PostFlow as an AI Marketing Lab: Our LinkedIn Research

Why we built PostFlow — the lab instead of the black box

As co-preneurs at Reruption we don’t just build solutions for clients; we experiment with our own products. PostFlow is our LinkedIn AI lab: a product that automates content generation, sequencing, measurement and optimization. The goal was never just reach — it was to create a reproducible, data-driven framework that explains why content works and how to transfer those insights to real business goals.

From over 5,000 of our own posts we created a research collective: we test hypotheses about content psychology, measure engagement triggers and build data models that predict reach, comments and lead acquisition. These insights feed directly into our client projects — because research is only useful if it translates into revenue, talent or brand value.

What 5,000+ posts taught us

When you run that many experiments, you quickly notice: LinkedIn is not an algorithmic monolith but an interplay of human psychology and platform mechanics. Three recurring learnings stand out.

• Interaction beats production: Posts that explicitly invite responses (questions, dilemmas, polls) not only generate more comments, they extend visibility through the algorithm’s engagement feedback.
• Concrete stories outperform abstract theses: Personal anecdotes with a clear recommended action get significantly higher click and share rates than general industry statements.
• Sequences multiply effects: A single viral story is lucky, but success becomes repeatable with multipart series: sequences increase follower retention, profile visits and direct messages.

These findings are not just observable — we quantify them. Metrics like comment-to-impression rate, share velocity, profile-visit lift and conversion rate in downstream funnels are our KPIs. The numbers allow us to set priorities: which lever brings real leads, and which only vanity impressions?

Content psychology: what really works on LinkedIn

Behind every successful LinkedIn strategy is psychology. We extracted patterns that consistently work:

• Social proof: Mentions of clients, numbers or case studies build trust.
• Conflict and solution: An obvious problem plus a pragmatic solution sparks curiosity.
• Reciprocity: Helpful, immediately actionable tips boost shares and comments.
• Identification: Audience-specific language (e.g., “CMOs who…”) increases the perceived relevance of posts.

In practice this means: our best templates combine a personal element (story), a clear rupture (problem) and a concrete benefit (actionable step). In PostFlow we use these patterns to build generation prompts for LLMs — not generic, but template-based with variable fields for persona, hook and CTA.

Data models in PostFlow: from embeddings to engagement prediction

PostFlow is less a pure generator and more an experimentation stack. Our models can be explained in three layers:

1) Representation & Retrieval

For contextual search and topic matching we use semantic embeddings. With them we measure similarities between topics, identify content gaps and build a curated repository of reusable snippets. Embeddings allow us to combine old posts with new ideas and recognize cross-post synergies.

2) Engagement and outcome models

This layer predicts how a post will perform. Features include text length, hook type, call-to-action phrasing, image usage, timestamp, author history and prior interaction trajectories. For predictions we use a combination of classic gradient-boosting models for structured features and fine-grained classifiers based on embeddings. Target variables are comment rate, share rate, profile-visit lift and lead conversion.

3) Sequence optimizer & reward models

Order and timing of posts have a sequential effect: a kickoff post followed by educational posts often ends in direct messages. We use Markov-like models and reinforcement learning approaches to evaluate sequences — not just single posts. Reward functions include both short-term engagement and long-term metrics like brand awareness or lead quality.

Technically, we combine LLM-powered generation for text, embedding-based search for reuse and more classical ML models for performance forecasting. This creates a hybrid system that pairs creative freedom with measurable predictability.

How we run experiments: from hypothesis to production

Our experimentation process is intentionally pragmatic. We keep it to five steps:

1. Hypothesis: A clear, testable statement. Example: “Posts about personal setbacks generate 25% more comments among B2B marketing audiences.”
2. Design: Define control and test groups, select KPIs, timeframe and significance level.
3. Generation: LLM-based production with template context — multiple variants per hypothesis.
4. Deployment & Monitoring: Live posting via PostFlow, real-time tracking, early stopping on negative signals.
5. Analysis & Iteration: statistical significance testing, qualitative comment analysis, feedback into model parameters.

Example: For a sequence hypothesis we tested 120 posts in 8 weeks with varying hook intensity. Result: a 4-part sequence with an explicit CTA at the end increased the direct-message rate by 37%. Insights like these are immediately operationalizable: we build sequence templates from them and provide those to clients.

Practical levers: copilots, sequences and reach strategies

PostFlow enables several levers marketing teams can use right away:

Copilots for creators and executives

Our copilots combine contextual prompts with personal data (tone, role, topic preferences). They deliver not only drafts but alternative tones, hook variants and suitable CTAs. This reduces creation time while preserving authenticity — a central point in personal branding.

Sequence toolkit

Instead of single posts we deliver multipart series: Hook → Evidence → How-to → CTA. For employer branding, for example, sequences start with company values, continue with employee stories and end with concrete job opportunities. That builds trust and measurable application incentives.

Reach levers

These include timing optimization, comment seeding (targeted replies from the company page) and cross-posting signals. A common lever: early engagement through internal amplification — a short employee reshare program can triple a post’s visibility.

How research flows into client projects — concrete examples

Our PostFlow insights are not academic; they land in product and consulting projects:

• Lead generation for e-commerce (Internetstores): We transferred sequence templates from PostFlow into LinkedIn campaigns to generate high-quality leads for a subscription model. The combination of storytelling and targeted CTAs significantly increased conversion rates into qualified sales leads.
• Employer branding & recruiting (Mercedes‑Benz): Insights into candidate psychology helped sharpen messaging in the recruiting chatbot strategy — more personal approach, fewer generic texts. This improved candidate engagement and shortened initial screening phases.
• Content analytics integration (FMG): Our data modeling approaches were integrated into internal dashboards to cluster documents and content by relevance, enabling more targeted PR and thought-leadership efforts.

We deliberately call this way of working Co-Preneuring: we test, learn and transfer — quickly enough to seize market opportunities immediately.

Concrete recommendations for marketing teams and founders

If you want to apply PostFlow learnings in your company, start with these steps:

• 1. Hypothesis backlog: Create 5 testable hypotheses (e.g., “Personal anecdotes increase profile visits by 20%”).
• 2. Sequences instead of single posts: Plan 3–4 post types as series — Hook, Use Case, How-to, CTA.
• 3. Measure the right KPIs: Beyond impressions, track comment rate, share rate, profile visits and above all message conversions.
• 4. Internal amplification: Activate employees as early amplifiers; track the lift.
• 5. Operationalize copilots: Implement a simple prompt workflow for executives so content remains authentic.

A small experiment plan template we recommend: 4 weeks, 8 posts, A/B test of 2 hook types, metrics: comment rate, profile visits, MQLs. After 4 weeks: decide, iterate or scale.

Risks, limits and compliance

AI-powered content strategies are powerful but not risk-free. We recommend clear guardrails: transparency around AI usage, data protection when handling personal data and a review process for sensitive content. PostFlow has in-product checks to avoid legal and ethical pitfalls — a must, especially for employer branding and CEO personal branding.

Conclusion & call to action

PostFlow is more than a tool; it’s our experimental lab that combines creativity with measurable science. From >5,000 posts we’ve extracted patterns, developed data models and created proven sequences that can be transferred directly into client projects — from lead generation to employer branding to personal branding for executives.

If you want to make your LinkedIn marketing more systematic, experimental and results-oriented, talk to us. We’ll show how PostFlow learnings can be integrated into your projects — fast, practical and with clear KPIs. Contact us for a short assessment or an AI PoC that takes your content strategy to the next level.