The smartphone penetration in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.3 percentage points. The penetration is estimated to amount to 97 percent in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smartphone penetration in countries like Mexico and Canada.

Key Mobile Payments StatisticsTop Mobile Payments AppsFinance App Market LandscapeMobile Payments Transaction VolumeMobile Payments UsersMobile Payments Adoption by CountryMobile Payments TPV in...

In a survey conducted between October 2024 and September 2025, some ** percent of respondents in the United States stated that they had paid with their smartphone for everyday purchases in the past 12 months.The Global survey is part of Statista Consumer Insights, providing you with exclusive consumer survey results on more than *** industries and topics.

The most used digital wallets in the United States typically originate from within the country, with only one app reaching a MAU of more than 120 million. This is according to a ranking of the biggest apps that can function as a digital wallet for payments, based on a minimum of 50,000 monthly active users (MAU). Almost all popular wallets in the U.S. come from the country itself, with the likes of Venmo and Zelle being only available to this particular country. Nevertheless, digital wallets are typically not a popular payment method at POS in the United States.

This dataset, titled "U.S. Consumer Cell Phone Data," provides anonymized records of consumer purchases and ownership of cell phones across the United States. Each entry in the dataset includes vital information such as the name, address, and purchase-related details of individual buyers. The data offers insights into consumer behavior, trends in cell phone adoption, and geographic patterns of mobile technology use.

Key features of the dataset include:

1. Name: The names of cell phone buyers, enabling linkages with other consumer records for demographic and behavioral analysis (while respecting privacy guidelines).

2. Address: Detailed geographic information, including street addresses, cities, states, and ZIP codes. This allows for regional analysis of purchasing trends and the potential for mapping market penetration of specific phone brands or models.

3. Device Details: While not explicitly stated in the description, such datasets often include information about the types of cell phones purchased, brands, models, and potentially the purchase date.

This dataset could be invaluable for businesses, marketers, and researchers aiming to understand consumer preferences, improve mobile marketing strategies, or tailor product offerings to specific regions.

In summary, the U.S. Consumer Cell Phone Data is a robust resource for analyzing the intersection of consumer behavior and mobile technology, provided that its usage adheres to relevant privacy and ethical standards.

Tax-Provision Time Series for Fiserv, Inc.. Fiserv, Inc. provides payments and financial services technology solutions in the United States, Europe, the Middle East and Africa, Latin America, the Asia-Pacific, and internationally. It operates through the Merchant Solutions and Financial Solutions segments. The company provides merchant acquiring and digital commerce services, mobile payment services, security and fraud protection solutions, stored-value solutions, software-as-a-service, and pay-by-bank solutions, as well as Clover, a point-of-sale and business management platform through various channels, including direct sales teams, strategic partnerships with agent sales forces, independent software vendors, independent sales organizations, financial institutions, and other strategic partners. It also offers debit card processing services, debit network services, security and fraud protection products, bill payment; person-to-person payments, account-to-account transfers, credit card processing services, prepaid card processing services, card production services, print services, government payment processing, student loan processing, and customer loan and deposit account processing; digital banking; financial and risk management; and professional services and consulting, check processing, automated clearing house, and real-time payments. It serves large enterprise, small business, banks, credit union, large financial institution, fintech, public sectors, and software providers. Fiserv, Inc. was incorporated in 1984 and is headquartered in Milwaukee, Wisconsin.

About this dataset

Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services

How to use the dataset

If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates

Research Ideas

1. Predicting readmission rates for different hospital conditions
2. Analyzing relationships between hospital ownership and quality of care
3. Studying the relationship between hospital type and patient experience

Acknowledgements

This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project

Columns

File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...

Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Comprehensive dataset containing 70 verified Mobile Communications America locations in United States with complete contact information, ratings, reviews, and location data.

About this dataset

Context

This list ranks the 10 cities in the Mobile County, AL by Multi-Racial Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Multi-Racial Black Population: This column displays the rank of cities in the Mobile County, AL by their Multi-Racial Black or African American population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Multi-Racial Black Population: The Multi-Racial Black population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Multi-Racial Black. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Mobile County Multi-Racial Black Population: This tells us how much of the entire Mobile County, AL Multi-Racial Black population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

• Small business c-level decision makers and owners
• Full Contact info for 21Mil US Business Contacts
• @4Mil US Companies
• Business Email, Postal, Telemarketing, Mobile Phones
• Small Business Contact Datasets
• Technographics and Firmographic Databases
• Safe for you to Email

