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Galit Shmuéli
          SRITNE Chaired Prof.
            of Data Analytics



De-mystifying Predictive Analytics
De-Mystefying Predictive Analytics
De-Mystefying Predictive 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
De-Mystefying Predictive Analytics
Overall Behaviour


Case Studies


 Past             Present          Future




          “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

Measurement              Data          Models
Determine        Draw sample,       Data Mining
Outcome and      Split into         algorithms
Predictors       training/holdout   & Evaluation

Deployment
Re-evaluation
More 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
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
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
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
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
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
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

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
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



                      Training
                    4,000 customers




                       Holdout
                    1,000 customers
Classification & Regression Trees


          No




                     Yes   No   Yes



     No        Yes
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
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                                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
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?
Big Data            Cloud Computing

                             Real-time data




                             Unstructured
Social Media                    data




               Mobile 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

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De-Mystefying Predictive Analytics

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