Improving Café Reputation: Machine Learning Analytics for Predicting Customer Engagement on Google Maps

Abstrak

Background: Online reviews is a powerful tool in shaping customer decisions, as they significantly influence a business’s reputation and the ability to attract new customer. Given the growing reliance on digital platforms, understanding engagement levels is crucial for business that want to enhance online presence. By analyzing these customer activities, business owners can leverage Machine Learning (ML) analytics to predict engagement on Google Maps reviews. Objective: This study aimed to develop the most suitable ML model in order to predict customer engagement levels in café business on Google Maps, and determine the online review features that have the greatest impact on engagement. Additionally, the analysis aimed to provide actionable recommendations to help business owners improve online reputation and engagement strategies. Method: A total of 5,626 online reviews data were collected using web scraping methods during the analysis. The data was then preprocessed by extracting major review features, calculating engagement levels, and addressing class imbalance with SMOTE method. In the study, K-Means clustering was used to segment engagement levels, while sentiment analysis through VADER Lexicon was applied to measure sentiment content. Various ML models were trained and validated using a 10-fold cross-validation method. Finally, Analysis was conducted using Spearman's correlation to identify relationships among features derived from the best-performing ML models. Results: The result of the analysis showed that Random Forest model achieved the highest accuracy and PR AUC in predicting engagement levels. The four most influential factors were review length (16.23%), photos (15.57%), total rating (12.35%), and author review count (10.19%). Spearman's correlation analysis showed a positive relationship among review length, photos, and author review count, signifying the combined impact on engagement levels. Conclusion: This study described the effectiveness of Random Forest model in predicting engagement levels in Google Maps reviews. Specifically, the model identified review length, photos, total rating, and author review count as the key factors influencing engagement. These results would provide valuable guidance for business owners that desire to improve customer engagement and online reputation. Building on this, future studies should explore larger datasets, integrate additional features, and examine how the engagement contribute to long-term customer retention. Keywords: Online Reputation Management, Customer Engagement, Behavior, Machine Learning, Google Maps Review, Predictive Analytics