a practical look at data preparation jason brown cognicase inc. data mining irmac: data warehouse...

A Practical Look at Data Preparation

Jason BrownJason BrownCognicase Inc.Cognicase Inc.

Data MiningData Mining

IRMAC: Data Warehouse SIGNovember 5, 2002

2

Agenda

• Crash Course in Data Mining– What– Why– How

• The virtuous cycle– Data Preparation

• Case Study– Background– Going through the cycle

• Data Preparation

• Q&A

3

The Crash Course

•What

•Why

•How

4

Definitions

Data Mining:The process of exploration and analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules.

Knowledge Discovery

Data Mining is not Data Warehousing, OLAP etc.

5

Definitions

Modeling:• Not an ER type Data Model• A data mining model is computational, full of

algorithms• A model can be descriptive or predictive.

– A descriptive model helps in understanding underlying processes or behavior.

– A predictive model uses known values (input) to predict an unknown value (output)

6

Two Types of Data Mining

• Directed– Know specifically what we are looking for

• Who is likely to respond to our offer?• What our customers going to be worth to us over their lifetime?

– Model is a Black Box

Input Output

7

Two Types of Data Mining

• Undirected– Not exactly sure what we are looking for

• How should we define our Customer Segments?• What is interesting about all of our point of sale data?

– Model is a Transparent Box

Input Output

8

Modeling Techniques

Decision Trees

If …….. Then ……..

Rule Induction

Neural Networks

Nearest Neighbour

Clustering

9

Modeling TechniquesDecision Trees

• The tree is built based on the input of a training data set– Training Data Set is based on historical data– Over sample the data that reflects your question

• Each record of the Model Set is run through the branches of the tree until the record reaches a leaf

10

Modeling TechniquesDecision Trees

Age < 23

YNIncome < 12 000

N

N

Y

Y

Male ?

28% 37%

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Modeling TechniquesNeural Networks

• Neural networks are a nonlinear model -similar to a ‘brain’.

• The network is built based on the input of a training set.

• Model sets run through this network will return accurate results based on the patterns identified in the training set.

• Very Complex

12

Modeling TechniquesClustering

• Clustering finds groups of records that are similar.

• For example, customers can be clustered by:– income

– age

– ytd revenue

13

Modeling TechniquesClustering

MaleIncome < 12 000Age < 23Coke Buyers

MaleIncome < 12 000Age < 23Non Coke Buyers

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Modeling TechniquesNearest Neighbour

• Model is built based on the input of a training set.

• Classifies a record by calculating the distances between the record criteria and the training data set

• Then it assigns the record to the class that is most common among its nearest neighbours

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Modeling TechniquesNearest Neighbour

Records plotted based on:

IncomeGenderAge

Bought Coke

Bought Coke

Bought Coke

Did Not Did Not

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Modeling TechniquesRule Induction

• A technique that infers generalizations from the information in the data

IF age < 19 AND purchase is coke THEN 40% purchase chips

• Describes the data, allows us to visualize what is going on

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The Crash Course

•What

•Why

•How

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The Reasons to Mine Data

Expenses

IncreaseProfit

Revenues

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The Reasons to Mine Data

• For Marketing/CRM– Targeting prospects – Predicting future customer behaviour– Costs Revenues

• For Research– Identify drugs likely to be successful– Costs

• For Process Improvement– Identify causes of production failures– Costs

20

The Crash Course

•What

•Why

•How

21

Process

• Many different processes for Data Mining– Vendor Driven

• SAS - SEMMA– Sample, Explore, Modify, Model, Assess

• SPSS - 5 A’s– Assess, Access, Analyze, Act, Automate

– Consulting Companies– The Virtuous Cycle

• Michael Berry and Gordon Linoff

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Business Problem

Transform Data

Act

Measure

ProcessThe Virtuous Cycle

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• Define the business problem• Understand the business and the rules• Determine if Data Mining fits the need• Understand the value to the business of

solving the problem

Business Problem

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Data for Data Mining

• Type of Data Values– Categorical

• Defined set of values• Ontario, Quebec, PEI …

– Ranks• High, Medium, Low• 0 – 20 000, 20 001 – 35 000, 35 001 – 50 000

– Intervals• Date• Time • Temperature

– True Numeric• Values that support numeric operations

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Transform DataSteps

Identify Data1

Prepare Model Set6

Add Derived Variables5

Transpose to Right Granularity4

Validate & Clean3

Obtain Data2

Conduct Modeling7

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Transform Data

Identify Data1

Step 1 - Identify Data

• What data is required to meet the modeling need?

• What data is available?

