LinkedIn Engager Analytics (linkedin-engager-analytics)

The linkedin-engager-analytics skill transforms vanity metrics (likes, reactions, and comments) into qualified business intelligence. It ingests engagement lists from any LinkedIn post, parses member titles, companies, and seniority levels, and segments engagers into three actionable cohorts: Peer, Aspirational, and Prospect (ICP).

Engagers on technical and operational posts represent the highest-intent warm leads on B2B social media. A practitioner who likes or comments on an in-depth post about database migration is actively thinking about that exact operational challenge.

When to use this skill

Ideal Use Cases:

• Auditing the audience quality on your own viral or high-performing posts to measure true ICP resonance.
• Analyzing engagement on competitor posts to identify active, dissatisfied buyers in your market.
• Mining comment sections of industry benchmark reports to build warm outbound sales lists.
• Prioritizing which commenters deserve custom 1-on-1 direct message outreach versus quick replies.
• Evaluating whether a paid creator or influencer partnership actually delivered qualified decision-makers.

Disambiguation ('Not for X, use Y'):

• Not for replying to comments in the thread -> use linkedin-reply-handler.
• Not for monitoring comment velocity over time -> use linkedin-thread-monitor.
• Not for rewriting someone's profile -> use linkedin-profile-optimizer.
• Not for drafting the original post -> use linkedin-post-writer.
• Not for drafting comments on the post -> use linkedin-comment-drafter.

The Three-Tier ICP Segmentation Framework

Every person who interacts with a target post is categorized into one of three strategic tiers:

1. The Prospect Tier (Target Buyers & Inbound Leads)

• Profile: Exact match for your Ideal Customer Profile (ICP). Includes decision-makers and technical budget holders (e.g. CTO, VP of Engineering, Head of Infrastructure, Chief Information Security Officer) at companies within your target revenue or employee count.
• Conversion Velocity: Commenters convert at 4x the rate of likers; likers convert at 3x the rate of cold outbound.
• Strategic Action: Route directly to founder CRM / outbound sequence. Trigger high-touch, personalized connection request referencing their specific comment or reaction.

2. The Peer Tier (Co-Creators & Ecosystem Allies)

• Profile: Founders, technical operators, and creators building adjacent products at similar stage or scale (e.g. Seed/Series A founders, senior staff engineers, indie hackers).
• Strategic Action: Cultivate for mutual distribution, podcast guest appearances, product feedback exchanges, and community alliance.

3. The Aspirational Tier (Amplifiers & Capital Allocators)

• Profile: High-profile industry luminaries, Tier-1 venture capitalists, executives at Fortune 500 tech companies (e.g. Google, Microsoft, Stripe), and major keynote speakers.
• Strategic Action: Nurture with respectful, additive follow-ups; tag in high-substance quarterly updates; invite to private dinner roundtables or advisory chats.

Lead Qualification Scoring Rubric (0–100 Scale)

The engine evaluates each profile across four objective dimensions:

1. Title & Seniority Fit (35 Points Max):
   • C-Suite / VP / Founder / Head of Dept = 35 pts
   • Director / Staff / Principal Engineer = 25 pts
   • Senior Engineer / Individual Contributor = 15 pts
   • Student / Unrelated Professional = 0 pts
2. Company Relevance & Size (30 Points Max):
   • Exact Target Employee Band (e.g., 20–250 employees in B2B SaaS) = 30 pts
   • Adjacent Industry or Enterprise (>1,000 employees) = 18 pts
   • Unknown / Agency / Freelance = 5 pts
3. Engagement Depth & Intent (25 Points Max):
   • Detailed technical comment or diagnostic question = 25 pts
   • Brief comment or praise = 15 pts
   • Post Repost with thoughts = 20 pts
   • Reaction / Like only = 8 pts
4. Geography & Market Accessibility (10 Points Max):
   • Core Operating Region (e.g. US, Canada, Western Europe) = 10 pts
   • Secondary Accessible Market = 6 pts
   • Non-target territory = 0 pts

Qualification Threshold: Total Score >= 70 qualifies as a Tier-1 Warm Lead.

The 4-Touch Warm Outbound Sequence

For profiles scoring >= 70, the skill recommends this multi-channel conversion sequence:

• Touch 1 (Public Thread, Hour 0–2): Author leaves an insightful, warm reply to their comment.
• Touch 2 (LinkedIn Connection Request, Hour 4–6): Send personalized note referencing the post: "Hi [Name], loved your point on Redis caching in our thread today. Would love to stay connected here."
• Touch 3 (Value Asset Share via DM, Day 2): Share a relevant un-gated document: "Hey [Name], put together this internal schema migration checklist after our discussion—thought it might be useful for your team."
• Touch 4 (Soft Discovery Invite, Day 5): Low-pressure invitation to compare notes over a 15-minute call.

