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Machine Learning Applications for Consumer Behavior Prediction

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Innovations in Smart Cities Applications Volume 6 (SCA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 629))

Abstract

Customers are growing increasingly interested in the degree of service that businesses can provide. Because the services provided by different providers are not significantly different, there is greater competition among firms to maintain and improve the quality of their services. This research study investigates how machine learning can help businesses learn more about their clients and provide them with more personalized services. It also acts as a pattern on how firms can improve the effectiveness of their marketing efforts to acquire new clients and establish long-term connections with existing customers.

In marketing, the prediction of consumer behavior is frequently used to schematize service offerings and develop targeted marketing programs. We have implemented six different machine-learning algorithms to improve further our ability to forecast consumer behavior. We have presented six machine learning models to improve performance, including Random Forest, Gradient Boosting, Logistic Regression, LightGBM, XgBoost, and Decision Tree, to achieve better results. Gradient Boosting, according to this study’s findings, is the best overall model for performance and efficiency.

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Authors and Affiliations

  1. Research Laboratory on New Economy and Development (LARNED), Faculty of Legal Economic and Social Sciences AIN SEBAA, Hassan II University of Casablanca, Casablanca, Morocco

    Nouri Hicham & Sabri Karim

Authors
  1. Nouri Hicham
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  2. Sabri Karim
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Corresponding author

Correspondence to Nouri Hicham .

Editor information

Editors and Affiliations

  1. Faculty of Science and Technology, Computer Engineering Department, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Faculté des sciences et techniques de Tanger, Université Abdelmalek Essaâdi, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. Interdisciplinary Centre of Social Science, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

    Domingos Santos

  4. DiSAC-Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, Castelo Branco, Portugal

    Rogerio Dionisio

  5. FST Al-Hoceima, Physics Department, Abdelmalek Essaadi University, Al-Hoceima, Morocco

    Nabil Benaya

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Hicham, N., Karim, S. (2023). Machine Learning Applications for Consumer Behavior Prediction. In: Ben Ahmed, M., Boudhir, A.A., Santos, D., Dionisio, R., Benaya, N. (eds) Innovations in Smart Cities Applications Volume 6. SCA 2022. Lecture Notes in Networks and Systems, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-031-26852-6_62

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  • DOI: https://doi.org/10.1007/978-3-031-26852-6_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26851-9

  • Online ISBN: 978-3-031-26852-6

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