The insights table contains insight data and derived intelligence about companies. Use it to prioritize accounts based on buying signals, identify companies going through growth or change, and layer intent-like signals into your targeting. All insights are company-level only.
Where the data comes from
Insights are derived from multiple sources: technographic data from website and stack detection, hiring data from job boards and career pages, funding data from press and regulatory filings, and engagement-style signals where available. Data is updated with each monthly release; recency varies by signal type.
Counts and coverage
Insight record count depends on your subscription and which products (e.g. technographics, hiring, funding) are enabled. Not every company has insights; fill rates are highest for companies with a clear web presence and for venture-backed companies (funding). Use the fill-rate query below to see coverage for your dataset.
Table stats
| Metric | Value |
|---|
| Total records | Varies by subscription; one row per insight event or snapshot |
| Update frequency | Monthly (aligned with contacts and companies release) |
| Primary key | RBID_ORG |
| Main foreign key | RBID_ORG → org.RBID |
Data dictionary
Fill rates vary significantly by insight type. Technographic data is available for companies with a detectable web/tech footprint; funding data is richest for venture-backed companies; hiring signals depend on public job postings.
Identifiers
| Field | Description | Example |
|---|
RBID_ORG | RevenueBase’s unique identifier for a company | rb-oab75n3pe |
Technographic signals
RevenueBase derives technographic signals from a variety of sources across the web, primarily from skills and technologies listed in a company’s job postings and from Google Play and Apple App Store listings. The presence of a technographic indicator means we detected that technology in use at some point in time. Because we don’t currently have a reliable way to determine when a company stops using a particular technology, these signals are additive only — they expand over time but don’t contract. A technology that appears in a company’s profile may no longer be actively in use. Treat technographic data as a cumulative footprint of a company’s technology stack rather than a real-time snapshot of what’s deployed today.
| Field | Description | Example |
|---|
ABM_TECH_ORG | There is evidence of particular activity based management technologies in use | 6Sense;Demandbase;Uberflip;Terminus;Engagio;Triblio |
ANALYTICS_TECH_ORG | There is evidence of particular analytics technologies in use | Tableau;Power BI;Segment;Looker;Qlik |
APPLICATION_SECURITY_TECH_ORG | There is evidence of particular application security technologies in use | Synopsys;Checkmarx;Snyk;Veracode;Semgrep |
CLOUD_PROVIDER_TECH_ORG | There is evidence of particular cloud providers in use | AWS;Google Cloud;Microsoft Azure;IBM Cloud;Oracle Cloud;Alibaba |
CLOUD_SECURITY_TECH_ORG | There is evidence of particular cloud security technologies in use | Datadog;Wiz;Sysdig;Lacework |
CMS_TECH_ORG | There is evidence of particular content management systems in use | Hubspot;Wordpress;Wix;Squarspace;Drupal;Optimizely;Contentful;Adobe Experience Manager;Sitecore;sanity;Prismic;Adobe CQ;Strapi;Episerver |
CONVERSATION_INTELLIGENCE_TECH_ORG | There is evidence of particular conversation intelligence technologies in use | Chorus;Gong;Aircall;Clari;Dialpad |
CRM_TECH_ORG | There is evidence of particular customer relationship management technologies in use | Salesforce CRM;Hubspot;Microsoft Dynamics;Zoho;Pipedrive CRM;SugarCRM |
DEVELOPMENT_TECH_ORG | There is evidence of particular development technologies in use | MongoDB;PostgreSQL;Couchbase;Neo4j:TigerGraph |
E_COMMERCE_PLATFORM_TECH_ORG | There is evidence of particular e-commerce platform technologies used | WooCommerce;BigCommerce; Shopify;Saleforce Commerce Cloud;Oracle Commerce |
EMAIL_HOSTING_TECH_ORG | There is evidence of particular email hosting providers in use | Google;Microsoft |
EMAIL_SECURITY_TECH_ORG | There is evidence of particular email security technologies in use | Mimecast;Symantec;Proofpoint;Fortinet;Microsoft Defender;Barracuda |
ERP_TECH_ORG | There is evidence of particular ERP technologies in use | SAP ecc;SAP s/4hana;Infor Cloudsuite Industrial |
MARKETING_AUTOMATION_TECH_ORG | There is evidence of particular Marketing Automation technologies in use | Mailchimp;Hubspot;Klaviyo;Pardot;Marketo;Salesforce Marketing Cloud |
MARTECH_CATEGORIES_ORG | G2 martech sub-category | eCommerce Platforms & Carts;Live Chat & Chatbots;Display & Programmatic Advertising;Native/Content Advertising;Business/Customer Intelligence & Data Science |
SALES_AUTOMATION_TECH_ORG | There is evidence of particular sales automation technologies in use | Salesloft;Outreach;Yesware;Apollo |
HAS_WEB_APP_ORG | If the business maintains a web application | Yes |
HAS_MOBILE_APP_ORG | If the business has either a Google or Apple mobile app available | Yes |
Hiring signals
RevenueBase accumulates job postings over a rolling 12 to 18 month window and tallies the total number of posts for each job function. Postings older than 12 months are automatically sunset. Keep in mind that these values are directional rather than precise — job postings fluctuate daily as companies open, close, and re-list roles. A single posting may also represent multiple headcount for the same position.
