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--- |
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license: cc-by-4.0 |
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task_categories: |
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- tabular-classification |
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- reinforcement-learning |
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language: |
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- en |
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- multilingual |
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tags: |
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- recommendation-system |
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- recommendation |
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- machine-learning |
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- email |
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- tabular |
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- marketing |
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- click-through-rate-prediction |
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pretty_name: XCampaign Dataset |
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size_categories: |
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- 10M<n<100M |
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--- |
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# XCampaign Dataset |
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<h4> |
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<a href="https://github.com/zidcenek/Active-Learning-for-Email-Interaction-Dynamics" target="_blank"> 💻Github Repo</a> |
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<a href="https://dl.acm.org/doi/10.1145/3746252.3760832" target="_blank">📖Paper Link</a> |
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</h4> |
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## Introduction |
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This repository contains the Mailprofiler's **XCampaign Dataset** -- provided by Mailprofiler; |
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[XCampaign](https://xcampaign.info/switzerland-en/) represents an email campaign management platform. |
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The dataset was published alongside our CIKM 2025 paper *Active Recommendation for Email Outreach Dynamics*. |
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The dataset of almost 15 million interactions captures user-level interactions with periodic marketing mailshots, |
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including whether an email was opened and the time-to-open (TTO). |
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## Dataset and Fields |
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The **XCampaign Dataset** includes the following fields: |
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- `mailshot_id`: (or template id) identifier of the mailshot campaign |
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- `user_id`: anonymized recipient identifier |
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- `opened`: binary label (\(1\) if opened, \(0\) otherwise) |
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- `time_to_open`: time delta between send and open (a parseable string of a timedelta `0 days 09:39:32`) |
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## Global Statistics |
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All statistics below are computed from the full dataset. |
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- `Rows`: 14,908,085; `Users`: 131,918; `Mailshots`: 160 |
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- Global open rate: 9.09% |
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- Per-mailshot open rate: $9.13\% \pm 3.58\%$ |
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- Per-user open rate: mean $12.33\% \pm 20.46\%$ |
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- Time-to-open (opened only): mean 1d 17h 25m; median 6h 25m |
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- Fraction opened within 1h: 25.9%; within 24h: 71.2%; within 7d: 93.0% |
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- Sent to users at each mailshot: $93,175 \pm 19,162$ |
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- Item \(\times\) User interaction matrix density: 70.63% |
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## How to Use and Cite |
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The XCampaign Dataset is made available under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**. |
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This license allows you to share and adapt the dataset for any purpose, **including commercial use**, as long as you provide appropriate credit. |
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If you use this dataset in your work, please **cite the following paper**, which introduced the dataset: |
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### Plain Text Citation |
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> Čeněk Žid, Rodrigo Alves, and Pavel Kordík. 2025. Active Recommendation for Email Outreach Dynamics. In *Proceedings |
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> of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25)}*. Association for |
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> Computing Machinery, New York, NY, USA, 5540–5544. https://doi.org/10.1145/3746252.3760832 |
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### BibTeX Citation |
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```bibtex |
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@inproceedings{10.1145/3746252.3760832, |
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author = {\v{Z}id, \v{C}en\v{e}k and Kord\'{\i}k, Pavel and Alves, Rodrigo}, |
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title = {Active Recommendation for Email Outreach Dynamics}, |
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year = {2025}, |
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isbn = {9798400720406}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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url = {https://doi.org/10.1145/3746252.3760832}, |
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doi = {https://doi.org/10.1145/3746252.3760832}, |
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booktitle = {Proceedings of the 34th ACM International Conference on Information and Knowledge Management}, |
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pages = {5540–5544}, |
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numpages = {5}, |
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keywords = {email outreach, reinforcement learning, shallow autoencoder}, |
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location = {Seoul, Republic of Korea}, |
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series = {CIKM '25} |
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} |
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``` |
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Global open rate and distribution of per-user open rates. |
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## Time to Open (TTO) |
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Time-to-open is heavy-tailed: while the median is about 6.4 hours, most opens occur within a week. Specifically, |
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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 |
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emphasize the main mass of the distribution. The CDF and histogram are shown in Figure~\ref{fig:tto}. |
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Distribution of time-to-open for opened emails. |
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CDF of time-to-open for opened emails. |
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The heavy-tailed TTO suggests robust objectives and appropriate censoring strategies. The two user segments motivate |
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segment-aware priors and exploration strategies; mailshot-level heterogeneity motivates per-mailshot features or random effects. |
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## Dataset Versions |
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The current version of the dataset contains 12 months of data (2024-04 -- 2025-03). Future dataset might |
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include additional months of data. The data collection is still ongoing. |
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## Acknowledgements |
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Čeněk Žid's research was supported by the Grant Agency of the Czech Technical University (SGS20/213/OHK3/3T/18). |
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We warmly thank *Mailprofiler* for providing the dataset for this research. |
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<p align="center"> |
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<a href="https://fit.cvut.cz/en" target="_blank"> |
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<img src="assets/logo-fit-en-modra.jpg" alt="FIT CTU" height="60"/> |
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</a> |
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<a href="https://xcampaign.info/switzerland-en/" target="_blank"> |
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<img src="assets/Xcampaign_logo.svg" alt="XCampaign" height="60"/> |
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</a> |
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<a href="https://www.recombee.com/" target="_blank"> |
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<img src="assets/recombee_logo.png" alt="Recombee" height="60"/> |
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</a> |
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</p> |
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