{"id":9435,"date":"2026-04-08T14:59:17","date_gmt":"2026-04-08T14:59:17","guid":{"rendered":"https:\/\/phantombuster.com\/blog\/?p=9435"},"modified":"2026-04-08T14:59:17","modified_gmt":"2026-04-08T14:59:17","slug":"weekly-engaged-accounts-list-linkedin-signals","status":"publish","type":"post","link":"https:\/\/phantombuster.com\/blog\/linkedin-automation\/weekly-engaged-accounts-list-linkedin-signals\/","title":{"rendered":"Workflow: build a weekly \u2018engaged accounts\u2019 list from multiple LinkedIn signals"},"content":{"rendered":"
Building a solid pipeline from LinkedIn engagement takes more than just pulling one signal and hoping it tells the full story. The accounts showing real interest leave multiple traces. They like your posts, comment on them, view your profile, and sometimes send connection requests. Each signal alone is noisy. Combined, they’re useful.<\/p>\n

This workflow shows how to extract, merge, and score multiple engagement signals each week. The pacing reduces avoidable account risk and keeps your outreach focused on fit and intent. By the end, you’ll have a repeatable weekly process that\u00a0compounds over time.<\/p>\n

Automation should amplify good behavior, not replace judgment. – PhantomBuster Product Expert, Brian Moran<\/a><\/p><\/blockquote>\n

Why combining signals beats chasing one at a time<\/h2>\n

The problem with single-signal lists<\/h3>\n

Relying on one engagement signal creates a noisy list. For instance, if you only export post likers, you’ll capture everyone who clicked “Like” once, including competitors, students, and habitual engagers. You still don’t know who’s actually leaning in. Likewise, if you export profile viewers, you’ll miss people who engaged with content but didn’t click through to your profile.<\/p>\n

In short, you have partial context. Single-signal extraction also makes generic outreach more tempting. Without layered context, prioritization breaks, and your reply rates drop.<\/p>\n

The multi-signal advantage<\/h3>\n

Aggregating likes, comments, profile views, and inbound connection requests into a single list surfaces people showing repeated interest. An individual who liked a post, viewed your profile, and sent a connection request is warmer than someone who just liked a post once.<\/p>\n

Layering signals also gives you a practical scoring model. More signals mean greater intent, so you can rank your list by engagement depth, not just recency. This keeps you outcome-oriented. You’re not optimizing for “how many people can I message today?” Instead, it’s “which conversations are most likely to move forward?”<\/p>\n

The compounding effect matters. Each week’s list builds on the last. Accounts that engage again naturally rise to the top, so you don’t have to start from scratch. For additional ideas on signals to track,\u00a0this Reddit discussion\u00a0covers website visits, content downloads, and event attendance as supplementary indicators worth testing.<\/p>\n

Step 1: Extract post engagers\u00a0(likes and comments)<\/h2>\n

What to extract and why<\/h3>\n

Post likes and comments are high-volume interest signals. People interacting with your content are already paying attention. Comments carry more weight than likes because they take more effort. A thoughtful commenter is often easier to start a conversation with than a one-click like. Extract both. Likes give you volume. Comments give you context.<\/p>\n

How to extract with PhantomBuster<\/h3>\n

Use PhantomBuster’s\u00a0LinkedIn Post Likers Export automation to pull users who liked your posts. As of April 2026, LinkedIn’s UI typically exposes up to ~3,000 likers per post\u2014plan for pagination caps. If you hit that threshold, prioritize your most recent posts or the ones closest to your ICP.<\/p>\n

Use PhantomBuster’s LinkedIn Post Commenters Export automation to extract commenters and their comment text. Enable Watcher (incremental) mode so each weekly run only captures new comments and routes results to PhantomBuster Leads automatically. This reduces duplicates and supports a weekly cadence.<\/p>\n

Start with one or two posts per week and adjust based on reply rate and session stability. If you post frequently, choose the posts that generate relevant engagement, not just the ones with the most reactions.<\/p>\n

Step 2: Extract profile viewers<\/h2>\n

Why profile views matter<\/h3>\n

Profile viewers are often a “warm” signal. Someone took an extra step to click through and learn more about you. This can indicate curiosity about your offer, role, or content.<\/p>\n

