| | --- |
| | license: cc-by-4.0 |
| | task_categories: |
| | - tabular-classification |
| | - reinforcement-learning |
| | language: |
| | - en |
| | - multilingual |
| | tags: |
| | - recommendation-system |
| | - recommendation |
| | - machine-learning |
| | - email |
| | - tabular |
| | - marketing |
| | - click-through-rate-prediction |
| | pretty_name: XCampaign Dataset |
| | size_categories: |
| | - 10M<n<100M |
| | --- |
| | |
| | # XCampaign Dataset |
| | <h4> |
| | <a href="https://github.com/zidcenek/Active-Learning-for-Email-Interaction-Dynamics" target="_blank"> 💻Github Repo</a> |
| | <a href="https://dl.acm.org/doi/10.1145/3746252.3760832" target="_blank">📖Paper Link</a> |
| | </h4> |
| | |
| | ## Introduction |
| | This repository contains the Mailprofiler's **XCampaign Dataset** -- provided by Mailprofiler; |
| | [XCampaign](https://xcampaign.info/switzerland-en/) represents an email campaign management platform. |
| | The dataset was published alongside our CIKM 2025 paper *Active Recommendation for Email Outreach Dynamics*. |
| |
|
| | The dataset of almost 15 million interactions captures user-level interactions with periodic marketing mailshots, |
| | including whether an email was opened and the time-to-open (TTO). |
| |
|
| | ## Dataset and Fields |
| | The **XCampaign Dataset** includes the following fields: |
| | - `mailshot_id`: (or template id) identifier of the mailshot campaign |
| | - `user_id`: anonymized recipient identifier |
| | - `opened`: binary label (\(1\) if opened, \(0\) otherwise) |
| | - `time_to_open`: time delta between send and open (a parseable string of a timedelta `0 days 09:39:32`) |
| |
|
| | ## Global Statistics |
| | All statistics below are computed from the full dataset. |
| | - `Rows`: 14,908,085; `Users`: 131,918; `Mailshots`: 160 |
| | - Global open rate: 9.09% |
| | - Per-mailshot open rate: $9.13\% \pm 3.58\%$ |
| | - Per-user open rate: mean $12.33\% \pm 20.46\%$ |
| | - Time-to-open (opened only): mean 1d 17h 25m; median 6h 25m |
| | - Fraction opened within 1h: 25.9%; within 24h: 71.2%; within 7d: 93.0% |
| | - Sent to users at each mailshot: $93,175 \pm 19,162$ |
| | - Item \(\times\) User interaction matrix density: 70.63% |
| |
|
| |
|
| | ## How to Use and Cite |
| |
|
| | The XCampaign Dataset is made available under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**. |
| |
|
| | This license allows you to share and adapt the dataset for any purpose, **including commercial use**, as long as you provide appropriate credit. |
| |
|
| | If you use this dataset in your work, please **cite the following paper**, which introduced the dataset: |
| |
|
| | ### Plain Text Citation |
| | > Čeněk Žid, Rodrigo Alves, and Pavel Kordík. 2025. Active Recommendation for Email Outreach Dynamics. In *Proceedings |
| | > of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25)}*. Association for |
| | > Computing Machinery, New York, NY, USA, 5540–5544. https://doi.org/10.1145/3746252.3760832 |
| |
|
| | ### BibTeX Citation |
| | ```bibtex |
| | @inproceedings{10.1145/3746252.3760832, |
| | author = {\v{Z}id, \v{C}en\v{e}k and Kord\'{\i}k, Pavel and Alves, Rodrigo}, |
| | title = {Active Recommendation for Email Outreach Dynamics}, |
| | year = {2025}, |
| | isbn = {9798400720406}, |
| | publisher = {Association for Computing Machinery}, |
| | address = {New York, NY, USA}, |
| | url = {https://doi.org/10.1145/3746252.3760832}, |
| | doi = {https://doi.org/10.1145/3746252.3760832}, |
| | booktitle = {Proceedings of the 34th ACM International Conference on Information and Knowledge Management}, |
| | pages = {5540–5544}, |
| | numpages = {5}, |
| | keywords = {email outreach, reinforcement learning, shallow autoencoder}, |
| | location = {Seoul, Republic of Korea}, |
| | series = {CIKM '25} |
| | } |
| | ``` |
| |
|
| |  |
| | Global open rate and distribution of per-user open rates. |
| |
|
| | ## Time to Open (TTO) |
| | Time-to-open is heavy-tailed: while the median is about 6.4 hours, most opens occur within a week. Specifically, |
| | 93.0\% of opens arrive within 7 days, so 7.0\% arrive later than 7 days. The plots below are truncated at 7 days to |
| | emphasize the main mass of the distribution. The CDF and histogram are shown in Figure~\ref{fig:tto}. |
| |
|
| |  |
| | Distribution of time-to-open for opened emails. |
| |
|
| |  |
| | CDF of time-to-open for opened emails. |
| |
|
| | The heavy-tailed TTO suggests robust objectives and appropriate censoring strategies. The two user segments motivate |
| | segment-aware priors and exploration strategies; mailshot-level heterogeneity motivates per-mailshot features or random effects. |
| |
|
| | ## Dataset Versions |
| | The current version of the dataset contains 12 months of data (2024-04 -- 2025-03). Future dataset might |
| | include additional months of data. The data collection is still ongoing. |
| |
|
| |
|
| | ## Acknowledgements |
| | Čeněk Žid's research was supported by the Grant Agency of the Czech Technical University (SGS20/213/OHK3/3T/18). |
| | We warmly thank *Mailprofiler* for providing the dataset for this research. |
| |
|
| |
|
| | <p align="center"> |
| | <a href="https://fit.cvut.cz/en" target="_blank"> |
| | <img src="assets/logo-fit-en-modra.jpg" alt="FIT CTU" width="200"/> |
| | </a> |
| | |
| | <a href="https://xcampaign.info/switzerland-en/" target="_blank"> |
| | <img src="assets/Xcampaign_logo.svg" alt="XCampaign" width="220"/> |
| | </a> |
| | |
| | <a href="https://www.recombee.com/" target="_blank"> |
| | <img src="assets/recombee_logo.png" alt="Recombee" width="150"/> |
| | </a> |
| | </p> |
| | |
