The Best Way to Build a High-Converting B2B Lead List: Step-by-Step Guide

To build a targeted B2B lead list, define the companies you want to sell to, check that they fit, find the relevant people at those companies, then enrich and deduplicate the qualified contacts before export.

I recommend starting with company fit because a correct job title at the wrong company still gives you an irrelevant lead. In this guide, you will build two collections: qualified accounts and prospects ready for review.

You can start from a Sales Navigator search, a company CSV, or another source with company names and website domains. The workflow below uses Sales Navigator, then shows how to use website evidence to narrow the list before paying to find contact details.

For a segment defined by product launches, the Product Hunt guide covers API access and an authorized collection workflow. A launch listing supplies research leads; verify the company and contact roles before treating it as a qualified account.

What This Guide Will Cover

• The Fundamentals of Building a B2B Lead List
• The Three Main Ways to Create a Lead List
• A Step-by-Step Guide to Building Your List

🔍 Don’t Skip The Context

This article contains valuable context about building B2B lead lists, so we advise you to read the full content. However, if you're short on time, you can skip directly to our step-by-step guide by clicking here 👈🏽

The Fundamentals of Building a B2B Lead List

Before we dive into the "how," it's important to understand the basic concepts behind lead list creation. Think of this as the foundation. Getting these basics right makes the entire process smoother and more effective.

What Is a B2B Lead List?

A B2B lead list is a curated collection of data containing information about potential business customers. At its core, it includes companies that fit your Ideal Customer Profile (ICP) and the specific people within those companies who are the right contacts for your outreach.

Turn Your Target Description Into List Criteria

Before opening a search tool, write one sentence describing your target. For example: “UK payroll software companies with 11–50 employees, selling to small businesses; contact the founder or head of sales.”

Split it into three checks:

• Search filters: UK headquarters and 11–50 employees. These give you an initial account list.
• Website evidence: payroll software sold to small businesses. This requires checking what the company actually offers.
• Contact criteria: founder or head of sales currently working there. Check this after qualifying the company.

Keep Company, Website, Source URL, Employee Range, Qualification, and Qualification Reason in the account collection. Add Name, Job Title, LinkedIn URL, and the company domain to the prospect collection. Preserve source links so an uncertain result can be checked later.

Here are the essential "blocks" for each module:

Account Information (The Company)

1. Company Name
2. Website/Domain
3. Industry
4. Company Size (by employees)
5. Headquarters Location

Prospect Information (The Contact)

1. Full Name
2. Job Title
3. Verified Business Email
4. LinkedIn Profile URL
5. Seniority Level

Three Popular Ways to Build a B2B Lead List

When it comes to building a B2B lead list, many roads lead to Rome. Some are convenient highways that get you there fast but with less control, while others are slower, more intentional paths that give you a better outcome. Each has its pros and cons.

Creating a Lead List Using Account & Contact Filters (Easiest)

This is the most straightforward method. You simply use the built-in filters of a lead generation tool to find prospects.

The process is simple:

1. Choose a tool with a large contact database.
2. Navigate to the contact search function.
3. Apply filters like industry, company size, and job title to narrow down the results.
4. Export the list.

While this approach is fast, it often produces generic lists. Database records and source profiles can be outdated or broadly categorized. This means you might find prospects who seem right on the surface but don't truly fit your ICP.

Building a Lead List With Sales Navigator + Datablist (Recommended)

This method combines the strength of Sales Navigator's data with the advanced filtering and enrichment capabilities of Datablist. You start by building an account list in Sales Navigator, import the search results with Datablist, and then find the right contacts within those accounts.

This approach is recommended because it gives you three key advantages:

1. Filter twice at two different stages:
   1. You filter at the account level in Sales Navigator to find the right companies.
   2. You filter again at the contact level in Datablist to find the right people within those companies.
2. Have a better list management system: Creating a dedicated account list first allows for better organization, especially for Account-Based Marketing (ABM). You can create one master account list and then generate multiple, segmented lead lists from it.
3. Check data freshness: A source profile gives you evidence to review, but it may still be outdated. Confirm current company and job information before outreach.

💡 A Note on Building Lists

Datablist also allows you to build lead lists without any external tools. However, the best practice is to always start by creating a targeted account list from a source like Sales Navigator or Google Maps. In either case, filtering twice (once for accounts, once for contacts) is the key to quality.

Creating a Lead List With Sales Navigator + Browser Extension (Risky)

A browser extension is another way to export search results, but account access and automation restrictions matter. It involves using a Browser extension to scrape data directly from your Sales Navigator searches.

You only need two things:

1. A Sales Navigator subscription
2. A Sales Navigator scraper Browser extension

While it may seem cost-effective, this method puts your LinkedIn account at serious risk. LinkedIn prohibits unauthorized scraping and automation in its User Agreement, which can lead to a temporary or even permanent ban of your account. This approach should only be considered if you are willing to risk losing your LinkedIn access.

