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.

Key Features & Stats:

200M+ Decision-Makers: Includes C-level executives, VPs, Directors, and Managers.

95% Accuracy: Email & Phone numbers verified for maximum deliverability.

Bi-Weekly Updates: Never waste time on outdated leads with our frequent data refreshes.

50+ Data Points: Comprehensive firmographic, technographic, and contact details.

Core Fields:

Direct Work Emails & Personal Emails for effective outreach.

Mobile Phone Numbers for cold calls and SMS campaigns.

Full Name, Job Title, Seniority for better personalization.

Company Insights: Size, Revenue, Funding data, Industry, and Tech Stack for a complete profile.

Location: HQ and regional offices to target local, national, or international markets.

Top Use Cases:

Cold Email & Calling Campaigns: Target the right people with accurate contact data.

CRM & Marketing Automation Enrichment: Enhance your CRM with enriched data for better lead management.

ABM & Sales Intelligence: Target the right decision-makers and personalize your approach.

Recruiting & Talent Mapping: Access CEO and senior leadership data for executive search.

Instant Delivery Options:

JSON – Bulk downloads via S3 for easy integration.

REST API – Real-time integration for seamless workflow automation.

CRM Sync – Direct integration with your CRM for streamlined lead management.

Enterprise-Grade Quality:

SOC 2 Compliant: Ensuring the highest standards of security and data privacy.

GDPR/CCPA Ready: Fully compliant with global data protection regulations.

Triple-Verification Process: Ensuring the accuracy and deliverability of every record.

Suppression List Management: Eliminate irrelevant or non-opt-in contacts from your outreach.

US Business Contacts | B2B Email Database | Sales Leads | CRM Enrichment | Verified Phone Numbers | ABM Data | CEO Contact Data | US B2B Leads | US prospects data

Premium B2B Marketing Database - 18+ Million Company Records

Accelerate your B2B sales and marketing success with our comprehensive business database featuring over 18 million verified company records and 70 million employee profiles. Our 20+ year data expertise delivers superior quality and coverage compared to competitors.

Core Database Statistics

Company Records: 18,243,524 (verified businesses)

Employee Records: 70,420,010 (professional profiles)

Business Email Addresses: 38,731,006 (verified and deliverable)

Phone Numbers: 9,728,410 (direct business lines)

Geographic Coverage: Complete US business landscape

Industry Classification: Full SIC code taxonomy

Advanced Targeting Categories

Geographic Targeting: Target businesses by precise location parameters including nationwide campaigns, state-level focus, Metropolitan Service Areas (MSA), zip code radius, city and county targeting, and carrier route precision for local market penetration.

Business Profile Segmentation: Segment companies by annual revenue (sales volume), employee count (startup to enterprise), year founded (established vs. emerging), business type (small business, corporation, public company), facility ownership status, stock exchange listings (NYSE, NASDAQ, ASE), and franchise operations.

Industry Classification (SIC Codes): Leverage Standard Industrial Classification codes for precision targeting across 2-digit (broad categories), 4-digit (sub-industries), 6-digit (niche markets), and 8-digit (hyper-specific) classifications covering all major industries including Manufacturing, Healthcare, Technology, Financial Services, Professional Services, and more.

Employee & Decision Maker Targeting: Identify key decision makers by job title (C-level, VP, Director, Manager), department focus (IT, Marketing, Finance, Operations), purchasing authority levels, seniority positions, and functional roles across technical, administrative, and strategic positions.

Multi-Channel Campaign Applications

Deploy across all major B2B marketing channels:

Email Marketing: Direct outreach to verified business email addresses

LinkedIn Advertising: Professional network targeting with job title precision

Social Media: Facebook, Instagram, and Twitter/X B2B campaigns

Search Advertising: Google, BING and YouTube business targeting

Direct Mail: Physical address campaigns for high-value prospects

Telemarketing: Direct phone outreach to decision makers

Account-Based Marketing: Multi-touch ABM campaign coordination

Data Quality & Sources

Our business database aggregates from multiple verified sources:

Business registration and licensing records

Professional association memberships and directories

Industry publications and trade organizations

Conference and trade show participation data

Online business profiles and corporate websites

Financial reporting and SEC filing information

Employment databases and HR records

Technical Delivery & Integration

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

Delivery Methods: Secure FTP, API integration, direct download portals

Integration Options: CRM systems, marketing automation platforms, ad platforms

Custom Selections: 1,000+ selectable business and employee attributes

Update Frequency: Monthly data refreshes with real-time validation

Minimum Orders: Flexible based on targeting complexity and campaign size

Account-Based Marketing (ABM) Excellence

Specifically designed for sophisticated ABM strategies:

Target Account Identification: Find companies matching ideal customer profiles

Decision Maker Mapping: Multiple contacts within target accounts

Account Prioritization: Focus on high-revenue, high-employee companies

Personalized Outreach: Industry and company-specific messaging

Multi-Touch Coordination: Synchronized campaigns across channels

Unique Value Propositions

20+ Year Data Heritage: Established industry expertise and proven track record

Superior Data Coverage: More extensive and accurate than competitors

Real-Time Validation: Continuous data refreshing and quality assurance

Advanced Segmentation: Combine multiple targeting criteria for precision

Compliance Management: Built-in suppression lists and opt-out handling

Technical Flexibility: API access and custom integration support

Ideal Customer Profiles

Technology Companies: Software, SaaS, hardware, and IT services

Professional Services: Consulting, legal, accounting, and advisory firms

Financial Services: Banks, insurance, investment, and fintech companies

Healthcare Organizations: Medical devices, pharmaceuticals, and healthcare IT

Manufacturing Companies: Industrial equipment, automotive, and consumer goods

Marketing Agencies: Digital agencies serving B2B clients

Sales Organizations: Inside sales, field sales, and business development teams

Performance Optimization Features

Lookalike ...

Mexico BTS: Mfg: Difficulty in Hiring: Manager and Sales: Greater data was reported at 8.340 % in Mar 2019. This records a decrease from the previous number of 9.060 % for Feb 2019. Mexico BTS: Mfg: Difficulty in Hiring: Manager and Sales: Greater data is updated monthly, averaging 4.995 % from Aug 2000 (Median) to Mar 2019, with 224 observations. The data reached an all-time high of 15.170 % in Aug 2017 and a record low of 1.460 % in Dec 2014. Mexico BTS: Mfg: Difficulty in Hiring: Manager and Sales: Greater data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.S003: Business Tendency Survey.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Explore the progression of average salaries for graduates in Marketing Director from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Marketing Director relative to other fields. This data is essential for students assessing the return on investment of their education in Marketing Director, providing a clear picture of financial prospects post-graduation.

Director, Destination Marketing Job #: 1788 Jurisdiction: Out-of-Scope Department: Destination Marketing Communications, Service Regina & Tourism

Mexico BTS: Mfg: Difficulty in Hiring: Manager and Sales: Difficulty data was reported at 10.070 % in Mar 2019. This records an increase from the previous number of 5.290 % for Feb 2019. Mexico BTS: Mfg: Difficulty in Hiring: Manager and Sales: Difficulty data is updated monthly, averaging 12.725 % from Aug 2000 (Median) to Mar 2019, with 224 observations. The data reached an all-time high of 28.800 % in Oct 2001 and a record low of 2.170 % in Nov 2014. Mexico BTS: Mfg: Difficulty in Hiring: Manager and Sales: Difficulty data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.S003: Business Tendency Survey.

Access over 10 million verified C-level executive profiles across all industries worldwide. Includes emails, job titles, and company data. Perfect for enterprise sales, partnerships, and executive outreach. Best Price Guarantee.

CRM or Customer Relationship Management is a system that allows businesses to manage contacts, collaborate effectively, increase productivity & grow your business. Traditionally Customer Relationship Management has been a sales tool but now CRM systems, like Salesforce, allow people to do more.

