Datasets, terminology & data quality
How online, offline, and scrape-trace data fit together — plus practical guidance on validation and match accuracy.
Online primary dataset
Our primary dataset consists of online data — derived from online sources such as co-reg databases, online forms, and publisher relationships.
Collecting data via online sources is the most direct path to the most actionable email signals and intent. Consumer attributes from online profiles may be less exhaustive than offline / skip-traced attributes; pair channels accordingly.
| Field | Description |
|---|---|
FIRST_NAME |
— |
LAST_NAME |
— |
DIRECT_NUMBER |
— |
MOBILE_PHONE |
— |
PERSONAL_ADDRESS |
— |
PERSONAL_CITY |
— |
PERSONAL_PHONE |
— |
PERSONAL_STATE |
— |
PERSONAL_ZIP |
— |
PERSONAL_ZIP4 |
— |
SOCIAL_CONNECTIONS |
— |
AGE_RANGE |
— |
CHILDREN |
— |
GENDER |
— |
HOMEOWNER |
— |
MARRIED |
— |
NET_WORTH |
— |
INCOME_RANGE |
— |
BUSINESS_EMAIL |
— |
BUSINESS_EMAIL_VALIDATION_STATUS |
— |
PROGRAMMATIC_BUSINESS_EMAILS |
— |
BUSINESS_EMAIL_LAST_SEEN |
— |
PERSONAL_EMAIL |
— |
ADDITIONAL_PERSONAL_EMAILS |
— |
PERSONAL_EMAIL_VALIDATION_STATUS |
— |
PERSONAL_EMAIL_LAST_SEEN |
— |
SHA256_PERSONAL_EMAIL |
— |
SHA256_BUSINESS_EMAIL |
— |
LAST_UPDATED |
— |
COMPANY_ADDRESS |
— |
COMPANY_DESCRIPTION |
— |
COMPANY_DOMAIN |
— |
COMPANY_EMPLOYEE_COUNT |
— |
COMPANY_LINKEDIN_URL |
— |
COMPANY_NAME |
— |
COMPANY_PHONE |
— |
COMPANY_REVENUE |
— |
COMPANY_SIC |
— |
COMPANY_NAICS |
— |
COMPANY_CITY |
— |
COMPANY_STATE |
— |
COMPANY_ZIP |
— |
COMPANY_INDUSTRY |
— |
COMPANY_LAST_UPDATED |
— |
DEPARTMENT |
— |
JOB_TITLE |
— |
LINKEDIN_URL |
— |
PROFESSIONAL_ADDRESS |
— |
PROFESSIONAL_ADDRESS_2 |
— |
PROFESSIONAL_CITY |
— |
PROFESSIONAL_STATE |
— |
PROFESSIONAL_ZIP |
— |
PROFESSIONAL_ZIP4 |
— |
SENIORITY_LEVEL |
— |
JOB_TITLE_LAST_UPDATED |
— |
Offline dataset
Our offline dataset supports direct outreach — outbound calling, canvassing, and high-confidence identity matching when someone must be exactly who they claim offline.
| Field | Description |
|---|---|
SKIPTRACE_MATCH_BY |
— |
SKIPTRACE_PERSON_TITLE_OF_RESPECT |
— |
SKIPTRACE_NAME |
— |
SKIPTRACE_ADDRESS |
— |
SKIPTRACE_CITY |
— |
SKIPTRACE_STATE |
— |
SKIPTRACE_ZIP |
— |
SKIPTRACE_LANDLINE_NUMBERS |
— |
SKIPTRACE_WIRELESS_NUMBERS |
— |
SKIPTRACE_CREDIT_RATING |
— |
SKIPTRACE_EXACT_AGE |
— |
SKIPTRACE_ETHNIC_CODE |
— |
SKIPTRACE_CARRIER_ROUTE |
— |
SKIPTRACE_LANGUAGE_CODE |
— |
SKIPTRACE_IP |
— |
Scrape-trace dataset
Scrape-trace adds validation for B2B by combining skip tracing with live web crawling — cross-checking records against the freshest public sources alongside offline matches.
