Build an evidence-backed customer-angle matrix
Produce distinct ad-angle hypotheses with traceable customer evidence, safe claim boundaries, and a human-reviewed brief for each test.
- Preparation
- A focused research and review session, followed by separate creative production and campaign testing.
- Difficulty
- Moderate
- Task
- Find evidence-backed ad angles
Before you start
Prepare the data, boundary, and owner.
- Write the audience, product, market, and campaign decision that the research must support.
- Gather recent reviews, de-identified support excerpts, interview notes, and campaign learning that you may use for this purpose.
- Use an organisation-approved ChatGPT workspace and confirm its current data controls before upload.
- Create a restricted Google Sheet for the working evidence and name the person who will review the analysis.
- Keep customer evidence inside your own approved tools. AIWorkDex has no intake, file upload, survey hosting, or email capture.
Actionable steps
Build a narrow route, then test it.
Each step names its input, output, supporting Tools, and inspectable evidence.
Define the decision and evidence boundary
Write one decision question, the customer group, the market, the date range, and the evidence sources you may use. Record excluded sources and known gaps.
Why it matters: A fixed scope keeps the analysis tied to a real campaign decision and makes its limits visible.
- Input
- The campaign problem, intended audience, product facts, approved evidence sources, and usage permissions.
- Output
- A short research brief with the decision question, cohort, sources, exclusions, owner, and review date.
Tools in this stepGoogle SheetsEvidence for this step
AI-assisted analysis needs a defined scope, repeatable checks, documented human oversight, and ongoing review.
National Institute of Standards and Technology: AI RMF 1.0, MAP 3.3–3.5; MEASURE 2.1 and 2.6; Appendix C, pages 40–41
Prepare the minimum useful evidence
Create one row per review, excerpt, or interview observation. Keep a non-identifying evidence key, source type, date, product context, and exact customer language. Remove names, email addresses, order numbers, and unrelated personal details.
Why it matters: The analysis needs enough context for review. Extra personal data adds avoidable risk and does not improve the angle.
- Input
- Approved reviews, support excerpts, interview notes, campaign learning, and the research brief.
- Output
- A restricted, de-identified evidence sheet with one source item per row.
Tools in this stepGoogle SheetsEvidence for this step
Customer review text can be analysed systematically to extract and organise customer requirements.
Journal of Integrated Design and Process Science: Abstract; article overview of the extraction and mapping processAI-assisted analysis needs a defined scope, repeatable checks, documented human oversight, and ongoing review.
National Institute of Standards and Technology: AI RMF 1.0, MAP 3.3–3.5; MEASURE 2.1 and 2.6; Appendix C, pages 40–41Google Sheets supports shared review with separate view, comment, and edit permissions.
Google Workspace: Work the way you want; Control spreadsheet access
Extract customer tensions with evidence
Ask ChatGPT to classify only the supplied rows as problems, motivations, objections, desired outcomes, or use cases. Require the evidence key, exact supporting excerpt, confidence, and a short rationale for every label. Keep Unknown as a valid result.
Why it matters: Structured output can reveal repeated customer needs while preserving the row that supports each interpretation.
- Input
- The de-identified evidence sheet, fixed category definitions, and the research brief.
- Output
- A labelled table with source keys, excerpts, categories, confidence, rationales, and Unknown rows.
Tools in this stepGoogle SheetsChatGPT data analysisEvidence for this step
Customer review text can be analysed systematically to extract and organise customer requirements.
Journal of Integrated Design and Process Science: Abstract; article overview of the extraction and mapping processChatGPT data analysis can process uploaded files, clean data, combine tables, and create structured outputs.
OpenAI: How data analysis works in ChatGPT; Work on tables in real-timeAI-assisted analysis needs a defined scope, repeatable checks, documented human oversight, and ongoing review.
National Institute of Standards and Technology: AI RMF 1.0, MAP 3.3–3.5; MEASURE 2.1 and 2.6; Appendix C, pages 40–41
Audit the extracted tensions
Review every low-confidence and Unknown row. Check a varied sample of the remaining labels against the original evidence. Correct invented, merged, or over-broad themes before counting them.