We also surface role-based signals derived from the RevenueBase contact database. These include binary indicators like whether a company has a CISO on staff, as well as counts such as IT_ROLE_COUNT_ORG that reflect the number of contacts we’ve identified in a given function. These counts are based on current job titles in our database and will shift as contacts are added, updated, or removed. As with hiring signals, treat these data points as directional indicators of a company’s organizational structure and investment areas rather than exact headcounts.
Data lag and trend indicators: All open position columns (*_OPEN_ROLES_COUNT_ORG) and role count columns (*_ROLE_COUNT_ORG) reflect trends, not real-time values. There is a 2-3 month lag between the data source and the dataset. These fields should be used to identify hiring trends and organizational patterns, not as real-time metrics for current job openings or exact team sizes. Use them for directional signals about company growth, investment areas, and organizational structure.
| Field | Description | Example |
|---|
ACCOUNT_EXECUTIVE_OPEN_ROLES_COUNT_ORG | Number of open job postings for account executives positions at the organization | 1 |
BUSINESS_DEVELOPMENT_OPEN_ROLES_COUNT_ORG | Number of open job postings for business development positions at the organization | 350 |
BUSINESS_DEVELOPMENT_ROLE_COUNT_ORG | Number of people with business development titles associated with the organization | 662 |
CUSTOMER_SUCCESS_OPEN_ROLES_COUNT_ORG | Number of open job postings for customer success positions at the organization | 2 |
CUSTOMER_SUCCESS_ROLE_COUNT_ORG | Number of people with customer success titles associated with the organization | 29 |
DEMAND_GENERATION_OPEN_ROLES_COUNT_ORG | Number of open job postings for demand generation positions at the organization | 1 |
DEVOPS_OPEN_ROLES_COUNT_ORG | Number of open job postings for development operations positions at the organization | 5 |
DEVOPS_ROLE_COUNT_ORG | Number of people with development operations titles associated with the organization | 14 |
ENGINEER_ROLE_COUNT_ORG | Number of people with engineering titles associated with the organization | 26 |
GRC_OPEN_ROLES_COUNT_ORG | Number of open job postings for governance, risk, and compliance positions at the organization | 450 |
GRC_ROLE_COUNT_ORG | Number of people with governance, risk, and compliance titles associated with the organization | 3477 |
IOS_DEV_ROLE_COUNT_ORG | Number of people with IOS developer titles associated with the organization | 589 |
IT_OPEN_ROLES_COUNT_ORG | Number of open job postings for information technology positions at the organization | 2 |
IT_ROLE_COUNT_ORG | Number of people with information technology titles associated with the organization | 476 |
MARKETING_OPEN_ROLES_COUNT_ORG | Number of open job postings for marketing positions at the organization | 1 |
MARKETING_ROLE_COUNT_ORG | Number of people with marketing titles associated with the organization | 3 |
MOBILE_DEV_ROLE_COUNT_ORG | Number of people with mobile developer titles associated with the organization | 1 |
NETWORK_INFRASTRUCTURE_OPEN_ROLES_COUNT_ORG | Number of open job postings for network infrastructure positions at the organization | 6 |
NETWORK_INFRASTRUCTURE_ROLE_COUNT_ORG | Number of people with network infrastructure titles associated with the organization | 32 |
OPERATIONS_OPEN_ROLES_COUNT_ORG | Number of open job postings for operations positions at the organization | 324 |
OPERATIONS_ROLE_COUNT_ORG | Number of people with operations titles associated with the organization | 1 |
QA_ROLE_COUNT_ORG | Number of people with quality assurance titles associated with the organization | 18 |
