Romanov moscow-spring sim2011-finished

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Customer-telecommunications company’s relationship simulation

model (RSM), based on non-monotonic business rules approach and formal

concept analysis method.

Russian Plekhanov University of Economics

Victor Romanov

Roman Veynberg

Alina Poluektova

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THE TC-COMPANIES HAVEPROBLEMS WITH CUSTOMER PROFILE FITTING

Customer Satisfaction ChangeWave Research

AT&T’s low churn rate – despite its relatively poor Very Satisfied rating and its high percentage of dropped calls

Sprint/Nextel (35%) is second in terms of customer satisfaction, with Tmobile (23%) and AT&T (23%) lagging well behind.

ChangeWave Research: April 27, 2010

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FITTING SERVICES TO CUSTOMER PROFILE

We propose: To increase profit, to hold clients and attract new ones TC-companies can use customers’ personal data and services data (customer’s consumption level) for service fitting to the consumption profile of specific customer.

For discovery the uniform consumption profile groups of customers and business rules we propose to apply the Formal Concept Analysis Method for making specific services adjusted for each category of clients.

To ensure operability in conditions when customer’s data may be incomplete or contradictory, extracted business rules should be considered within the frame of non-monotonic logic, and realized as defeasible theory rules.

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Why business rules?

Dynamic competition economy

In big and medium business a lot of documents contain business rules.

It is difficult to find and change them

EDM new conception propose extract business rule as

different component,

This makes possible more easy update

them

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What business rule is

static void processLoanRequest(Borrower borrower, Loan loan) { System.out.println("Processing request from " + borrower.getName()); // Approve or reject the loan checkLoanConditions(borrower, loan); // Display the verdict if (loan.isApproved()) { System.out.println("==> Loan is approved :-)"); } else { System.out.println("==> Loan is rejected :-("); for (Object msg : loan.getMessages()) { System.out.println("==> Because " + msg); } } } /** * Check conditions on the borrower and the loan using hard-coded policies */ static void checkLoanConditions(Borrower borrower, Loan loan) { // Check maximum amount if (loan.getAmount() > 1000000) { loan.addToMessages("The loan cannot exceed 1,000,000"); loan.reject(); } // Check repayment and score if (borrower.getYearlyIncome() > 0){ int val = loan.getYearlyRepayment() * 100 / borrower.getYearlyIncome(); if ((val>=0) && (val<30) && (borrower.getCreditScore()>=0) && (borrower.getCreditScore()<200)) { loan.addToMessages("debt-to-income too high compared to credit score"); loan.reject(); } if ((val>=30) && (val<45) && (borrower.getCreditScore()>=0) && (borrower.getCreditScore()<400)) { loan.addToMessages("debt-to-income too high compared to credit score"); loan.reject(); } if ((val>=45) && (val<50) && (borrower.getCreditScore()>=0) && (borrower.getCreditScore()<600)) { loan.addToMessages("debt-to-income too high compared to credit score"); loan.reject(); } if ((val>=50) && (borrower.getCreditScore()>=0) && (loan.getAmount() >

Applications codesIT

Business Logic

Business

Business rule is the assertion at the natural or formallanguage, which for each state of business system defines permissible decisions on business control

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Business Rule Management System

The sources where rules originated from

Applications

Processes Personell

Documents

Business rules management system

The rules are stored and updated

The rules are extracted and executed

The rules are inserted

User Applications

Rules repository

Rules Server

Rules+

Metadata

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BUSINESS RULES SYSTEM ARCHITECTURE

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Formal Concept Analysis Customers regarded as objects with their attributes - personal data, realty, employment positions may be named as CONTEXT.The FORMAL CONCEPT of customers is a collections (subset of) whole set of customers with their attributes set, such that each member of the collection has in common all attributes from this particular attribute set. Formal concept lattice may be used for visualization telecommunication company’s customer groups, that make possible for management assign to these groups corresponding set of discount options. Besides selecting the group of customers FCA method provide possibility by mean data mining approach extract new rules from customer database.Application of non-monotonic rules expands possibility taking in account not only strict implications, but also rules with fixed level of support and confidence.

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The business rules formal definitionAt the theory level of first level logic (FOL) business rules have

statement view IF-THEN and expresses logical consequence or implication.

IF (conditions), then (the list of actions),

else(alternate list of actons).

p is a assertion, named as

antecedent, which is describing state of business conditions

q – assertion named as consequent, describing

decision which are offering in this conditions.

IF p, THEN q, where

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CUSTOMER’S ATRIBUTESCUSTOMER’S ATRIBUTES

PERSONAL DATA sitizen agegendersingle

JOB DATA studentheadInc_hInc_mInc_l

SERVICE CONSUMPTION DATA sms \mmsLoc_callInt_callConfGprs

ACTION EFFECT Act1_eff Act2_eff Act3_eff

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Formal context “customers” Context part 1

Context part 2 (continuation)

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Concept lattice “customers”

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The rules discovered The rules discovered by FCA look like this: different kind

of if customers satisfy different conditions and for them different marketing actions are effective:

•IF head = true AND Inc_m = true AND Cons_mid = true THEN Act1_eff;

Rule 1

•IF gender_male = true AND student = true AND sitizen = true AND Inc_l = true AND sms \mms = true AND Cons_mid = true AND THEN Act2_eff;

Rule 2

•IF sitizen true AND Int_call true AND Gprs true AND Inc_h true AND Cons_high true AND THEN Act3_eff;

Rule 3

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Rules quality criteria Let M – attribute set and G objects set. The rules are defined as the implication X⇒Y,

where X,Y M, X ⊆ Y =. The implication means that all objects of context which contain attributes X also

contain attribute Y. That is in the situation X manager ought make decision Y.

support

Supp=card(ψ(X)/ card(G)) - is a rate of context

objectsK := (G,M, I),

which contain attributes X

Is defined as conf( X⇒Y)=supp(XY)/

supp(X)

3

Is defined as supp(X Y)/

supp(Y) supp(X)

confidence lift conviction

Conviction conv(X⇒Y)=1-

supp(Y)/1- conf(X⇒Y)

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The rules discovered with confidence <100 %

IF Loc_call AND Cons_mid AND Single THEN Act2_eff (confidence = 80%)

IF Cons_mid THEN Act2_eff (confidence = 70%)

IF Head THEN Act3_eff (confidence = 67%)

IF single AND Loc_call THEN Act2_eff (confidence = 62%)

IF Cons_low THEN Act3_eff (confidence = 50%)

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CONCLUSION: The formal concept analysis software, being applied to

client data, shows, that action1 is efficient for “Head, medium income and medium consumption” category of clients;

action2 is efficient for “male, student, low income, medium consumption and use sms\mms” category of clients ;

action3 is efficient for “citizen, use international call, GPRS with high income and consumption” category of clients

So, proposed approach transforms user data into TC-company’s services, fitted to customer profile and

stimulate consumption increasing so as company’s profit