LinkedIn Post Experimentation

Source Traceability

Primary source: Growth Hacking LinkedIn by Bjorn Radde, especially sections 2.1 "Phases of growth hacking", 2.2 "Growth Hacking LinkedIn", 3.4.1 "Posts", 3.7 "Social Selling Index", and 4.1 "LinkedIn analysis tools". Guidance is transformed and paraphrased.

Reference Routing

| Need | Read | |------|------| | Experiment model and source notes | references/core/knowledge.md | | Experiment design rules | references/core/rules.md | | Test templates and analysis examples | references/core/examples.md | | Run a content experiment | workflows/run-post-experiment.md |

Workflow

1. Turn a content idea into a hypothesis about audience, topic, format, or

response.

1. Choose one variable to test.
2. Define metrics before publishing: impressions, engagements, comments, saves,

sends, profile visits, followers, newsletter subscriptions, or qualified conversations.

1. Publish, respond to comments, and collect results after a sensible window.
2. Decide whether to repeat, revise, or stop the content angle.

Output Format

markdown
# LinkedIn Content Experiment

## Hypothesis
[Audience + content variable + expected signal.]

## Test Design
- Variable:
- Control or comparison:
- Format:
- Publishing window:
- Engagement plan:

## Metrics
| Metric | Why It Matters | Target |
|--------|----------------|--------|

## Decision Rules
- Repeat:
- Revise:
- Stop:

## Learning Log
- What happened:
- What to try next:

Quality Bar

• Do not optimize five variables at once.
• Prefer learning from qualified response over raw reach.
• Treat analytics as directional, not perfect truth.
• Include comment quality and audience fit, not just impressions.