Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enha...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/11403 |
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| author | Dimitris C. Gkikas Prokopis K. Theodoridis |
| author_facet | Dimitris C. Gkikas Prokopis K. Theodoridis |
| author_sort | Dimitris C. Gkikas |
| collection | DOAJ |
| description | User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships among key metrics including event count, sessions, purchase revenue, transactions, and bounce rate, were examined using descriptive statistics, revealing factors affecting user engagement. Machine learning classifiers, such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), were assessed for their effectiveness in classifying engagement levels. DTs achieved a classification accuracy of 97.98%, outperforming NB (65.00%) and k-NN (97.90%). Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. This study highlights the significance of artificial intelligence (AI) integration in digital marketing as a best practice for optimizing decision-making processes. |
| format | Article |
| id | doaj-art-c8d941d6c7c34f4286e45e9ea14b1f13 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c8d941d6c7c34f4286e45e9ea14b1f132024-12-13T16:23:48ZengMDPI AGApplied Sciences2076-34172024-12-0114231140310.3390/app142311403Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User EngagementDimitris C. Gkikas0Prokopis K. Theodoridis1School of Agricultural Sciences, University of Patras, 30200 Messolonghi, GreeceSchool of Social Sciences, Hellenic Open University, 26335 Patras, GreeceUser engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships among key metrics including event count, sessions, purchase revenue, transactions, and bounce rate, were examined using descriptive statistics, revealing factors affecting user engagement. Machine learning classifiers, such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), were assessed for their effectiveness in classifying engagement levels. DTs achieved a classification accuracy of 97.98%, outperforming NB (65.00%) and k-NN (97.90%). Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. This study highlights the significance of artificial intelligence (AI) integration in digital marketing as a best practice for optimizing decision-making processes.https://www.mdpi.com/2076-3417/14/23/11403predictive analyticsdata miningdigital marketingGoogle Analyticskey performance indicatorsuser behavior |
| spellingShingle | Dimitris C. Gkikas Prokopis K. Theodoridis Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement Applied Sciences predictive analytics data mining digital marketing Google Analytics key performance indicators user behavior |
| title | Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement |
| title_full | Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement |
| title_fullStr | Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement |
| title_full_unstemmed | Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement |
| title_short | Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement |
| title_sort | predicting online shopping behavior using machine learning and google analytics to classify user engagement |
| topic | predictive analytics data mining digital marketing Google Analytics key performance indicators user behavior |
| url | https://www.mdpi.com/2076-3417/14/23/11403 |
| work_keys_str_mv | AT dimitriscgkikas predictingonlineshoppingbehaviorusingmachinelearningandgoogleanalyticstoclassifyuserengagement AT prokopisktheodoridis predictingonlineshoppingbehaviorusingmachinelearningandgoogleanalyticstoclassifyuserengagement |