A survey conducted in the United States in January 2024 shows online shoppers' most appreciated types of customer service. Most of the respondents, around ** percent, answered that they value prompt replies to their inquiries. The quality of responses and the competency of the associates are also high on the users' list of preferences, with ** percent and ** percent, respectively. Only about ** percent of the respondents consider their previous interactions with customer service reps important.

This dataset comes from the Annual Community Survey question related to satisfaction with the quality of the city’s online services. Respondents are asked to provide their level of satisfaction related to “Tempe's online services (registration, payment, etc.)” on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Online Service Satisfaction performance measure. The performance measure dashboard is available at 2.05 Online Services Satisfaction Rate.Additional Information Source: Community Attitude Survey ( Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey results  Publish Frequency: Annual  Publish Method: Manual  Data Dictionary

We want you to make the most of these shared investments by putting them to frequent use. But, we also need to protect our shared investments and make sure that we are using them in ways that do not impact others’ ability to use them.

For logistics service providers (LSPs), improving customer satisfaction and obtaining customer re-use intention are key to gaining sustainable competitive advantages and success. Logistics service quality (LSQ) is a concern for logistics service providers, retailers, and customers. The proposed model, which is based on the stimuli-organism-response theory and the logistics service quality framework, integrates operational quality, resource quality, information quality, personal contact quality, customization quality, and customer satisfaction to study logistics service re-use intentions. The data were obtained from an online survey using a structured questionnaire given to those with experience in logistics service. Using partial least squares structural equation modeling on 810 respondents who were adult Chinese customers, this study discovered that operational, resource, information, personal contact, and customization qualities positively affect the satisfaction of logistics service customers, while customer satisfaction positively affects re-use intention. Moreover, the results of the mediation analysis revealed that customer satisfaction mediated the connection between the five components of LSQ and the re-use intention of logistics services. The originality of the study lies in its comprehensive examination of the direct and indirect effects of service quality dimensions on customer satisfaction and logistics service re-use intention in the context of logistics services. This study provides valuable insights into the importance of customer satisfaction in the logistics industry and highlights the need for logistics companies to prioritize customer satisfaction and improve their overall performance and competitiveness.

The experimental dataset was the QoSDataset2 from the publicly released WS-DREAM and the Web service searching engines: xmethods.net. The experimental dataset was the QoSDataset2 from the publicly released WS-DREAM, and the Web service searching engines: xmethods.net. The dataset includes 5301 Web services,214 Services Users and response time. We constructed three user-service matrices with different of size = 100 × 100, size = 100 × 150, size = 150 × 100 by randomly extracting a certain number of users and services.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Coal Oil Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Coal Oil Point map area data layers. Data layers are symbolized as shown on the associated map sheets.

The dataset contains the details of the answers* to the questionnaire to detect the satisfaction of users who use the online booking service for physical, telephone and virtual appointments with the Municipality of Milan offices. The survey was launched in July 2020 in accordance with the Digital Administration Code (Article 7(3) of Legislative Decree No 82/2005, as amended by Article 8(1) of Legislative Decree No 179/16) and the questionnaire was updated in February 2024. The questionnaire can be completed online by the user after making the reservation and allows them to express their satisfaction with the accessibility, simplicity and effectiveness of the service. The user accesses the questionnaire via a link in the confirmation and reminder email sent by the booking system. The results of the survey are published monthly on https://www.comune.milano.it/comune/amministrazione-transparente/servizi-distributi/servizi-in-rete. Some interpretative notes: * The columns “Could you specify where you experienced the most difficulties?” are assessed only by those who gave a negative assessment in the question “Overall how easy was it to use this online service?” * The columns ‘Overall how easy was it to use this online service?’ and ‘Overall how satisfied are you with the online appointment booking service’ can range from 1 (not at all) to 4 (very) * The values in the column “Would you speak well of this service to other people?” correspond to the following rating levels: 0 to 6 detractors, 7 and 8 passive, 9 and 10 promoters * The ‘Planned start’ column shows the date of the appointment * The ‘Creation date’ column shows the date on which the appointment was booked (*) closed questions only

The dataset contains the details of the replies (*) to the questionnaire for detecting the satisfaction of users who use the online service for requesting birth certificates, activated from the second half of November 2020 in compliance with the indications of the Digital Administration Code (Legislative Decree 82/2005, Article 7(3) - amended by Legislative Decree 179/16, Article 8(1)).

