Update Frequency: N/A

A log of the Unified Call Center's service requests.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Introduction

Call Center Statistics: Call centers play a crucial role in customer service, serving as the main point of contact between businesses and their clients. With the evolution of technology, including AI, automation, and cloud systems, call center operations have been significantly improved, boosting efficiency and enhancing the customer experience.

Analyzing call center statistics provides essential insights into areas like response times, problem resolution, and the success of multichannel support. These metrics allow businesses to assess their operational performance, fine-tune customer service approaches, and ensure that customer interactions are managed effectively, resulting in greater satisfaction and loyalty.

I'm excited to share my latest PwC Call Center Data Analysis Dashboard, developed as part of the PwC Switzerland Virtual Internship Program through Forage. This interactive dashboard provides deep insights into call center operations, helping enhance customer experience and operational efficiency using Power BI. It tracks key metrics such as call volume, average handling time, customer satisfaction scores, and agent performance, enabling data-driven decision-making. By leveraging analytics, businesses can optimize processes, improve service quality, and identify training opportunities for agents. Explore how Power BI transforms raw data into actionable insights, driving efficiency and better customer satisfaction in call center operations.

Update Frequency: "An automated scan for dataset updates occurs every day at 3:45 a.m."

For up to date information on service requests please visit, https://city.milwaukee.gov/ucc .

A log of the Unified Call Center's service requests.

From potholes, abandoned vehicles, high weeds on vacant lots, and curbside trash to faulty traffic signals, the City of Milwaukee's Unified Call Center (UCC) makes it easy to submit service requests to solve problems. The UCC also allows you to track your service requests. Each time you complete a service request online, you will be assigned a tracking number that you can use to see when a City of Milwaukee representative expects to investigate or take care of your request.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This dataset captures monthly data from HSS' phone system and includes metrics pertaining to Calls Answered, Average Speed of Answer, Abandonment Rate, In-person Assistance. This data supports the City's Performance Measures requirements. In April of 2023 HSS switched to a new phone system - WEBEX (Finess).

This record contains data on the performance against service standards in relation to: performance against service standards in relation to replies to MP letters; outstanding out of service standard MP letters; performance against service standard for customer complaints, The number of calls to Hampshire's Sky Customer Service Centre. Includes data previously published in UKBA percentage of complaints responded to within service standards. Published by Hampshire County Council. *Licensed under [Open Government Licence] Open Government Licence. Openness rating: Open Data *Certificate: Raw Level Provided by: http://www.followthesteps.net/sky-contact-phone-number/ & http://www.faqtory.co/sky/ The information on this page (the dataset metadata) is also available in JSON format. API: /api/2/rest/package/uk-visas-immigration-customer-service-standards Read more about this site's CKAN API » http://data.gov.uk/data/api

Call Center Analytics Market Outlook

The global call center analytics market size is projected to grow from $2.5 billion in 2023 to $9.2 billion by 2032, reflecting a compound annual growth rate (CAGR) of 15.3% from 2024 to 2032. This growth is primarily driven by the increasing demand for enhanced customer experience management and the rising adoption of advanced analytics solutions in call centers across various sectors.

One of the significant growth factors in the call center analytics market is the need for organizations to analyze and improve customer interactions. As businesses strive to offer superior customer service and experience, they are increasingly turning to analytics solutions that can provide insights into customer behavior, preferences, and pain points. This, in turn, helps businesses to enhance their service quality, reduce costs, and increase customer satisfaction and loyalty. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) in analytics tools is further propelling the market growth by enabling more accurate predictions and personalized customer interactions.

Another key driver is the growing importance of workforce optimization in call centers. Call center analytics solutions play a crucial role in managing and optimizing the performance of call center agents. These solutions can track and analyze various performance metrics, such as call handling time, first-call resolution rate, and customer satisfaction scores. By leveraging such data, organizations can identify areas for improvement, provide targeted training to agents, and ensure efficient resource allocation. This focus on enhancing workforce productivity and efficiency is significantly contributing to the market's expansion.

The increasing regulatory scrutiny and the need for compliance management are also fueling the demand for call center analytics. In sectors such as BFSI and healthcare, organizations are required to adhere to strict regulatory standards to ensure data security and privacy. Call center analytics solutions help these organizations to monitor and report compliance-related metrics, identify potential risks, and take proactive measures to mitigate them. This capability is becoming increasingly important, given the rising incidences of data breaches and the growing emphasis on regulatory compliance across industries.

Regionally, North America is expected to dominate the call center analytics market during the forecast period. This is attributed to the presence of key market players, the high adoption rate of advanced technologies, and the strong focus on customer experience management in the region. However, Asia Pacific is anticipated to witness the highest growth rate, driven by the rapid digital transformation, the increasing number of call centers, and the rising investments in analytics solutions by organizations in countries such as India and China.

