latest Data: April - June 2020 .Monthly summary of service requests received, Origin of service request, closed with aging analysis and service notification type of problem. Historic Data: Jan - March 2022; 2021; 2020; 2019; 2018; 2017; 2016.read more

Some location data has a data quality issue and has temporarily removed while we resolve the issue. WE will work on making it available as the highest priority.

Please review this brief video for a better understanding of how these data are created: https://www.youtube.com/watch?v=lvTCjVHxpAU

This data represents police response activity. Each row is a record of a Call for Service (CfS) logged with the Seattle Police Department (SPD) Communications Center. Calls originated from the community and range from in progress or active emergencies to requests for problem solving. Additionally, officers will log calls from their observations of the field.

Previous versions of this data set have withheld approximately 40% of calls. This updated process will release more than 95% of all calls but we will no longer provide latitude and longitude specific location data. In an effort to safeguard the privacy of our community, calls will only be located to the “beat” level. Beats are the most granular unit of management used for patrol deployment. To learn more about patrol deployment, please visit: https://www.seattle.gov/police/about-us/about-policing/precinct-and-patrol-boundaries.

As with any data, certain conditions and qualifications apply:

1) These data are queried from the Data Analytics Platform (DAP), and updated incrementally on a daily basis. A full refresh will occur twice a year and is intended to reconcile minor changes.

2) This data set only contains records of police response. If a call is queued in the system but cleared before an officer can respond, it will not be included.

3) These data contain administrative call types. Use the “Initial” and “Final” call type to identify the calls you wish to include in your analysis.

We invite you to engage these data, ask questions and explore.

This dataset contains daily statistics on calls to the Fulton County Board of Health Call Center during the COVID-19 pandemic. Statistics include the number of calls received and answered, the total and average connect time, the average time to answer and the average queued time. The dataset was updated from a spreadsheet emailed by the Board of Health. Daily updates of the dataset were discontinued after October 19, 2022.

This data set includes information on Do Not Call and robocall complaints reported to the Federal Trade Commission. The data set contains information reported by consumers, including the telephone number originating the unwanted call, the date the complaint was created, the time the call was made, the consumer’s city and state locations reported, the subject of the call, and whether the call was a robocall. None of the information about the reported calls is verified.

Poland Mobile Phone: Phone Calls: Domestic: In One Network data was reported at 45,679.000 min mn in 2017. This records an increase from the previous number of 45,440.000 min mn for 2016. Poland Mobile Phone: Phone Calls: Domestic: In One Network data is updated yearly, averaging 35,324.000 min mn from Dec 2003 (Median) to 2017, with 15 observations. The data reached an all-time high of 45,679.000 min mn in 2017 and a record low of 4,118.000 min mn in 2003. Poland Mobile Phone: Phone Calls: Domestic: In One Network data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.TB001: Mobile Phone Statistics.

In 2022, contacting victims via text was the most common method employed by fraudsters, being used in 321,374 fraud cases reported to the Federal Trade Commission (FTC) in the United States. Contacting victims via phone call was the second most common method, with 294,659 reported cases.

This service provides web services used to obtain call-related data for patients. Users of this service are intended to be healthcare providers

Analysis of ‘Do Not Call (DNC) Reported Calls Data 7/4/19 - 7/10/19’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/52244cd0-67b0-4d5e-931b-7137eebaf3e4 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

This data set includes information on Do Not Call and robocall complaints reported to the Federal Trade Commission. The data set contains information reported by consumers, including the telephone number originating the unwanted call, the date the complaint was created, the time the call was made, the consumer’s city and state locations reported, the subject of the call, and whether the call was a robocall. None of the information about the reported calls is verified.

--- Original source retains full ownership of the source dataset ---

In 2023, almost seven in 10 French online users made voice or video calls using the internet, while in the case of users who had engaged with the internet in the last three months the number surpassed seven percent. Between 2008 and 2019, the share of the French digital population who made voice or video calls over the internet experienced a constant but moderate increase. Between 2019 and 2021, the online voice and video calling penetration experienced a sudden increase, as usage trends were accelerated by the global outbreak of the COVID-19 pandemic in 2020.