The questionnaire can be completed online by the user after applying for the certificate and allows them to express their satisfaction with the accessibility, simplicity and effectiveness of the service. The user accesses the questionnaire via a link received by email at the end of the request.

The results of the survey are published monthly on https://www.comune.milano.it/comune/amministrazione-transparente/servizi-distributi/servizi-in-rete.

The survey on the online service for requesting birth certificates ended on 29 December 2022.

(*) closed questions only

Introduction

The English Retail & E-Commerce Chat Dataset is a large-scale, high-quality collection of over 12,000 chat conversations between customers and call center agents, focused exclusively on Retail and E-Commerce domains. Designed to reflect real-world service interactions, this dataset supports the development of robust conversational AI and NLP models tailored for English-speaking audiences.

Participant & Chat Overview

Topic Diversity

This dataset spans a wide range of Retail and E-Commerce conversation types:

This diversity enables training of models that handle varied intents, scenarios, and outcomes within customer service workflows.

Language Nuance & Realism

The dataset is rich in linguistic diversity and mirrors real conversational tone and structure used in English-speaking regions:

This linguistic authenticity ensures the development of culturally fluent AI models for English Retail & E-Commerce use cases.

Conversational Structure & Flow

The conversations reflect natural dialogue dynamics and are organized into various types of interaction styles:

Each conversation includes common dialogue stages such as:

Online Food Order Dataset

Description: The dataset contains information collected from an online food ordering platform over a period of time. It encompasses various attributes related to Occupation, Family Size, Feedback etc..

Attributes:

Demographic Information:

Age: Age of the customer. Gender: Gender of the customer. Marital Status: Marital status of the customer. Occupation: Occupation of the customer. Monthly Income: Monthly income of the customer. Educational Qualifications: Educational qualifications of the customer. Family Size: Number of individuals in the customer's family. Location Information:

Latitude: Latitude of the customer's location. Longitude: Longitude of the customer's location. Pin Code: Pin code of the customer's location. Order Details:

Output: Current status of the order (e.g., pending, confirmed, delivered). Feedback: Feedback provided by the customer after receiving the order.

Purpose: This dataset can be utilized to explore the relationship between demographic/location factors and online food ordering behavior, analyze customer feedback to improve service quality, and potentially predict customer preferences or behavior based on demographic and location attributes.

Coast to Coast is a leading provider of ink and toner products, carrying a vast selection of premium ink and toner replacement cartridges. Founded in 2000, the company has grown by selling high-quality products while emphasizing excellent customer service. With a strong focus on employee ownership and a second chance company, Coast to Coast prioritizes its workforce, offering excellent opportunities for professional and personal growth.

The company offers a wide range of products, including toner cartridges, computers, digital signage, managed print services, office supplies, and software. With partnerships with top brands, Coast to Coast ensures compatibility and quality for its customers. With a strong online presence, customers can shop online, manage their accounts, and access support resources. Coast to Coast is committed to providing an excellent customer experience, backed by its A+ rating with the Better Business Bureau and its Champion of Business Ethics Partner title.

Techsalerator’s Business Technographic Data for Jamaica: Unlocking Insights into Jamaica's Technology Landscape

Techsalerator’s Business Technographic Data for Jamaica provides a detailed and comprehensive dataset essential for businesses, market analysts, and technology vendors seeking to understand and engage with companies operating within Jamaica. This dataset offers in-depth insights into the technological landscape, capturing and organizing data related to technology stacks, digital tools, and IT infrastructure used by businesses in the country.

Please reach out to us at info@techsalerator.com or visit Techsalerator Contact.

Top 5 Most Utilized Data Fields

• Company Name: This field lists the names of companies in Jamaica, enabling technology vendors to target potential clients and allowing analysts to assess technology adoption trends within specific businesses.

• Technology Stack: This field outlines the technologies and software solutions a company uses, such as accounting systems, customer management software, and cloud services. Understanding a company's technology stack is key to evaluating its digital maturity and operational needs.

• Deployment Status: This field indicates whether the technology is currently deployed, planned for future deployment, or under evaluation. Vendors can use this information to assess the level of technology adoption and interest among companies in Jamaica.