Why it matters: AI labels can flatten different customer situations or create a pattern that the source material does not support.
- Input
- The labelled table, original de-identified evidence, category definitions, and a named reviewer.
- Output
- A corrected table, review notes, and a record of the rows checked by a person.
Tools in this stepGoogle SheetsChatGPT data analysisEvidence for this step
Customer review text can be analysed systematically to extract and organise customer requirements.
Journal of Integrated Design and Process Science: Abstract; article overview of the extraction and mapping processAI-assisted analysis needs a defined scope, repeatable checks, documented human oversight, and ongoing review.
National Institute of Standards and Technology: AI RMF 1.0, MAP 3.3–3.5; MEASURE 2.1 and 2.6; Appendix C, pages 40–41
Build the customer-angle matrix
Group the reviewed rows without merging distinct situations. For each candidate angle, record the customer situation, tension, desired progress, objection, product relevance, evidence keys, representative excerpts, strength of evidence, gaps, and a creative hypothesis.
Why it matters: The matrix separates a customer angle from the hook, format, or asset used to express it.
- Input
- The audited labels, verified product facts, previous campaign learning, and current brand guidance.
- Output
- A matrix of distinct angle hypotheses with traceable evidence and visible gaps.
Tools in this stepGoogle SheetsChatGPT data analysisEvidence for this step
Customer review text can be analysed systematically to extract and organise customer requirements.
Journal of Integrated Design and Process Science: Abstract; article overview of the extraction and mapping processGoogle Sheets supports shared review with separate view, comment, and edit permissions.
Google Workspace: Work the way you want; Control spreadsheet access
Review claims before creative work
For each angle, write the likely express and implied claims. Remove or qualify claims that exceed verified product facts. Send regulated, objective, health, safety, performance, price, or effectiveness claims through the organisation’s normal legal and compliance review.
Why it matters: Customer language can inspire an angle without proving an objective claim about the product.
- Input
- The angle matrix, product substantiation, intended creative message, target markets, and review policy.
- Output
- A claim-safe angle row with allowed wording, prohibited wording, evidence links, and required approvals.
Tools in this stepGoogle SheetsEvidence for this step
Under US FTC guidance, advertisers need a reasonable basis for objective express and implied claims before an advert runs.
Federal Trade Commission: What truth-in-advertising rules apply; How the FTC determines deception; What evidence a company must have
Fill a defined gap only when needed
If the existing evidence cannot answer one important question, run a small consented Google Forms survey. Ask only about that gap. Disable email collection, omit names and order numbers, restrict access, hide response summaries, stop collection when the planned sample closes, and review every response before adding it to the matrix.
Why it matters: A bounded survey can test a missing assumption without creating a broad customer-data collection exercise.
- Input
- One documented evidence gap, consent wording, a suitable customer group, access controls, and a retention decision.
- Output
- A de-identified response sheet, review record, and a clear decision to update or leave the angle unchanged.
Tools in this stepGoogle SheetsGoogle FormsEvidence for this step
Google Forms does not record account usernames unless the setting to collect email addresses is enabled.
Google Docs Editors Help: Step 1: Check form settings; Limit users to one responseGoogle Forms responses can be linked to Google Sheets for row-level review.
Google Docs Editors Help: View responses; View all responses in a spreadsheetAI-assisted analysis needs a defined scope, repeatable checks, documented human oversight, and ongoing review.
National Institute of Standards and Technology: AI RMF 1.0, MAP 3.3–3.5; MEASURE 2.1 and 2.6; Appendix C, pages 40–41
Choose the next angle tests
Select a small set of distinct, supported angles. Give each one a separate creative brief with the audience, tension, promise boundary, evidence, concept, format, brand checks, owner, and test measure. Keep execution variants under their parent angle.
Why it matters: Separate briefs let the team test different customer ideas instead of mistaking new wording or crops for new angles.
