Individual contact records — verified emails, titles, seniority, direct dials.
The contacts table contains individual person records with professional contact information and email verification data. This is the primary table for building prospect lists, outbound campaigns, and enrichment workflows. Each row is one person at one organization; if a contact holds multiple current positions, they appear in multiple rows.
Contact records are built from professional profiles, public sources, and verified email and phone data. RevenueBase continuously verifies email deliverability using its internal email verification system and re-verifies addresses on a regular cadence. Titles, seniority, and department are normalized from raw job titles so you can filter and segment consistently.
Fill rate is the percentage of rows where a field is non-null. Rates vary by segment (e.g. US and larger companies often have higher fill rates for phone and seniority).
Experienced Transportation Security Officer with a demonstrated history of working in the government administration industry. Skilled in Negotiation, Team Building, Public Speaking, Management, and Marketing. Strong military and protective services professional with a Doctor of Philosophy - PhD focused in Public Service Leadership Emergency Management from Capella University.
The first set of address fields (CITY, STATE_NAME, STATE_CODE, COUNTRY_NAME, COUNTRY_CODE, COUNTRY_REGION, CONTINENT) describes where the person is located on their professional profile. The fields prefixed with JOB_LOCATION_ describe where the person works from — usually the office location if they work from an office. These two address sets do not always match: someone might live in Boston, MA and work in Providence, RI. The person’s location (the first set) typically has a higher fill rate, so use those fields when you search for people by location.
Field
Description
Example
CITY
The city where the person lives
Lexington
STATE_NAME
The state/province/region of the country that the person is located in
District of Columbia
STATE_CODE
The abbreviation of the state/province/region of the country that the person is located in
DC
COUNTRY_NAME
The name of the country the person is located in
United States
COUNTRY_CODE
The code for the country the person is located in
US
COUNTRY_REGION
The global region that the person is located in
NORAM
CONTINENT
The continent that the person is located in
North America
JOB_LOCATION_CITY
The city in where the person works
New York
JOB_LOCATION_STATE
Name of State or Province of the country that the person is located
New York
JOB_LOCATION_STATE_CODE
The state/province/region of the country that the person is located in
The contact’s last update timestamp. Any field update (except email and phone related fields) will trigger a change in this value. LinkedIn successful profile revalidation (even with no updates) will also update this timestamp
2013-01-01 00:00:00
REVENUEBASE_CONTACT_VERIFICATION_SUMMARY
Verification Summary showing details on a record observed specifing when and how it was verified
Verified by RevenueBase | Experience last observed on LinkedIn on September 7, 2025: Proposal Manager at Metaphase | Profile: linkedin.com/in/jamie-gregory-28878356 | Email last verified on October 15, 2025 via RevenueBase’s Email Verification Tool | Next verification scheduled: November, 2025 | Learn more at https://revenuebase.ai/verified-by-revenuebase
All emails in the dataset have been verified as valid by RevenueBase’s email verification system. If an email address exists in the dataset, it means it has passed verification and is considered deliverable.
For the safest outbound sends, filter on EMAIL_LAST_VERIFIED_AT >= DATEADD(day, -30, CURRENT_DATE()) to prioritize recently verified addresses. Since all emails in the dataset are valid, you don’t need to filter on EMAIL_STATUS.
Join PER_LATEST to ORG_LATEST on RBID_ORG = RBID when you need full firmographic or HQ address data. Join to INSIGHTS_LATEST on RBID_ORG = RBID_ORG when you need company-level insight or intent signals.
Build a prospect list (VP+ in engineering or sales at mid-size software companies)
What you’re finding: Decision-makers and influencers at software companies in a size band that fits your ICP.Why these fields: All emails in the dataset are verified as valid. EMAIL_LAST_VERIFIED_AT helps prioritize recently verified addresses. JOB_LEVEL and JOB_FUNCTION narrow to buying roles. LINKEDIN_INDUSTRY_ORG and EMPLOYEE_COUNT_MAX_ORG (denormalized) avoid a join while filtering by ICP.Logic: Restrict to verified emails, seniority in C-Suite/VP/Director, departments that typically own tooling decisions, and a single industry + employee range. Order by EMAIL_LAST_VERIFIED_AT so the freshest data appears first.
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Ask AI
SELECT PER.FIRST_NAME, PER.LAST_NAME, PER.EMAIL_ADDRESS, PER.JOB_TITLE, PER.JOB_LEVEL, PER.COMPANY_NAME, ORG.EMPLOYEE_COUNT_MAXFROM RELEASES.RELEASE.PER_LATEST AS PERINNER JOIN RELEASES.RELEASE.ORG_LATEST AS ORGON PER.RBID_ORG = ORG.RBIDWHERE JOB_LEVEL IN ('C-Team', 'VP', 'Director') AND JOB_FUNCTION IN ('Engineering', 'Sales') AND LINKEDIN_INDUSTRY LIKE '%Software%' AND EMPLOYEE_COUNT_MAX BETWEEN 100 AND 1000ORDER BY EMAIL_LAST_VERIFIED_AT DESCLIMIT 500;
What you’re finding: How complete key fields are for a subset (e.g. one industry or country) so you can set expectations for list build size and which filters to use.Why these fields: Same as the global fill-rate query, but run with a WHERE clause so rates reflect only the segment you care about.
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SELECT COUNT(*) AS total_records, ROUND(COUNT(EMAIL_ADDRESS) * 100.0 / COUNT(*), 1) AS email_fill_pct, ROUND(COUNT(CELLPHONE) * 100.0 / COUNT(*), 1) AS phone_fill_pct, ROUND(COUNT(JOB_TITLE) * 100.0 / COUNT(*), 1) AS title_fill_pctFROM RELEASES.RELEASE.PER_LATESTWHERE LINKEDIN_INDUSTRY LIKE '%Software%' AND COUNTRY_NAME = 'United States';
