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Journey Segmentation of Turkish Tobacco Users Using Sequence Clustering Techniques

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Intelligent and Fuzzy Techniques: Smart and Innovative Solutions (INFUS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1197))

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Abstract

In this paper, using individual tobacco panel data, a novel and behavioral approach based on sequence clustering techniques is proposed to have a deeper understanding of different behavior types of Turkish tobacco users during the consecutive price markups of 6th April and 2nd May in 2019. To achieve this, main brands before markups are determined for each of the 5052 individuals. Then, having some prior assumptions, their purchase behaviors are obtained as time-stamped event sequences. Finally, while a portion of the obtained sequences which are less complex are segmented with empirical analyses, the rest of them are segmented using hierarchical clustering with optimal matching event (OME) distance. Results suggested seven main type of behavior among the tobacco users in Turkey during the markup period.

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References

  1. Ngai, E.W.T., Xiu, L., Chau, D.C.K.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)

    Article  Google Scholar 

  2. Teichert, T., Shehu, E., von Wartburg, I.: Customer segmentation revisited: the case of the airline industry. Transp. Res. Part A: Policy Pract. 42(1), 227–242 (2008)

    Google Scholar 

  3. Cooil, B., Aksoy, L., Keiningham, T.L.: Approaches to customer segmentation. J. Relat. Mark. 6(3–4), 9–39 (2008)

    Google Scholar 

  4. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  5. Saunders, J.A.: Cluster analysis for market segmentation. Eur. J. Mark. 14(7), 422–435 (1980)

    Article  Google Scholar 

  6. Tsai, C.-Y., Chiu, C.-C.: A purchase-based market segmentation methodology. Expert Syst. Appl. 27(2), 265–276 (2004)

    Article  Google Scholar 

  7. Kim, K., Ahn, H.: Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In: Artificial Intelligence and Simulation, pp. 409–415 (2005)

    Google Scholar 

  8. Namvar, M., Gholamian, M.R., KhakAbi, S.: A two phase clustering method for intelligent customer segmentation. In: 2010 International Conference on Intelligent Systems, Modelling and Simulation (2010)

    Google Scholar 

  9. Piccarreta, R., Studer, M.: Holistic analysis of the life course: methodological challenges and new perspectives. Adv. Life Course Res. 41, 100251 (2018)

    Article  Google Scholar 

  10. Ritschard, G., Burgin, R., Studer, M.: Exploratory mining of life event histories. In: McArdle, J.J., Ritschard, G. (eds.) Contemporary Issues in Exploratory Data Mining in Behavioral Sciences, pp. 221–253. Routeledge, New York (2018)

    Google Scholar 

  11. Studer, M., Ritschard, G.: What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures. J. R. Stat. Soc. Ser. A (Stat. Soc.) 179(2), 481–511 (2015)

    Article  MathSciNet  Google Scholar 

  12. Terragni, A., Hassani, M.: Analyzing customer journey with process mining: from discovery to recommendations. In: 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), Barcelona, pp. 224–229 (2018)

    Google Scholar 

  13. Kamakura, W.A.: Sequential market basket analysis. Mark. Lett. 23(3), 505–516 (2012)

    Article  Google Scholar 

  14. Silberer, G.: Analyzing sequences in marketing research. In: Diamantopoulos, A., Fritz, W., Hildebrandt, L. (eds.) Quantitative Marketing and Marketing Management. Gabler Verlag, Wiesbaden (2012)

    Google Scholar 

  15. Studer, M., Müller, N., Ritschard, G., Gabadinho, A.: Classer, discriminer et visualiser des séquences d’événements. In: Revue des nouvelles technologies de l’information (RNTI), E-19, pp. 37–48 (2010)

    Google Scholar 

  16. Gabadinho, A., Ritschard, G., Müller, N.S., Studer, M.: Analyzing and visualizing state sequences in R with TraMineR. J. Stat. Softw. 40(4), 1–37 (2011)

    Article  Google Scholar 

  17. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236 (1963)

    Article  MathSciNet  Google Scholar 

  18. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2017)

    MATH  Google Scholar 

  19. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

Download references

Acknowledgement

This study is an outcome of the collaboration project conducted by IPSOS and ITUNOVA (2020) entitled “Exploration of the Shopping Journeys and Customer Churn in FMCG Sector in Turkey Using Data Analytics Techniques”.

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Authors and Affiliations

  1. Department of Management Engineering, Istanbul Technical University, 34367, Istanbul, Turkey

    Ahmet Talha Yiğit

Authors
  1. Ahmet Talha Yiğit
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  2. Tolga Kaya
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  3. Utku Doğruak
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Corresponding author

Correspondence to Ahmet Talha Yiğit .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Sezi Cevik Onar

  3. Department of Industrial Engineering, Istanbul Technical University, İstanbul, Turkey

    Basar Oztaysi

  4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey

    Irem Ucal Sari

  5. Industrial Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Selcuk Cebi

  6. Industrial Engineering Department, Galatasaray University, Istanbul, Turkey

    A. Cagri Tolga

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Yiğit, A.T., Kaya, T., Doğruak, U. (2021). Journey Segmentation of Turkish Tobacco Users Using Sequence Clustering Techniques. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_11

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