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Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm

Author

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  • Aidin Salamzadeh

    (Department of Business Management, Faculty of Management, University of Tehran, Tehran 141556311, Iran)

  • Pejman Ebrahimi

    (Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöllő, Hungary)

  • Maryam Soleimani

    (Department of Management, Economics and Accounting, Payame Noor University, Tehran 1599959515, Iran)

  • Maria Fekete-Farkas

    (Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences (MATE), 2100 Gödöllő, Hungary)

Abstract

The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users’ point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of “customization” has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers.

Suggested Citation

  • Aidin Salamzadeh & Pejman Ebrahimi & Maryam Soleimani & Maria Fekete-Farkas, 2022. "Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm," JRFM, MDPI, vol. 15(10), pages 1-16, September.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:10:p:424-:d:922960
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    References listed on IDEAS

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    4. Chiara Rinaldi & Marlene D’Aguilar & Matt Egan, 2022. "Understanding the Online Environment for the Delivery of Food, Alcohol and Tobacco: An Exploratory Analysis of ‘Dark Kitchens’ and Rapid Grocery Delivery Services," IJERPH, MDPI, vol. 19(9), pages 1-16, May.
    5. Fagerstrøm, Asle & Eriksson, Niklas & Sigurdsson, Valdimar, 2020. "Investigating the impact of Internet of Things services from a smartphone app on grocery shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
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    Cited by:

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    2. Branislav Popović & Marko Janev & Lidija Krstanović & Nikola Simić & Vlado Delić, 2022. "Measure of Similarity between GMMs Based on Geometry-Aware Dimensionality Reduction," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    3. Sohail Ahmad Javeed & Rashid Latief & Umair Akram, 2023. "The Effects of Board Capital on Green Innovation to Improve Green Total Factor Productivity," Sustainability, MDPI, vol. 15(13), pages 1-18, June.

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