SendGuard900K: Massive Dataset for Metadata-Based Quality Assurance of Email Marketing

SendGuard900K: Massive Dataset for Metadata-Based Quality Assurance of Email Marketing This work was supported in part by the Project ‘‘SendGuard—Improving the Security of Recipients Innovative Tool Based on Machine Learning Technology and Artificial Intelligence to Fight with the Problem of Spam and Phishing in Marketing and Transactional E-Mail Messages" co-financed from the Funds of European Regional Development Fund under Project POIR.01.01.01-00-0202/19-02. Overview We introduce SendGuard900K, a large-scale real-world dataset designed to support research on quality assurance, security, and performance analysis of email marketing campaigns using metadata and aggregated behavioral signals. The dataset originates from the SendGuard project, whose goal is to develop a significantly improved AI-driven service for analyzing email marketing and transactional messages. The project addresses one of the most pressing global challenges in digital communication: ensuring that legitimate, personalized messages reach their recipients effectively in an ecosystem dominated by spam, phishing, and abuse. SendGuard900K contains rich campaign-level metadata collected from production systems between 2022 and 2024, spanning campaigns created by a large number of independent companies across multiple industries and geographies. The dataset is designed to enable reproducible research on email quality, deliverability, engagement, and abuse detection—without exposing message content or personally identifiable information. Files This dataset includes two CSV files: SendGuard900K.csv — The main file containing all records in the dataset. SendGuard900K-preview.csv — A sample of 100 rows extracted from the main file, provided solely for the purpose of enabling Zenodo’s preview functionality. Data summary The detail technical info about data including columns names, types and inteprpretation is given in the Technical Notes of this record. Time period: 2022–2024 Granularity: Campaign-level Scale: ~900,000 email campaigns Creators: Campaigns generated by a large number (over 4000) of independent companies Data types: Integer metrics, categorical descriptors, timestamps Privacy: No personal data, no message bodies, no recipient-level records Potential Research Use-cases The following examples illustrate some potential research directions enabled by the dataset; however, they are not exhaustive, and the actual scope of possible analyses and applications can easily extend beyond the cases listed below. 1. Deliverability and Engagement Prediction Task: Regression or classificationPotential targets: campaign_unique_opens_count campaign_unique_clicks_count derived open rate or click-through rate time-windowed engagement metrics (e.g. _30, _60, _1440) 2. Phishing Risk Modeling Task: Classification or risk scoring (regression)Potential targets: campaign_phishing_count campaign_complaint_count campaign_moderation campaign_moderation_rejected_count 3. Spam and Abuse Detection Task: Binary or multiclass classificationPotential targets: campaign_moderation campaign_moderation_rejected_count campaign_complaint_count campaign_phishing_count