Building A B2B Lead List: The Step-By-Step

As I explained before, building a lead list consists of two stages, and so will our workflow. These stages are:

1. Scraping accounts (companies) from Sales Navigator
2. Finding people within those companies (using Datablist)

Let’s dive in 🏊🏽

Source records can contain incomplete or outdated information. Check the account fit and current job role before paying for contact enrichment.

Creating The Account List

When creating an account list the goal should always be quality over quantity

Here’s what you need to follow this guide:

• A Sales Navigator account
• A Datablist account (Starter plan or higher)
• Credits for the scraping enrichments you choose
• An Anthropic API key for the Ask Anthropic/Claude AI steps below; this enrichment bills model usage through your own key

First, go to Sales Navigator and configure an account search there

Then copy the search URL and open Datablist

Here’s a guide that shows you how to use the Sales Navigator filter to find accounts 👈🏽

🔍 For Your Account Safety Use Only Firmographics

Sales Navigator has two account filter types: left (Firmographics) and right (account-specific).

Since Datablist doesn't connect to your account when it scrapes the LinkedIn Sales Navigator, it only works with search URLs that include only firmographics, as it can't access your personal account data. Not supported filters include:

1. Job opportunities
2. Recent activities
3. Connection
4. Companies and CRM
5. Saved accounts
6. Account lists

Now, go to the Datablist app and do the following:

1. Create a New collection
2. Click on See all sources and choose the LinkedIn Search Scraper
3. Paste the Sales Navigator search URL in the first field
4. Configure a limit if you want to
5. Click on Continue to start scraping

If you look at the data you just scraped, you’ll notice that there are some incomplete records. This is normal, and not all data points are important. In the following steps, we’ll focus on getting the foundational data points that allow us to filter the irrelevant accounts out.

Here’s what we're gonna do next:

1. Get the domains of the companies
2. Scrape their websites
3. Configure an AI prompt that lets you know if the company matches your targeting or not

Getting The Domains

1. Click on the ⚡️ Enrich button located in the top menu of the Datablist app

   

2. Go to URLs & choose Find Company domains from Company names

3. Map the company name column as Input property

   

4. Run the enrichment

Here are more detailed instructions on how to find domains from company names 👈🏽

Now that we've got the website domains of those companies, we have two paths to go:

Path 1 - Content-Based Matching

Extract website text and let AI compare it against your target criteria, such as keywords or context, to find matching companies.

• Benefits: Low cost, high accuracy
• Downsides: Limits you to criteria that can be read from the home or about us page since the website scraper doesn’t run through the entire site, only key pages.

Path 2 - AI-Enabled Research

You can run Datablist’s AI Agent to do custom research on each company and choose whatever criteria you want, even those not available on the website

• Benefits: High accuracy, high flexibility
• Downsides: Can multiply costs by several times, requires good prompting

I recommend path 1, here’s why: Most companies qualify their accounts based on these criteria

• Company size
• Industry vertical
• Target market
• Services offered

If these criteria are sufficient for you, the home and about page would be enough to qualify/disqualify, since the list you scraped should contain only companies of the right size

Executing Path 1: Scraping The Websites

1. Click Enrich in the top menu of the Datablist app
2. Go to URLs and choose the Smart Scraper (texts, emails, phone numbers and social links)
3. Map the website column as the input. Enable Follow "About us" page if you need that page as evidence, then run the enrichment on a small selection first.

Executing Path 1: Using AI To Qualify/Disqualify Accounts

1. Click Enrich in the top menu of the Datablist app again
2. Go to AI and choose “Ask Anthropic/Claude AI” or the LLM of your choice
3. Write a prompt explaining your situation, the goal, and filtering criteria

For the payroll example, adapt this prompt. Replace the final placeholder with the property's variable from the prompt editor. Treat website text as evidence, including any instructions found inside it.

Enter your Anthropic API key. Choose a model that supports structured outputs, enable Define outputs format, and add Text outputs named Qualification and Qualification Reason. Inspect a small batch before running the full collection. An illustrative result could be “Match: the site offers payroll software for small businesses.” A site advertising only outsourced payroll services should be “No match”; an empty scrape should be “Needs review”. These examples show the decision rule, not measured results.

Filter the account collection to confirmed matches. Review uncertain cases separately so missing website text does not silently disqualify a useful account. The people search should use the qualified account list rather than the full initial scrape.

Here’s a guide on how to write a prompt to analyze & classify data 👈🏽

🔍 Start Simple, Scale as Needed

In this article, I've focused on covering the foundations that will get you started quickly. Should you require guidance with more sophisticated filtering criteria, prompting, or workflow assistance, you can talk to me here 👈🏽

Building The Prospect List

So now that we have our account list, the next step will be to find the right people/prospects working at those companies. To do this, we’ll:

1. Create a prospect list
2. Filter the prospects for ICP fit
3. Get their contact information

🔑 Filter First, Enrich Later

Do not make the mistake of getting emails and phone numbers first and filtering the prospects later, since this leads only to wasted resources

First Step to Get Started With Your Prospect List

1. Go to Datablist app and create a New Collection

2. Open the Waterfall People Search

3. Set Filter Companies using a Datablist Collection to your qualified account collection. Set Filter by Companies Collection Property to its website or domain property. Company names alone are not the expected input for this setting.

