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US B2B Contact Database | 200M+ Verified Records | 95% Accuracy | API/CSV/JSON Elevate your sales and marketing efforts with America's most comprehensive B2B contact data, featuring over 200M+ verified records of decision-makers, from CEOs to managers, across all industries. Powered by AI and refreshed bi-weekly, this dataset ensures you have access to the freshest, most accurate contact details available for effective outreach and engagement.

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Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

✅ Depth Beyond Digits Each contact includes 150+ data points:

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✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

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Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.

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Description

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

Input variables:

Bank client data 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. Other attributes 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Bytemine provides a high-accuracy telemarketing data solution designed for businesses that rely on phone-based outreach and direct contact with consumers and professionals across the United States. Our US telemarketing contact database includes verified phone numbers, personal and work emails, and direct dials — all updated regularly and maintained at over 95 percent data accuracy.

Whether you're building outbound call campaigns, running SMS marketing efforts, or enhancing your CRM with up-to-date phone numbers and contact details, Bytemine gives you the scale and reliability needed to connect with your audience effectively. Our data is structured, searchable, and available via both platform and API, giving your team the tools to operate at scale with full control.

Our telemarketing contact data includes:

Verified US phone numbers, including mobile and landline Direct dials with high connection rates Work and personal email addresses matched to each record 50+ additional attributes such as name, location, job title, company, industry, income range, and more Compliance-aware delivery and access, with usage guidelines for outreach teams

Bytemine’s telemarketing data is built for performance. We prioritize accuracy, freshness, and reachability by sourcing our data directly from trusted B2B and B2C platforms, employment systems, and consumer data aggregators. Our internal processes ensure that contact records are verified and updated consistently, helping you reduce bounce rates, minimize wasted dials, and increase connect rates across your campaigns.

Use cases include:

Cold calling and outbound sales outreach Telemarketing campaigns across regions or industries Consumer research, polling, and data collection Call center list building and automation CRM and database enrichment with fresh phone numbers SMS marketing and mobile-first promotions Direct contact for financial services, insurance, home services, healthcare, and more

With over 95 percent personal contact accuracy and a wide range of targeting filters, our dataset allows teams to segment by job title, location, industry, income, education, homeowner status, and more — giving you precise control over who you contact and how.

Delivery and access options:

Searchable platform for list building, filtering, and export RESTful API for integrating telemarketing data directly into your sales or dialing system Batch enrichment for updating and validating existing phone number lists

Bytemine’s telemarketing data is trusted by marketing agencies, outbound sales teams, call centers, survey providers, insurance firms, home service businesses, and any organization that needs scalable, accurate, and compliant contact data.

If your business relies on calling and direct engagement, Bytemine delivers the telemarketing infrastructure to improve contact rates, reduce drop-offs, and maximize ROI from every outreach effort.

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

API Features:

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Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

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• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...

Retail Sales in the United States increased 0.60 percent in August of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Crime report data is provided for Louisville Metro Police Divisions only; crime data does not include smaller class cities.The data provided in this dataset is preliminary in nature and may have not been investigated by a detective at the time of download. The data is therefore subject to change after a complete investigation. This data represents only calls for police service where a police incident report was taken. Due to the variations in local laws and ordinances involving crimes across the nation, whether another agency utilizes Uniform Crime Report (UCR) or National Incident Based Reporting System (NIBRS) guidelines, and the results learned after an official investigation, comparisons should not be made between the statistics generated with this dataset to any other official police reports. Totals in the database may vary considerably from official totals following the investigation and final categorization of a crime. Therefore, the data should not be used for comparisons with Uniform Crime Report or other summary statistics.Data is broken out by year into separate CSV files. Note the file grouping by year is based on the crime's Date Reported (not the Date Occurred).Older cases found in the 2003 data are indicative of cold case research. Older cases are entered into the Police database system and tracked but dates and times of the original case are maintained.Data may also be viewed off-site in map form for just the last 6 months on Crimemapping.comData Dictionary:INCIDENT_NUMBER - the number associated with either the incident or used as reference to store the items in our evidence roomsDATE_REPORTED - the date the incident was reported to LMPDDATE_OCCURED - the date the incident actually occurredUOR_DESC - Uniform Offense Reporting code for the criminal act committedCRIME_TYPE - the crime type categoryNIBRS_CODE - the code that follows the guidelines of the National Incident Based Reporting System. For more details visit https://ucr.fbi.gov/nibrs/2011/resources/nibrs-offense-codes/viewUCR_HIERARCHY - hierarchy that follows the guidelines of the FBI Uniform Crime Reporting. For more details visit https://ucr.fbi.gov/ATT_COMP - Status indicating whether the incident was an attempted crime or a completed crime.LMPD_DIVISION - the LMPD division in which the incident actually occurredLMPD_BEAT - the LMPD beat in which the incident actually occurredPREMISE_TYPE - the type of location in which the incident occurred (e.g. Restaurant)BLOCK_ADDRESS - the location the incident occurredCITY - the city associated to the incident block locationZIP_CODE - the zip code associated to the incident block locationID - Unique identifier for internal databaseContact:Crime Information CenterCrimeInfoCenterDL@louisvilleky.gov

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1. Why build a dataset?

I wanted to run data analysis and machine learning on a large dataset to build my data science skills but I felt out of touch with the various datasets available so I thought... how about I try and build my own dataset?

2. Why gaming data?

I wondered what data should be in the dataset and settled with online digital game purchases since I am an avid gamer. Imagine getting sales data from the PlayStation Store or Xbox Microsoft Store, this is what I was aiming to replicate.

3. Scope of the dataset

I envisaged the dataset to be data created through the purchase of a digital game on either the UK PlayStation Store or Xbox Microsoft Store. Considering this, the scope of dataset varies depending on which column of data you are viewing, for example: - Date and Time: purchases were defined between a start/end date (this can be altered, see point 4) and, of course, anytime across the 24hr clock - Geographically: purchases were setup to come from any postcode in the UK - in total this is over 1,000,000 active postcodes - Purchases: the list of game titles available for purchase is 24 - Registered Banks: the list of registered banks in the UK (as of 03/2022) was 159

4. Over 42,000 rows isn't enough?

To generate the dataset, I built a function in Python. This function, when called with the number of rows you want in your dataset, will generate the dataset. For example, calling function(1000) will provide you with a dataset with 1000 rows.

Considering this, if just over 42,000 rows of data (42,892 to be exact) isn't enough, feel free to check out the code on my GitHub to run the function yourself with as many rows as you want.

Note: You can also edit the start/end dates of the function depending on which timespan you want the dataset to cover.

5. Disclaimer - this is still a work in progress!

Yes, as stated above, this dataset is still a work in progress and is therefore not 100% perfect. There is a backlog of issues that need to be resolved. Feel free to check out the backlog.

One example of this is how on various columns, the distributions of data is equal, when in fact for the dataset to be entirely random, this should not be the case. An example of this issue is the Time column. These issues will be resolved in a later update.

Last updated: 24/04/2022

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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.

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