Data-driven E-commerce UI Personalization: Going Beyond Product Recommendations
The dataset includes 1. online store customer behavior data (clickstream) from 1.04.-30.11.2023, used to cluster customers and evaluate the effectiveness of implemented modifications (catalog: learning-dataset) 2. clustering results to verify the effectiveness of implemented changes (catalog: clustering) 3. detailed data for calculation of macro-conversion indicators (catalog: macro-conversion-indicators) 3. detailed data for calculation of micro-conversion indicators (catalog: micro-conversion-indicators)
Steps to reproduce
Behavioral data is the basis for clustering. The study used the K-means algorithm, with k=4. The results of macro-conversion and micro-conversion are calculated based on the corresponding customer actions included in the learning dataset. Actions included in the calculation of PCRs are derived from the expected customer journey.
Narodowe Centrum Badań i Rozwoju