13 Best Free Datasets for Call Centers and Telcos
Alexandra Quinn | July 1, 2025
Customer service chatbots and co-pilots and smart call center analysis applications are prime use cases for AI and generative AI. These AI systems and agents can provide real-time recommendations, support customer service at scale, generate insights that can be used in downstream applications to reduce churn and increase revenue, and more.
How can customer service organizations grow and optimize their use of data and AI? For data scientists tasked with building and training AI models and LLMs, open and free telecom and call center datasets are an important starting point.
But these datasets can be hard to come by, since they include personal customer information and business competitive information, which is why not many organizations share this data. This blog post is here to help.
Top Free Datasets for Call Centers and Telcos
Here are 13 excellent open datasets and data sources for telco and call center data for AI.
Call Center Data
A dataset containing metrics about incoming calls, answered calls, abandoned calls, service rate, and more.
Call Center Analysis
A 5,000 row dataset containing information about the agent’s name, time of call, if the issue was answered and resolved, the speed of answer, call duration and satisfaction score.
Telco Customer Churn
Customer churn information for a fictitious telco company, offered by IBM. Data includes whether the customer churned in the last month, gender, monthly charges, services the customer signed up for, and more.
Telco Customer Churn (2)
This second customer churn is a Hugging Face dataset with more than 4,000 rows of training data. It includes data like customer churn information, average monthly GB download, long distance charges, churn, service types, billing, etc.
Call Center Metrics for the Health Service System
Monthly data from a home phone system. It includes metrics about answered calls, average response speed, abandonment rate, in-person assistance, and more.
MTA NYCT Customer Engagement Statistics
Performance metrics about the volume and responsiveness of the New York City Transit Subway and Bus customer engagement and service teams, between May 2017 and May 2022.
DOHMH Call Center Summary
Data regarding US Department of Health services, which can be used to evaluate quality of service, gather customer feedback and determine required staff size.
NYCHA Customer Contact Centers
Data regarding customer contact center locations, phone numbers and schedules in New York.
MTA NYCT Paratransit Call Center Performance
A dataset containing information about the percentage of answered calls and their average response time in New York.
911 Performance Dashboard Beta
Washington DC’s 911 (emergency) call center, providing insight into daily operations.
311 City Service Requests
Service requests to DC’s request center in 2017. These include abandoned automobiles, parking meter repair, bulk trash pickup.
A second 311 service request dataset covers snow removal requests.
Question-Answering (QA) System for the Telecom Domain
Question and answer pairs for retrieving and using in 3rd Generation Partnership Project (3GPP) technical documents.
The Future of Telco and Call Center Data for Generative AI
Financial, retail and other institutions that harness the power of GenAI and AI can enhance customer satisfaction, reduce churn, scale operations, increase their revenue, and gain a competitive advantage.
Here are a few demos showcasing these abilities:
1. Smart Call Center Analysis Application - A call analysis platform for call centers. The application analyzes customers calls and generates actionable insights for agents, management and downstream applications. It helps support agents, create tailored recommendations for customers, and more. This improves the customer experience, helps optimize first call resolution, enhances operational efficiency, supports decision-making and helps meet compliance regulations. Watch the smart call center analysis app demo.
2. Real-time Gen AI Co-Pilot (Wealth Management Use Case) - A gen AI co-pilot that amplifies a client relationship management scenario in private banking, emphasizing personalized service, strategic investment advice and proactive support for the client’s needs.Watch the wealth management co-pilot demo here.
3. Hyper-personalized Banking Gen AI Agent with MongoDB - A gen AI banking agent that provides clients with hyper-personalized recommendations for credit card choices, based on MLRun and MongoDB. Watch the hyper-personalized agent demo here.
