Draup Account Intelligence

**Overview**
Draup Account Intelligence provides a holistic and dynamic view of global companies by aggregating critical business, financial, operational, and leadership data into one unified dataset. It empowers sales, business development, and market intelligence teams with detailed company profiles that span foundational information (headquarters, industry verticals, employee counts, R&D workforce size) through to deeper insights on financial performance, IT spend, and strategic priorities.
Beyond static company profiles, the dataset delivers dynamic intelligence — including leadership movement tracking, competitor landscapes, outsourcing relationships, and curated signal alerts that highlight key developments and market shifts. Supplemented with regulatory filings, earnings transcripts, and shareholder data, Draup Account Intelligence offers a comprehensive lens into target accounts, connecting granular operational signals with broader corporate strategy.

**Methodology & Quality**
Draup aggregates data from diverse proprietary and public sources, then standardizes it into consistent company, profile, and workload taxonomies. Our processes normalize financial structures, align technology stacks to vendor hierarchies, and tag usage intensity to reduce noise. Leadership movements, job postings, and company signals are refreshed frequently to ensure timeliness. Filings and transcripts are mapped systematically to company entities, creating a reliable and auditable data trail.

**Key Tables Include**
• Company Overview: firmographics, headquarters, employee count, R&D workforce
• Company Financials: overall, by business unit, and by region
• IT Spend & Strategic Priorities: category-wise spend, initiatives, mapped use cases
• Technology Stack: vendor–product adoption, workloads, usage intensity
• Company Locations: workforce distribution, site types, workloads
• Deals & Service Providers: outsourcing contracts, project scope, vendor relationships
• Leadership Movements: executive transitions across roles, functions, and geographies
• Job Postings (Company-Level): standardized job roles, skills, and hiring trends
• Company Signals & Competitors: curated events, partnerships, competitive landscapes
• Filings & Transcripts: SEC filings, international filings, earnings transcripts & attendees
• Shareholdings: ownership details, filing dates, shareholding percentages

**Use Cases:**
***Analyze the availability of talent for a given role in a specific location.***

Measure the talent pool size and distribution for target roles within chosen geographies to support workforce planning and sourcing.

SELECT
profile_info.company_name,
profile_info.business_function,
COUNT(DISTINCT profile_info.profile_id) AS talent_count
FROM profile_info
WHERE profile_info.job_role ILIKE '%Software Engineer%'
AND profile_info.location ILIKE '%San Francisco%'
GROUP BY profile_info.company_name, profile_info.business_function
ORDER BY talent_count DESC;

***Identify the most common certifications for a DevOps Engineer role in the USA.***

Identify widely held certifications among professionals in a role and geography to align upskilling, hiring, and credentialing strategies.

SELECT
profile_certifications.certification_name,
COUNT(DISTINCT profile_certifications.profile_id) AS holders_count
FROM profile_certifications
JOIN profile_info
ON profile_certifications.profile_id = profile_info.profile_id
WHERE profile_info.job_role = 'Devops Engineer'
AND profile_info.location ILIKE 'United States'
AND profile_certifications.certification_name IS NOT NULL
GROUP BY profile_certifications.certification_name
ORDER BY holders_count DESC
LIMIT 10;

***Which competitors is Snowflake hiring talent from and who are they losing their talent to?***

Draup’s talent movement capabilities reveal a company's inbound and outbound talent flows across roles, functions, and geographies. Identify key feeder and destination companies to inform talent strategy, poaching risk, or market positioning.

SELECT
company_name AS competitor_company,
executive_name,
from_title AS previous_title,
to_title AS current_title,
movement_date
FROM
company_leadership_movement
WHERE
affiliated_entity_name = 'Snowflake Inc.'
AND company_name IS NOT NULL
AND movement_date >= DATE_SUB(CURRENT_TIMESTAMP(), INTERVAL '1' YEAR)
ORDER BY
movement_date DESC;

***List VP-and-above R&D leaders who left Pfizer in the past 12 months and their new employers***

Track high-level executive movements from any company using Draup’s leadership change data. Filter by function, designation, and time period to uncover exits and transitions; useful for competitive intelligence, recruitment targeting, or succession mapping.

