{"id":9469,"date":"2026-04-09T10:07:52","date_gmt":"2026-04-09T10:07:52","guid":{"rendered":"https:\/\/phantombuster.com\/blog\/?p=9469"},"modified":"2026-04-09T10:07:52","modified_gmt":"2026-04-09T10:07:52","slug":"phantombuster-fresh-lead-list-weekly","status":"publish","type":"post","link":"https:\/\/blogv2.phantombuster.com\/blog\/linkedin-automation\/phantombuster-fresh-lead-list-weekly\/","title":{"rendered":"How to use PhantomBuster to build a fresh lead list every week (instead of one huge list)"},"content":{"rendered":"

If you run the same LinkedIn search every week, you\u2019ll usually see the same profiles near the top. The goal isn\u2019t to \u201cstart over,\u201d it\u2019s to keep a cumulative master list and pull a weekly view of net-new leads.<\/p>\n

PhantomBuster Automations are cumulative by default. They append results to the same destination and use that history to avoid duplicates. The reliable way to get a fresh weekly list is to keep your master list intact, then filter for what was added since the last run.<\/p>\n

This guide shows a repeatable workflow to build a weekly net-new lead system that keeps data clean and reduces avoidable LinkedIn risk.<\/p>\n

Why starting fresh each week fails: What to do instead<\/h2>\n

The common mistake: Deleting history to \u201cclean up\u201d<\/h3>\n

Many BDRs assume the cleanest approach is to delete last week\u2019s data, rerun the same search, and export a new file.<\/p>\n

That breaks the deduplication logic. PhantomBuster solves that and relies on your existing output history to recognize profiles it already processed. If you delete the master list, clear your output, or switch to a new output destination, the next run can treat previously captured profiles as new.<\/p>\n

You’ll get duplicate entries, wasted execution capacity, and a higher chance you contact the same person twice.<\/p>\n

The cumulative approach: How PhantomBuster keeps a net-new output<\/h3>\n

PhantomBuster appends results to a single output destination, such as a CSV file, a Google Sheet, or the LinkedIn Leads page in your dashboard. On the next run, it checks what\u2019s already been processed and skips duplicates.<\/p>\n

Your \u201cmaster list\u201d grows over time. Your \u201cweekly list\u201d is a filtered view of what was added since a given date.<\/p>\n

How to set up your Phantom for weekly fresh leads<\/h2>\n

Step 1: Choose the right Automation and lock in your search URL<\/h3>\n

Use a search export Automation. These are built for repeatable, cumulative lead extraction.<\/p>\n

Paste your LinkedIn search URL into the Automation\u2019s configuration. Make sure the search type is People, not Jobs or Posts, so the output stays consistent.<\/p>\n

Your search URL is the foundation of the workflow. Too broad, and you create noise. Too narrow, and you\u2019ll run out of new profiles quickly. Aim for a search that can produce new results over time, even after deduplication removes repeats.<\/p>\n

Step 2: Turn on duplicate removal and use Watcher mode when it fits<\/h3>\n

In your Automation settings, enable Remove duplicate profiles. This helps prevent reprocessing profiles that show up again in LinkedIn\u2019s ranked search results.<\/p>\n

If your Automation offers Watcher mode, enable it when your input list changes over time. Watcher mode is a good fit for workflows where new profiles get added continuously, like job changes, new followers, or new commenters.<\/p>\n

Enable duplicate removal by default, and use Watcher mode when you want the Automation to focus on new additions since the last run.<\/p>\n

Step 3: Schedule weekly runs and keep activity patterns steady<\/h3>\n

Set the Automation to run repeatedly on a weekly schedule, for example every Monday morning. This gives you predictable lead flow without manual exports.<\/p>\n

If you run multiple LinkedIn Automations, avoid launching them all at the same time. Spread them across the day so your activity looks like a normal work pattern, not a single burst.<\/p>\n

You can also set a batch size. If you leave the \u201crows to process per launch\u201d setting blank, you can create long runs, timeouts, or unusual activity spikes that are avoidable.<\/p>\n

Note:<\/strong> When you scale, set a steady batch size, such as 100 to 500 rows per run, depending on the Automation and your overall LinkedIn activity.<\/p>\n

\u201cAvoid slide and spike patterns. Gradual ramps outperform sudden jumps.\u201d
\n\u2013 PhantomBuster Product Expert, Brian Moran<\/a><\/p><\/blockquote>\n

Three ways to extract only this week\u2019s new leads<\/h2>\n

Method A: Google Sheets filtering: recommended for most BDRs<\/h3>\n

Connect a Google Sheet as your output destination. PhantomBuster will append new rows to the same sheet each week, which becomes your cumulative master list.<\/p>\n

Create a second tab called \u201cThis week\u2019s leads.\u201d Filter the master tab by the capture timestamp. If Column A contains a timestamp, you can use:<\/p>\n

=FILTER(‘Sheet1′!A:Z,’Sheet1’!A:A>= TODAY()-7)<\/p>\n

This second tab is filtered for weekly outreach, while the master tab stays intact for deduplication.<\/p>\n

This approach is useful for teams because it\u2019s visible, shareable, and easy to extend with columns like owner, status, segment, and last touch date.<\/p>\n

Method B: JSON export: Useful for ad hoc snapshots<\/h3>\n

Open your Automation, go to the Console, then the Files or Results tab, and download the JSON output.<\/p>\n

In many cases, JSON reflects the latest execution output, while a cumulative CSV reflects everything appended over time. If you need the latest run only, JSON is often the fastest path.<\/p>\n

If you want to work in a spreadsheet tool, convert JSON to CSV with a converter you trust, then import it.<\/p>\n

Method C: Zapier or Make routing: For hands-off delivery<\/h3>\n

If your output goes to Google Sheets, you can use Zapier or Make to trigger on a new row and push leads into the next step of your system.<\/p>\n

Common routes include:<\/p>\n