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mindweave/email-campaigns

Email Marketing Campaign Analytics Dataset (Free Sample) This is a free sample with 4,025 rows. The full dataset has 56,462 rows across 4 tables. Email campaign performance data for a simulated B2B SaaS company running 120 campaigns over 18 months. 15,000 subscribers across 5 segments, 40,000 email events (sends, opens, clicks, bounces, unsubscribes). Features realistic engagement curves: declining open rates over time, segment-specific behavior, A/B test results, and two… See the full description on the dataset page: https://huggingface.co/datasets/mindweave/email-campaigns.

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face
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Email Marketing Campaign Analytics Dataset (Free Sample)

This is a free sample with 4,025 rows. The full dataset has 56,462 rows across 4 tables.

Email campaign performance data for a simulated B2B SaaS company running 120 campaigns over 18 months. 15,000 subscribers across 5 segments, 40,000 email events (sends, opens, clicks, bounces, unsubscribes).

Features realistic engagement curves: declining open rates over time, segment-specific behavior, A/B test results, and two anomalies — a deliverability crisis (IP blacklisted for 2 weeks) and a viral campaign that 3x'd normal click rates.

Ideal for: MarTech platform testing, email deliverability analysis, campaign optimization ML, customer segmentation, and marketing dashboards.

Sample tables

TableSample Rows
campaigns20
events3,000
segments5
subscribers1,000
Total4,025

Full dataset

The complete dataset includes all tables with full row counts:

TableFull Rows
campaigns157
events41,300
segments5
subscribers15,000
Total56,462

Formats included: CSV, Parquet, SQLite

[Get the full dataset on Gumroad](https://mindweavetech.gumroad.com)

About

Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.

Every dataset features:

  • Enforced foreign key relationships across all tables
  • Realistic statistical distributions (not uniform random)
  • Temporal patterns (seasonal, time-of-day, day-of-week)
  • Injected anomalies for ML training and anomaly detection
  • Deterministic generation (same seed = same output)

Browse all datasets: https://mindweavetech.gumroad.com