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Transform DataStep2 - Obtain Data

Identify Data

Obtain Data

1

2

• OLTP• Data Warehouse• Data Marts and OLAP• Self Reported• External

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Transform DataStep 3: Validate & Clean

Identify Data

Obtain Data

Validate & Clean

1

3

2

• Data Issues:– Missing– Fuzzy– Incorrect– Outliers

• Solutions:– Change Source– Filter Out– Ignore– Integrate– Predict– Derive a New Variable

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Transform DataStep 4: Transpose to right granularity

Identify Data

Obtain Data

Transpose to Right Granularity

Validate & Clean

1

4

3

2

• Data sets for Data Mining need one view, one record

• Grain must be consistent throughout– Aggregates can be problematic– Atomic data is often required to build data set

• Training data sets cast from point in time of event looking back

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Transform DataStep 5: Add Derived Variables

Identify Data

Obtain Data


Validate & Clean

Add Derived Variables

1

5

4

3

2

• Combined Columns• Summarizations• Features from Columns• Time Series

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Transform DataStep 6: Prepare Model Set

Identify Data

Obtain Data


Validate & Clean


Prepare Model Set

1

6

5

4

3

2

• The Actual Input to the modeling

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Transform DataStep 7: Conduct Modeling

Identify Data

Obtain Data


Validate & Clean


Prepare Model Set

Conduct Modeling

1

6

5

4

3

2

7

• Get our result– Decision Trees– Neural Networks– Clustering – Nearest Neighbour– Rule Induction

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Act

The Business has to actually do something with the results or what was the point?

Marketing or Retention Campaigns

Business Changes

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Measure

• Answer 2 Questions– Was the Data Mining effort accurate? – Were the Business Actions successful?

• Use different sets of data to compare real results– Actioned Customers vs. Non Actioned

• Accuracy Types– Absolute

• Our prediction was 80% of Group D would buy Coke and 78% really did

– Relative• Our prediction was 80% of Group D would buy Coke but

57 % really did, however Group C which we predicted had a 60% propensity to buy Coke actually bought Coke 42% of the time

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And back around

Business Problem

Transform Data

Act

Measure

36

The Case Study

37

The Case Study

• Background– The Business– Data Warehouse Overview– Strengths and Challenges

• The Project– Business Problem– Transform Data– Act– Measure

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The Case Study



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The Business

• One of the top 3 (4?) cellular phone providers in Canada

• Recent Acquisitions– Clearnet– Quebectel

• Important Business Concepts– Handset– Subscriber– Client– Activity - Activations, Deactivations– Churn– Usage

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Cubes

Catalogues

Sources

Staging

Integration (3N F)

Reports

Data Marts(Dimensional)

DW Environment

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• Commitment to Data Warehousing• Prior Experience in Data Mining• Tools already Established• Strong business support for the outcomes Data

Mining would provide

Strengths at

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• Data Warehouse still in midst of major re-architecture effort

• Ongoing billing system integration projects• A data mart for data mining had existed

(Clearnet) but it was a victim of both of the above– Successful at Churn Prediction

Challenges at

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The Case Study



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Using the Virtuous Cycle

Business Problem

Transform Data

Act

Measure

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Business Problems

• Churn Modeling– predict which subscriber is likely to leave

• Behavioural Segmentation– clustering subscribers into subgroups based on some

commonality– revenue, usage, demographic

• Client Value Estimation– the present value of all future profits generated throughout

the lifetime of that client

46

Transform Data Steps

Identify Data

Obtain Data


Validate & Clean


Prepare Model Set

Conduct Modeling

1

6

5

4

3

2

7

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Identify Data

EVERY POSSIBLE VARIABLE RELATED TO A SUBSCRIBER!

• Business Wanted:

1

• They provided a detailed list, by subject area, of the variables that they believed were required to conduct the kind of Data Mining Activities desired.

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• 19 Subject Areas identified with up to 75 variables each - What is the Priority?

• Avoid the big bang - How much can we actually do?

• Where to source the data from?• Resources - Who is going to do it?