SELECT
executive_name,
from_title AS title_at_pfizer,
company_name AS new_employer,
to_title AS new_title,
movement_date
FROM
company_leadership_movement
WHERE
affiliated_entity_name = 'Pfizer'
AND business_function = 'R&D'
AND (from_title LIKE '%VP%' OR from_title LIKE '%President%' OR from_title LIKE '%Chief%')
AND movement_date >= current_timestamp - INTERVAL '1' YEAR
ORDER BY
movement_date DESC;

***See how does your peers’ workforce split by function or location compare with you.***

Benchmark workforce distribution across functions and geographies against competitors to identify over- or under-invested areas in your organization.

WITH workforce_distribution AS (
SELECT
profile_info.company_name,
profile_info.business_function,
profile_info.location,
COUNT(*) AS headcount
FROM profile_info
WHERE profile_info.business_function IS NOT NULL
AND profile_info.location IS NOT NULL
GROUP BY profile_info.company_name, profile_info.business_function, profile_info.location
),
total_workforce AS (
SELECT
workforce_distribution.company_name,
SUM(workforce_distribution.headcount) AS total_headcount
FROM workforce_distribution
GROUP BY workforce_distribution.company_name
),
workforce_percentage AS (
SELECT
workforce_distribution.company_name,
workforce_distribution.business_function,
workforce_distribution.location,
workforce_distribution.headcount,
ROUND((workforce_distribution.headcount * 100.0) / total_workforce.total_headcount, 2) AS percentage_split
FROM workforce_distribution
JOIN total_workforce
ON workforce_distribution.company_name = total_workforce.company_name
)
SELECT
CASE
WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'
ELSE 'Peer Companies'
END AS company_group,
workforce_percentage.business_function,
workforce_percentage.location,
SUM(workforce_percentage.headcount) AS total_headcount,
ROUND(AVG(workforce_percentage.percentage_split), 2) AS avg_percentage_split
FROM workforce_percentage
GROUP BY
CASE
WHEN workforce_percentage.company_name = 'Microsoft' THEN 'Microsoft'
ELSE 'Peer Companies'
END,
workforce_percentage.business_function,
workforce_percentage.location
ORDER BY
workforce_percentage.business_function,
workforce_percentage.location;

***Analyze the historical talent movement and attrition patterns in a location***

Analyze career transitions, exits, and movements over time within a geography to anticipate attrition risks and strengthen retention strategies.

WITH profile_experience_expanded AS (
SELECT
profile_id,
company_name,
UNNEST(msa) AS location,
start_date,
end_date
FROM profile_experiences
WHERE start_date IS NOT NULL
),
location_joins AS (
SELECT
location,
EXTRACT(YEAR FROM start_date) AS start_year,
COUNT(DISTINCT profile_id) AS joins
FROM profile_experience_expanded
GROUP BY location, EXTRACT(YEAR FROM start_date)
),
location_exits AS (
SELECT
location,
EXTRACT(YEAR FROM end_date) AS end_year,
COUNT(DISTINCT profile_id) AS exits
FROM profile_experience_expanded
WHERE end_date IS NOT NULL
GROUP BY location, EXTRACT(YEAR FROM end_date)
)
SELECT
COALESCE(location_joins.location, location_exits.location) AS location,
COALESCE(location_joins.start_year, location_exits.end_year) AS year,
COALESCE(location_joins.joins, 0) AS total_joins,
COALESCE(location_exits.exits, 0) AS total_exits,
COALESCE(location_exits.exits, 0) - COALESCE(location_joins.joins, 0) AS net_attrition
FROM location_joins
FULL OUTER JOIN location_exits
ON location_joins.location = location_exits.location
AND location_joins.start_year = location_exits.end_year
ORDER BY location, year;