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Customer Behaviour Hidden Markov Model

Author

Listed:
  • Ales Jandera

    (BERG Faculty, Technical University of Kosice, Nemcovej 3, 04200 Kosice, Slovakia)

  • Tomas Skovranek

    (BERG Faculty, Technical University of Kosice, Nemcovej 3, 04200 Kosice, Slovakia)

Abstract

In this work, the Customer behaviour hidden Markov model (CBHMM) is proposed to predict the behaviour of customers in e-commerce with the goal to forecast the store income. The model consists of three sub-models: Vendor, Psychology and Loyalty, returning probabilities used in the transition matrix of the hidden Markov model, deciding upon three decision-states: “Order completed”, “Order uncompleted” or “No order”. The model outputs are read by the Viterbi algorithm to estimate if the order has been completed successfully, followed by the evaluation of the forecasted store income. The proposed CBHMM was compared to the baseline prediction represented by the Google Analytics tracking system mechanism (GA model). The forecasted income computed using CBHMM as well as the GA model followed the trend of real income data obtained from the store for the year 2021. Based on the comparison criteria the proposed CBHMM outperforms the GA model in terms of the R-squared criterion, giving a 5% better fit, and with the PG value more than 3 dB higher.

Suggested Citation

  • Ales Jandera & Tomas Skovranek, 2022. "Customer Behaviour Hidden Markov Model," Mathematics, MDPI, vol. 10(8), pages 1-10, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1230-:d:789761
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    References listed on IDEAS

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    1. Liu Chang & Yacine Ouzrout & Antoine Nongaillard & Abdelaziz Bouras & Zhou Jiliu, 2013. "The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, July.
    2. Hernández, Blanca & Jiménez, Julio & Martín, M. José, 2010. "Customer behavior in electronic commerce: The moderating effect of e-purchasing experience," Journal of Business Research, Elsevier, vol. 63(9-10), pages 964-971, September.
    3. Martínez, Andrés & Schmuck, Claudia & Pereverzyev, Sergiy & Pirker, Clemens & Haltmeier, Markus, 2020. "A machine learning framework for customer purchase prediction in the non-contractual setting," European Journal of Operational Research, Elsevier, vol. 281(3), pages 588-596.
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    Cited by:

    1. Donghun Lee, 2022. "Knowledge Gradient: Capturing Value of Information in Iterative Decisions under Uncertainty," Mathematics, MDPI, vol. 10(23), pages 1-20, November.
    2. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.

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