Identify DataIT Challenges

1

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• First we asked the business to rate each variable as H, M or L priority– Almost everything was given an H

• Then we asked the business to rank the subject areas in order of importance– Hard to convince them of the value– Hard to find consensus– Necessary for determining a release strategy

Identify DataPrioritizing

1

50

Obtain Data

• For each Subject Area and each variable we assessed and documented the following:– Where can it be sourced from (and when)?– What are the known issues?– Q&A back and forth on the variables with business– Identified possible additional variables

2

51

+ =Release Strategy:•1 Release a Quarter

•3 planned releases

•15 of 19 Subject Areas

Obtain Data

2

52

Obtain Data

• Data Mining Access tool against sources– Data Warehouse– OLTP– External Data

• Creation of flat files to feed to Data Mining team– Multi source– Validate and Clean

• Build a Data Mart for Data Mining– Part of the Data Warehouse Architecture

2

53

Obtain Data

Sources

Staging

Integration (3N F)

Data Marts(Dimensional)

Data Mart forData Mining

54

Validate & Clean Data

Validate & Clean3

Staging

Integration (3N F)

Data Marts

Data Mart forData Mining

• Clean?– Not Fuzzy– Correct - mostly– Missing and Outliers

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• The Grain is Subscriber by Bill Cycle• Monthly Snapshots of subscriber Data• Fact Data Cast to Bill Cycle

Transpose to Right Granularity4

BILLED_AMOUNTS

SUBSCRIBER_ID (FK)MONTH (FK)

LOTS_OF_BILLED_REVENUE_FACTSUPDATE_DTLOAD_DT

PRODUCT_SKU

PRODUCT_SKU_KEY

PRODUCT_INFOUPDATE_DTLOAD_DT

SUBSCRIBER_BILL_CYCLE_SNAPSHOT

SUBSCRIBER_IDMONTH

CURRENT_HANDSET_KEY (FK)FIRST_HANDSET_KEY (FK)SUBSCRIBER_PERSONAL_INFOSUBSCRIBER_DEMOGRAPHIC_INFOCONTRACT_INFORATE_PLAN_INFOMUCH_MORE_INFOLOAD_DTUPDATE_DT

VALUE_ADDED_SERVICES

VALUE_ADDED_SERVICES_KEY

VALUE_ADDED_SERVICES_DESVALUE_ADDED_SERVICES_CDVALUE_ADDED_SERVICES_IDUPDATE_DTLOAD_DT

VALUE_ADDED_SERVICES_BILLED

MONTH (FK)SUBSCRIBER_ID (FK)VALUE_ADDED_SERVICES_KEY (FK)

VALUE_ADDED_SERVICES_AMTUPDATE_DTLOAD_DT

FACTS_5SUBSCRIBER_ID (FK)MONTH (FK)

LOTS_OF_FACTSUPDATE_DTLOAD_DT






LOTS_OF_FACTSUPDATE_DTLOAD_DTSUBSCRIBER_BILL_CYCLE_SNAPSHOT

SUBSCRIBER_IDMONTH

CLIENT_ACCOUNT_ID (FK)CLIENT_SNAPSHOT_MONTH (FK)CURRENT_HANDSET_KEY (FK)FIRST_HANDSET_KEY (FK)SUBSCRIBER_PERSONAL_INFOSUBSCRIBER_DEMOGRAPHIC_INFOCONTRACT_INFORATE_PLAN_INFOMUCH_MORE_INFOLOAD_DTUPDATE_DT

CLIENT_ACCOUNT_BILL_CYCLE_SNAPSHOTCLIENT_ACCOUNT_IDCLIENT_SNAPSHOT_MONTH

INFO_ON_THE_ACCOUNTACCOUNT_NO_OF_CANCELLED_SUBSACCOUNT_NO_OF_SUSPENDED_SUBSACCOUNT_NO_OF_ACTIVE_SUBSLOAD_DTUPDATE_DT

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• Combined Columns– PERCENT_BUCKET_USED

• IN_BUCKET_CALLS / RATE_PLAN_BUCKET_MINUTES)

• Summarizations– Sphere of Influence

• UNIQUE_CALLED_NUMBER_CNT• UNIQUE_CALLING_NUMBER_CNT


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• Features from Columns– CONTRACT_INDICATOR (Y/N Flag)

• Was the the date of snapshot for that subscriber between COMMIT_START_DATE and COMMIT_END_DATE

• Time Series


BLOCKED_USAGE_WORK

SUBSCRIBER_ID: NUMBER(22)USAGE_DT: DATE

BLOCKED_CNT: NUMBER(22)UPDATE_DT: DATELOAD_DT: DATE

BLOCKED_USAGE

MONTH: NUMBER(8)SUBSCRIBER_ID: NUMBER(22)

BLOCKED_CNT: NUMBER(22)UPDATE_DT: DATELOAD_DT: DATE


MONTH: NUMBER(8)SUBSCRIBER_ID: NUMBER(22)VALUE_ADDED_SERVICES_KEY: NUMBER

VALUE_ADDED_SERVICES_AMT: NUMBER(22,2)UPDATE_DT: DATELOAD_DT: DATE


SUBSCRIBER_ID: NUMBER(22)MONTH: NUMBER(8)

