B2B contact data for outbound is the database of people and companies you search to build a prospect list: who works where, in what role, and how to reach them. Good data is broad, fresh, filterable and honest about what it does not know. For agencies, it is the layer that turns a written ideal client profile into real names, and the place where most stale-list problems start.
Nerdy Joe is owned by Agency Growth Partners (AGP), so this is our own system and we are not neutral about it. This article covers the data layer inside Artemis, the system AGP runs, and where it sits in the AGP growth machine. It also gives you criteria to judge any data source, including the one you use today.
Where data sits in the outbound sequence
The data layer comes right after strategy. The previous stage, covered in our guide to the ideal client profile for agency outbound, defines who to target. Data is where that definition meets reality.
Artemis runs a five-step loop: Discover, Enrich, Personalize, Engage, Book. The data layer covers the first two. Discover finds the people and companies that fit. Enrich fills in the full record and attaches company and intent context before anyone writes a line of copy.
Get this layer wrong and every later stage inherits the problem. Bad titles produce irrelevant emails, stale addresses produce bounces, and missing context produces generic copy.
What is inside the Artemis data layer
Artemis unifies contact and company data from many sources into one database. Here is what AGP publishes about it.
| Element | What AGP states |
|---|---|
| People profiles | 693M |
| Companies | 67M |
| People with an email address | 366M |
| People with a phone number | 166M |
| Sources | 20+ sources, unified into one schema |
| Freshness | Coverage updated daily |
| People filters | 50 ICP filters |
| Company filters | 34 ICP filters |
| Search | Natural-language search returning a ranked list with fit and intent scoring |
| Enrichment | A LinkedIn URL or email returns a full record |
A few of these numbers are worth reading carefully. Not every person profile has an email or a phone number, and the figures above show that plainly. That honesty matters more than the headline count, because a list built from records that claim to be complete but are not will cost you in bounces.
Coverage: why "20+ sources, one schema" matters
No single dataset covers every market well. Some sources are stronger on large companies, others on small businesses, others on specific regions or contact types.
Unifying many sources into one schema means a search runs across all of them at once, and each person or company ends up as one record instead of five conflicting ones. It also means filters behave the same way regardless of which source supplied a field.
Inside Artemis, each record names the dataset that supplied it. That matters when something looks wrong: you can see where the data came from instead of treating the database as a black box.
Filters: turning an ICP into a search
Artemis offers 50 filters for people and 34 for companies. The ones agencies use most map directly to the ICP worksheet:
- Title and seniority. Separates the person who signs from the person who does the work.
- Industry. Narrows to the sub-industry your offer actually fits.
- Headcount and revenue. Keeps the list inside the size band where you win.
- Funding. Flags companies with fresh capital to spend.
- Technology. Finds companies running a tool your offer depends on or replaces.
- Location. Matches the markets and time zones you plan to serve.
- Email status and email type. Limits the list to contacts with the kind of address you are willing to send to.
- Reachability. Shows whether a contact can actually be reached by email or phone.
The last three filters are the ones most DIY builders ignore. They are also the ones that protect deliverability before a list is even exported.
Natural-language search with fit and intent scoring
Filters work well when you know exactly what you want. Artemis also supports describing the ideal customer in plain language, then returns a ranked list with fit and intent scoring.
The practical benefit is ordering. Instead of a flat list of thousands of contacts in no particular order, the best-fit companies come first. AGP's page shows example fit scores of 96%, 93%, 91% and 88%; those are examples from AGP's site, not a benchmark for any specific search.
Fit and intent answer different questions:
| Score | Question it answers | What you do with it |
|---|---|---|
| Fit | Does this company and person match the ICP? | Decide whether they belong on the list at all |
| Intent | Is there a sign they may need help now? | Decide who goes first and what the email opens with |
A high-fit, low-intent contact is worth reaching, but maybe later or with a softer offer. A high-fit, high-intent contact is where the first sends should go.
Enrichment: what a full record contains
Enrichment starts from something you already have. In Artemis, a LinkedIn URL or an email returns a full record with:
- Current role
- Firmographics (company size, industry, revenue and similar details)
- Skills
- Education
- Location
- Verified work emails
- Phone numbers
Agencies use enrichment in three common situations. First, when a client hands over a list of target accounts or event attendees that has names but no usable contact details. Second, when a prospect replies from a personal address and you want the full work context. Third, when a record looks thin and you want to confirm the person is still in the role before sending.
Intent context: why every contact needs it
Inside Artemis, every contact arrives with company and intent context attached. That context is what makes the next stage, personalization, possible.
A name, title and email are enough to send an email. They are not enough to send a relevant one. The difference is knowing that the company just raised money, is hiring a sales team, opened a new plant or brought in a new marketing leader.
Those signals are what AGP's copy is written from. Our guide to signal-based cold email personalization shows how a signal becomes a first line that does not read like a template.
Good data vs stale lists
Here is the comparison that matters most when you are judging any data source, whether it is Artemis, another provider or a spreadsheet a client sent you.
| Trait | Good outbound data | Stale list |
|---|---|---|
| Freshness | Coverage refreshed continuously, emails checked close to send | Exported months ago, never rechecked |
| Email status | Every email carries a deliverability status | Emails with no status, or "valid" with no date |
| Unknown fields | Left empty when the source does not know | Filled with guessed patterns like first.last@ |
| Source | Each record names where it came from | No way to tell where a row came from |
| Context | Company and intent context attached | Name, title and email only |
| Filtering | Built from ICP filters for this campaign | Built for someone else's campaign, or for no campaign |
| Exclusivity | Pulled fresh for your segment | Sold to many buyers who all email the same people |
Stale lists fail in predictable ways. People have changed jobs, so emails bounce or reach the wrong person. Companies have grown or shrunk out of your size band. Guessed addresses look complete in a spreadsheet and fail on send.