• Industry Sector: This field specifies the industry in which the company operates, such as tourism, agriculture, or retail. Knowing the industry helps vendors tailor their products to sector-specific demands and emerging trends in Jamaica.

• Geographic Location: This field identifies the company's headquarters or primary operations within Jamaica. Geographic information aids in regional analysis and understanding localized technology adoption patterns across the island.

Top 5 Technology Trends in Jamaica

• Tourism and Hospitality Tech: With tourism being a vital sector in Jamaica, there is a growing adoption of digital solutions such as booking systems, customer relationship management (CRM) tools, and digital marketing platforms to enhance visitor experiences and streamline operations.

• Renewable Energy Solutions: As Jamaica prioritizes sustainability, the demand for renewable energy technologies like solar power and wind energy is rising. Companies are increasingly investing in these solutions to reduce their environmental impact and operational costs.

• E-commerce and Digital Payments: The trend towards digital commerce is expanding in Jamaica, with businesses embracing e-commerce platforms and digital payment solutions to reach a broader audience and simplify transactions.

• Cybersecurity: As the reliance on digital platforms grows, so does the focus on cybersecurity. Jamaican companies are investing in data protection, secure communications, and cyber threat mitigation strategies to safeguard their digital assets.

• Cloud Computing and IT Services: Cloud-based solutions are becoming more prevalent in Jamaica, offering scalable and cost-effective alternatives to traditional IT infrastructure. This trend is especially noticeable in sectors such as education, finance, and healthcare.

Top 5 Companies with Notable Technographic Data in Jamaica

• National Commercial Bank Jamaica (NCB): A major player in Jamaica’s banking sector, NCB is advancing its digital offerings with online banking, mobile applications, and enhanced cybersecurity measures to improve customer experience and security.

• Digicel Jamaica: A leading telecommunications provider, Digicel is driving digital connectivity with high-speed internet, mobile services, and investments in fiber-optic networks, enhancing digital access across the country.

• Jamaica Public Service Company (JPS): Jamaica’s primary electricity provider, JPS is integrating renewable energy technologies such as solar and wind power into its infrastructure, supporting Jamaica’s sustainability goals.

• GraceKennedy Ltd: A significant retail and distribution company, GraceKennedy is leveraging e-commerce platforms and cloud-based solutions to streamline operations and enhance customer engagement.

• Sagicor Group Jamaica: A prominent player in the financial services sector, Sagicor Group is investing in digital transformation through advanced IT infrastructure, customer management systems, and secure online services.

Accessing Techsalerator’s Business Technographic Data

For those interested in accessing Techsalerator’s Business Technographic Data for Jamaica, please contact info@techsalerator.com with your specific needs. Techsalerator offers customized quotes based on the required number of data fields and records, with datasets available for delivery within 24 hours. Ongoing access optio...

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

The NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011] developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The dataset includes downscaled projections from 33 models, as well as ensemble statistics calculated for each RCP from all model runs available. The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).

The dataset contains the details of the replies (*) to the questionnaire for detecting the satisfaction of users who use the online service for requesting surveys and copies of building files, activated in May 2021 in compliance with the indications of the Digital Administration Code (Legislative Decree 82/2005, Article 7(3) - amended by Legislative Decree 179/16, Article 8(1)). The questionnaire can be completed online by the user after applying for the certificate and allows them to express their satisfaction with the accessibility, simplicity and effectiveness of the service. The user accesses the questionnaire via a link received by email at the end of the request. The results of the survey are published monthly on https://www.comune.milano.it/comune/amministrazione-transparente/servizi-distributi/servizi-in-rete. Some interpretative notes: * The columns “Could you specify where you experienced the most difficulties?” are assessed only by those who gave a negative assessment in the question “Overall how easy was it to use this online service?” * The values in the column “Would you speak well of this service to other people?” correspond to the following rating levels: 0 to 6 detractors, 7 and 8 passive, 9 and 10 promoters (*) closed questions only

PO.DAAC provides several ways to discover and access physical oceanography data, from the PO.DAAC Web Portal to FTP access to front-end user interfaces (see http://podaac.jpl.nasa.gov). That same data can also be discovered and accessed through PO.DAAC Web Services, enabling efficient machine-to-machine communication and data transfers.

Context

A fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3.

Data Description 7043 observations with 33 variables

CustomerID: A unique ID that identifies each customer.

Count: A value used in reporting/dashboarding to sum up the number of customers in a filtered set.

