Scope: ABKE (AB客) GEO (Generative Engine Optimization) for B2B export companies. Goal: quantify ROI with traceable lead-to-deal evidence (CRM + sales records), not vanity metrics.
Annual ROI (Year 1) = (Attributable Gross Profit from GEO − Total GEO Investment) ÷ Total GEO Investment
If gross profit is not available, ABKE can temporarily use attributable revenue as a proxy, but the ROI statement must clearly label it as revenue-based ROI.
Boundary note: Costs unrelated to GEO (e.g., offline exhibitions, non-GEO ad spend) should be excluded unless they are intentionally bundled in the project scope and documented.
3.1 AI-referred leads (traceable)
Leads that can be linked to AI-driven discovery paths (e.g., buyer states they found you via ChatGPT/Gemini/DeepSeek/Perplexity; or landing pages built for GEO are the first-touch entry; or a consistent “AI referral” source is recorded in CRM).
3.2 Qualified inquiries (MQL/SQL) and pipeline value
Count inquiries that pass your documented qualification rules (e.g., target country/industry/application, minimum order value, required certifications). Record stage movement and expected value in CRM for auditability.
3.3 Closed-won deals and attributable gross profit
Deals marked as closed-won with contracts/invoices. Attribute only the portion of revenue/profit supported by documented touchpoints (e.g., AI-referred first touch + GEO content as key assist).
3.4 Marginal CAC change (cost per incremental customer)
Compare the marginal acquisition cost before vs. after GEO deployment. This is especially relevant if GEO reduces reliance on paid bidding for the same customer segments.
What is not sufficient by itself: “AI visibility”, generic ranking claims, or untraceable mentions without lead/deal linkage are treated as supporting indicators, not ROI outputs.
Limitation disclosure: AI platforms do not always provide deterministic referral logs. ABKE therefore emphasizes CRM-level traceability (source fields + sales evidence) rather than relying solely on platform-side referral data.
Awareness: ROI is framed around the pain shift: from keyword-based discovery to AI question-based supplier selection.
Interest: ABKE differentiates via a full-chain GEO system (knowledge assets → slicing → content factory → distribution → AI cognition → CRM).
Evaluation: ROI relies on measurable objects: leads, inquiries, pipeline stages, closed-won, CAC deltas—recorded in CRM.
Decision: Risk control through scope boundaries, attribution rules, and evidence capture methods (not unverifiable “AI ranking” promises).
Purchase: Delivery is auditable via implementation steps (research → asset modeling → content system → GEO site cluster → distribution → continuous optimization).
Loyalty: Knowledge slices and distribution records become reusable digital assets that reduce marginal acquisition cost over time; ongoing optimization updates the enterprise “AI-understandable profile”.