SALES_OPEN_ROLES_COUNT_ORG | Number of open job postings for sales positions at the organization | 6 |
SALES_ROLE_COUNT_ORG | Number of people with sales titles associated with the organization | 4 |
SECURITY_OPEN_ROLES_COUNT_ORG | Number of open job postings for security positions at the organization | 500 |
SECURITY_ROLE_COUNT_ORG | Number of people with security titles associated with the organization | 6 |
ANDROID_DEV_ROLE_COUNT_ORG | Number of people with Android developer titles associated with the organization | 467 |
HAS_CIO_ORG | If the business has a chief information officer | Yes |
HAS_CISO_ORG | If the business has a chief information security officer | Yes |
Funding & financial signals
| Field | Description | Example |
|---|
FUNDING_ROUND_NUMBER_OF_INVESTORS_ORG | If the business received funding, the last known number of investors | 2 |
LAST_FUNDING_AMOUNT_ORG | If the organization received funding, the last funding amount | 15400000 |
LAST_FUNDING_DATE_ORG | If the organization received funding, the last funding date | 2022-09-22 |
LAST_FUNDING_TYPE_ORG | If the business received funding, the last funding type | Post-IPO Equity |
LEAD_INVESTORS_ORG | If the business received funding, the last set of lead investors | Exact Sciences |
TOTAL_FUNDING_AMOUNT_ORG | If the organization received funding, the total amount of funding to date | 175500000 |
MONTHLY_GOOGLE_ADSPEND_ORG | Estimated monthly Google Ads budget | 1370 |
Growth indicators
| Field | Description | Example |
|---|
EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG | Percentage of latest month’s employee growth rate as a percentage. | 4 |
MONTHLY_ORGANIC_TRAFFIC_ORG | Estimated monthly search result clicks | 4943 |
MONTHLY_PAID_TRAFFIC_ORG | Estimated monthly pay per click number | 372 |
TOTAL_MONTHLY_TRAFFIC_ORG | Estimated monthly web traffic | 5315 |
Mobile App Store Data
We pull this data from the Google Play and Apple App Store several times per year.
| Field | Description | Example |
|---|
TOTAL_REVIEWS_ORG | Total reviews of the two top applications from Google and Apple. | 1373 |
LAST_APPSTORE_UPDATE_ORG | The date of the last update to this particular application | 2021-12-24 |
LAST_PLAYSTORE_UPDATE_ORG | The last date of an update to the most frequently downloaded application | 2023-11-09 |
APPSTORE_UPDATE_COUNT_ORG | The number of updates to this top application | 8 |
APPSTORE_APP_CATEGORY_ORG | The applestore classification of this application | Sports |
TOP_APPLEAPPSTORE_URL_ORG | The latest apple store app for the organization. Where there are multiple latest apps, the ‘top’ app is the one with the most updates and the highest rating | https://apps.apple.com/ng/app/id1541201440 |
TOP_APPSTORE_RATING_ORG | The rating of the top apple store app for the organization | 4.5 |
TOP_APPSTORE_REVIEW_COUNT_ORG | The total reviews for the organizations’ top apple store application | 35 |
PLAYSTORE_APP_CATEGORY_ORG | The playstore classification of this application | Finance |
PLAYSTORE_DOWNLOAD_COUNT_ORG | The total number of downloads for this application | 272740 |
TOP_PLAYSTORE_URL_ORG | The most frequently downloaded playstore app for the organization | https://play.google.com/store/apps/details?id=com.todo1.davivienda.mobileapp.hn&hl=es_419 |
TOP_PLAYSTORE_RATING_ORG | The latest rating of the playstore app with the most downloads | 3.6 |
TOP_PLAYSTORE_REVIEW_COUNT_ORG | The total of reviews for the most highly downloaded application | 1291 |
Joining this table
Join INSIGHTS_LATEST to ORG_LATEST on RBID_ORG = RBID to attach firmographics. To get insights for contacts, join PER_LATEST to INSIGHTS_LATEST on per.RBID_ORG = insights.RBID_ORG.