How to extract with PhantomBuster<\/h3>\n

Use\u00a0PhantomBuster’s Sales Navigator Profile Viewers Export automation to extract visible viewers. Enable incremental mode so weekly launches only fetch new viewers and avoid duplicates. If you have Sales Navigator, you’ll generally see more viewer detail than on a free account. If you don’t have Sales Navigator, you can still work with LinkedIn data, but it’ll be more limited. Run this extraction once per week. A weekly cadence captures new viewers without creating volume spikes.<\/p>\n

Step 3: Extract inbound connection requests<\/h2>\n

Why inbound requests are high-intent<\/h3>\n

Someone who sends you a connection request has already decided you’re worth knowing. It’s one of the strongest engagement signals you can act on. Inbound requests aren’t cold outreach. The prospect initiates them. The more reliable play here is to accept, engage, and qualify, but not pitch immediately.<\/p>\n

How to extract with PhantomBuster<\/h3>\n

Use\u00a0PhantomBuster’s LinkedIn Auto Invitation Accepter to accept incoming requests, add a short welcome message, and log new connections directly into PhantomBuster Leads for follow-up. It paces acceptances and records each new connection in Leads, so your follow-up view stays up to date without spreadsheet work. If you send a welcome message, keep it simple and professional. For example: “Thanks for connecting. What brought you to my profile?” This invites context without forcing a pitch.<\/p>\n

Step 4: Aggregate and deduplicate your list<\/h2>\n

Why deduplication matters<\/h3>\n

The same person can appear across multiple signals, as they could have engaged with your content multiple times. Without deduplication, you risk double-messaging, and your list size becomes misleading. Double-messaging\u00a0makes you look like spam and harms trust. Deduplication also makes scoring possible. You need to know how many signals each person triggered if you want to prioritize intent. More signals equal greater intent.<\/p>\n

How to merge and deduplicate<\/h3>\n

Export each signal to CSV or Google Sheets. Use the LinkedIn profile URL as your unique identifier. It’s consistent across exports and works well as a merge key. Send each automation’s output to PhantomBuster Leads. If you use Sheets, import to separate tabs, combine with VSTACK\/UNIQUE on the LinkedIn URL, then pivot to count signals. Or, send outputs to PhantomBuster Leads. It centralizes results and auto-deduplicates by LinkedIn URL, so you can skip most spreadsheet maintenance.<\/p>\n

Assign points per signal, then sum points per person to create a total intent score. Sort by score, highest to lowest. Here’s a table with suggested scores. Treat these as a starting point\u2014adjust weights to your ICP and reply-rate data.<\/p>\n\n\n\n\n<\/colgroup>\n\n\n\n\n\n\n
Signal<\/strong><\/td>\nIntent Level<\/strong><\/td>\nSuggested Score<\/strong><\/td>\n<\/tr>\n
Inbound Connection Request<\/strong><\/td>\nHigh<\/td>\n10<\/td>\n<\/tr>\n
Profile View<\/strong><\/td>\nMedium-High<\/td>\n5<\/td>\n<\/tr>\n
Comment on Your Post<\/strong><\/td>\nMedium<\/td>\n5<\/td>\n<\/tr>\n
Like on Your Post<\/strong><\/td>\nLow<\/td>\n1<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Step 5: Score and filter for ICP fit<\/h2>\n

Why scoring prevents volume-first outreach<\/h3>\n

Not all engagement is useful. A student or competitor liking your post is noise for your pipeline. Scoring helps you prioritize people who show both intent and repeated engagement, helping you target outreach better.<\/p>\n

How to score your list<\/h3>\n

Assign points per signal using the table above. Sum points per person to create a total intent score, then sort highest to lowest. This changes how you work the list. Someone with a score of 16 is a better outreach candidate than someone with a score of 1 from a single like.<\/p>\n

How to filter for ICP fit<\/h3>\n

Scoring isn’t enough. You need to narrow your list down further so you’re spending time on the right accounts. To do that, you need to enrich profiles with basic firmographic and role data. Run PhantomBuster’s AI LinkedIn Profile Enricher on your deduplicated Leads list so each profile has title, company, industry, location, and employee count\u00a0for filtering. Once enriched, filter your list:<\/p>\n