As of March 2021, Facebook Messenger was the mobile messaging and video calls app found to collect the largest amount of data from global iOS users, with over 30 data points collected across 14 segments. Line ranked second with 26 data points, while WeChat collected a total number of 23 data points from iOS users. The most collected data segments for messaging and video call apps were users' contact information and user content.

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

Archive of Calls for Service data from 2012 to 2019 provided as a single source. Staging for sub-layers for specified time periods.

Spam calls are unsolicited, nuisance calls made to a large number of recipients, with the intended goal to persuade the recipient either to buy a service/product or to divulge sensitive personal information. Throughout the given period from 2017 to 2020, Brazil has remained the country with the highest average number of spam calls and had almost 50 received spam calls per user per month in 2020. In 2020, Hungary had the highest increase of spam calls, with an average of 28.3 calls, which was 1132 percent more than they had in 2019 when they only had 2.3 spam calls per user and month.

Please review this brief video for a better understanding of how these data are created: https://www.youtube.com/watch?v=lvTCjVHxpAU This data represents police response activity. Each row is a record of a Call for Service (CfS) logged with the Seattle Police Department (SPD) Communications Center. Calls originated from the community and range from in progress or active emergencies to requests for problem solving. Additionally, officers will log calls from their observations of the field. Previous versions of this data set have withheld approximately 40% of calls. This updated process will release more than 95% of all calls but we will no longer provide latitude and longitude specific location data. In an effort to safeguard the privacy of our community, calls will only be located to the “beat” level. Beats are the most granular unit of management used for patrol deployment. To learn more about patrol deployment, please visit: https://www.seattle.gov/police/about-us/about-policing/precinct-and-patrol-boundaries. As with any data, certain conditions and qualifications apply: 1) These data are queried from the Data Analytics Platform (DAP), and updated incrementally on a daily basis. A full refresh will occur twice a year and is intended to reconcile minor changes. 2) This data set only contains records of police response. If a call is queued in the system but cleared before an officer can respond, it will not be included. 3) These data contain administrative call types. Use the “Initial” and “Final” call type to identify the calls you wish to include in your analysis. We invite you to engage these data, ask questions and explore.

This data set contains statistical data about phone numbers on the Registry, telemarketers and sellers accessing phone numbers on the Registry, and complaints consumers submit to the FTC about telemarketers allegedly violating the Do Not Call rules for Fiscal Year 2013. Statistical data on Do Not Call (DNC) complaints is based on unverified complaints reported by consumers, not on a consumer survey.

In 2023, the majority of contact center workers in the United States stated they agreed artficial intelligence (AI) had improved customer service when it came to customer information tasks during their workday. 40 percent agreed that AI had made their work easier.

This statistic shows the geographical distribution of contact centers worldwide as of December 2016. During the survey, 24 percent of contact center industry leaders said their organization was located in Europe.

Indonesia Ship Calls: Commercial Port: International: Central Java data was reported at 1,773.000 Unit in 2017. This records an increase from the previous number of 1,301.000 Unit for 2016. Indonesia Ship Calls: Commercial Port: International: Central Java data is updated yearly, averaging 1,133.000 Unit from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 1,773.000 Unit in 2017 and a record low of 1,015.000 Unit in 2012. Indonesia Ship Calls: Commercial Port: International: Central Java data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.TD008: Ship Calls: International Voyage: by Commercial Port.

Introduction

Welcome to the Indian English Call Center Speech Dataset for the Travel domain designed to enhance the development of call center speech recognition models specifically for the Travel industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data:

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Travel domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Travel domain call center conversational AI and ASR models for the Indian English language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This statistic shows the over-the-phone and video interpreting market size in the United States from 2012 to 2019. In 2019, the U.S. over-the-phone and video interpreting market was worth 1.2 billion U.S. dollars.