How can we objectively verify whether ABKE (AB客) actually ranks in AI search recommendations (GEO performance)?

Why “AI Search Ranking” Must Be Tested (Not Claimed)

In the generative AI search workflow, buyers often ask an AI system a full question (e.g., “Who is a reliable supplier for X?”) rather than typing keywords. A GEO vendor’s credibility can be checked by whether the vendor’s own brand is cited or recommended by AI answer engines in a controlled test.

• Awareness need: Understand what “AI recommendation” means (citation vs. suggestion vs. list ranking).
• Interest need: See the test logic and what makes it different from SEO rank checks.
• Evaluation need: Require reproducible evidence (queries, logs, source URLs, multi-domain coverage).
• Decision need: Reduce vendor-selection risk by using a transparent protocol and acceptance criteria.

Definitions (What to Measure)

Note: Some AI tools do not always show citations. If citations are unavailable, you can still track brand mention + list position, but the evidence strength is lower.

Reproducible A/B Test Protocol (7 Days)

A. Control variables (must be fixed)

• Tools: ChatGPT, Perplexity, Gemini, Bing Copilot (same set for all runs).
• Language: e.g., English (or Chinese) — choose one and keep constant.
• Region: e.g., United States / Singapore / Mainland China — lock the same region where possible.
• Time window: 7 consecutive days (e.g., 2026-03-15 to 2026-03-21). Run the same query set each day.
• Session hygiene: Incognito/private mode, logged-out where possible, and clear cookies between tools.

B. Query set (20–50 fixed prompts)

Build a list that mixes brand terms, category terms, and comparison terms. Do not change wording during the 7 days.

C. Data to collect (every run)

1. Query ID (fixed number, e.g., Q01–Q30).
2. Tool (ChatGPT / Perplexity / Gemini / Bing Copilot).
3. Date & time (ISO 8601 format recommended, e.g., 2026-03-15T10:30:00+08:00).
4. Answer capture: screenshot or exported conversation record.
5. ABKE mention: Yes/No.
6. Position: Top1 / Top3 / Not in Top3 / Not listed (if a list exists).
7. Cited URLs: copy all visible citations; store full URL strings.
8. Distinct domains count: extract domains from cited URLs and count unique domains.

D. Pass/Fail evidence criteria (procurement-friendly)

• Reproducibility: Same query set produces trackable outcomes across 7 days (variance is allowed, but logs must be complete).
• Evidence completeness: Provide the full query list + incognito screenshots/exports + timestamps.
• Source robustness: Provide citation source URLs with ≥10 distinct domains across the whole dataset.

Common Pitfalls and Limits (What This Test Cannot Guarantee)

• Model volatility: AI answers can change due to model updates and retrieval index refresh cycles; a 7-day window reduces but does not eliminate variance.
• Personalization: Logged-in states, location signals, and browsing history can bias results; incognito and cookie clearing reduce this risk.
• Citation availability: Some tools may not show sources for every answer; treat non-cited answers as weaker evidence.
• Language mismatch: Testing in English while your market content is only Chinese (or vice versa) may under-report performance.

Delivery SOP (What ABKE Can Provide as a Test Package)

For buyer-side verification and internal approval, the recommended deliverables are:

• Query Set File: CSV/Sheet with 20–50 fixed queries, language, region, and query intent tags (brand/category/comparison).
• Evidence Pack: Incognito screenshots or exported logs for each tool, with timestamp and query ID.
• Citation Appendix: Full list of cited URLs + unique domain summary (≥10 domains), including the mapping: Query ID → URL(s).
• Result Dashboard: Counts of mention rate, citation rate, Top1/Top3 frequency per tool and per query type.

Acceptance can be based on your procurement threshold (e.g., minimum Top3 frequency for category queries), but the protocol above ensures the measurement is auditable.