[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128276-en":3,"doc-seo-128276-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128276,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Improving Café Reputation - Machine Learning Analytics for Predicting Customer Engagement on Google Maps","Online reviews shape customer decisions by strongly influencing business reputation and the ability to attract new visitors. As digital platforms become increasingly central, understanding customer engagement levels on review systems is essential for strengthening online presence. This study develops suitable machine learning models to predict engagement on Google Maps, identifies the most impactful review features, and provides actionable guidance for improving reputation and engagement strategies.","Journal of  \nInformation Systems Engineering and Business Intelligence  \n\n| Vol.11, No.1, February 2025\u003Cbr>Available online at: [http://e-journal.unair.ac.id/index.php/JISEBI](http://e-journal.unair.ac.id/index.php/JISEBI)\u003Cbr>Improving Café Reputation: Machine Learning Analytics for Predicting Customer Engagement on Google Maps\u003Cbr>Siti Anisah 1)* , Meditya Wasesa 2) \u003Cbr>1) 2) School of Business and Management, Institut Teknologi Bandung, Bandung, Indonesia Jl. Ganesha No.10, Bandung\u003Cbr>1)[siti_anisah@sbm-itb.ac.id](siti_anisah@sbm-itb.ac.id),2)[meditya.wasesa@itb.ac.id](meditya.wasesa@itb.ac.id) |\n| --- |\n| Abstract\u003Cbr>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.\u003Cbr>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.\u003Cbr>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.\u003Cbr>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.\u003Cbr>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.\u003Cbr>Keywords: Online Reputation Management, Customer Engagement, Behavior, Machine Learning, Google Maps Review, Predictive Analytics Article history: Received 24 September 2024, first decision 2 January 2025, accepted 17 March 2025, available online 28 March 2025 |\n| I. INTRODUCTION\u003Cbr>The rapid expansion of online platforms is transforming the way business engage with customer, particularly for small enterprises. This is because consumer choices are increasingly influenced by online reviews. According to thereport published by Statista in December 2022, this trend signified that the share of online reviews on Google grew from 67% in 2020 to 71% in 2021. This increase shows the growing impact of online reviews on customer perceptionsand decision-making [1] . Given this trend, analyzing customer engagement on online review platforms is","cbCaiqP3eIX5FyaN","https://ap.wps.com/l/cbCaiqP3eIX5FyaN","pdf",857134,2,1,12,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Role of online reviews and engagement\n# Methodology\n## Data collection and preprocessing\n## Feature extraction, SMOTE, and clustering\n## Sentiment analysis and model validation\n## Correlation analysis\n# Results\n## Best-performing model and predictive performance\n## Most influential review factors\n## Spearman correlation findings\n# Conclusion","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To develop an appropriate machine learning model for predicting customer engagement levels in cafés on Google Maps and to determine which online review features most affect engagement.\"},{\"question\":\"How is the dataset built and prepared?\",\"answer\":\"Web scraping collects 5,626 Google Maps reviews. The data is preprocessed by extracting review features, computing engagement levels, and handling class imbalance using SMOTE.\"},{\"question\":\"Which model performs best and what factors matter most?\",\"answer\":\"Random Forest achieves the highest accuracy and PR AUC. The most influential factors are review length, photos, total rating, and author review count.\"}]","Improving Café Reputation - Machine Learning Analytics for Predicting Customer Engagement on Google Maps | PDF",1785946471,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"improving-cafe-reputation-machine-learning-analytics-for-predicting-customer-engagement-on-google-maps","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/improving-cafe-reputation-machine-learning-analytics-for-predicting-customer-engagement-on-google-maps/128276/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this study?","Question",{"text":76,"@type":77},"To develop an appropriate machine learning model for predicting customer engagement levels in cafés on Google Maps and to determine which online review features most affect engagement.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the dataset built and prepared?",{"text":81,"@type":77},"Web scraping collects 5,626 Google Maps reviews. The data is preprocessed by extracting review features, computing engagement levels, and handling class imbalance using SMOTE.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performs best and what factors matter most?",{"text":85,"@type":77},"Random Forest achieves the highest accuracy and PR AUC. 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