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(1)

Telecommunication Case Modelling – Call Center

C.Chudzian, J.Granat, W.Traczyk

Decision Support Systems Laboratory

National Institute of Telecommunications

(2)

The goal

Selecting prospective clients for targeting a

marketing campaign based on the existing data

(call center data, billing data and others).

(3)

Model of the call center

List of the clients

Call center

Outgoing script

Incoming script

Data base Clients

Reports

(4)

The data is distributed

Call center Switch

Target group of clients

Analysis

(5)

Process of building a table for data mining

Billing

Service data Client

data SS7

Call Center

New

attributes Data

aggregation

Synchronization

in time Data mining

table New

attributes

Data aggregation

New

attributes Data

aggregation

(6)

Billing

Call Center

Classification of clients

Switch data

No

List of clients

YES

(7)

SAS Enterprise Miner

(8)

MiningMart requirements

Data source

Concept

1

Concept

..

Concept

Operator Operator

data prepared for mining

Column set

Table View

Relational data model Column set

Table View

Relational data model

Column set

Table View

Relational data model

Data prepared for

mining

(9)

The set of operators

Client Call class

Time ...

CDR

A1 A2 ...

Client

(10)

Client Features III Client Features II

Preprocessing (part I)

Process details for all clients

UnSegment

Process details for a client

SpecifiedStatistics

Segmentation

Features (I,II,III) for all clients

Client Features I

RowSelectionByQuery SpecifiedStatistics

TimeIntervalManualDiscretization RowSelectionByQuery

SpecifiedStatistics

UnSegment UnSegment

JoinByKey

Features I for all clients Features II for all clients Features III for all clients

(11)

Preprocessing (part II)

Features(I,II,III) for all clients

GenericFeatureConstruction All features for all clients

Call Center Data

Service users

UnionByKey Service info

GenericFeatureConstruction

Service info (switch prefered)

UnionByKey

Mining Concept

(12)

NIT case - HCI view

(13)

Business data

(14)

NIT case – preprocessing steps

(15)

Conclusions

Conceptual modeling improves:

• the understanding of the data preparation process

• maintenance of the data preparation process

• Knowledge transfer for other people

We do not need to use programming language

(16)

Questions & discussion

J.Granat@itl.waw.pl

?

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