Classifications on Online Shoppers Purchasing Intention
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Date
2020-09-01T06:12:32Z
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Abstract
Due to today’s transition from visiting physical stores to online shopping, predicting customer
behaviour in the context of e-commerce is gaining importance. It can in-crease customer
satisfaction and sales, resulting in higher conversion rates and a competitive advantage, by
facilitating a more personalized shopping process. By utilizing clickstream and supplementary
customer data, models for predicting customer behaviour can be built. This study analyses
machine learning models to predict a purchase, which is a relevant use case as applied by a
large German clothing retailer. Next, to comparing models this study further gives insight into
the performance differences of the models on sequential clickstream and the static customer
data, by conducting a descriptive data analysis and separately training the models on the
different datasets. The results indicate that a Random Forest algorithm is best suited for the
prediction task, showing the best performance results, reasonable latency, offering
comprehensibility and a high robustness. Regarding the different data types, models trained
on sequential session data outperformed models trained on the static customer data by far.
The best results were obtained when combining both datasets.
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Classifications on Online Shoppers Purchasing Intention, ELDHO M M, CHETAN, KAVYA A S, 1NH16CS032, 1NH16CS026, 1NH16CS050