Finding qualified business contacts has always been one of the hardest parts of lead generation. Most companies do not publish neatly organized prospect lists, and buying third party databases often leads to outdated information, questionable accuracy, or compliance concerns. That is why many professionals prefer building a lead list from public business websites using information that businesses have chosen to make publicly available.
The challenge is not finding data. The challenge is collecting it efficiently, organizing it properly, and avoiding a messy spreadsheet filled with duplicates, broken links, and irrelevant contacts. A structured workflow can solve that problem.
This guide explains how to build a lead list from public business websites by combining search terms, website extraction, email discovery, and Google Sheets automation. Whether you are a marketer, recruiter, sales professional, consultant, or business owner, understanding this process can help you create cleaner prospecting databases while staying mindful of platform rules and data collection requirements.
Understanding the Goal of This Workflow
At its core, this workflow transforms simple search ideas into a usable contact database.
You start with business search terms, identify relevant company websites, scan those websites for publicly displayed email addresses, clean the results, and store everything inside Google Sheets.
The workflow is intentionally straightforward. Instead of trying to scrape massive amounts of information from multiple platforms at once, it focuses on gathering publicly available contact information from approved sources and organizing it efficiently.
That distinction matters. Many platforms restrict automated collection of their content, so always review the terms of service of any data source before collecting information. For example, Google Maps terms prohibit scraping or exporting Maps content outside approved services.
Why Businesses Still Publish Contact Emails
Some people assume public email discovery no longer works because many companies use contact forms instead of displaying addresses.
In practice, plenty of businesses still publish email addresses on their websites. Local service companies, agencies, law firms, manufacturers, distributors, nonprofits, and professional service providers frequently include contact information on homepages, contact pages, footer sections, or staff directories.
Even when public emails are limited, the process can still uncover valuable opportunities that would otherwise require hours of manual research.
Setting Up Your Google Sheet
The foundation of the workflow is a simple Google Sheet containing two tabs.
The first tab should be named searches. Each row contains a search idea such as:
"Plumbers in Austin"
"Dental clinics in Chicago"
"Marketing agencies in Toronto"
The second tab should be named emails. This tab serves as the final destination for cleaned email results.
Keeping these tabs separate creates a clear distinction between input data and output data. After working with lead generation systems for years, one pattern appears repeatedly: workflows become unreliable when raw search data and processed results get mixed together.
How the Workflow Works Step by Step
Step 1: Start With a Manual Trigger
Begin with a manual trigger that launches the workflow when needed.
This approach is especially useful during testing because it allows you to control execution and monitor results before processing larger datasets.
The workflow then reads search terms from the searches tab inside Google Sheets.
Step 2: Fetch Search Results or Approved Source Pages
Next, an HTTP Request node retrieves content associated with each search term.
The source could be a search results page, an industry directory, a business database with approved access, or an API endpoint.
Many experienced automation builders make the mistake of scaling too early. Start with small batches. Processing ten searches successfully is far more valuable than launching thousands of requests that fail halfway through execution.
Step 3: Extract Business Website URLs
Once page content has been retrieved, a Code node extracts business website URLs.
This stage requires filtering.
Not every link represents a legitimate business website. You should remove:
Static file links
Tracking URLs
Advertisement redirects
Platform profile links
Empty records
Social media URLs when not needed
The objective is to isolate actual business websites that may contain public contact information.
Why URL Cleaning Matters
Poor URL filtering creates a domino effect.
Every invalid link passed into the next stage increases processing time, wastes requests, and introduces noise into your final lead list. Strong filtering early in the workflow usually improves overall accuracy dramatically.
Step 4: Loop Through Websites One at a Time
After collecting websites, use a Loop Over Items node to process each site individually.
This approach improves workflow stability and reduces the chance of overwhelming target servers.
Adding a short delay between requests is considered good practice. It helps avoid excessive request rates and creates a more responsible collection process.
Experienced operators understand that speed is rarely the bottleneck. Data quality almost always matters more than processing volume.
Step 5: Scan Websites for Public Email Addresses
Each website is then opened through another HTTP Request node.
