README.md

metadata

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

💻Github Repo 📖Paper Link

Introduction

This repository contains the Mailprofiler's XCampaign Dataset -- provided by Mailprofiler; XCampaign 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

@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.