Develop Louisville Focuses on the full range of land development activities, including planning and design, vacant property initiatives, advanced planning, housing & community development programs, permits and licensing, land acquisition, public art and clean and green sustainable development partnerships.Data Dictionary:“LBA” is the abbreviation for the Louisville and Jefferson County LBA Authority, Inc."Parcel ID" is an identification code assigned to a piece of real estate by the Jefferson County Property Valuation Administration. The Parcel ID is used for record keeping and tax purposes.“IMPROV” stands for whether or not the real estate parcel had an “improvement” (i.e., a structure) situated on it at the time it was sold. “1” indicates that a structure existed when the parcel was sold and “0” indicates that the parcel was an empty, piece of land.“APPLICANT” is the individual(s) or active business entity that submitted an Application to Purchase the real estate parcel and whose application was presented to and approved by the LBA’s Board of Directors. The Board of Directors must approve each application before a transfer deed is officially recorded with the Office of the County Clerk of Jefferson County, Kentucky.“SALE DATE” is the date that the Applicant signed the transfer deed for the respective real estate parcel.“SALE AMOUNT” is the amount that the Applicant paid to purchase the respective real estate parcel.“SALE PROGRAM” is the LBA’s disposition program that the Applicant participated in to acquire the real estate parcel.The Office of Community Development defines each “Sale Program” as follows:Budget Rate (“Budget Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land.Cut It Keep It - Applicant requested to maintain the empty piece of land situated on the same block as a real estate parcel owned by the Applicant. Applicant must retain ownership of the lot for three (3) years before the Applicant can sell it.Demo for Deed (“Last Look – Demo for Deed”) – Applicant requested to demolish the structure situated on the real estate parcel and retain the land for a future use.Flex Rate (“Flex Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land but did not have proof of funding or a timeline as to when the project would be completed.Metro Redevelopment – The real estate parcel was part of a redevelopment project being considered by Metro Government.Minimum Pricing Policy – The pricing policy that was approved by the LBA’s Board of Directors and in effect as of the real estate parcel’s sale date.RFP (“Request for Proposals”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Save the Structure (“Last Look – Save the Structure”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Side Yard – The Applicant requested to acquire the LBA’s adjoining piece of land to make the Applicant’s occupied, real estate parcel larger and more valuable.SOI (“Solicitation of Interest”) – The LBA assembled two (2) or more real estate parcels and the Applicant submitted a redevelopment project for the subject parcels.For more information about each of the current disposition programs that the LBA offers, please refer to the following website pages:https://louisvilleky.gov/government/community-development/vacant-lot-sales-programshttps://louisvilleky.gov/government/community-development/vacant-structures-saleContact:Connie Suttonconnie.sutton@louisvilleky.gov

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Data for the main experiments, additional experiments, Sales manager experiment, Demographic survey. We examine a business case, where people need to make a decision about a management accounting innovation (i.e. costing system). We show that compensating decision-makers on long-term performance metrics that include the costs of the innovation (instead of metrics that exclude them) increases their likelihood of adopting the innovative management accounting system. Data files: Stata 14, separate Data file and Do file (i.e., the program to run analyses) for the different experiments (Main experiment; Additional experiment one testing process; Additional experiment two to mitigate concerns; Sales manager experiment; Demographic survey Supplemental material: Data files Structure data package: Separate files for each of the experiments Ethical clearance: IRB-2019-10-008, Nanyang Technological University (NTU) Supplemental data for the related publication is also available at the Taylor & Francis website, https://doi.org/10.1080/09638180.2024.2440430.

Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Column Name	Data Type	Description	Business Impact
product_id	String	Unique product identifier (FB000001-FB002176)	Product tracking and inventory management
category	Categorical	Product type (Dresses, Tops, Bottoms, Outerwear, Shoes, Accessories)	Category performance analysis
brand	Categorical	Fashion brand name (Zara, H&M, Forever21, Mango, Uniqlo, Gap, Banana Republic, Ann Taylor)	Brand comparison and market positioning
season	Categorical	Collection season (Spring, Summer, Fall, Winter)	Seasonal trend analysis and forecasting
size	Categorical	Clothing size (XS, S, M, L, XL, XXL) - Null for accessories	Size demand optimization
color	Categorical	Product color (Black, White, Navy, Gray, Beige, Red, Blue, Green, Pink, Brown, Purple)	Color preference analysis
original_price	Numerical	Base product price ($15.14 - $249.98)	Pricing strategy development
markdown_percentage	Numerical	Discount percentage (0% - 59.9%)	Markdown effectiveness analysis
current_price	Numerical	Final selling price after discounts	Revenue and margin analysis
purchase_date	Date	Transaction date (2024-2025 range)	Time series analysis and seasonality
stock_quantity	Numerical	Available inventory (0-50 units)	Inventory optimization
customer_rating	Numerical	Product rating (1.0-5.0 scale) - Includes nulls	Quality assessment and customer satisfaction
is_returned	Boolean	Return status (True/False)	Return rate calculation and analysis
return_reason	Categorical	Specific return reason (Size Issue, Quality Issue, Color Mismatch, Damaged, Changed Mind, Wrong Item)	Return pattern analysis