The resulting HTML is passed into a Code node that searches for email patterns using validation rules or regular expressions.
Common locations where public emails appear include:
Homepage footers
Contact pages
About pages
Staff directories
Support sections
Career pages
The extraction process should keep only valid-looking email addresses while removing blanks and malformed entries.
Validating Email Quality
Not every detected email should be saved.
Some websites contain placeholder addresses, obsolete contacts, or technical email references that are irrelevant for outreach.
Applying validation checks improves final lead quality and reduces unnecessary follow-up work later.
Cleaning and Deduplicating Results
Raw extraction almost always produces duplicates.
A business might display the same email address across multiple pages, or multiple search terms may lead to the same company website.
Before exporting data, apply filtering and duplicate removal.
This stage often gets overlooked because it feels less exciting than extraction. Ironically, it is one of the most valuable parts of the entire workflow.
A clean spreadsheet with 500 verified contacts is usually more useful than a cluttered database containing 5,000 repeated entries.
Manual Review Still Has Value
Automation is powerful, but human review remains important.
For high-value prospects, manually checking extracted contacts can reveal useful context such as company size, service offerings, geographic focus, and decision-maker information.
Many successful sales teams combine automation with selective human research rather than relying entirely on either approach.
Exporting Clean Data to Google Sheets
Once emails have been validated and deduplicated, append them to the emails tab.
Google Sheets remains one of the most practical destinations for lead data because it is accessible, collaborative, and easy to connect with CRM systems.
Before expanding to larger datasets, run several small tests.
This helps identify extraction issues, filtering gaps, and formatting inconsistencies before they affect thousands of records.
Common Mistakes to Avoid
One common mistake is using low-quality search terms. Generic searches often produce irrelevant results, while highly targeted searches tend to generate better leads.
Another issue is ignoring source restrictions. Not every platform permits automated collection. Compliance should be evaluated before launching any workflow.
A third mistake is prioritizing quantity over relevance. Large lead lists may look impressive, but conversion rates usually improve when targeting is precise.
Finally, many users neglect data hygiene. Duplicate emails, formatting errors, and outdated contacts can quickly reduce the value of an otherwise effective system.
Current Trends in Lead List Building
As of 2026, lead generation is shifting toward higher-quality first-party and publicly available data sources.
Businesses are becoming more cautious about privacy, regulators continue to increase scrutiny around data practices, and buyers expect more personalized outreach.
This means that simply collecting thousands of contacts is no longer enough. Successful prospecting increasingly depends on accurate data, contextual research, and responsible collection methods.
Workflows that emphasize data quality, transparency, and compliance are becoming more sustainable than aggressive scraping strategies.
My Take
The biggest misunderstanding about building a lead list from public business websites is that people think the hard part is finding email addresses. It isn't. The hard part is creating a repeatable process that produces accurate, relevant, and usable contacts without drowning in junk data.
After watching lead generation evolve over the past decade, one thing remains true: quality beats volume. A carefully curated list of legitimate business contacts consistently outperforms enormous databases filled with duplicates and low-intent prospects. The strongest workflows are not necessarily the fastest. They are the ones that balance automation, validation, and human judgment.
Looking ahead, lead generation will likely become even more focused on data quality and compliance. Organizations that invest in cleaner workflows, responsible collection methods, and thoughtful prospect research will have a significant advantage over those still chasing raw volume.
FAQ
Is it legal to collect public email addresses from business websites?
The answer depends on jurisdiction, intended use, and the source's terms of service. Publicly displayed information is not automatically exempt from privacy regulations or platform restrictions. Always review applicable laws, compliance requirements, and source-specific terms before collecting or using contact information.
What is the best way to remove duplicate emails?
Most automation platforms provide filtering and deduplication tools before export. Google Sheets can also remove duplicates after import. Performing deduplication before saving results generally produces cleaner datasets and reduces storage clutter.
Why are some business websites not returning email addresses?
Many companies use contact forms instead of publishing direct email addresses. Others hide email information behind scripts, images, or spam protection tools. In those cases, no public email may be available for extraction, which is increasingly common among larger organizations.