Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement

Dimitris C. Gkikas, Prokopis K. Theodoridis

Applied Sciences 2024 article cited by 18

Source Open-access PDF More in Business & Industry

importance 3/10 ai assisted Digital Marketing and Social Media digital marketing stage: research

Problem

Online retailers need to classify user engagement levels from Google Analytics metrics to improve digital marketing decisions.

Why it matters

The abstract states the goal is to generate recommendations helping decision-makers and marketers.

What the AI did

Machine learning classifiers (decision trees, Naive Bayes, k-NN) were trained on Google Analytics engagement metrics to classify user engagement levels, with pruning applied for optimization.

Results

Decision trees achieved 97.98% classification accuracy, outperforming Naive Bayes (65.00%) and k-NN (97.90%).

Impact

Decision trees classified engagement levels with 97.98% accuracy, supporting marketing recommendations.

Record

Venue	Applied Sciences
Published	2024
Work type	article
Authors	Dimitris C. Gkikas, Prokopis K. Theodoridis
Citations	18 (OpenAlex, at import time)
References	29
Model or method	Decision trees, Naive Bayes, k-nearest neighbors
Organization	not stated in source
Primary source	https://doi.org/10.3390/app142311403
Open access	gold
Keywords	online shopping, user engagement, decision trees, Google Analytics, classification, digital marketing

Importance rating

3 / 10

incremental / niche

Marketing engagement classification aids decision-makers, but abstract shows no scale of affected users or business impact; incremental niche advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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