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

AbstractThis paper's main goal is to understand the impact of control systems on the relationship between a salesperson's effort and their performance. The control systems have two perspectives: outcome and behavior. The difference between them is how a salesperson is evaluated in regards to their job. If the sales manager is only concerned with sales results, the control system is based on outcome. However, if the sales manager is concerned with a salesperson's capacities, abilities and knowledge, the control system is based on behavior. Based on the theories developed by Anderson and Oliver (1987) and Oliver and Anderson (1994), we proposed a theoretical framework to explain sales performance, positioning the control system as a moderating variable. We undertook two descriptive field investigations to test the proposed model's hypotheses for pharmaceutical sales representatives and car dealer salespersons. We estimated four regression equations to test the hypotheses. The first conclusion is that the perception of product innovation by salespeople actually influences the launch of new products. The second conclusion showed that salesperson effort has a negative relationship with performance. Third, we demonstrated the moderating effect control systems have on the relationship between salesperson experience and sales efforts.

Target over 5 million C-level executives across Europe with verified emails, job titles, and LinkedIn URLs. Perfect for strategic B2B sales, partnerships, and executive outreach. GDPR-compliant. Best Price Guarantee.

Assume the car seat sales manager, who manages 400 stores, wants to be the company's top sales manager. The manager wonders how many sales other stores are currently making. However, the manager is only aware of his or her own sales data and has access to information about other stores. To determine the sales amount of other stores, the manager will create a sales predictive model, allowing the manager to estimate how much carseat sales are doing in other stores.

Dataset has 400 observations (each at a different store) and 11 variables:

|Variable| Description| |:-------------|:-------------| |sales | Unit sales in thousands| |comp_price | Price charged by competitor at each location| |income | Community income level in thousands of dollars| |advertising | Local ad budget at each location in thousands of dollars| |population | Regional pop in thousands| |price | Price for car seats at each site| |shelveLoc | Bad, Good or Medium indicates quality of shelving location| |age | Age level of the population| |education | Ed level at location| |urban | Factor levels 'Yes' or 'No' are used to indicate whether a store is in an urban or rural location.| |us | Factor levels 'Yes' or 'No' are used to indicate whether a store is in the United States or not.|

The data management platforms market is set to record a valuation of USD 30 billion in 2025 and grow to USD 90 billion by 2035 at a CAGR of 13.2% during the forecast period. Companies are increasingly embracing AI-based data management, cloud-based analytics, and real-time data integration applications to improve business intelligence as well as customer insights. Additionally, machine learning, big data, and regulation-friendly data handling will drive industry growth.

Contracts and Deals Analysis

Company	Contract Value (USD Million)
Salesforce and Own Company	Approximately USD 1,850 - USD 1,950
Databricks and SAP	Approximately USD 500 - USD 600

Country-wise Analysis

Country	CAGR (2025 to 2035)
USA	10.2%
UK	9.9%
European Union	10.1%
Japan	10.0%
South Korea	10.4%

Competitive Outlook

Company Name	Estimated Market Share (%)
Oracle BlueKai	20-25%
Adobe Audience Manager	15-20%
Salesforce DMP	12-17%
Nielsen DMP	8-12%
Lotame	5-9%
Other Companies (combined)	20-30%

Global Email Address & Contact Data Solutions: 293M+ Verified Emails and Phone Numbers for B2B & B2C Outreach Boost your marketing and sales strategies with Forager.ai's Global Contact Data and Email address Data. Our comprehensive database offers access to over 293 million verified email addresses, along with phone number data and detailed B2B Email data and contact information. Whether you're focused on expanding your B2B Email outreach or improving lead generation, our solutions provide the tools you need to engage decision-makers and drive success.

