Predictive Buyer Behavior Model as Customer Retention Optimization Strategy in E-commerce
DOI:
https://doi.org/10.52985/insyst.v6i1.379Keywords:
Customer Retention Optimization Strategy, E-Commerce, Predictive Behavior Model, Random ForestAbstract
Lazada is one of the rapidly growing E-commerce platforms in this digital era. One of the main challenges faced by Lazada is customer retention, where customers make purchases once or a few times before switching to other platforms. Therefore, it is important to understand buyer behavior in E-commerce through customer prediction to identify factors influencing retention. This study employs the Random Forest (RF) method to analyze Lazada customer data and formulate more effective marketing strategies. The analysis is conducted by loading preprocessed datasets into the KNIME workflow and utilizing various nodes and algorithms available in KNIME to build and evaluate predictive models. The Random Forest model is trained multiple times to achieve the highest Accuracy rate, which is 72.472%, with a fairly high level of agreement and a balanced trade-off between recall and precision. Additionally, this model successfully predicts that customers purchasing electronic equipment are potentially churning at a rate of 3.85%. Subsequently, customer strategy analysis for customer retention optimization in the E-commerce industry is conducted through data visualization using Tableau. Predictive analysis of customer behavior serves as a strong foundation for formulating effective retention strategies in the E-commerce industry. With this approach, Lazada can enhance customer experience and ensure sustainability in facing the increasingly fierce competition in the digital market.
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