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LAB-006 / Measurement before content production

B2B AI Visibility Scorecard

A repeatable benchmark for testing whether a B2B brand appears for buyer questions across answer engines—and whether the mention is accurate.

Companion to the YouTube video
Keyword demand
4,400 / month
Workflow
55 minutes
Verified
2026-07-23
PACK PREVIEWVERSION 2.0.0
LAB-006

AI search visibility

B2B AI visibility scorecard

2 FILESSOURCE-LINKEDEDITABLE
Visual field guide16 pages of topic-specific research, technical diagrams, charts, worked examples, controls, and primary-source links.
Implementation bundleEditable 30-question prompt set, visibility log, component scorecard, protocol, and monthly benchmark, plus the field guide, prompt contracts, and full source notes.
DEFINED OUTCOME

Create a question set, citation benchmark, accuracy rubric, and monthly measurement log for one buying journey.

PACK CONTENTS

What you will leave with

  • Buyer-question generator
  • Mention and citation log
  • Accuracy and prominence rubric
  • Monthly change narrative

OPERATING METHOD

Use the pack in 4 passes

  1. 01

    Bound the question set

    Choose questions by buying stage, problem, category, comparison, risk, and implementation. Freeze the set before benchmarking.

  2. 02

    Capture observable evidence

    Record model, date, prompt, answer, brand mention, citation, prominence, accuracy, and competitors. Preserve screenshots where permitted.

  3. 03

    Score the response

    Separate presence, citation, factual accuracy, recommendation strength, and source quality. One score should not hide the failure mode.

  4. 04

    Connect to owned evidence

    Map gaps to crawlable pages, primary research, entity clarity, digital PR, or technical accessibility. Re-test on a fixed cadence.