How It Works Step-by-Step

When running linkedin-engager-analytics, execute the following 5-phase audit:

1. Post Verification & Data Ingestion:
   • Ingest the LinkedIn post URL and validate the URN via lib.url_parser.parse_post_url(url).
   • Retrieve the list of reactors (names, headlines, reaction types) and commenters (text, timestamps, author profile data).
2. ICP Boundary Definition:
   • Ingest user's ICP definition (target job titles, company size brackets, industry sectors, geographic constraints).
   • If not specified, default to standard B2B Tech Founder ICP (CTO, VP Eng, Head of Product, 10–250 employees).
3. Segmentation & Scoring:
   • Run profile parsing across all engagers against the 100-point rubric.
   • Assign each profile to Prospect, Peer, or Aspirational buckets based on title, company, and engagement depth score.
   • Highlight high-intent signals (e.g. a VP of Engineering commenting "We need this" or "How does pricing work?").
4. Actionable Report Generation:
   • Output structured markdown summary with breakdown percentages, top 10 prospects with direct connection angles, and CSV-ready export format.
5. Approval Staging:
   • Await user review before exporting data into CRM pipelines or initiating contact sequences.

Approval Gate

Exporting qualified lead lists or staging automated connection requests triggers an approval gate:

[ENGAGER ANALYTICS COMPLETE]
Target Post: https://www.linkedin.com/posts/architecture-migration-case-study/
Total Engagers Analyzed: 148 (112 Likes, 36 Comments)

Audience Breakdown:
- Prospects (ICP Match): 28 profiles (18.9%)
- Peers (Founders/Engineers): 64 profiles (43.2%)
- Aspirational (VCs/Execs): 14 profiles (9.5%)
- Non-ICP / General: 42 profiles (28.4%)

Top Identified High-Intent Prospects:
1. Alex Mercer — VP Engineering @ DataBridge (240 employees) [Score: 92] [Comment: "Is this SOC2 compliant?"]
2. Elena Rostova — Head of Infrastructure @ CloudPay (120 employees) [Score: 84] [Liked post]
3. Marcus Vance — CTO @ MedTechAI (45 employees) [Score: 88] [Commented on Redis caching]

Approval Action: STAGE CRM EXPORT (28 ICP PROSPECTS)
Approval Token: 77a01c44-5511-4dd2-88ee-1234567890ab
Status: READY FOR USER APPROVAL TO SAVE CSV.

Examples

Example 1: Full Engagement Audit Teardown

User Input: "Analyze the 50 engagers on our latest post about cutting AWS bill by 60%."

### Audience ICP Fit Teardown

#### Summary Metrics:
- **Total Engagers Processed:** 50
- **Total Prospects (ICP):** 12 (24%)
- **Total Peers:** 22 (44%)
- **Total Aspirational:** 6 (12%)
- **General / Other:** 10 (20%)

#### Tier 1: Qualified Prospects (Target for Outbound Outreach)

| Name | Current Title | Company | Lead Score | Warm Outreach Angle |
| :--- | :--- | :--- | :--- | :--- |
| **Sarah Jenkins** | VP of Engineering | ApexScale (85 emp) | 94 | Comment: "Our AWS bill is $18k/mo for 4 services". Reference her AWS bill comment; offer internal cost-allocation spreadsheet. |
| **David Miller** | Head of Cloud Ops | FinMetrics (140 emp) | 88 | Comment: "Did you use Karpenter for node scaling?". Answer his Karpenter question and offer a 15-min teardown of spot-instance setup. |
| **Rachel Kim** | CTO & Co-founder | HealthSync (30 emp) | 82 | Reaction: "Insightful". Send connection request referencing health data compliance during cloud cost migrations. |
| **Arun Patel** | Director of Infrastructure | LogiSys (400 emp) | 76 | Reaction: "Like". Soft intro: "Noticed your reaction on our AWS post—how is LogiSys handling multi-region egress costs?" |

#### Tier 2: High-Value Aspirational Profiles

| Name | Role / Company | Significance | Next Action |
| :--- | :--- | :--- | :--- |
| **Michael Chen** | Partner @ Sequoia Capital | Focuses on cloud infrastructure | Send thoughtful connection request; add to quarterly founder update memo. |
| **Lisa Sterling** | VP Engineering @ Datadog | Category-defining enterprise leader | Leave substantive comment on her next post to build mutual recognition. |

Example 2: Competitor Post Lead Hijacking

User Input: "Audit comments on our competitor's pricing increase post to find churn candidates."

### Competitor Post Intelligence Teardown
- **Competitor URL:** `https://www.linkedin.com/feed/update/urn:li:activity:7129988776655443322/`
- **Total Comments Analyzed:** 34
- **Dissatisfied Buyer Leads Identified:** 9

#### High-Intent Churn Signals:
1. **Mark Henderson (Director of IT @ FinCloud):** "A 40% price hike with zero advance notice makes this impossible to justify to our CFO. Exploring alternatives now."
   - *Recommended Angle:* Soft DM: "Saw your comment regarding unexpected price hikes on [Competitor]. We built an open migration tool for FinCloud's exact stack—zero seat fees."
2. **Kavita Desai (Head of Product @ EduSphere):** "Does anyone have recommendations for open-source self-hosted alternatives?"
   - *Recommended Angle:* Public Additive comment outlining 3 self-hosted open-source options including our own.