DATASET DESIGN
Buyer-Question Dataset Methodology For AEO
Buyer-question datasets should represent how decisions unfold, not a bag of keyword variants. This specification defines the fields, provenance and quality controls required before a curated dataset is released.
DATASET DESIGN
The 240-Question AI Visibility Library
Buyer Question Atlas v1 contains 240 researcher-authored questions and monitoring prompts across definition, diagnosis, measurement, tool, technical, content, local, service-buying and ROI themes. Version 1.0 was published September 6, 2026.
Each prompt record is designed to preserve source theme, buyer intent, persona, journey stage, geography, engine, wording and run conditions. Public guides consolidate related questions; monitoring prompts remain dataset records for controlled testing.
RESEARCH ASSET240 Questions And Monitoring Prompts
Version 1.0, published September 6, 2026. Structured to prevent thin content, uncontrolled prompt drift and unrepeatable visibility reporting.
DATASET DESIGN
Dataset Schema
J
Journey stage
Discovery, evaluation, shortlist, risk or procurement.
A
Audience
Role, responsibility and decision influence.
T
Trigger
The event that makes the question commercially relevant.
Q
Question
Natural buyer language with controlled variants.
E
Evidence need
The proof an adequate answer should contain.
DATASET DESIGN
Corpus Construction
Inputs may include sales objections, search queries, public community questions, review themes, product documentation and stakeholder interviews. Each row must preserve source class, capture date and inclusion rationale. Personally identifiable or confidential sales information must be removed before publication.
DATASET DESIGN
Release Status
Specification first
This is a methodological schema. No proprietary buyer-question corpus is represented as complete or observed on this page. Curated releases will carry their own version, provenance and license statement.
DATASET DESIGN
From Question Library To Content Decision
| Question Type | Primary Use | Publishing Rule |
|---|---|---|
| Distinct informational intent | Guide or research article | Create one canonical page with sufficient evidence depth |
| Commercial evaluation | Money-page section, FAQ or buying guide | Link directly to the relevant service and disclose fit |
| Close semantic variant | FAQ or subsection | Consolidate to avoid cannibalization |
| Customer-style monitoring prompt | Controlled visibility testing | Keep as a versioned dataset record, not a thin page |
Next Useful Step
Request An AI Visibility Diagnostic
Choose one company, one competitive set and one revenue-critical buyer journey. We will identify the decision questions, representation gaps and evidence requirements that matter most.