Analysis of E-Commerce Consumer Purchasing Patterns Using the Naïve Bayes Algorithm
DOI:
https://doi.org/10.30865/ijics.v10i1.9531Keywords:
E-commerce, Consumer Purchasing Patterns, Naïve Bayes, Transaction Data, Classification.Abstract
The rapid growth of e-commerce has generated a large volume of transactional data; however, its utilization is often limited to sales reports. In fact, transaction data holds significant strategic potential to be analyzed in order to understand customer purchasing patterns. This study aims to analyze consumer purchasing behavior in e-commerce by applying the Naïve Bayes algorithm as a probabilistic classification method. The dataset used in this research consists of 30 transaction records, including attributes such as purchase frequency, transaction value, product type, payment method, and promotion, with the purchasing decision (buy or not buy) as the class attribute. The research stages include calculating prior probabilities and conditional probabilities for each attribute with respect to the purchasing decision classes. The results indicate that the majority of transactions belong to the buy class with a probability of 0.63, while the not-buy class has a probability of 0.37. Purchase frequency and transaction value are identified as the most influential factors, where medium to high purchase frequency and medium to large transaction values show a strong tendency toward buying decisions. Furthermore, electronic products and non-cash payment methods, particularly e-wallets and credit cards, exhibit high probabilities associated with purchase decisions. Promotions also contribute positively to encouraging purchases, although they are not the sole determining factor. Overall, this study demonstrates that the Naïve Bayes algorithm is effective in identifying customer purchasing patterns and can support strategic decision-making in e-commerce businesses.
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