CLIENT_ACCOUNT_ID: NUMBER(22)CLIENT_SNAPSHOT_MONTH: NUMBER(8)SUBSCRIBER_CD: VARCHAR2(50)TIME_REMAINING_ON_CONTRACT: NUMBERCONTRACT_END_DT: DATECONTRACT_START_DT: DATECONTRACT_TERM: NUMBERCONTRACT_IND: VARCHAR2(1)SUBSCRIBER_STATUS_CD: VARCHAR2(50)ACTIVATION_CHANNEL_ORG_CD: VARCHAR2(50)ACTIVATION_REASON_CD: VARCHAR2(50)ACTIVATION_DT: DATEPOSTAL_CODE: VARCHAR2(20)PROVINCE_STATE_CD: VARCHAR2(50)LAST_NM: VARCHAR2(60)LANGUAGE_CD: VARCHAR2(50)BIRTHDATE: DATEGENDER: VARCHAR2(1)DRIVERS_LICENSE: VARCHAR2(20)FIRST_NM: VARCHAR2(50)SOCIAL_INSURANCE_NO: VARCHAR2(20)MOBILE_PHONE_NO: VARCHAR2(50)DEACTIVATION_REASON_CD: VARCHAR2(50)DEACTIVATION_DT: DATEREVENUE_BAND_CD: VARCHAR2(50)SEGMENT_CD: VARCHAR2(50)SUSPEND_REASON_CD: VARCHAR2(50)SUSPEND_DT: DATECURRENT_HANDSET_KEY: NUMBERFIRST_HANDSET_KEY: NUMBERUPDATE_DT: DATELOAD_DT: DATEAGE_OF_CURRENT_HANDSET: NUMBERPREPAID_IND: VARCHAR2(1)REVENUE_IND: VARCHAR2(1)RATE_PLAN_BUCKET_MINUTES: NUMBER(9,2)RATE_PLAN_GROUP_CD: VARCHAR2(50)RATE_PLAN_CD: VARCHAR2(50)SUBSCRIBER_TENURE_DAYS: NUMBERSUBSCRIPTION_ID: VARCHAR2(50)TECHNOLOGY_TYPE_CD: VARCHAR2(50)AGE: NUMBER(4,1)

PRODUCT_SKU_KD2

PRODUCT_SKU_KEY: NUMBER

PRODUCT_GROUP_DES: VARCHAR2(240)PRODUCT_GROUP_CD: VARCHAR2(50)PRODUCT_GROUP_ID: VARCHAR2(50)ENGLISH_PRODUCT_DES: VARCHAR2(240)PRODUCT_CD: VARCHAR2(50)REFURBISHED_HANDSET_IND: VARCHAR2(1)WEB_READY_IND: VARCHAR2(1)SIM_CARD_IND: VARCHAR2(1)PRODUCT_ID: VARCHAR2(50)UPDATE_DT: DATELOAD_DT: DATE

DROPPED_USAGE_WORK

SUBSCRIBER_ID: NUMBER(22)USAGE_DT: DATE

DROPPED_CNT: NUMBER(22)UPDATE_DT: DATELOAD_DT: DATE

DROPPED_USAGE


DROPPED_CNT: NUMBER(22)UPDATE_DT: DATELOAD_DT: DATE

BUCKET_USAGE


UPDATE_DT: DATELOAD_DT: DATEPERCENT_BUCKET_USED: NUMBER

BILLED_USAGE_3_MTH_AVG


TOTAL_CALLS_MOU_3_MTH_AVG: NUMBER(22,5)LONG_DISTANCE_CALLS_MOU_3_MTH_: NUMBER(22,5)WEEKEND_CALLS_MOU_3_MTH_AVG: NUMBER(22,5)EVENING_CALLS_MOU_3_MTH_AVG: NUMBER(22,5)PEAK_CALLS_MOU_3_MTH_AVG: NUMBER(22,5)UPDATE_DT: DATELOAD_DT: DATE

BILLED_USAGE


TOTAL_CALLS_MOU: NUMBER(22,5)TOTAL_CALLS_CNT: NUMBERLONG_DISTANCE_CALLS_MOU: NUMBER(22,5)LONG_DISTANCE_CALLS_CNT: NUMBERWEEKEND_CALLS_MOU: NUMBER(22,5)WEEKEND_CALLS_CNT: NUMBEREVENING_CALLS_MOU: NUMBER(22,5)EVENING_CALLS_CNT: NUMBERPEAK_CALLS_MOU: NUMBER(22,5)PEAK_CALLS_CNT: NUMBERUPDATE_DT: DATELOAD_DT: DATEOUT_BUCKET_CALLS_MOU: NUMBER(22,5)IN_BUCKET_CALLS_MOU: NUMBER(22,5)OUT_BUCKET_CALLS_CNT: NUMBERIN_BUCKET_CALLS_CNT: NUMBERROAMING_CALLS_MOU: NUMBER(22,5)ROAMING_CALLS_CNT: NUMBER