Through the North American Bat Monitoring Program, Bat Conservation International and U.S. Geological Survey (USGS) collaborated with the U.S. Fish and Wildlife Service to provided technical and science support to assistance in U.S. Fish and Wildlife Services’ Species Status Assessment (“SSA”) for the northern long-eared bat (Myotis septentrionalis), little brown bat (Myotis lucifugus), and tri-colored bat (Perimyotis subflavus). We conducted analyses to estimate changes in bat echolocation activity recorded during mobile transect surveys. Bat activity recorded during mobile acoustic transects provide an index of abundance and can be used to determine changes in populations over time (Roche et al. 2011, Jones et al. 2013). We hypothesized that mobile transect surveys would detect changes in populations for Myotis lucifugus, Myotis septentrionalis, and Perimyotis subflavus over the past decade related to two main stressors on North American bat populations: the emergence of White-nose Syndrome (WNS) and increases in installed wind energy facilities. We obtained data stored in the North American Bat Monitoring Program (NABat) (U.S. Fish and Wildlife Service, 3-Species Status Assessment - Mobile Transect Acoustic Monitoring Data Accessed 2020-11-23. NABat Request Number 11. Database Version v5.3.0), West Virginia (West Virginia Division of Natural Resources), and New York (New York State Department of Environmental Conservation). West Virginia and New York have mobile acoustic sampling programs that began in 2009 but their mobile acoustic data have not been contributed to the NABat Program database. These data were joined with stressor and habitat covariates (year of Pd arrival, wind energy risk index, habitat composition) with SSAmobile_04_combineData.R. A dataset for each species was created by filtering for grid cells within a species range (as defined by the USFWS). The following data were removed from final analyses: • Data from Canada were removed due to our inability to calculate a comparable wind energy index in Canada (see below) • Data collected from September to April as this does not represent the summer maternity season • Data where no observations of a species were recorded on any run at a site (i.e., all zeros) were removed to prevent zero inflation • Sites with only one run were removed due to the lack of information they provide for trend analysis. Note: Sites with multiple runs within a single year were retained for analysis because these data provide information on the effect of day of year and sampling variability. To determine changes in bat populations, we first modeled bat activity as counts of echolocation call sequences recorded along mobile acoustic transects. We used three categories of variables to model the count of call sequences along a transect: 1) Stressors to populations — We examined the influence of WNS and wind energy development over time 2) Spatial variation in activity — We used latitude, longitude, and habitat covariates to account for changes in activity across landscapes 3) Sampling variation — We accounted for day of year, sampled transect length, detector type, and ID software used. We then predicted the number of call sequences at each spatial scale and year. Finally, we derived the rate of change in population from the change in the predicted number of call sequences.

Bytemine provides access to one of the largest and most accurate US phone number databases available, featuring over 80 million verified mobile numbers. Our data includes both B2C and B2B contacts, enriched with comprehensive personal and professional details that support a wide range of use cases — from sales and marketing outreach to lead enrichment, identity resolution, and platform integration.

Our US Phone Number Data includes:

80 million+ verified US mobile numbers B2C and B2B contacts with name, email, location, and more Work emails and personal emails 57 contact-level data points including job title, company name, seniority, industry, geography, and more

This dataset gives you unmatched access to individuals across the United States, allowing you to connect with professionals and consumers directly through mobile-first campaigns. Whether you're targeting executives, small business owners, or general consumers, Bytemine provides the precision and scale to reach the right audience.

All phone numbers in our database are:

Verified and regularly updated Matched with accurate metadata (name, email, job, etc.) Compliant with data usage policies Sourced through direct licensing from trusted partners including B2B platforms, employment systems, and verified consumer data sources

This data is ideal for:

Cold calling and phone-based outreach SMS marketing and mobile-based campaigns CRM and marketing automation enrichment Identity resolution and contact matching Prospect list building and segmentation B2B and B2C marketing and retargeting App-based user targeting and onboarding Customer data platforms that need verified mobile identifiers

With access to both business and consumer profiles, Bytemine’s US Phone Number Data allows companies to execute highly targeted and personalized campaigns. Each contact is enriched with up to 57 attributes, giving your team deep insight into who the contact is, where they work, and how best to reach them.

Data can be accessed in two flexible ways:

1. Via our web platform — search, filter, and download targeted lists
2. Via API — automate contact discovery, data enrichment, or validation at scale

Our API makes it easy to integrate contact data into your existing tools, workflows, or SaaS platform. Whether you're building a lead generation engine, contact enrichment feature, or an internal prospecting tool, Bytemine delivers the clean, structured data needed to power it.

