Customer Analytics of Orchid Pot Business during the First Corona Virus Outbreak Period in Thailand
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Abstract
CORONA VIRUS 2019, also known as COVID-19, began to spread in December 2019. The first affected covid-19 person was found in Wuhan district, China, then COVID-19 impacted worldwide, including Thailand. This research uses the Data Mining technique by applying the Association Rule to understand customer behavior in people who buy orchid pots during the first coronavirus pandemic in Thailand. This study applies to the database of customer purchase transactions who buy orchid pots. This research adopts the FP-Growth model to understand the groups of products customers typically buy. Finally, the Association rule generates seven rules of orchid pot types that customers purchase in the same basket. Each rule shows Confidence, Lift, and Conviction range from 0.833 – 0.857, 2.629 – 5.602, 4.098 – 5.929, respectively. This study also deployed Predictive modeling by utilizing the Generalized linear model, Deep learning, Random Forest, and Gradient Boosted Tree. As a result of the Predictive model, Gradient Boosted Tree without Auto feature selection and feature extractions methods produce the lowest relative error at 15.2%. The association rule finds an orchid pot that customers purchased one of the items in the group. The expected result of this study is that orchid entrepreneurs can adopt this outcome from Association Rule and Predictive Modeling Analytics when a problematic situation similar to the COVID-19 pandemic happens again.
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