Cancel-for-Any-Reason Insurance Recommendation Using Customer Transaction-Based Clustering

In the travel insurance industry, cancel-for-any-reason insurance, also known as a cancellation protection service (CPS), is a recent attempt to strike a balance between customer satisfaction and service provider (SP) profits. However, some exceptional circumstances, particularly the COVID-19 pandemic, have led to a dramatic decrease in SP revenues, especially for non-refundable tickets purchased early with CPS. This paper begins by presenting a risk group segmentation of customers in an online ticket reservation system. Then, a CPS fee is recommended depending on the different customer risk groups provided by the cluster segmentation via different clustering algorithms such as centroid-based K-means, hierarchical agglomerative, DBSCAN, and artificial neural network-based SOM algorithms. According to the implemented cluster metrics, which include the Silhouette index, Davies-Bouldin index, Entropy index, and DBCV index, the SOM algorithm presents the most appropriate result. After predicting the new customer cluster, a CPS fee will be calculated with the proposed adaptive CPS method based on the cluster segmentation weights. Determining the weight of each cluster is related to the total CPS revenue threshold for all clusters defined by the SP. Therefore, to avoid a loss for SPs, the total CPS revenue will be kept constant with the threshold that the SP has been adjusted. The experimental results based on real-world data show that the risk group segmentation of customers helps to maintain a balance between CPS fees and SP profits. Finally, according to the calculated weights, the proposed model pegs the SP gain/loss variation with a 0.00012 exchange ratio.

[1]  Xiao Xu,et al.  Density peaks clustering using geodesic distances , 2017, International Journal of Machine Learning and Cybernetics.

[2]  Hans-Peter Kriegel,et al.  The (black) art of runtime evaluation: Are we comparing algorithms or implementations? , 2017, Knowledge and Information Systems.

[3]  Yu Xue,et al.  A robust density peaks clustering algorithm using fuzzy neighborhood , 2017, International Journal of Machine Learning and Cybernetics.

[4]  Frank Nielsen,et al.  Introduction to HPC with MPI for Data Science , 2016, Undergraduate Topics in Computer Science.

[5]  Arthur Zimek,et al.  Density-Based Clustering Validation , 2014, SDM.

[6]  Yu Xue,et al.  A novel density peaks clustering with sensitivity of local density and density-adaptive metric , 2018, Knowledge and Information Systems.

[7]  Hans-Peter Kriegel,et al.  DBSCAN Revisited, Revisited , 2017, ACM Trans. Database Syst..

[8]  Sabri Serkan Gulluoglu.  Segmenting customers with data mining techniques , 2015, 2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC).

[9]  María Teresa Ballestar,et al.  Customer segmentation in e-commerce: Applications to the cashback business model , 2017, Journal of Business Research.

[10]  J. Santos Domínguez-Menchero,et al.  Optimal purchase timing in the airline market , 2014 .

[11]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[12]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  L. Infante,et al.  Hierarchical Clustering , 2020, International Encyclopedia of Statistical Science.

[14]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Viet Hoang Vu,et al.  An airfare prediction model for developing markets , 2018, 2018 International Conference on Information Networking (ICOIN).

[16]  Leonardo O. Iheme,et al.  Artificial Neural Networks in Customer Segmentation , 2019, 2019 27th Signal Processing and Communications Applications Conference (SIU).

[17]  Sebastián Maldonado,et al.  Integrated framework for profit-based feature selection and SVM classification in credit scoring , 2017, Decis. Support Syst..

[18]  Christian Hennig,et al.  Recovering the number of clusters in data sets with noise features using feature rescaling factors , 2015, Inf. Sci..

[19]  R. J. Kuo,et al.  Integration of self-organizing feature map and K-means algorithm for market segmentation , 2002, Comput. Oper. Res..

[20]  Muhammad Zein Hanafi,et al.  Segmentation of Customers’ Experiences of YouTube Streaming Application Users in South Jakarta using K-means Method , 2020, 2020 International Conference on Smart Technology and Applications (ICoSTA).

[21]  Kadim Tasdemir,et al.  Topology-Based Hierarchical Clustering of Self-Organizing Maps , 2011, IEEE Transactions on Neural Networks.

[22]  Zhaleh Sadreddini,et al.  A Novel Cancellation Protection Service in Online Reservation System , 2020, IEEE Access.

[23]  Quanwang Wu,et al.  Clustering with Local Density Peaks-Based Minimum Spanning Tree , 2021, IEEE Transactions on Knowledge and Data Engineering.

[24]  Anastasia A. Lantseva,et al.  Data-driven Modeling of Airlines Pricing , 2015 .

[25]  Jian Cao,et al.  OTPS: A decision support service for optimal airfare Ticket Purchase , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[26]  Fahad Shahbaz Khan,et al.  Airline ticket price and demand prediction: A survey , 2019, J. King Saud Univ. Comput. Inf. Sci..

[27]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[28]  Kai Meng Tay,et al.  Application of self-organizing map to failure modes and effects analysis methodology , 2017, Neurocomputing.

[29]  Andrew Y. Ng,et al.  Learning Feature Representations with K-Means , 2012, Neural Networks: Tricks of the Trade.

[30]  Alexander N. Gorban,et al.  SOM: Stochastic initialization versus principal components , 2016, Inf. Sci..

[31]  Trupti M. Kodinariya,et al.  Review on determining number of Cluster in K-Means Clustering , 2013 .

[32]  Data Segmentation via t-SNE, DBSCAN, and Random Forest , 2020, ArXiv.

[33]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.

[34]  Feiping Nie,et al.  Local PurTree Spectral Clustering for Massive Customer Transaction Data , 2017, IEEE Intelligent Systems.

[35]  Joshua Zhexue Huang,et al.  PurTreeClust: A purchase tree clustering algorithm for large-scale customer transaction data , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[36]  Donald C. Wunsch,et al.  An Information-Theoretic-Cluster Visualization for Self-Organizing Maps , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[37]  B D Satoto,et al.  Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster , 2018, IOP Conference Series: Materials Science and Engineering.

[38]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[39]  Rocco Fazzolari,et al.  AW-SOM, an Algorithm for High-Speed Learning in Hardware Self-Organizing Maps , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.

[40]  Bhavuk Chawla,et al.  Airfare Analysis And Prediction Using Data Mining And Machine Learning , 2017 .

[41]  Fang-Ming Hsu,et al.  Segmenting customers by transaction data with concept hierarchy , 2012, Expert Syst. Appl..

[42]  Saulo Martiello Mastelini,et al.  Deep Regressor Stacking for Air Ticket Prices Prediction , 2017, SBSI.

[43]  Daniel K Hartline,et al.  t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis. , 2019, Marine genomics.

[44]  Lior Rokach,et al.  Data Mining and Knowledge Discovery Handbook, 2nd ed , 2010, Data Mining and Knowledge Discovery Handbook, 2nd ed..

[45]  Hari Krishna Kanagala,et al.  A comparative study of K-Means, DBSCAN and OPTICS , 2016, 2016 International Conference on Computer Communication and Informatics (ICCCI).