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
Measurement
Draw sample,
Split into
training/holdout
Dat...
5Examples of
Predictive Analytics
Applications
Problem
Identification
Outcome: redemption
Predictors: customer,
shop & product info
Measurement
From similar past
campaig...
Problem
Identification
Outcome: performance
Predictors: employee &
training info
Measurement
From past
training efforts
(s...
Problem
Identification
Measurement
Outcome: renewal
Predictors: customer &
membership info
Data
Past renewal
campaigns
(su...
Example 4: Product-level demand forecasting
Problem
Identification
Actions
Update Orders, Pricing, Promo
Get More Data, Re...
Problem
Identification
Outcome: pay/not
Predictors: customer,
product, transaction info
Measurement
Past deliveries
(payme...
Predictive Analytics:
It’s all about correlation, not causation
Algorithms search for correlation between the
outcome and ...
Causality?
www.tylervigen.com
The Causal Explanation Process
Determine
Outcome and
Causes
Measurement
Assign records to
treatment(s)
Collect data on
inp...
What causes average
customer to redeem?
Example 1:
Personalized Offer
Change coupon design/type
Collect new data (gender)
...
Improve service
Change target market
Actions
What causes average
member not to renew?
Example 3:
Customer Churn
Problem Id...
Modify payment policy
Change website design
Train delivery staff
Actions
What causes average transaction
to result in non-...
Toolkit for Determining Causality
Gold Standard:
Controlled, Randomized Experiment
Beyond A/B Testing:
Multiple factors and
Interactions between factors
Causal Explanation with
Observational Data
(not a controlled experiment)
Self Selection
Current Practice
Compare
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:
O...
Asia Analytics Lab @ ISB
facebook.com/groups/asiaanalytics
NASSCOM Big Data and Analytics Summit 2014 - Predicting, Explaining and the Business Analytics Toolkit - Galit Shmuéli
NASSCOM Big Data and Analytics Summit 2014 - Predicting, Explaining and the Business Analytics Toolkit - Galit Shmuéli
NASSCOM Big Data and Analytics Summit 2014 - Predicting, Explaining and the Business Analytics Toolkit - Galit Shmuéli
NASSCOM Big Data and Analytics Summit 2014 - Predicting, Explaining and the Business Analytics Toolkit - Galit Shmuéli
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NASSCOM Big Data and Analytics Summit 2014 - Predicting, Explaining and the Business Analytics Toolkit - Galit Shmuéli

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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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NASSCOM Big Data and Analytics Summit 2014 - Predicting, Explaining and the Business Analytics Toolkit - Galit Shmuéli

  1. 1. Galit Shmuéli SRITNE Chaired Professor of Data Analytics Predicting, Explaining and the Business Analytics Toolkit
  2. 2. 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)
  3. 3. Today’s Talk 1. Predictive Analytics: The process & applications 2. Prediction is not explanation 3. The Explanatory Analytics toolkit
  4. 4. Will the customer pay? What causes non-payment?
  5. 5. Past Present Future Case Studies Overall Behaviour “Presonalized” Behaviour
  6. 6. The Predictive Analytics Process Determine Outcome and Predictors Measurement Draw sample, Split into training/holdout Data Data Mining algorithms & Evaluation Models Predict New Records; Get More Data; Re-Evaluate Actions What to Predict? Why? Implications? Problem Identification:
  7. 7. 5Examples of Predictive Analytics Applications
  8. 8. Problem Identification Outcome: redemption Predictors: customer, shop & product info Measurement From similar past campaign (redeemers and non-redeemers) Data Predictive Algorithms Expected 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
  9. 9. Problem Identification Outcome: performance Predictors: employee & training info Measurement From 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 Algorithms Expected gain per employee Models & Evaluation
  10. 10. Problem Identification Measurement Outcome: renewal Predictors: customer & membership info Data Past 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 Algorithms Expected gain per person Models & Evaluation
  11. 11. Example 4: Product-level demand forecasting Problem Identification Actions Update Orders, Pricing, Promo Get More Data, Re-Evaluate Historic info Data Forecasting; Expected gain Models & Eval Measurement Outcome: month-ahead weekly forecasts of #units purchased, per item Predictors: past demand for this & related items, special events, economic outlook, social media Item-level weekly demand forecasts
  12. 12. Problem Identification Outcome: pay/not Predictors: customer, product, transaction info Measurement Past deliveries (payments and non-payments) Data Predict payment probability Example 5: COD Prediction Reconfirm with suspect deliveries More Data & Update Model Actions Predictive Algorithms Expected gain per delivery Models & Evaluation
  13. 13. 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!
  14. 14. Causality? www.tylervigen.com
  15. 15. The Causal Explanation Process Determine Outcome and Causes Measurement Assign records to treatment(s) Collect data on inputs+output Data Statistical 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:
  16. 16. What causes average customer to redeem? Example 1: Personalized Offer Change coupon design/type Collect new data (gender) Actions Problem Identification: Tailor training Prepare employees Incentivize learning Actions Example 2: Employee Training What causes average employee to succeed? Problem Identification:
  17. 17. Improve service Change target market Actions What causes average member not to renew? Example 3: Customer Churn Problem Identification: Create flexible designs Open new locations Actions Example 4: Demand Forecasting What causes high/low demand? Problem Identification:
  18. 18. Modify payment policy Change website design Train delivery staff Actions What causes average transaction to result in non-payment? Example 5: Cash-On-Delivery Prediction Problem Identification:
  19. 19. Toolkit for Determining Causality
  20. 20. Gold Standard: Controlled, Randomized Experiment
  21. 21. Beyond A/B Testing: Multiple factors and Interactions between factors
  22. 22. Causal Explanation with Observational Data (not a controlled experiment) Self Selection
  23. 23. Current Practice Compare online/offline performance stats
  24. 24. Turns out: online and offline users differ on “awareness” Awareness of electronic services provided by Government of India
  25. 25. Performance Evaluation: % Using Agent Naïve Comparison: Online system → Less agents After correcting for self-selection: Online system → More agents for “unaware” users! Aware Unaware
  26. 26. Asia Analytics Lab @ ISB facebook.com/groups/asiaanalytics

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