Mailbox providers notice. Google's sender guidelines tell senders to keep their user-reported spam rate below 0.1% and to prevent it from ever reaching 0.3%. Irrelevant emails to the wrong people are a fast way to earn spam reports, and bounces from dead addresses tell providers you are not maintaining your list. Clean data is the first line of defense; the second is the setup covered in our guide to agency cold email sending infrastructure.
Questions to ask any data provider
Use these whether you are comparing vendors, auditing your current tool or evaluating an agency that will source data for you. Our roundup of the best B2B data providers applies the same lens to the main self-serve options.
- How many sources feed the database, and are they merged into one record per person?
- How often is coverage refreshed?
- Does every email carry a deliverability status, and when was it last checked?
- Can I see which source supplied each record?
- Are unknown fields left empty, or filled with guesses?
- Can I filter by email status, email type and reachability before exporting?
- Can I check a contact before revealing its details, so I only pay for or pull what I need?
- Does each contact come with company and intent context?
- Can I enrich from an email or LinkedIn URL?
- Is the list built fresh for my segment, or pulled from a shared static file?
If a provider cannot answer questions 3, 4 and 5 clearly, treat their data as unverified until you prove otherwise. Running it through a separate checker is a sensible minimum; our guide to the best email verification tools covers the main options.
How the data layer works in practice
At AGP, a dedicated growth engineer builds, runs and tunes each client's engine, including the lists. Whether a growth engineer runs it or you build it yourself, the sequence looks like this:
- Translate the ICP into filters. Each line of the strategy document becomes a people or company filter.
- Run search and review the ranked results. Fit and intent scoring put the best candidates at the top.
- Spot-check samples. Review titles, company sizes and industries against the ICP before anything is exported. Do this with any provider.
- Enrich the shortlist. Records get full role, firmographic and contact details with company and intent context attached.
- Hand off to verification. Contacts are enriched and verified before anything sends.
That last step is its own stage. Our guide to verified lead lists for agencies covers how search results become a send-ready list, including deliverability status, check-before-reveal and list hygiene. For how all the stages connect from first email to signed contract, see how AGP's growth machine works end to end.
Who this is not for
A large unified database with deep filters is more than some agencies need.
- Agencies with a short, known target list. If you sell to 40 named accounts, careful manual research may beat any database.
- Agencies selling mainly to consumers. This data layer is built for B2B buyers.
- Teams without a written ICP. More data makes a vague definition worse, not better. Start with strategy.
- Anyone planning to export everything and blast it. No data source protects a sender who ignores filters, verification and volume limits.
What to do next
If you are running outbound today, pull 50 random rows from your current list and check them by hand: is the person still in the role, does the company still fit, and does the email have a known status? That one hour tells you more about your data than any vendor page.
Then compare what you find against the good-versus-stale table above. If more than a few rows fail, the problem is the data layer, and it will keep showing up as bounces and silence until you fix it. The full AGP growth machine guide shows how the data layer feeds every stage after it.
Want this built for your agency?
If you want to see what your ICP looks like as a live, scored search before you commit to anything, book a call with David. He will walk through your targeting and tell you honestly whether the data side is your bottleneck.
Sources
- Agency Growth Partners, Artemis (database coverage, filters, natural-language search, enrichment, accuracy practices): https://agencygrowth.partners/artemis
- Agency Growth Partners, home: https://agencygrowth.partners/
- Agency Growth Partners, Services: https://agencygrowth.partners/services
- Google, Email sender guidelines FAQ (spam rate thresholds): https://support.google.com/mail/answer/14229414?hl=en
- Booking page, David Tile: https://agency-growth-partners.cal.com/david/nerdy-joe
Frequently asked questions
What is B2B contact data?
B2B contact data is information about business buyers and their companies: names, titles, work emails, phone numbers, company size, industry, revenue, technology and location. Outbound teams use it to find and reach the people who fit their ideal client profile.
Where does B2B contact data come from?
Most providers combine many sources, such as public company information, professional profiles, email and phone verification and licensed datasets, then merge them into one record per person and company. Ask any provider which sources a record came from and how often it is refreshed.
How often should B2B contact data be updated?
As often as possible, because people change jobs and companies change size, funding and tools. Look for providers that refresh coverage continuously and re-verify emails close to the time you send, not once a year.
What is the difference between contact data and enrichment?
Contact data is the database you search to find people. Enrichment starts from something you already have, such as an email address or a LinkedIn URL, and fills in the rest of the record: role, company details, verified emails and phone numbers.
Is buying a B2B email list a good idea?
Static purchased lists are usually a poor choice for cold outreach because they age quickly, often lack verification status and are shared with other senders. Building a fresh, filtered and verified list from a maintained database is safer for deliverability.
What is intent data in B2B outbound?
Intent data is any signal that a company may be in market now, such as hiring, funding, leadership changes or expansion. It helps rank a list so the most likely buyers are contacted first and gives the email a timely reason to exist.