SUBSCRIBER_ID_KD2_TEMP

SUBSCRIBER_ID: NUMBER(22)

ACCOUNT_CYCLE_BILL_DAY: NUMBER(2)

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Prepare Model Set

Prepare Model Set6

FACTS_5



FACTS_4



FACTS_3



FACTS_2



FACTS_1



BILLED_AMOUNTS


LOTS_OF_BILLED_REVENUE_FACTSUPDATE_DTLOAD_DT

DROPPED_AND_BLOCKED_USAGE

DROPPED_CALLS_CNTUPDATE_DTLOAD_DT

EQUIPMENT_ACTIVITY_SNAPSHOT


HANDSET_COUNTABLE_DATAUPDATE_DTLOAD_DT

PRODUCT_SKU

PRODUCT_SKU_KEY



SUBSCRIBER_IDMONTH








BLOCKED_USAGE

BLOCKED_CALLS_CNTUPDATE_DTLOAD_DT

CLIENT_ACCOUNT_BILL_CYCLE_SNAPSHOT

CLIENT_ACCOUNT_IDCLIENT_SNAPSHOT_MONTH

INFO_ON_THE_ACCOUNTACCOUNT_NO_OF_CANCELLED_SUBSACCOUNT_NO_OF_SUSPENDED_SUBSACCOUNT_NO_OF_ACTIVE_SUBSLOAD_DTUPDATE_DT

08/01/2002 1,735,436.26 281,032.21 388,970.91 0.00 516,911.31 462,667.55 749,330.77 6,371.95 500,183.42 08/14/2002

• Select Sample– Population– Data for given

modeling effort

• Denormalize completely

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Prepare Model Set

Prepare Model Set6

Denormalize completely

Example of Denormalization for Data Mining

Handset 1 Handset 2 Handset 3 Handset 40 1 0 0

BILLED_USAGE

MONTH (FK)SUBSCRIBER_ID (FK)

TOTAL_CALLS_MOUTOTAL_CALLS_CNTLONG_DISTANCE_CALLS_MOULONG_DISTANCE_CALLS_CNTWEEKEND_CALLS_MOUWEEKEND_CALLS_CNTEVENING_CALLS_MOUEVENING_CALLS_CNTPEAK_CALLS_MOUPEAK_CALLS_CNTUPDATE_DTLOAD_DTOUT_BUCKET_CALLS_MOUIN_BUCKET_CALLS_MOUOUT_BUCKET_CALLS_CNTIN_BUCKET_CALLS_CNTROAMING_CALLS_MOUROAMING_CALLS_CNT

BILLED_USAGE_3_MTH_AVG


TOTAL_CALLS_MOU_3_MTH_AVGLONG_DISTANCE_CALLS_MOU_3_MTH_WEEKEND_CALLS_MOU_3_MTH_AVGEVENING_CALLS_MOU_3_MTH_AVGPEAK_CALLS_MOU_3_MTH_AVGUPDATE_DTLOAD_DT

BUCKET_USAGE


UPDATE_DTLOAD_DTPERCENT_BUCKET_USED

PRODUCT_SKU

PRODUCT_SKU_KEY



SUBSCRIBER_IDMONTH








60

Prepare Model Set

Prepare Model Set6

Example of Casting a a Test Set for Churn Modeling

• Sliding Windows Concept

O c t N o v D e c J a n F e b M a r A p r M a y J u n J u l

M o d e l S e t F e b 3 2 1 X PM o d e l S e t M a r 3 2 1 X PM o d e l S e t A p r 3 2 1 X P

S c o r e S e t 3 2 1 X P P

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Conduct Modeling

7

• Done by business team with help from Vendor (SAS)– Decision Trees and Neural Networks (Churn)– Clustering (Segmentation)

Decision Trees

Neural Networks

Clustering

62

Various Marketing and CRM Activities

The Action

63

Measuring

• Not there yet– Intend to compare actioned vs. non-actioned results– 50 % to be actioned

64

And the cycle continues ...

Business Problem

Transform Data

Act

Measure

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AQ&Q U E S T I O N SQ U E S T I O N SA N S W E R SA N S W E R S

Jason [email protected]

a practical look at data preparation jason brown cognicase inc. data mining irmac: data warehouse...

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