LinkedIn Profile Post Competitor Intelligence

Analyze LinkedIn profile post datasets to track publishing cadence, engagement momentum, breakout posts and competitor content changes.

Why use this Actor

Teams can collect LinkedIn posts but still need recurring answers about who is publishing more, gaining engagement and producing breakout content. This Actor is deliberately separated from source extraction. It accepts supplied public-data rows or an Apify Dataset and turns them into recurring decision intelligence. That architecture reduces dependence on source-site markup, login flows and proxy behavior while letting upstream collection tools change without rebuilding the intelligence layer.

Key features

• Compares current and previous post snapshots
• Computes deterministic contentMomentumScore from engagement and cadence
• Identifies breakout and declining content patterns
• Produces agent-ready actions instead of raw post rows
• Reads inline rows or an Apify Dataset with read-only permissions.
• Compares an optional previous snapshot and emits deterministic agentAction output.
• Runs at 256 MB with no mandatory paid LLM or browser dependency.

Example

{
  "currentItems": [
    {
      "id": "p1",
      "author": "Ana",
      "text": "AI agents in sales",
      "reactions": 220,
      "comments": 31,
      "postedAt": "2026-09-08",
      "url": "https://linkedin.example/p1"
    },
    {
      "id": "p2",
      "author": "Ana",
      "text": "New product launch",
      "reactions": 410,
      "comments": 58,
      "postedAt": "2026-09-09",
      "url": "https://linkedin.example/p2"
    },
    {
      "id": "p3",
      "author": "Bruno",
      "text": "Hiring trends",
      "reactions": 95,
      "comments": 12,
      "postedAt": "2026-09-09",
      "url": "https://linkedin.example/p3"
    }
  ],
  "previousItems": [
    {
      "id": "p1",
      "author": "Ana",
      "text": "AI agents in sales",
      "reactions": 160,
      "comments": 20,
      "postedAt": "2026-09-08",
      "url": "https://linkedin.example/p1"
    }
  ]
}

The run writes one structured report to the default Dataset and stores the same object in INTELLIGENCE_REPORT. Downstream automations can route the report by agentAction, while the current source Dataset can be preserved as the baseline for the next scheduled run.

Use cases

• B2B content intelligence
• Founder monitoring
• Competitor research
• Social selling
• Content strategy
• AI agents

A common workflow is: collect authorized public data, store it in an Apify Dataset, run this Actor with current and previous Dataset IDs, route the resulting action to a workflow or agent, and preserve the current Dataset for the next comparison.

Pricing

The product uses one primary pay-per-event unit: One decision-ready intelligence report written to the default dataset. The configured price is $0.008 per report before any Marketplace tiering or future approved changes. There is no separate start-fee design in the QuanMatrix product model.

Limitations

• Analyzes supplied public-data datasets; it does not bypass authentication or private-data controls.

• Signals are deterministic heuristics based on observed snapshot fields.

• Input field aliases are normalized conservatively.

• The Actor does not bypass authentication, private-account controls, robots restrictions, or source-platform access rules.

• Decision scores are prioritization aids based on observed fields and snapshot differences, not guarantees about future outcomes.

Output and automation

Every result contains ok, mode and agentAction, plus source counts and product-specific score fields. The output schema exposes both the default Dataset and the stored intelligence report so the Actor can be used from Tasks, schedules, API calls, agents and other Apify workflows.