**Author details**

Goksu Tuysuzoglu<sup>1</sup> , Derya Birant2 \* and Aysegul Pala3

\*Address all correspondence to: derya@cs.deu.edu.tr

1 Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Turkey

2 Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey

3 Department of Environmental Engineering, Dokuz Eylul University, Izmir, Turkey

### **References**


[5] Lei KS, Wan F. Applying ensemble learning techniques to ANFIS for air pollution index prediction in Macau. In: International Symposium on Neural Networks (ISNN'12); 11-14 July 2012. Berlin, Heidelberg: Springer; 2012. pp. 509-516

strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection, (iii) boosting, and (iv) voting. In the experimental studies, ensemble

• Multistrategy ensemble learning that combines several ensemble strategies can be ad-

• Text mining, web mining, and process mining have been used in many engineering fields. However, there is very limited usage of them in environmental engineering. Future research

• Some ontologies can be developed for environmental domain. We believe that the future environmental data mining studies will be supported by the ontologies to extract semantic

relationships, to improve accuracy, and to develop better decision support systems.

\* and Aysegul Pala3

1 Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, Turkey

[1] Stojić A, Stojić SS, Reljin I, Čabarkapa M, Šoštarić A, Perišić M, Mijić Z. Comprehensive analysis of PM10 in Belgrade urban area on the basis of long-term measurements. Environmental Science and Pollution Research. 2016;**23**:10722-10732. DOI: 10.1007/

[2] Srivastava AN. Greener aviation with virtual sensors: A case study. Data Mining and

[3] Al Abri ES, Edirisinghe EA, Nawadha A. Modelling ground-level ozone concentration using ensemble learning algorithms. In: Proceedings of the International Conference on Data Mining (DMIN'15); 27-30 July 2015; Las Vegas. USA: The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing

[4] Bougoudis I, Demertzis K, Iliadis L. HISYCOL a hybrid computational intelligence system for combined machine learning: The case of air pollution modeling in Athens. Neural

Computing and Applications. 2016;**27**:1191-1206. DOI: 10.1007/s00521-015-1927-7

Knowledge Discovery. 2012;**24**:443-471. DOI: 10.1007/s10618-011-0240-z

2 Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey

3 Department of Environmental Engineering, Dokuz Eylul University, Izmir, Turkey

methods are tested on different real-world environmental datasets.

In the future, the following studies can be carried out:

dressed, instead of a single ensemble strategy.

, Derya Birant2

\*Address all correspondence to: derya@cs.deu.edu.tr

can focus on these subjects.

**Author details**

14 Data Mining

Goksu Tuysuzoglu<sup>1</sup>

**References**

s11356-016-6266-4

(WorldComp); 2015. pp. 148-154


[18] Muñoz-Mas R, Lopez-Nicolas A, Martínez-Capel F, Pulido-Velazquez M. Shifts in the suitable habitat available for brown trout (*Salmo trutta* L.) under short-term climate change scenarios. Science of the Total Environment. 2016;**544**:686-700. DOI: 10.1016/j. scitotenv.2015.11.147

**Chapter 2**

Provisional chapter

**Estimating Customer Lifetime Value Using Machine**

DOI: 10.5772/intechopen.76990

With the rapid development of civil aviation industry, high-quality customer resources have become a significant way to measure the competitiveness of the civil aviation industry. It is well known that the competition for high-value customers has become the core of airline profits. The research of airline customer lifetime value can help airlines identify high-value, medium-value and low-value travellers. What is more, the airline company can make resource allocation more rational, with the least resource investment for maximum profit return. However, the models that are used to calculate the value of customer life value remain controversial, and how to design a model that applies to airline company still needs to be explored. In the paper, the author proposed the optimised China Eastern Airlines passenger network value assessment model and examined its fitting degree with the TravelSky value score. Besides, the author combines China Eastern Airlines passenger network value assessment model score with loss model score to help airlines find their significant customers.

