Bayesian Network Classifiers for Predicting Long-Life Customer Spending Behavior

1. Bayesian Network Classifiers for Identifying the Slope of the customer Lifecycle of Long-Life Customers Authored by: Bart Baesens, Geert Vertraeten, Dirk Poel, Michael Petersen, Patrick Kenhove, Jan Vanthienen Presentation by: Oksana Myachina, Jeff Janies

2. INTRODUCTION • Acquiring a new customer is more costly, than selling additional products to existing ones. • Traditional brand strategies should be replaced by customer strategies. • It’s very important to make informed decisions on customers level.

3. CRM is successful only if customers remain at least to a certain extent ,loyal to the company in case. • Research shows large heterogeneity in long-term customers spending. • Responding to this fact , the study explained in the paper,was performed.

4. The relevance of estimation of a customer’s spending evaluation • Traditional relationship marketing claims: - loyal customers raise their spending - generate new customers - ensure diminishing serving costs - have reduced consumer price sensitivity • RM main idea : the longer customer stays loyal to company, the more Profit it has

5. 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

6. What is the aim of the study? To elaborate an accurate indication of customer’s future spending evaluation To account for heterogeneity within the group of long-life customer To estimate whether newly acquired customers will increase or decrease their future spending

7. Aim and Methodology • 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

8. Naïve Bayes classifiers • Often work well in practice • Learns the class-conditional probabilities P( Xi = xi | C = cl) • New test cases are classified by using Bayes’ rule to compute the posterior probability of each class cl given the vector of observed variable values (see handout)

9. Naïve Bayes Classifier

10. TANs • Tree Augmented Naïve Bayes Classifiers (TANs) • Extension of the Naïve Bayes Classifiers • Relax the independence assumption by allowing arcs between the variables • The class variable has no parents and each variable has as parents the class variable and at most one other variable • The variables are only allowed to form a tree structure

11. Tree Augmented Naïve Bayes classifier

12. GBN: Learning Algorithm • Assumes an a priori ordering of the variables • D-separation plays a pivotal role in the structure learning algorithm • A four phase algorithm • Create a draft • Add and remove arcs based on the concept of d-separation and conditional independence • Establish parameters

13. Multinet Bayesian Network Classifiers • GBN and TANs assume relations between the variables are the same for all classes • Multinet Bayesian networks allows for more flexibility and is composed of a separate, local network for each class and prior probability distribution of the class node • (see handout for formulas)

14. Other Methods used, but not discussed • CL multinet • C4.5 and C4.5rules • White-box classifiers for classification decisions • Linear Discriminant Analysis (LDA) • Well-known benchmark statistical classifiers • Quadratic Discriminant Analysis (QDA) • Well-known benchmark statistical classifiers

15. Training • Naïve Bayes and TAN used Matlab toolbox of Kevin Murphy • GBN and GBN multinet classifiers used PowerPredictor software

16. Data Set • Variables of the Study • Time Frame • Attributes, Values, and Encodings

17. Performance Classification • Measured by area under the Receiver operating characteristic curve (AUROC) • Uses a 2D graph of the sensitivity on the Y-axis (true alarms) versus the false alarms on the X -axis

18. Performance Classification • Percentage of correctly classified (PCC) • This is the most commonly used measure of performance of a classifier • Contingency table analysis to detect statistically significant performance differences between classifiers.

19. Accuracy and AnalysisAUROC ROC

20. The results • Naïve Bayes and TAN did not remove any attributes • TAN added 14 arcs to the Naïve Bayes classifier with minimal performance improvement • GBN multinet looks simpler, but bad performance • GBN classifier was able to prune 12 attributes

21. The Unrestricted

22. Practical implementation • Marketing investment decision • Monitor of customer-acquisition policies • To design an a-priori segmentation scheme for a company's customer base

23. THANK YOU!