AI Visibility Dashboard · Category-Led GEO Strategy

Background

As AI assistants increasingly answer procurement and product-selection questions directly, being mentioned and being ranked first have become new visibility metrics. A low-voltage electrical brand with four product categories found that a healthy overall average concealed enormous category-level gaps. Applying one content strategy across all four meant spreading budget evenly across completely different competitive positions.

The challenge

• No common measurement: exposure, first-choice, top-three, share of voice and visible rank lived in separate exports and could not be compared.
• Results were not reproducible: answers varied by model, by Web versus App, and by question round, so a one-off spot check was not representative.
• Averages hid the real story: overall exposure was 59.5%, but the strongest category reached 85.6% and the weakest only 12.5%.
• Content could not be prescribed: knowing a brand was not recommended did not reveal whether the gap was specifications, certifications, case studies or service policies.

Solution: building the dashboard

• A fixed test protocol: 10 consecutive rounds, 760 real AI answers, two mainstream AI platforms, Web and App reported separately.
• Five core indicators: brand exposure, TOP1 recommendation occupancy, TOP3 recommendation occupancy, AI share of voice and average visible recommendation rank — each with its denominator documented (e.g. exposure = 452 mentions / 760 tests).
• Four drill-down dimensions: by model, by device, by category and by brand, so every card traces back to its original rounds.
• A composite GEO score shown alongside every indicator, preventing exposure-only misinterpretation.
• Round-level filtering and refresh, so any campaign or content release can be compared before and after on the same protocol.

Current strategy: four category playbooks

• Switches & sockets (74.5% exposure, 9.5% TOP1, 34.5% TOP3, 2.00 average rank): secure in the top three but never first — fight for TOP1 with differentiated positioning and comparison-led, scenario-led Q&A content.
• Intelligent PDU (85.6% / 16.9% / 30.0% / 2.19): strong mid-table presence but the master brand is absent — bind category terms directly to the brand name.
• Structured cabling (70.5% / 2.5% / 7.0% / 3.74): mentioned but rarely recommended — strengthen the evidence chain and scenario content.
• Low voltage (12.5% / 0.0% / 0.6% / 5.00): almost entirely absent — the most urgent gap, requiring baseline coverage and reference projects first.
• Budget follows the type of gap rather than the category: win first place, complete the evidence, bind the brand, or simply get covered — each with its own content assets.

The decision evidence chain

• Must 1 · Publicly crawlable: place facts on third-party sources AI can read, not only on the corporate website.
• Must 2 · Structured: checklists and tables beat prose because models extract them more reliably.
• Must 3 · Repeated across sources: the same fact needs to appear in five or more sources before AI repeats it as consensus.
• Four evidence layers, filled per category: specifications (structured, comparable metrics), certifications (3C / CE / UL / TÜV / KNX / ISO and scenario-specific approvals), case studies (landmark + scale + year + role) and service policies (warranty, response time, channel and technical support).
• The point is to turn brand claims into decision evidence that AI can extract, restate and cross-check.

Results

Baseline period: 10 rounds

Baseline readings

The first baseline: brand exposure 59.5%, TOP1 recommendation occupancy 6.7%, TOP3 17.4%, AI share of voice 8.0% and average visible recommendation rank 2.69. By model, exposure was similar across the two platforms while first-choice recommendations differed sharply (0.3% versus 13.2%); by device, the Web GEO score of 46.6 exceeded App at 37.5. These are not outcome claims but a comparable baseline for every subsequent content iteration.