CRED Data FAQ

Last updated: January 6, 2026

DATA SOURCING & COVERAGE

Where does CRED get its data from?

We ingest data from 150+ sources spanning professional profiles, company registries, digital signals, web activity, commercial datasets, and proprietary scrapers. All inputs run through our normalization, matching, and quality pipelines before reaching the platform.

How much does CRED actually cover?

CRED maintains one of the largest commercial datasets in the industry, with 45 million companies and over 1 billion people globally. High-priority companies and senior decision makers receive faster refresh cycles and deeper enrichment to ensure the data you rely on is always current and commercially relevant.

Do you track both companies and decision makers?

Yes.

  • Companies: revenue, employees, digital spend, funding, web traffic, product footprint, deal activity.

  • People: job roles, experience, skills, seniority, education, location, and interest signals.

Do you cover sponsorship and commercial deal data?

Yes. We track historical deal activity, news-based signals, event participation, and partnership updates using a mix of ingestion feeds, web extraction, and LLM-based deal harvesting.

DATA ACCURACY & VALIDATION

How accurate is the data?

Accuracy is reinforced through:

  • cross-source validation across 150+ inputs

  • real-value-only outlier detection

  • imputation models that replace unreliable estimates

  • human-in-the-loop QA for top-tier entities

  • field-level recency and quality scoring

We explicitly avoid passing unverified or stale data into customer workflows.

Do you use predictive or imputed data?

Only where real data is missing or unreliable.
We apply:

  • Imputation models: generate realistic values using verified features

  • Similarity models: benchmark a company against its closest peers

Both outperform simple heuristics and stabilize insights.

How do you detect inaccurate records?

We flag anomalies through:

  • deviation from historical trends

  • deviation from industry benchmarks

  • outliers detected purely from real data

  • inconsistencies across sources

Flagged entries route to human review or model-driven correction.

DATA RECENCY & REFRESH CYCLE

How often does the data on companies and decision makers get refreshed?

We receive new data daily, but it must pass through ingestion, matching, pipelines, and model execution. Today, updates are delivered weekly, and we’re moving to twice-weekly refresh cycles for tighter recency on senior roles and company attributes.

How fresh is contact data?

We ensure that the 11M decision makers from ~5M of the worlds largest companies are no older than a month old.

How do you decide which data gets refreshed first?

We prioritize entities with the highest commercial value and platform interaction. Refresh priority is based on:

  • sponsorship activity

  • revenue scale

  • digital spend

  • workforce size

  • web traffic and app usage

  • recent customer interactions in CRED

These entities receive faster ingestion, more enrichment passes, and more rapid delivery.

Can customers request refreshes on demand?

Yes. You can trigger a refresh for specific profiles or lists. Heavy usage may require credits due to scraping costs.

DATA PRIORITIZATION

What data gets prioritized in the platform?

We classify a company as high-priority if it hits any one of several quantitative thresholds across business scale and digital footprint. Examples include:

  • strong deal or funding activity

  • high annual revenue

  • 500+ employees

  • major digital marketing spend

  • significant web traffic or app usage

  • sustained engagement inside CRED

High-priority companies — and their decision-makers — get more complete coverage, deeper enrichment, and faster refresh cycles. Lower-signal companies are deprioritized unless they meet other high-value criteria.

MATCHING, CLEANING & NORMALIZATION

How does CRED match entities across 150+ sources?

We use a multi-stage pipeline:

  1. Identifier matching across emails, domains, names, and IDs

  2. Candidate generation using similarity search

  3. Feature scoring across names, roles, industries, locations

  4. ML-based match prediction

  5. Human review for ambiguous cases

This creates a single clean entity record from many inputs.

How do you remove duplicates?

All incoming entities are scored for similarity and merged into a canonical record. Ambiguous cases are routed to human reviewers for validation.

Do you normalize data from all sources?

Yes. All data is standardized into CRED’s schema — titles, industries, locations, currencies, metrics, and hierarchies are aligned to ensure consistency.

SAMPLE SIZES & INSIGHTS

How do you determine sample size for insights and reports?

We use tiered sampling:

  • full database for large-scale benchmarks

  • high-priority companies for industry-level insights

  • user-defined cohorts for scoring and ICP analysis
    We require minimum sample thresholds before surfacing insights to avoid misleading outputs.

Are CRED insights statistically reliable?

Yes. We exclude low-signal entities, apply outlier filters, and use verified-only baselines for any statistical output.

SCRAPING & ENRICHMENT

Do you scrape professional profiles or social data?

Yes — selectively and legally.
We scrape publicly visible information via authenticated sessions where permitted. Fields include work experience, titles, skills, followers, engagement, brand pages, and account metadata.

When do you scrape instead of relying on feeds?

When:

  • an entity hasn’t refreshed recently

  • real data conflicts across sources

  • a profile is commercially important

  • a customer requests a refresh

  • an update is time-sensitive (e.g., role changes)

Do you support enrichment for social platforms?

Yes. We support enrichment from social networks to capture engagement patterns, brand signals, and creator activity.

CUSTOMER IMPACT & USE CASES

Can I trust CRED’s data for sales intelligence and targeting?

Yes. Our validation stack, refresh cadence, and prioritization model are designed to surface accurate, recent, commercially meaningful data for sponsorship, partnership, and deal workflows.

Does CRED replace multiple data vendors?

In most cases, yes.
We consolidate a broad ecosystem of 150+ sources into a single cleaned, normalized dataset.

Can I export or sync data to my CRM?

Yes. You can export lists, apply scoring, and sync enriched data directly into your CRM.

SECURITY & COMPLIANCE

Is data secure?

Yes. All data is hosted in Google Cloud, encrypted at rest and in transit, with role-based access controls and full audit logging.

Is your scraping compliant?

Yes. We only scrape data that is publicly visible to logged-in users, within allowed usage contexts. Sensitive or private data is never collected.

TROUBLESHOOTING & SUPPORT

Why does a profile look outdated?

It usually means the most recent update hasn’t propagated yet through the pipelines. High-priority entities refresh more quickly, but you can request a manual update anytime.

How do I report incorrect data?

Submit the entity URL via support. We’ll run it through cross-source validation, enrichment, and targeted refresh pipelines.

How fast do fixes go live?

Minor fixes: 24–48 hours
Large resync or reprocessing: 5–10 days