Data-driven E-commerce UI Personalization: Going Beyond Product Recommendations

Published: 29 December 2023| Version 1 | DOI: 10.17632/sxmgyvxpv9.1
Contributor:
Adam Wasilewski

Description

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)

Files

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.

Categories

e-Commerce, Design for Personalization, k-means Clustering

Funding

National Centre for Research and Development

Licence