In the context of customer relationship management, customer lifetime value (CLV) or customer equity (CE) becomes important because it is a disaggregate metric to evaluate marketing decisions [1], which can be utilised to allocate resources appropriately and identify profitable consumers [2]. Companies are looking forward to better approaches to create value and optimise their market offerings to appeal to customers and make profits [3]. Many firms are utilising CLV regularly to control and supervise the strategies of marketing as well as evaluate the business success. For companies, it is of interest to know how much net benefit it can expect from their customers. It is recognised that clv has become a significant component of

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: customer lifetime value, estimating, machine learning

Estimating Customer Lifetime Value Using Machine

**Learning Techniques**

Learning Techniques

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76990

Sien Chen

Sien Chen

Abstract

1. Introduction


### **Estimating Customer Lifetime Value Using Machine Learning Techniques** Estimating Customer Lifetime Value Using Machine Learning Techniques

DOI: 10.5772/intechopen.76990

Sien Chen Sien Chen

[18] Muñoz-Mas R, Lopez-Nicolas A, Martínez-Capel F, Pulido-Velazquez M. Shifts in the suitable habitat available for brown trout (*Salmo trutta* L.) under short-term climate change scenarios. Science of the Total Environment. 2016;**544**:686-700. DOI: 10.1016/j.

[19] Bravo-Moncayo L, Naranjo JL, García IP, Mosquera R. Neural based contingent valuation of road traffic noise. Transportation Research Part D: Transport and Environment.

[20] Kühnlein M, Appelhans T, Thies B, Nauss T. Improving the accuracy of rainfall rates from optical satellite sensors with machine learning—A random forests-based approach applied to MSG SEVIRI. Remote Sensing of Environment. 2014;**141**:129-143. DOI: 10.1016/j.

[21] Fan C, Xiao F, Wang S. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. Applied Energy.

[22] Araya DB, Grolinger K, ElYamany HF, Capretz MA, Bitsuamlak G. An ensemble learning framework for anomaly detection in building energy consumption. Energy and

[23] Jovanović RŽ, Sretenović AA, Živković BD. Ensemble of various neural networks for prediction of heating energy consumption. Energy and Buildings. 2015;**94**:189-199. DOI:

[24] Knudby A, Brenning A, LeDrew E. New approaches to modelling fish–habitat relationships. Ecological Modelling. 2010;**221**:503-511. DOI: 10.1016/j.ecolmodel.2009.11.008 [25] Kocev D, Džeroski S. Habitat modeling with single-and multi-target trees and ensembles. Ecological Informatics. 2013;**18**:79-92. DOI: 10.1016/j.ecoinf.2013.06.003

[26] Zhang Z, Ma C, Xu J, Huang J, Li L. A novel combinational forecasting model of dust storms based on rare classes classification algorithm. In Geo-Informatics in Resource Management and Sustainable Ecosystem (GRMSE'14); October 2014. Berlin, Heidelberg:

[27] Mathanker SK, Weckler PR, Taylor RK, Fan G.AdaBoost and support vector machine classifiers for automatic weed control: Canola and Wheat. In: 2010 Pittsburgh, Pennsylvania*,* 20-23 June 2010; American Society of Agricultural and Biological Engineers. 2010. p. 1 [28] Lima AR, Cannon AJ, Hsieh WW. Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy. Computers & Geosciences.

[29] Luo Q, Kathuria A. Modelling the response of wheat grain yield to climate change: A sensitivity analysis. Theoretical and Applied Climatology. 2013;**111**:173-182. DOI: 10.1007/

[30] Mohammed AA, Yaqub W, Aung Z. Probabilistic forecasting of solar power: An ensemble learning approach. Intelligent Decision Technologies. Smart Innovation, Systems and

Technologies. 2015;**39**:449-458. DOI: 10.1007/978-3-319-19857-6\_38

scitotenv.2015.11.147

16 Data Mining

rse.2013.10.026

10.1016/j.enbuild.2015.02.052

Springer; 2015. pp. 520-537

s00704-012-0655-5

2017;**50**:26-39. DOI: 10.1016/j.trd.2016.10.020

2014;**127**:1-10. DOI: 10.1016/j.apenergy.2014.04.016

2013;**50**:136-144. DOI: 10.1016/j.cageo.2012.06.023

Buildings. 2017;**144**:191-206. DOI: 10.1016/j.enbuild.2017.02.058

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76990

### Abstract

With the rapid development of civil aviation industry, high-quality customer resources have become a significant way to measure the competitiveness of the civil aviation industry. It is well known that the competition for high-value customers has become the core of airline profits. The research of airline customer lifetime value can help airlines identify high-value, medium-value and low-value travellers. What is more, the airline company can make resource allocation more rational, with the least resource investment for maximum profit return. However, the models that are used to calculate the value of customer life value remain controversial, and how to design a model that applies to airline company still needs to be explored. In the paper, the author proposed the optimised China Eastern Airlines passenger network value assessment model and examined its fitting degree with the TravelSky value score. Besides, the author combines China Eastern Airlines passenger network value assessment model score with loss model score to help airlines find their significant customers.

Keywords: customer lifetime value, estimating, machine learning
