GEO Case Study: Student Verhuis Service - Rankshift

GEO Case Study: Student Verhuis Service

TL;DR

Background and goals

Student Verhuis Service had dominated traditional SEO for years through its long partnership with Doublesmart. Strong rankings, solid CRO and successful Digital PR were already in place.

Instead of defending a Google-first position, the ambition shifted toward the next search layer: AI-driven discovery.

The objectives were:

GEO Strategy

To compete in AI search, Doublesmart and Student Verhuis Service needed a focused Generative Engine Optimization strategy. The strategy combined technical crawlability, AI-structured content, authority signals and functional usability inside LLM environments.

1. Technical foundation for AI crawlability

The first step was structural cleanup:

This ensured AI systems could access and understand core service logic, not just static content.

2. Answer Engine Optimization and content restructuring

A full AEO strategy was implemented:

Content moved from keyword targeting to answer clarity and extractability.

3. Authority and credibility signals

Original research and proprietary data were integrated into campaigns to strengthen trust signals.

Actions included:

These signals improved recognition inside LLM outputs as a reliable source.

4. AI-compatible pricing tool

A new AI-readable pricing tool was developed.

Unlike traditional form-based calculators, this version allowed LLM systems to interpret pricing logic. AI users could receive price indications directly within AI search environments.

This made the service not only discoverable, but functionally usable inside AI tools.

Execution

Execution took place over two years within a structured SEO for AI experiment.

Phase 1: Technical restructuring

Phase 2: Content rewriting and structural clarity

Phase 3: Authority amplification via data and PR insights from Rankshift

Phase 4: Conversion tool redevelopment for AI compatibility

Phase 5: Scaling visibility and experimentation

Long-term scalability initiatives included:

Each layer reinforced discoverability, trust and usability inside AI systems.

Measurement framework

Performance was tracked across five pillars:

1. Revenue attribution

Direct monthly revenue attributable to AI search environments.

2. Conversion performance

Structural conversion rate and comparison to industry benchmarks.

3. AI visibility footprint

Visibility growth across 189 high-priority moving industry search terms, clearly visualized using Rankshift.

4. National presence

Geographic visibility expansion beyond Amsterdam.

5. Recognition and industry validation

This was externally validated by winning the Dutch Search Award for Best Generative Engine Optimization Campaign in 2025, positioning Doublesmart as one of the best GEO agencies globally.

The framework connected AI visibility directly to measurable business growth.

Results

Revenue and growth

AI visibility

Student Verhuis Service became the first mover in its category to generate measurable income via AI search tools.

Why it worked

Several structural advantages explain the outcome:

This was not visibility layered on top of SEO. It was infrastructure designed for LLM environments.

Lessons and optimization opportunities

1. Technical accessibility is foundational

If AI systems cannot interpret tools and pricing logic, they cannot recommend them.

2. Authority signals amplify inclusion

Research, reviews and recognitions influence how AI models prioritize brands.

3. Functional usability matters

Being mentioned is valuable. Being operable inside AI environments is transformative.

4. Early experimentation compounds

Two years of structured experimentation created a durable advantage.

5. API-level integration is the next frontier

Direct data feeds and structured tables will likely become increasingly important for AI agent retrieval.

The core takeaway: AI search can drive measurable revenue when technical infrastructure, content structure and authority signals are aligned.