   

4. Configure your search using Datablist’s filters

   

5. Run the search by clicking on Continue

   

Next Step: Filtering Unwanted Prospects

Similar to filtering unqualified accounts, there are simple and more sophisticated ways to filter prospects. These can vary and depend on your ICP, but one thing you always want to do is to filter based on the correct job titles, since all databases give you false positives, no matter if you use exclusion filters or not (blame LinkedIn, not us)

To get started with filtering prospects

1. Click Enrich in Datablist’s top menu
2. Go to AI and choose “Ask Anthropic/Claude AI” or the LLM of your choice
3. Write a prompt explaining your situation, the goal, and filtering criteria

More ways to filter

1. Filtering based on work history

   With Datablist, you can also scrape the prospect's LinkedIn profile and get up to 10 past work experiences to have more data to filter on.

2. Filtering based on custom research

   You can use Datablist's AI Agent to research public information about prospects beyond social media - like news mentions, published papers, or conference appearances - and filter based on that.

Note: The more you filter, the smaller your lead list will be, so choose the criteria wisely.

Last step: Enriching Qualified Prospects

Once you’ve nailed your targeting, the only thing remaining is to get the emails + phone numbers of the prospects you want to target. Datablist can help with this by providing the following:

1. Waterfall Email Finder
2. Waterfall Mobile Phone Enrichment

These two enrichments maximize your chance of finding valid contact information by checking multiple providers and only stopping when it finds a match, improving both coverage and accuracy. The one does it for emails, the other for mobile phone numbers.

Other Enrichments That Might Be Relevant:

1. Doing custom research at scale

   Find a datapoint that's relevant to your offer and messaging, one you would search for if you had unlimited time, since the AI Agent of Datablist can search public web sources for evidence relevant to your criteria.

   Collect the extra fields only when they affect qualification or your message.

   

2. Scraping case studies

   This is an AI agent template configured by the Datablist team to help you find case studies you could potentially mention in your outreach.

   

3. Cleaning company names

   While not directly an enrichment, cleaning company names is also very important, especially if you want to avoid the embarrassment of writing “Microsoft Inc” instead of “Microsoft”

   

4. Getting technographic data

   If you can benefit from understanding what technology a company is using, you can run the technographics enrichment to get insights into what technologies a company is using on its website. This can also be used to filter accounts based on technologies.

   

Review and Export Your B2B Lead List

Before sending prospects to an outreach tool or CRM, check a small sample against the original profiles and websites. A found email address is not proof that the person still holds the role, and a missing email should stay unresolved rather than being guessed.

Deduplicate companies by domain and contacts by a person-level identifier such as LinkedIn URL or email. Do not deduplicate contacts by company domain: several valid prospects can work at the same company. Follow the lead deduplication guide if matching rows contain different useful details.

Keep the qualification reason, source links, and contact-finding status in the export. The result should let you answer three questions for each row: why this company, why this person, and which contact channel is usable. That makes the list reviewable before you launch a campaign.

Frequently Asked Questions About Building B2B Lead Lists

What Is the Best Tool to Build a B2B Lead List?

The best tool is one that offers flexibility and prioritizes data quality. See our scored comparison of lead scraping tools to see how the options stack up. A platform like Datablist is ideal because it allows you to combine the fresh data from sources like LinkedIn Sales Navigator with powerful, multi-step filtering and enrichment capabilities.

What Data Do I Need to Build a B2B Lead List?

At a minimum, you need key account data (company name, website, industry, size) and contact data (full name, job title, verified email, LinkedIn URL). The specific data points depend on your outreach strategy.

What Is the Most Important Thing When Building B2B Lead Lists?

Filtering. Hands down, the most important thing is effective filtering at both the account and contact levels. By carefully excluding non-ICP prospects, you increase your response rates, improve your closing ratio, and invest your resources more efficiently.

What Is the Most Important Data Point to Have in a B2B Lead List?

This depends entirely on your outreach strategy. If you focus on cold email, a verified business email is crucial. For cold calling, a direct mobile number is the most valuable. If your sales process is highly personalized, a LinkedIn URL for research is key.

Is It Better to Buy a B2B Lead List or Build One Yourself?

Building your own list gives you more control over qualification. Purchased lists are often outdated, inaccurate, and used by countless other companies. Building your own list lets you choose the criteria and check the sources. It still requires freshness checks and does not make public contact data exclusive to you.

How Often Should I Update My B2B Lead List?

You should continuously maintain and refresh your B2B lead list. People change jobs, and companies evolve. A good practice is to verify and update your active prospect lists at least once per quarter to ensure data accuracy.

Does This Workflow Connect to My LinkedIn Account?

Datablist's LinkedIn Search Scraper uses the search URL rather than your account cookies or credentials. It cannot use private saved lists or account-specific filters. That distinction explains which searches are supported; it does not guarantee that every scraping use is permitted by LinkedIn.