Bytemine’s phone number dataset is trusted by sales teams, marketing agencies, growth hackers, product teams, and data-driven platforms that rely on accurate contact information to engage the right audience.

If you need verified, mobile-first contact data for B2B or B2C outreach, Bytemine delivers the scale, accuracy, and flexibility required to grow your pipeline, enrich your database, and reach your customers directly.

About this dataset

Context

This list ranks the 10 cities in the Mobile County, AL by Hispanic Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic Other Race Population: This column displays the rank of cities in the Mobile County, AL by their Hispanic Some Other Race (SOR) population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Hispanic Other Race Population: The Hispanic Other Race population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Hispanic Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Mobile County Hispanic Other Race Population: This tells us how much of the entire Mobile County, AL Hispanic Other Race population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

English(the United States) Spontaneous Dialogue Smartphone speech dataset, collected from dialogues based on given topics, covering generic domain.

English(the United States) Scripted Monologue Smartphone speech dataset, collected from monologue based on given scripts, covering economy, entertainment, news, informal language, numbers, alphabet domains. Transcribed with text content and other attributes. Our dataset was collected from extensive and diversify speakers(349 speakers), geographicly speaking, enhancing model performance in real and complex tasks.Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Graph and download economic data for Mobile Cellular Subscriptions in the United States (ITCELSETSP2USA) from 1960 to 2023 about phone, telecom, and USA.

Snapshot img

Next-Generation Data Storage Market Size 2024-2028

The next-generation data storage market size is forecast to increase by USD 29.2 billion, at a CAGR of 8.08% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for data compliance in various sectors, particularly in data centers and mobile payments. The trend toward cloud computing is also driving market growth as businesses seek to store and process large amounts of data more efficiently. Big data, artificial intelligence (AI), machine learning, social media, and the Internet of Things (IoT) are generating massive amounts of data, necessitating advanced storage solutions.

However, challenges such as cyber threats, including distributed denial-of-service attacks, ransomware, viruses, worms, and malware, pose significant risks to data security and privacy. Compliance with data protection regulations and ensuring data security are becoming critical factors for companies in this market. High operating expenses for companies are also a challenge, as they must invest in research and development to stay competitive and offer innovative solutions to meet the evolving needs of businesses.

Request Free Sample

The market is experiencing significant growth due to the increasing data production from mobile devices, smart wearables, and connected devices. With the advent of 5G technology, the volume of data generated is expected to increase exponentially. E-commerce, smart technologies, automated systems, and mobile payments are driving the demand for cloud storage and data centers. Big data, data analytics, AI, and machine learning are transforming industries such as healthcare, finance, and retail. Security breaches, cyber threats, and distributed denial-of-service attacks are major concerns for organizations, leading to the adoption of advanced security measures. Flash memory and HDDs from non-volatile manufacturers are the preferred choices for low-latency data storage in smartphones, tablets, and laptops. The integration of AI and machine learning algorithms in data storage systems is enabling faster data processing and analysis. Social media platforms are generating massive amounts of data, further fueling the growth of the market.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  SAN
  NAS
  DAS


Deployment

  On-premise
  Cloud


Geography

  North America

    US


  Europe

    UK
    France


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Application Insights

The SAN segment is estimated to witness significant growth during the forecast period. The market is witnessing significant expansion due to the exponential growth of digital data in large-scale industries such as corporate information, healthcare with patient information, banking and financial services, online shopping, video, and pictures. To address the increasing demand for higher storage capacity and scalability, next-generation storage solutions like Storage Area Networks (SAN) have emerged. A SAN is a dedicated high-speed network that interconnects storage devices to multiple servers, providing each server with direct access to the storage. This setup allows for better flexibility, availability, and performance compared to Direct Attached Storage (DAS) or Network Attached Storage (NAS) systems.

In a collected environment, a backup server controls the primary server by connecting to the storage volume in case of system failure. Enterprise adoption of SAN storage devices is on the rise due to these advantages. Automatic cloud backups and the integration of the Internet of Things (IoT) further enhance the utility of next-generation data storage solutions.

