Bayesian Network Classifiers for Identifying the Slope of the customer

Lifecycle of Long-Life CustomersAuthored by: Bart Baesens, Geert Vertraeten, Dirk Authored by: Bart Baesens, Geert Vertraeten, Dirk

Poel, Michael Petersen, Patrick Kenhove, Jan Poel, Michael Petersen, Patrick Kenhove, Jan VanthienenVanthienen

Presentation by: Oksana Myachina, Jeff JaniesPresentation by: Oksana Myachina, Jeff Janies

INTRODUCTION

Acquiring a new customer is more costly, than Acquiring a new customer is more costly, than selling additional products to existing ones.selling additional products to existing ones.

Traditional brand strategies should be replaced by Traditional brand strategies should be replaced by customer strategies.customer strategies.

It’s very important to make informed decisions on It’s very important to make informed decisions on customers level.customers level.

CRM is successful only if customers CRM is successful only if customers remain at least to a certain extent ,loyal to remain at least to a certain extent ,loyal to the company in case.the company in case.

Research shows large heterogeneity in Research shows large heterogeneity in long-term customers spending.long-term customers spending.

Responding to this fact , the study Responding to this fact , the study explained in the paper,was performed.explained in the paper,was performed.

The relevance of estimation of a customer’s spending evaluation

Traditional relationship marketing claims:Traditional relationship marketing claims:

-- loyal customers raise their loyal customers raise their spending spending

-- generate new customersgenerate new customers -- ensure diminishing serving costsensure diminishing serving costs - have reduced consumer price - have reduced consumer price

sensitivity sensitivity

RM main idea : the longer customer stays loyal to RM main idea : the longer customer stays loyal to company, the more Profit it hascompany, the more Profit it has

Reinartz and Kumar state that LLC are not necessary:

- cheaper to serve - less price sensitive - more effective in bringing

new business to the company

Mail Company example

What is the aim of the study?What is the aim of the study?

To elaborate an accurate indication of To elaborate an accurate indication of customer’s future spending evaluationcustomer’s future spending evaluation

To account for heterogeneity within the To account for heterogeneity within the group of long-life customergroup of long-life customer

To estimate whether newly acquired To estimate whether newly acquired customers will increase or decrease their customers will increase or decrease their future spendingfuture spending

Binary classification problem: 'Will newly acquired customers increase or decrease their spending after their first purchase experiences?‘

Previous experience:- traditional statistical methods- nonparametric statistical models- neural networks

Innovation

- adaptation of Bayesian network classifiers

Aim and Methodology

Naïve Bayes classifiers

Often work well in practiceOften work well in practice Learns the class-conditional probabilities Learns the class-conditional probabilities

P( Xi = xi | C = cl)P( Xi = xi | C = cl) New test cases are classified by using New test cases are classified by using

Bayes’ rule to compute the posterior Bayes’ rule to compute the posterior probability of each class cl given the vector probability of each class cl given the vector of observed variable values of observed variable values (see handout)(see handout)

Naïve Bayes Classifier

TANs

Tree Augmented Naïve Bayes Classifiers (TANs)Tree Augmented Naïve Bayes Classifiers (TANs)

Extension of the Naïve Bayes ClassifiersExtension of the Naïve Bayes Classifiers

Relax the independence assumption by allowing Relax the independence assumption by allowing arcs between the variablesarcs between the variables

The class variable has no parents and each The class variable has no parents and each variable has as parents the class variable and at variable has as parents the class variable and at most one other variablemost one other variable

The variables are only allowed to form a tree The variables are only allowed to form a tree structurestructure

Tree Augmented Naïve Bayes classifier

GBN: Learning Algorithm

Assumes an a priori ordering of the variablesAssumes an a priori ordering of the variables D-separation plays a pivotal role in the structure D-separation plays a pivotal role in the structure

learning algorithmlearning algorithm A four phase algorithmA four phase algorithm

Create a draftCreate a draft Add and remove arcs based on the concept of Add and remove arcs based on the concept of

d-separation and conditional independenced-separation and conditional independence Establish parameters Establish parameters

Multinet Bayesian Network Classifiers

GBN and TANs assume relations between the GBN and TANs assume relations between the variables are the same for all classesvariables are the same for all classes

Multinet Bayesian networks allows for more Multinet Bayesian networks allows for more flexibility and is composed of a separate, local flexibility and is composed of a separate, local network for each class and prior probability network for each class and prior probability distribution of the class node distribution of the class node

(see handout for formulas)(see handout for formulas)

Other Methods used, but not discussed

CL multinetCL multinet

C4.5 and C4.5rulesC4.5 and C4.5rules White-box classifiers for classification decisionsWhite-box classifiers for classification decisions

Linear Discriminant Analysis (LDA)Linear Discriminant Analysis (LDA) Well-known benchmark statistical classifiers Well-known benchmark statistical classifiers

Quadratic Discriminant Analysis (QDA)Quadratic Discriminant Analysis (QDA) Well-known benchmark statistical classifiersWell-known benchmark statistical classifiers

Training

Naïve Bayes and TAN used Matlab toolbox Naïve Bayes and TAN used Matlab toolbox of Kevin Murphyof Kevin Murphy

GBN and GBN multinet classifiers used GBN and GBN multinet classifiers used PowerPredictor softwarePowerPredictor software

Data Set

Variables of the StudyVariables of the Study

Time FrameTime Frame

Attributes, Values, and EncodingsAttributes, Values, and Encodings

Performance Classification

Measured by area under the Receiver Measured by area under the Receiver operating characteristic curve (AUROC)operating characteristic curve (AUROC)

Uses a 2D graph of the sensitivity on the Uses a 2D graph of the sensitivity on the Y-axis (true alarms) versus the false Y-axis (true alarms) versus the false alarms on the X -axisalarms on the X -axis

Performance Classification

Percentage of correctly classified (PCC)Percentage of correctly classified (PCC)

This is the most commonly used measure This is the most commonly used measure of performance of a classifierof performance of a classifier

Contingency table analysis to detect Contingency table analysis to detect statistically significant performance statistically significant performance differences between classifiers.differences between classifiers.

Accuracy and AnalysisAUROC ROC

The results Naïve Bayes and TAN did not Naïve Bayes and TAN did not

remove any attributes remove any attributes

TAN added 14 arcs to the TAN added 14 arcs to the Naïve Bayes classifier with Naïve Bayes classifier with minimal performance minimal performance improvementimprovement

GBN multinet looks simpler, GBN multinet looks simpler, but bad performancebut bad performance

GBN classifier was able to GBN classifier was able to prune 12 attributes prune 12 attributes

The Unrestricted

Practical implementation

Marketing investment decisionMarketing investment decision

Monitor of customer-Monitor of customer-acquisition policiesacquisition policies

To design an a-priori To design an a-priori segmentation scheme for a segmentation scheme for a company's customer basecompany's customer base

THANK YOU!THANK YOU!