CoolFace
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mindweave/call-center-records

Call Center Records & Agent Performance Dataset (Free Sample) This is a free sample with 2,013 rows. The full dataset has 19,959 rows across 3 tables. Call detail records for a simulated insurance company call center with 25 agents handling 35,000 calls over 12 months. Includes IVR menu paths, agent assignments, call outcomes, hold times, transfer chains, and customer satisfaction scores. Features realistic patterns: Monday morning surge, lunch dip, seasonal peaks during open… See the full description on the dataset page: https://huggingface.co/datasets/mindweave/call-center-records.

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face
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Call Center Records & Agent Performance Dataset (Free Sample)

This is a free sample with 2,013 rows. The full dataset has 19,959 rows across 3 tables.

Call detail records for a simulated insurance company call center with 25 agents handling 35,000 calls over 12 months. Includes IVR menu paths, agent assignments, call outcomes, hold times, transfer chains, and customer satisfaction scores.

Features realistic patterns: Monday morning surge, lunch dip, seasonal peaks during open enrollment, and two anomalies — a system outage causing mass abandoned calls, and a viral social media complaint driving call volume 4x.

Ideal for: contact center analytics, workforce management, IVR optimization, agent performance dashboards, and customer experience analysis.

Sample tables

TableSample Rows
agents8
calls2,000
ivr_menu5
Total2,013

Full dataset

The complete dataset includes all tables with full row counts:

TableFull Rows
agents8
calls19,946
ivr_menu5
Total19,959

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