| Field | Description |
|---|---|
SKIPTRACE_MATCH_BY |
— |
SKIPTRACE_PERSON_TITLE_OF_RESPECT |
— |
SKIPTRACE_NAME |
— |
SKIPTRACE_ADDRESS |
— |
SKIPTRACE_CITY |
— |
SKIPTRACE_STATE |
— |
SKIPTRACE_ZIP |
— |
SKIPTRACE_LANDLINE_NUMBERS |
— |
SKIPTRACE_WIRELESS_NUMBERS |
— |
SKIPTRACE_CREDIT_RATING |
— |
SKIPTRACE_EXACT_AGE |
— |
SKIPTRACE_ETHNIC_CODE |
— |
SKIPTRACE_CARRIER_ROUTE |
— |
SKIPTRACE_LANGUAGE_CODE |
— |
SKIPTRACE_IP |
— |
Core data set summary
Terminology
| Term | Meaning |
|---|---|
| Online Data | Data that has been acquired through online sources and publishers. |
| Offline Data | Data that has been acquired via offline sources such as real estate databases, finance databases. |
| Skip Trace | Data has been matched to multiple sources (offline and online) to determine its accuracy. |
| Skip Scraped | Combining skip tracing data with scraping to not only cross check different databases—we also check latest online sources. |
| B2C | Business to Consumer — personal consumer data. |
| B2B | Business to Business — business data and not personal. |
| B2B2C | Business to Business to Consumer — business data matched on a personal level for greater accuracy. |
| Co-reg | Co-registration — data obtained from publishers across certain verticals. Opt-in data. |
Explanation of fields
| Field | Description |
|---|---|
FIRST_NAME |
First name from online coreg. |
LAST_NAME |
Last name from online coreg. |
SHA256_PERSONAL_EMAIL |
Sha256 encrypted email (most recent). |
PERSONAL_EMAIL |
Personal email from the sha256. |
PERSONAL_EMAIL_VALIDATION_STATUS |
Validation signal of personal email. |
PERSONAL_EMAIL_LAST_SEEN |
When the personal email was last seen by an ESP. |
SKIPTRACE_MATCH_BY |
Fields we used to skip trace the online data with offline data for more accuracy. |
SKIPTRACE_PERSON_TITLE_OF_RESPECT |
The title of the prospect. |
SKIPTRACE_NAME |
Full name of the prospect offline data. |
SKIPTRACE_ADDRESS |
Address of the prospect offline data. |
SKIPTRACE_CITY |
City of prospect offline data. |
SKIPTRACE_STATE |
State of prospect offline data. |
SKIPTRACE_ZIP |
ZIP of prospect offline data. |
SKIPTRACE_LANDLINE_NUMBERS |
Landline (home phone) of prospect offline data. |
SKIPTRACE_WIRELESS_NUMBERS |
Wireless (mobile) phone of prospect offline data. |
DNC |
National Do Not Call Registry tag. |
SKIPTRACE_B2B_MATCH_BY |
Fields used to take the B2B data then skip trace it against more information via online/offline. |
COMPANY_NAME |
Company name the prospect is associated with. |
COMPANY_DOMAIN |
Company domain of the company. |
COMPANY_DESCRIPTION |
AI generated description of the company and what they do. |
BUSINESS_EMAIL |
Business email of the prospect. |
BUSINESS_EMAIL_VALIDATION_STATUS |
Business email validation status and whether they have email signals. |
BUSINESS_EMAIL_LAST_SEEN |
When the business email last had a signal via ESP. |
SKIPTRACE_B2B_ADDRESS |
The business address which has been skip traced from another dataset. |
SKIPTRACE_B2B_LANDLINE_PHONE |
The business landline that has been skip traced from another dataset. |
SKIPTRACE_B2B_WIRELESS_PHONE |
The wireless phone (mobile) which has been skip traced from another dataset. |
SKIPTRACE_B2B_SOURCE |
The source of where we obtained the additional business info. |
SKIPTRACE_B2B_WEBSITE |
The business website (usually the root domain). |
LINKEDIN_URL |
LinkedIn URL of the prospect. |
Phone numbers — quality control
Multiple phone flavors exist inside the identity graph. For outbound dialing from call centres or similar workflows, rely on skip-traced numerics only — they reconcile across multiple offline sources.
Recommended fields:
SKIPTRACE_LANDLINE_NUMBERSSKIPTRACE_WIRELESS_NUMBERSDNC
Expect roughly 80–99% validity on hygiene checks after filtering to approved skip_trace fields — actual rates depend on list composition.
Email verification
B2B records resolve to employee-level granularity (often described as B2B2C) so you see both employer context and actionable contact points — usable for activation with stronger match coverage in paid environments.
We collect multiple email variants per person (programmatic vs deliverable workloads). Selecting the wrong validation tier can crater deliverability, so optimise per channel.
- Column S — Business email is the mailbox to use for cold outreach.
-
Column T — Verification tags.
- Valid (Catch-all) — typical organisation catch-all.
- Valid (Digital) — present in programmatic channels; may still be risky for SMTP sends.
- Valid (ESP) — receiving + sending telemetry from ESP partners — focus here for outbound email.
- Column V — Last seen, refreshed weekly / monthly / quarterly depending on ingestion batch freshness from ESP pipelines.
Adjusting skiptrace match fields for accuracy
Dialled or postal programs should prioritise strictly skip-traced rows — deterministic matches across tens of corroborating sources.
Matches between the online spine and offline append are expressed under SKIPTRACE_MATCH_BY. Narrowing filters to combinations such as address + email
typically boosts precision because multiple independent keys agree.