B2B Identity Graph Data: Best B2B Identity Graph Datasets & Databases
B2B identity graph data is information about the people of contact data at
different businesses and companies. B2B marketers and business sales teams
use B2B identity graphs data for consistent and targeted cross-channel
outreach to prospective business customers.
Alesco Phone ID Database - Identity Graph Data with over 598 Million Phone Number, covers 94% of the US population - available for licensing!
Factori_Identity Data ( Hashed email linked to unique Id with UID2.0)
True Influence - Proprietary B2B Intent Data Feed (USA)
Versium's Business REACH Account Based Audience Builder (Programmatic Audience, Facebook, Google, Linkedin, etc), B2B, USA, GDPR and CCPA Compliant
Versium's Business REACH Persona Audience Builder (Programmatic Audience, Facebook, Google, Linkedin and more), B2B, USA, GDPR and CCPA Compliant
Versium's Business REACH Digital, Enrichment (B2B), USA, GDPR and CCPA Compliance - Improve your match rates by 3x-5x, reach your targets online.
Versium's Business REACH Digital, B2B, USA, GDPR and CCPA Compliant - For improving your match rates by 3x-5x and reaching your targets online.
Versium's Business REACH Digital, API (B2B), USA, GDPR and CCPA Compliance - For improving your match rates by 3x-5x and reaching your targets online.
BIGDBM B2B2C File Data for USA (links business and consumer data)
Alesco Phone ID Database - Phone Number Data with over 598 Million Phone Number, covers 94% of the US population - available for licensing!
What is B2B Identity Graph Data?
B2B identity graph data is information about key officials at a prospective client business. This information includes email address, geographical location, contact details, device IDs, business IDs, and account username. This data is used to identify the contact details and personal information of the key players at target businesses.
How is B2B Identity Graph Data collected?
B2B identity graph data is collected through different sources. These sources include company websites, social media platforms, and business registration authorities. Data providers collect, organize, and analyze this information and create accurate graphs and datasets by cleaning and removing duplicates and unnecessary information, such as outdated email addresses and ex-directory numbers.
What are the typical attributes of B2B Identity Graph Data?
B2B identity graph data consists of these typical attributes:
• Business name and location: this refers to the business’s physical geographical location and its registered name. This also includes the employees and key officials of the business.
• Contact details: this refers to the personal and professional contact details of the business and employees.
• Type of business: this refers to the type of business, what they offer, the nature of the business, financial records, and other related information about the business.
What is B2B Identity Graph Data used for?
B2B identity graph data is used by B2B marketers and sales teams at businesses. This data is used to identify new prospect businesses. For a business to thrive in the market, B2B identity graph can be used as a tool to contact and build rapport among businesses. B2B identity graph data also helps eliminate wasted time and resources in gathering this data independently. B2B identity graph data also helps verify and validate the business’ identity and credibility.
How can a user assess the quality of B2B Identity Graph Data?
In assessing the quality of B2B identity graph data, users should consider the following quality aspects:
• Credible: this refers to how the gathered information performs when it’s verified and validated against other sources.
• Accurate: for B2B identity graph data to be considered high-quality, data must be free from error and updated regularly. Users must identify how the data was processed and cleaned.
• Accessible: the data must be accessible at all times without compromising PII.
• Complete: all the data included in the B2B identity graph data must be complete and consistent. The data must be organized according to categories and types.