Country: The country of the customer’s primary residence.

State: The state of the customer’s primary residence.

City: The city of the customer’s primary residence.

Zip Code: The zip code of the customer’s primary residence.

Lat Long: The combined latitude and longitude of the customer’s primary residence.

Latitude: The latitude of the customer’s primary residence.

Longitude: The longitude of the customer’s primary residence.

Gender: The customer’s gender: Male, Female

Senior Citizen: Indicates if the customer is 65 or older: Yes, No

Partner: Indicate if the customer has a partner: Yes, No

Dependents: Indicates if the customer lives with any dependents: Yes, No. Dependents could be children, parents, grandparents, etc.

Tenure Months: Indicates the total amount of months that the customer has been with the company by the end of the quarter specified above.

Phone Service: Indicates if the customer subscribes to home phone service with the company: Yes, No

Multiple Lines: Indicates if the customer subscribes to multiple telephone lines with the company: Yes, No

Internet Service: Indicates if the customer subscribes to Internet service with the company: No, DSL, Fiber Optic, Cable.

Online Security: Indicates if the customer subscribes to an additional online security service provided by the company: Yes, No

Online Backup: Indicates if the customer subscribes to an additional online backup service provided by the company: Yes, No

Device Protection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: Yes, No

Tech Support: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: Yes, No

Streaming TV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Streaming Movies: Indicates if the customer uses their Internet service to stream movies from a third party provider: Yes, No. The company does not charge an additional fee for this service.

Contract: Indicates the customer’s current contract type: Month-to-Month, One Year, Two Year.

Paperless Billing: Indicates if the customer has chosen paperless billing: Yes, No

Payment Method: Indicates how the customer pays their bill: Bank Withdrawal, Credit Card, Mailed Check

Monthly Charge: Indicates the customer’s current total monthly charge for all their services from the company.

Total Charges: Indicates the customer’s total charges, calculated to the end of the quarter specified above.

Churn Label: Yes = the customer left the company this quarter. No = the customer remained with the company. Directly related to Churn Value.

Churn Value: 1 = the customer left the company this quarter. 0 = the customer remained with the company. Directly related to Churn Label.

Churn Score: A value from 0-100 that is calculated using the predictive tool IBM SPSS Modeler. The model incorporates multiple factors known to cause churn. The higher the score, the more likely the customer will churn.

CLTV: Customer Lifetime Value. A predicted CLTV is calculated using corporate formulas and existing data. The higher the value, the more valuable the customer. High value customers should be monitored for churn.

Churn Reason: A customer’s specific reason for leaving the company. Directly related to Churn Category.

Source This dataset is detailed in: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

Downloaded from: https://community.ibm.com/accelerators/?context=analytics&query=telco%20churn&type=Data&product=Cognos%20Analytics

There are several related datasets as documented in: https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2018/09/12/base-samples-for-ibm-cognos-analytics

List of entities that sell or lease internet services provided by a Canadian Carrier to the Reseller on a wholesale basis.

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations:

ACWI

The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs.

ARS

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service.

EPA

The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse.

NWQMC

The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community.

USGS

The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

For logistics service providers (LSPs), improving customer satisfaction and obtaining customer re-use intention are key to gaining sustainable competitive advantages and success. Logistics service quality (LSQ) is a concern for logistics service providers, retailers, and customers. The proposed model, which is based on the stimuli-organism-response theory and the logistics service quality framework, integrates operational quality, resource quality, information quality, personal contact quality, customization quality, and customer satisfaction to study logistics service re-use intentions. The data were obtained from an online survey using a structured questionnaire given to those with experience in logistics service. Using partial least squares structural equation modeling on 810 respondents who were adult Chinese customers, this study discovered that operational, resource, information, personal contact, and customization qualities positively affect the satisfaction of logistics service customers, while customer satisfaction positively affects re-use intention. Moreover, the results of the mediation analysis revealed that customer satisfaction mediated the connection between the five components of LSQ and the re-use intention of logistics services. The originality of the study lies in its comprehensive examination of the direct and indirect effects of service quality dimensions on customer satisfaction and logistics service re-use intention in the context of logistics services. This study provides valuable insights into the importance of customer satisfaction in the logistics industry and highlights the need for logistics companies to prioritize customer satisfaction and improve their overall performance and competitiveness.