Designed to support your Email data-driven marketing efforts, Forager.ai delivers valuable insights with email data, phone number data, and contact details for both B2B and B2C audiences. Build meaningful connections and leverage high-quality, verified Email data to execute precise and effective outreach strategies.

Core Features of Forager.ai B2B Email Data Solutions: Targeted B2B Email Data: Gain access to a diverse collection of email addresses that help you execute personalized email campaigns targeting key decision-makers across industries.

Comprehensive Phone Number Data: Enhance your sales and telemarketing strategies with our extensive phone number database, perfect for direct outreach and boosting customer engagement.

B2B and B2C Contact Data: Tailor your messaging with B2B contact data and B2C contact Email address data that allow you to effectively connect with C-suite executives, decision-makers, and key consumer groups.

CEO Contact Information: Unlock direct access to CEO contact details, ideal for high-level networking, partnership building, and executive outreach.

Strategic Applications of Forager.ai Data: Online Marketing & Campaigns: Utilize our email address data and phone number information to run targeted online marketing campaigns, increasing conversion rates and boosting outreach effectiveness.

Database Enrichment: Improve your sales databases and CRM systems by enriching them with accurate and up-to-date contact data, supporting more informed decision-making.

B2B Lead Generation: Tap into our rich B2B Email data to expand your business networks, refine your outreach efforts, and generate high-quality leads.

Sales Data Amplification: Supercharge your sales strategies by integrating enriched contact data for better targeting and higher sales conversion rates.

Competitive Market Intelligence: Gain valuable insights into your competitors by leveraging our comprehensive contact data to analyze trends and shifts in the market.

Why Forager.ai Stands Out: Precision & Accuracy: With a 95%+ accuracy rate, Forager.ai ensures that your email data and contact information is always fresh, reliable, and ready to be used for maximum impact.

Global Reach, Local Relevance: Our Email address data solutions cover global markets while allowing you to focus on specific regions, industries, and audience segments tailored to your business needs.

Cost-Effective Solutions: We offer scalable, affordable B2B email data and B2B contact data packages, ensuring you get high-value results without breaking your budget.

Ethical, Compliant Data: We strictly adhere to GDPR guidelines, ensuring that all contact data is ethically sourced and legally compliant, protecting both your business and your customers.

Unlock the Power of Verified Email (Personal Email data & Business Email data) Contact Data with Forager.ai Explore the potential of our 293M+ verified email addresses and phone numbers to elevate your B2B email marketing, sales outreach, and data-driven initiatives. Our contact data solutions are tailored to support your lead generation, sales pipeline, and competitive intelligence efforts, giving you the tools to execute more effective and impactful campaigns.

Top Use Cases for Forager.ai Data Solutions: Lead Generation & B2B Prospecting

Cold B2B Email Outreach

CRM Enrichment & Marketing Automation

Account-Based Marketing (ABM)

Recruiting & Executive Search

Market Research & Competitive Intelligence

Flexible Data Licensing & Access Options: One-Time Data Files available upon request

24/7 API Access for seamless integration

Monthly & Annual Plans tailored to your needs

API Credits Roll Over with no expiration

Reach out to us today to discover how Forager.ai's high-quality Email data and contact data can transform your outreach strategies and drive greater business success.

Premium B2B leads from marketing decision makers including CMOs, Marketing Directors, and Digital Marketing Managers with budget authority.

Taiwan WPI: DS: Transport Equipment: Motorcycles data was reported at 99.230 1991=100 in Dec 1997. This records an increase from the previous number of 99.190 1991=100 for Nov 1997. Taiwan WPI: DS: Transport Equipment: Motorcycles data is updated monthly, averaging 99.890 1991=100 from Jan 1991 (Median) to Dec 1997, with 84 observations. The data reached an all-time high of 102.750 1991=100 in Jan 1993 and a record low of 98.220 1991=100 in Oct 1997. Taiwan WPI: DS: Transport Equipment: Motorcycles data remains active status in CEIC and is reported by Directorate-General of Budget, Accounting and Statistics, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.I051: Wholesale Price Index: 1991=100: Domestic Sales.