Insights + companies (signals with firmographics)
SELECT
co.COMPANY_NAME,
co.DOMAIN,
co.INDUSTRY_LINKEDIN,
co.EMPLOYEE_COUNT_MAX,
i.CRM_TECH_ORG,
i.SALES_ROLE_COUNT_ORG,
i.HAS_CIO_ORG,
i.EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG,
i.LAST_FUNDING_AMOUNT_ORG
FROM RELEASES.RELEASE.INSIGHTS_LATEST i
JOIN RELEASES.RELEASE.ORG_LATEST co ON i.RBID_ORG = co.RBID
WHERE co.EMPLOYEE_COUNT_MAX BETWEEN 50 AND 500;
Insights + contacts + companies (company-level insights with contact context)
SELECT
c.FIRST_NAME,
c.LAST_NAME,
c.EMAIL_ADDRESS,
c.JOB_TITLE,
co.COMPANY_NAME,
co.DOMAIN,
i.CRM_TECH_ORG,
i.SALES_ROLE_COUNT_ORG
FROM RELEASES.RELEASE.PER_LATEST c
JOIN RELEASES.RELEASE.ORG_LATEST co ON c.RBID_ORG = co.RBID
JOIN RELEASES.RELEASE.INSIGHTS_LATEST i ON c.RBID_ORG = i.RBID_ORG
ORDER BY i.LAST_FUNDING_DATE_ORG DESC;
How to calculate fill rates
Use this pattern to see how many records have each insight type and how complete key fields are.
SELECT
COUNT(*) AS record_count,
ROUND(COUNT(CRM_TECH_ORG) * 100.0 / COUNT(*), 1) AS tech_fill_pct,
ROUND(COUNT(SALES_ROLE_COUNT_ORG) * 100.0 / COUNT(*), 1) AS hiring_fill_pct,
ROUND(COUNT(LAST_FUNDING_AMOUNT_ORG) * 100.0 / COUNT(*), 1) AS funding_fill_pct,
ROUND(COUNT(EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG) * 100.0 / COUNT(*), 1) AS growth_fill_pct
FROM RELEASES.RELEASE.INSIGHTS_LATEST;
Sample queries
Find companies showing growth signals (hiring + headcount growth)
What you’re finding: Companies that are growing headcount and actively hiring — strong candidates for sales or hiring tools.
Why these fields: EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG and *_OPEN_ROLES_COUNT_ORG fields are trend indicators that show hiring patterns. We join to companies to filter by size (EMPLOYEE_COUNT_MAX) and to get COMPANY_NAME and DOMAIN. LAST_FUNDING_AMOUNT_ORG and LAST_FUNDING_TYPE_ORG add optional context for prioritization.
Logic: Require meaningful headcount growth and hiring trends, and limit to a mid-market size band. Order by growth rate so the hottest accounts are first. Note that open role counts reflect trends with a 2-3 month lag, not real-time job openings.
SELECT
co.COMPANY_NAME,
co.DOMAIN,
co.EMPLOYEE_COUNT_MAX,
i.EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG,
i.SALES_OPEN_ROLES_COUNT_ORG,
i.LAST_FUNDING_AMOUNT_ORG,
i.LAST_FUNDING_TYPE_ORG
FROM RELEASES.RELEASE.INSIGHTS_LATEST i
JOIN RELEASES.RELEASE.ORG_LATEST co ON i.RBID_ORG = co.RBID
WHERE i.EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG > 20
AND i.SALES_OPEN_ROLES_COUNT_ORG > 0
AND co.EMPLOYEE_COUNT_MAX BETWEEN 50 AND 500
ORDER BY i.EMPLOYEE_ON_LINKEDIN_GROWTH_RATE_ORG DESC;
Companies that recently adopted a specific technology
What you’re finding: Accounts that added a technology (e.g. Snowflake) in the last 6 months — good for competitive displacement or upsell plays.
Why these fields: technologies_detected (array) is filtered for the product name; tech_added_date restricts to recent adoption. We join to companies for name and domain. Array syntax in the WHERE clause depends on your warehouse (see call-out above).
Logic: Filter to technographic insights, require the technology to be in the detected list, and limit to the last 6 months. Order by tech_added_date descending so the most recent adopters appear first.
SELECT
co.COMPANY_NAME,
co.DOMAIN,
i.CRM_TECH_ORG,
i.DEVELOPMENT_TECH_ORG,
i.LAST_FUNDING_DATE_ORG
FROM RELEASES.RELEASE.INSIGHTS_LATEST i
JOIN RELEASES.RELEASE.ORG_LATEST co ON i.RBID_ORG = co.RBID
WHERE i.DEVELOPMENT_TECH_ORG ilike '%mongodb%'
ORDER BY i.LAST_FUNDING_DATE_ORG DESC;
Array containment syntax varies: Snowflake often uses ARRAY_CONTAINS(value, array) or array IN (SELECT value FROM table). BigQuery uses value IN UNNEST(array). Check your warehouse docs and adjust the filter accordingly.
