Episode 04 / 5
Where to Find Lead Data
Where B2B lead data comes from, how to combine databases, LinkedIn, open sources and enrichment, and how to build a list you can trust.
What you'll learn
- The four layers of lead data, and why no single tool is best at all of them.
- How to compare B2B databases, and test their coverage with fifty companies you know.
- Using LinkedIn and Sales Navigator for research, and why scraping is off limits.
- Open sources and signals: job posts, funding news, registers, fairs and rankings.
- Enrichment and waterfall enrichment: higher find rates, but still verify.
- Building a list with a known source and date on every row, and the GDPR basics.
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The problem is the data
In episode two, you built your ICP. Industry, size and persona are clear. You download a list of two thousand people from a lead database. The first campaign goes out. Some of the emails bounce. Some go to people who left the company long ago. And the list includes companies that don't fit your ICP at all. The problem wasn't the ICP. The problem is the data. In this episode, we'll look at where lead data comes from, and build a usable list step by step.
The four layers of lead data
First, let's be clear: lead data isn't one piece of information. It has four layers. Company data, also called firmographic data: industry, headcount, country, website. Person data: name, title, seniority, LinkedIn profile. Contact data: work email and phone. And signals: is the company hiring, did it raise funding, which technology does it use? Each layer has a different best source. Don't expect one tool to be perfect at all four.
Databases and LinkedIn
The first source is B2B databases: platforms that let you filter millions of company and person records. Roughly, there are three groups. The first is broad platforms you can use on your own. Apollo is an example. You can start on a free plan, and revealing contact details costs credits. You can also filter for companies that are hiring, raised funding, or use a particular technology. The second is tools focused on contact details. Like Lusha: you reveal a work email or phone number for credits. The third is enterprise platforms: ZoomInfo, Cognism and the like. Pricing comes by quote. They highlight features like buying intent signals or phone verified mobile numbers. But no database covers every country with the same depth. Test it for the countries you sell to. Search for fifty companies you know well with a trial account. For how many do you find the right person and a current email? That number decides, not the advert. The second source is LinkedIn. Sales Navigator is LinkedIn's paid sales tool: it filters companies and people by industry, size, title and seniority, and alerts you when someone changes jobs. But there's a clear limit here. LinkedIn's User Agreement explicitly bans scraping profiles and data with software, bots or browser extensions. Copying and distributing what you find without permission is banned too. Use LinkedIn for research: to find the right person and understand the context. Not as a bulk data source.
Open sources and signals
The third group is often free: open sources. The company's website: what do they sell, and who to? This is where you see ICP fit most clearly. Job postings: if they're hiring three sales reps, the sales team is growing. News and funding announcements. For startups, funding databases and local tech news sites help. Official company registers: many countries publish incorporations, capital increases and address changes, like Companies House in the UK. For listed companies, stock exchange filings. And association member lists, industry rankings and trade fair exhibitor lists. These give you companies, not people. That's fine: a solid list always starts with companies. In episode two, the seventh ICP question was signals. Hiring signals come from job postings, funding signals from news and registers. Technology signals come from technology detection tools or database filters. If a new sales director arrived, you'll see it in LinkedIn alerts. An imaginary example: Northline Logistics. They posted three sales jobs, opened a new warehouse in Valencia, and a new head of sales just joined. This company doesn't just fit the ICP. It's ready to talk right now. A signal answers the question: why now?
Enrichment and waterfall
Say the names are ready, but half the emails are missing. This is where enrichment comes in: filling a row's empty columns from other sources. You give a name and a company; you get back an email, a phone number or a headcount. Waterfall enrichment is the cascading version. If the first provider can't find it, the row goes to the second, then the third. Tools like Clay work this way; databases like Apollo offer a waterfall built in. The result: more rows get filled. But a waterfall raises the find rate; it doesn't guarantee accuracy. Every email you find still needs to be verified. We'll cover that in episode five.
A ready made list, or your own?
One day, this offer arrives: fifty thousand executives in Europe, a ready made list. If you can't get answers to four questions, don't buy it. Where was the data collected? When? On what legal basis? And is it built for your ICP, or is it the same file for everyone? Under the GDPR, buying data from someone else doesn't hand you their legal basis. You're responsible for being able to show why you may use it for marketing. Building your own list is slower. But you know where every row came from. In outbound, that knowledge is worth more than speed. And every piece of data has an expiry date. People change jobs, companies move, email addresses get closed. So write on every row when and where you got the data. Check the list right before the campaign.
From ICP to a ready list
Now let's bring it all together in an imaginary example. ICP: logistics companies in Spain, with fifty to five hundred employees, growing their sales team. Persona: sales director. One: the company list. We collect it from a database, an association member list and trade fair exhibitors. We remove duplicates by domain, meaning the website address, and take out the ones that don't fit by hand. Two: a signal column for each company. Three: in Sales Navigator, we find one or two people who match the persona at each company, and note them by hand. Four: work email and, if available, phone, from the database or a waterfall. For anyone we can't find, the LinkedIn channel remains. Five: the ready list. Each row starts with company details: company, domain, industry, headcount and signal. Then person details: name, title, LinkedIn, email, email status and phone. And at the end: source and date.
GDPR in practice
Finally, the law. This isn't legal advice; talk to a lawyer about your own situation. A name, a title and a work email are personal data too. In the European Union, the GDPR applies, and the UK has its own version of it. The GDPR accepts legitimate interest for direct marketing, but it's not an automatic permission. You weigh your interest against the person's rights. If you didn't get the data from them, you tell them at the latest in your first message. If they object, you stop. Information being public on the internet doesn't mean it can be used for any purpose. And remember the national email rules from episode three: some countries allow cold email to business addresses with an easy opt out, others ask for consent first. Check the rules for every country on your list. In practice, three rules: record the source of every row, say who you are and how to opt out in the message, and remove anyone who says no, permanently.
Which source to start with?
So which source should you start with? Look at which countries you sell to, your budget, and which layer is missing. Choose the tool for the gap, not for its advert. At TechSales, this is how we build lists too: we start from a persona definition, then build the list, clean it and score it. The rule is the same: a good list isn't bought, it's built. Try a small test this week. Pick fifty companies you know well, add a signal and the right person to each, and write the source and date on every row. That way you've measured your database's coverage in your market, and built your first list with known sources. You now have a list with known sources. But how many of its emails actually work? In the next episode, we'll cover email verification and list cleaning. We'll also see why bounced emails damage your sending account. You'll find the other episodes at techsales tr dot com, slash academy. See you next time.
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Next episode: 05 · Email Verification and List CleaningRead