Classifications on Online Shoppers Purchasing Intention
| dc.contributor.author | ELDHO, M M | |
| dc.contributor.author | CHETAN | |
| dc.contributor.author | KAVYA, A S | |
| dc.date.accessioned | 2020-09-01T06:12:32Z | |
| dc.date.available | 2020-09-01T06:12:32Z | |
| dc.date.issued | 2020-09-01T06:12:32Z | |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/12627 | |
| dc.language.iso | en | en_US |
| dc.subject | Classifications on Online Shoppers Purchasing Intention | en_US |
| dc.subject | ELDHO M M | en_US |
| dc.subject | CHETAN | en_US |
| dc.subject | KAVYA A S | en_US |
| dc.subject | 1NH16CS032 | en_US |
| dc.subject | 1NH16CS026 | en_US |
| dc.subject | 1NH16CS050 | en_US |
| dc.title | Classifications on Online Shoppers Purchasing Intention | en_US |
| dc.type | Other | en_US |