Get a glance at the market share of various segments Request Free Sample

The SAN segment accounted for USD 30.80 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

Europe is estimated to contribute 33% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

The market is experiencing significant expansion due to the exponential growth of digital data in various industries, including corporate information, healthcare with patient data, banking and financial services, online shopping, video, and pictures. This trend is particularly pronounced in large-scale industries, where the need for higher storage capacity and scalable solutions is paramount. The market's growth is driven by the benefits of next-gen

This comprehensive dataset delivers 387M+ U.S. phone numbers enriched with deep telecom intelligence and granular geographic metadata, providing one of the most complete national phone data assets available today. Designed for data enrichment, verification, identity resolution, analytics, risk modeling, telecom research, and large-scale customer intelligence, this file combines broad coverage with highly structured attributes and reliable carrier-grade metadata. It is a powerful resource for any organization that needs accurate, up-to-date U.S. phone number data supported by robust telecom identifiers.

Our dataset includes mobile, landline, and VOIP numbers, paired with detailed fields such as carrier, line type, city, state, ZIP code, county, latitude/longitude, time zone, rate center, LATA, and OCN. These attributes make the file suitable for a wide range of applications, from consumer analytics and segmentation to identity graph construction and marketing audience modeling. Updated regularly and validated for completeness, this dataset offers high-confidence coverage across all 50 states, major metros, rural areas, and underserved regions.

Field Coverage & Schema Overview

The dataset contains a rich set of fields commonly required for telecom analysis, identity resolution, and large-scale data cleansing:

Phone Number – Standardized 10-digit U.S. number

Line Type – Wireless, Landline, VOIP, fixed-wireless, etc.

Carrier / Provider – Underlying or current carrier assignment

City & State – Parsed from rate center and location metadata

ZIP Code – Primary ZIP associated with the phone block

County – County name mapped to geographic area

Latitude / Longitude – Approximate geo centroid for the assigned location

Time Zone – Automatically mapped; useful for outbound compliance

Rate Center – Telco rate center tied to number blocks

LATA – Local Access and Transport Area for telecom routing

OCN (Operating Company Number) – Carrier identifier for precision analytics

Additional metadata such as region codes, telecom identifiers, and national routing attributes depending on the number block

These data points provide a complete snapshot of the phone number’s telecom context and geographic footprint.

Key Features

387M+ fully structured U.S. phone numbers

Mobile, landline, and VOIP line types

Accurate carrier and OCN information

Geo-enriched records with city, state, ZIP, county, lat/long

Telecom routing metadata including rate center and LATA

Ideal for large-scale analytics, enrichment, and modeling

Nationwide coverage with consistent formatting and schema

Primary Use Cases 1. Data Enrichment & Appending

Enhance customer databases by adding carrier information, line type, geographic attributes, and telecom routing fields to improve downstream analytics and segmentation.

1. Identity Resolution & Profile Matching

Use carrier, OCN, and geographic fields to strengthen your identity graph, resolve duplicate entities, confirm telephone types, or enrich cross-channel identifiers.

1. Lead Scoring & Consumer Modeling

Build predictive models based on:

Line type (mobile vs landline)

Geography (state, county, ZIP)

Telecom infrastructure and regional carrier assignments Useful for ML/AI scoring, propensity models, risk analysis, and customer lifetime value studies.

1. Compliance-Aware Outreach Planning

Fields like time zone, rate center, and line type support compliant outbound operations, call scheduling, and segmentation of mobile vs landline users for regulated environments.

1. Data Quality, Cleansing & Validation

Normalize customer files, detect outdated or mismatched phone metadata, resolve carrier inconsistencies, and remove non-U.S. or structurally invalid numbers.

1. Telecom Market Analysis

Researchers and telecom analysts can use the dataset to understand national carrier distribution, regional line-type patterns, infrastructure growth, and switching behavior.

1. Fraud Detection & Risk Intelligence

Carrier metadata, OCN patterns, and geographic context support:

Synthetic identity detection

Fraud scoring models

Device/number reputation systems

VOIP risk modeling

1. Location-Based Analytics & Mapping

Lat/long and geographic context fields allow integration into GIS systems, heat-mapping, regional modeling, and ZIP- or county-level segmentation.

1. Customer Acquisition & Audience Building

Build highly targeted audiences for:

Marketing analytics

Look-alike modeling

Cross-channel segmentation

Regional consumer insights

1. Enterprise-Scale ETL & Data Infrastructure

The structured, normalized schema makes this file easy to integrate into:

Data lakes

Snowflake / BigQuery warehouses

ID graphs

Customer 360 platforms

Telecom research systems

Ideal Users

Marketing analytics teams

Data science groups

Identity resolution providers

Fraud & risk intelligence platforms

Telecom analysts

Consumer data platforms

Credit, insurance, and fintech modeling teams

Data brokers & a...