Component Analysis

The component segment of the call center analytics market is broadly categorized into software and services. The software segment includes various analytics tools and platforms that enable call centers to collect, analyze, and interpret data related to customer interactions. The services segment encompasses consulting, implementation, and support services that help organizations integrate and optimize these analytics solutions within their existing infrastructure.

In the software segment, the adoption of AI and ML-powered analytics tools is significantly transforming the way call centers operate. These advanced tools can analyze vast amounts of data in real-time, offering actionable insights that help call centers to enhance customer experience, improve agent performance, and achieve operational efficiency. Moreover, the increasing preference for cloud-based analytics solutions is further driving the growth of this segment, as they offer scalability, flexibility, and cost-effectiveness compared to traditional on-premises solutions.

The services segment is also witnessing substantial growth, as organizations seek expert guidance to implement and manage analytics solutions effectively. Consulting services are particularly in demand, as they provide organizations with strategic advice on selecting the right analytics tools, defining key performance indicators (KPIs), and developing data-driven decision-making processes. Additionally, the need for ongoing support and maintenance services to ensure the seamless functioning of analytics solutions is contributing to the expansion of this segment.

Introduction

Call center statistics: Call center, now often referred to as a contact center, stands as the most critical point of engagement between a brand and its customers. It is a complex, technology-driven ecosystem where data, efficiency, and human empathy must converge. The success of a call center directly correlates with a company's bottom line, affecting customer loyalty, churn rates, and overall revenue.

Three years ago, I was working in a call center as a quality controller. So, I knew analyzing call center statistics was an essential practice that transformed operational performance into business insights.

These metrics illuminate the health of the customer journey, revealing precisely how quickly, effectively, and satisfactorily customer issues are resolved. From the impact of AI tools to the vital importance of agent well-being, these data points guide every decision aimed at making the customer experience and ensuring the call center remains a competitive advantage. So, without any delay, let’s get started.

In 20223 there was a slight increase in the number of call center jobs created in several countries and regions. In the Philippines, for example, there were approximately ****** more call center jobs created than in 2022. India created the highest number of jobs globally within this industry, approximately ****** more when compared to the previous year. In 2022, the region that created the most call center jobs in the United States was in the *********.

1) Data Introduction • The Call Center Data includes a variety of data related to call center operations, including customer inquiries, call times, agent information, and service types that occur routinely at the call center.

2) Data Utilization (1) Call Center Data has characteristics that: • This dataset provides key indicators and details of call operation, including call ID, customer ID, counselor ID, call start/end time, call length, inquiry type, and call results. (2) Call Center Data can be used to: • Service Quality and Efficiency Analysis: Use call time, call result data to assess the performance of the counselor and the quality of service of the call center. • Analysis of trends by type of customer inquiry: By analyzing inquiry type and frequency data, you can identify key customer needs and trends, and use them to improve service.

Introduction

This US English Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native US English speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

To make 311 effective for residents, visitors, and business owners, 311 representatives must respond to calls in a timely and accurate manner.

Data driven Visualizations created using Call Center Data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This statistic depicts the call center market size in 2018, with a breakdown by region or country. In 2018, the call center market size in the United States was estimated to amount to around **** billion U.S. dollars.

🚀 Power BI Call Center Dashboard – Unlocking Insights from Data! 📊

I’m excited to share my latest Power BI project, where I built an interactive call center dashboard to analyze customer service performance and efficiency.

🔍 Key Features & Insights: ✅ Comprehensive KPIs to track total calls, call duration (hours & minutes), average call duration, and response time percentage. ✅ Visual breakdown of call distribution by day, state, channel, sentiment, and reason. ✅ Grid View Dashboard for detailed call logs, with filters for city, date, and channel, allowing easy data export. ✅ Advanced Power BI Techniques including data cleaning, modeling, DAX, time intelligence functions, and custom charts. ✅ Optimized data handling with a new Date Table in Power Query to improve time-based insights.

📈 Key Takeaways: This dashboard empowers decision-makers to monitor call center efficiency, optimize agent performance, and enhance customer experience by identifying trends and bottlenecks.

💡 Tech Stack Used: Power BI | DAX | Power Query | Data Visualization | Data Modeling

Between 2020 and 2023, the number of jobs created in the global call center industry fluctuated significantly. The industry with the highest amount of new jobs created in 2023 was **************************** (BPO), totaling almost *******.

Call Center (description updated 2/6/2023)

Introduction

This US Spanish Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Spanish-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native US Spanish speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making financial domain model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

Introduction

This Vietnamese Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Vietnamese-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Vietnamese speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

The Japanese call center services market generated roughly **** trillion Japanese yen in sales during the fiscal year 2023. For the upcoming years, revenues of call center agencies were forecast to stagnate, staying at around **** trillion yen by 2026.