- Input
- The reviewed matrix, claim decisions, production constraints, brand guidance, and campaign test plan.
- Output
- Human-approved creative briefs that preserve the link from each test to its angle and customer evidence.
Tools in this stepGoogle SheetsEvidence for this step
AI-assisted analysis needs a defined scope, repeatable checks, documented human oversight, and ongoing review.
National Institute of Standards and Technology: AI RMF 1.0, MAP 3.3–3.5; MEASURE 2.1 and 2.6; Appendix C, pages 40–41Under US FTC guidance, advertisers need a reasonable basis for objective express and implied claims before an advert runs.
Federal Trade Commission: What truth-in-advertising rules apply; How the FTC determines deception; What evidence a company must have
Success checks
Check facts and handovers before expanding.
- Every selected angle links to reviewed evidence keys and representative customer language.
- The matrix keeps distinct customer situations separate and records Unknown or weak evidence openly.
- A person checked all low-confidence rows and a varied sample of the remaining AI labels.
- Each creative brief separates the customer angle from its hook, format, and asset variations.
- Every express and implied product claim stays within verified support and the required market review.
- Any survey was consented, narrowly scoped, de-identified, access-controlled, and reviewed by a person.
- The team records the angle tested, the execution used, and the campaign result without treating one result as universal proof.
Failure modes
Stop and repair these conditions.
- The output contains generic hooks with no evidence key. Return to the customer rows and require a supporting excerpt for each theme.
- Several customer situations collapse into one broad angle. Split them and preserve the context that changes the buying decision.
- Names, email addresses, or order numbers remain in the working file. Stop the analysis and remove them before sharing or upload.
- The model invents a theme or alters customer wording. Correct the row and audit similar labels against the original evidence.
- The matrix ranks themes by count alone. Add evidence quality, source coverage, recency, product relevance, and important minority objections.
- A customer quote becomes an objective product claim. Remove it from the brief until suitable support and review exist.
- The team launches a broad survey before reading existing evidence. Stop and document the exact unanswered question first.
- The survey collects direct identifiers or exposes response summaries. Close it, repair the settings, remove unnecessary data, and review access.
- New crops or headlines are counted as new angles. Group them as executions under the same customer hypothesis.
- A person approves the creative without checking the evidence trail. Keep the brief in draft until the review is recorded.
Relevant Tools
Use each product for a defined part of the method.
Tool
Google Sheets
A shared spreadsheet for cleaning evidence, reviewing extracted themes, and maintaining a traceable customer-angle matrix.
See fit, limits, pricing context, and SourcesTool
ChatGPT data analysis
A file-analysis workspace for classifying return comments and producing reviewable summary tables.
See fit, limits, pricing context, and SourcesTool
Google Forms
An optional survey tool for gathering a small amount of consented customer evidence when existing sources leave a clear gap.
See fit, limits, pricing context, and SourcesSources
Inspect the material behind the method.
Product capabilities come from official documentation. Scope, testing, monitoring, and human oversight use the cited risk guidance.
- Journal of Integrated Design and Process Science: Systematic Service Product Requirement Analysis with Online Customer Review DataPeer-reviewed journal article · Published 2016-01-04
- Federal Trade Commission: Advertising FAQ's: A Guide for Small BusinessUS government business guidance · Published 2001-04-01
- Google Workspace: Google Forms: Online Form BuilderOfficial product page · Published 2026-09-18
- Google Docs Editors Help: Publish & share your form with respondersOfficial product documentation · Published 2026-09-18
- Google Docs Editors Help: View & manage form responsesOfficial product documentation · Published 2026-09-18
- Google Workspace: Collaborative, AI-powered spreadsheetsOfficial product page · Published 2026-09-18
- OpenAI: Improvements to data analysis in ChatGPTOfficial product announcement · Published 2024-05-16
- National Institute of Standards and Technology: Artificial Intelligence Risk Management Framework (AI RMF 1.0)Government framework · Published 2023-01-26