Prediction Customer Loyalty Using Random Forest Algorithm on Shopee Reviews
DOI:
https://doi.org/10.31294/p.v27i1.7940Keywords:
Customer loyalty, E-commerce, Prediction, Random Forest Algorithm, ShopeeAbstract
This research develops a Shopee customer loyalty prediction model using Random Forest algorithm, utilizing customer reviews from Google Play Store. One of the key issues in e-commerce is maintaining customer loyalty amidst intense competition, so it is important to identify loyal customers and understand the factors that influence their commitment. This study involves data collection through web scraping, data cleaning, loyalty labeling, and Random Forest-based prediction model building and evaluation. The evaluation process was conducted using a confusion matrix to measure accuracy, precision, recall, and F1-score. The model classified customers into loyal, neutral, and disloyal categories, with an overall accuracy of 97%. The model showed precision, recall, and F1-score of 0.98 for loyal customers, and 0.99, 1.00, and 0.99 for disloyal customers. However, identification of neutral customers is still a challenge, with precision, recall, and F1-score of 0.92, 0.85, and 0.88, respectively. The results of this study provide strategic insights for Shopee in improving customer retention strategies and demonstrate the effectiveness of the Random Forest algorithm in analyzing review data.
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