Lead Intelligence: Data Types, Legal Basics and Cost

Lead intelligence is the checked information about a company and a person that tells you whether to contact them, who exactly, and why now. It draws on six data types. Done well, it ends in a clean, verified list of decision-makers at companies that fit.

In my campaigns, that list decides more of the result than any tool. Better copy does not fix a bad list. A pricier database will not either.

A bad list also hurts your domain. Send to the wrong people and more of them hit the spam button. Google's Gmail sender guidelines set a ceiling in Postmaster Tools: keep the spam rate under 0.10% and never let it reach 0.30%. Google also says a high spam rate leads to more of your mail being classified as spam.

One disclosure: my team and I book B2B meetings mostly through cold email and LinkedIn. Clients pay their tool vendors directly, and every price below carries its source and date.

How is lead intelligence different from sales intelligence, enrichment and lead scoring?

Vendors blur these terms. Each one answers its own question.

Prospect intelligence is the same idea, stressing the time before first contact. Intent data is one narrow input.

Whether a small team should pay for it at all is answered in what intent data costs a small team.

Which six data types sit behind a good list?

1. Firmographic: industry, size, location, revenue band.
2. Contact: name, title, verified email.
3. Technographic: software the company already runs.
4. Intent: company-level topic research.
5. Trigger events: funding, hiring, a job change.
6. Engagement: replies, clicks and visits on your own channels.

One contact per company is rarely enough. Forrester's 2026 State of Business Buying research puts a typical purchase at 13 internal stakeholders and 9 external influencers (Forrester). Map several people per account.

Where does lead data come from, and is using it legal?

Usually yes, with duties that depend on who you contact. The data comes from public records, vendor databases, website tracking, partners and your own CRM.

In the UK, emailing a limited company needs no PECR consent, but a named work address is personal data, so UK GDPR applies (ICO B2B guidance). Sole traders count as individuals.

For them, PECR regulation 22 requires prior consent or a soft opt-in. A soft opt-in applies only if you collected the details during a sale or negotiation, you promote similar products, and every message offers a simple, free opt-out.

The same ICO guidance warns list buyers on three points. Public data is not agreement to marketing. A bought list needs a lawful basis. The right to object is absolute. That page is marked as under review after the 2025 Data (Use and Access) Act, so recheck it before you rely on it.

In the EU, GDPR Article 14 makes you tell people where you got their data and whether it was public. You must also name the legitimate interest you rely on, meaning the business reason you weighed against their rights. Do both within one month, and no later than your first message.

In practice, treat company-level data such as firmographics, technographics and intent as lower risk until you attach a named person. From that point the full personal data duties apply. Engagement data needs a suppression list that you check before every send.

This is not legal advice. For Germany, Austria and the Czech Republic, my team skips cold email unless a compliant basis exists, and runs LinkedIn outreach instead.

What does lead intelligence software cost in 2026?

List prices and credit rules, from each vendor's pricing page. Quote-only vendors publish no list price, so get a written quote before you budget.

Read the credit column before the price column, and ask each vendor what counts as a spent credit before a trial.

For visitor identification, a free tier is a cheap way to see whether real companies visit your site at all. The guide to website visitor identification explains what these tools can and cannot name.

Which tools should a small team buy, defer or skip?

Treat these cut-offs as a starting point; no study backs them. The database column says buy in every row because I trust a list from an established database such as Apollo, UpLead, ZoomInfo or RocketReach over a scraped one. In my experience, cheap freelancer lists bring low open rates and high bounces.

I add intent data only when the market is big. With roughly 2,000 target companies and signals on about 10, I skip it and write one offer that fits the whole market.

Build the list in this order:

1. Start from past and current clients, then find lookalikes.
2. Find out who actually decided on your past deals before you search a single title.
3. Search the titles that niche really uses.
4. Remove generic titles like Assistant, Manager and Associate.

VOLUME matters as much as fit. Even a perfect-fit prospect can be busy, unready or simply uninterested this month, so I never build a pipeline plan on 50-100 of them. My planning figure is simple: 30-35 booked meetings a month needs at least 10,000 people contacted a month. Lists age too, so check how fast B2B contact data goes stale before you pick a refresh plan.

How do you test a data provider in 30 days?

1. Week 1: pull the same sample of past-client lookalikes from two providers, built in the order above.
2. Week 2: verify every address with NeverBounce or DeBounce. I hold every list under 0.5% bounce before anything sends.
3. Week 3: hand-check a subset for current employer, real title and company fit.
4. Week 4: divide each provider's spend by its contacts that passed weeks 2 and 3, and keep the one with the lower cost per usable contact. Fix the list first. Then scale the volume.

Thinking of paying for more data? Bring your target list definition to a call and I will say what to buy first and what to leave out.