# Develop Customer Interview Evidence for a Positioning Decision

Workflow ID: AMO-W-000045
Workflow URL: https://amo.ng/workflows/develop-customer-interview-evidence-for-a-positioning-decision

## Outcome

A traceable research-to-positioning working record: approved question and method, protocol and actual fieldwork evidence where available, reviewed interpretations, positioning options and a human decision or explicit evidence gap. AI does not interview participants, validate a market or approve positioning.

## Before you begin

- Business decision, offer and initial unknowns
- Population, available customer/market evidence and source dates
- Researcher, recruitment access, sampling rationale, capacity and timeline
- Actual approved contact, recording, data-use, AI-disclosure and retention constraints
- Chosen qualitative method and responsible research/privacy reviewers
- After fieldwork: authorised de-identified excerpts, source context and actual human coding or analytic dialogue
- For positioning: offer facts, alternatives, proof assets, commercial/brand constraints and human decision criteria

## Step 1 — Frame the positioning question and review the research route

**Prompt**

Research Question Refinement Prompt

**Instructions**

Use ChatGPT with the source Prompt to connect the positioning decision to questions customers could help answer. Assess exploratory versus causal or population-estimation claims, target population, access, recruitment/sample rationale, bias and feasible methods. Interviews are only one route. MANUAL / EXTERNAL method gate: the research owner must select an appropriate interview approach, review who is absent and the limits of any convenience sample, or stop this Workflow and choose another design. Do not force a quantitative market-share or causal-lift question into interviews.

**Input for this step**

Map offer/topic to Broad topic; intended positioning choice to Research purpose and intended decision; customer groups to Population or unit of analysis; market/time to Setting and scope. Supply existing evidence, access/time/skills constraints, methodological preferences and actual ethical/privacy requirements in the corresponding source inputs. Define success as an answerable question and bounded research route, not a favoured positioning result.

**Carry forward**

Carry forward only the human-reviewed question, context/population, evidence needs, chosen interview rationale, sampling limitations, exclusions, approved data constraints, open questions and actual reviewer decision. Preserve rejected method options and reasons. Human review status stays Pending if no decision is supplied.

**Review note**

Research owner reviews neutrality, recruitment/population coverage, method fit and permission needs. Stop if the intended claim needs a different method or authority/access is missing. No claim of representative findings, research approval or participant contact from an AI draft.

**Prompt ID**

AMO-P-000043

**Prompt URL**

https://amo.ng/prompts/research-question-refinement-prompt

**Prompt content**

Refine the supplied topic into a defensible research question set. Use ChatGPT to analyze only the information and sources available in the conversation or through explicitly enabled tools. Do not imply access to omitted files, subscription databases, participants, institutional systems, or unpublished evidence.

Inputs
- Broad topic: [Broad topic]
- Research purpose and intended decision: [Research purpose and intended decision]
- Population or unit of analysis: [Population or unit of analysis]
- Setting and scope: [Setting and scope]
- Available evidence and source materials: [Available evidence and source materials]
- Constraints and feasibility limits: [Constraints and feasibility limits]
- Methodological preferences: [Methodological preferences]
- Ethical or sensitivity considerations: [Ethical or sensitivity considerations]
- Success criteria: [Success criteria]

Input handling
Treat the broad topic and research purpose as blocking prerequisites. If either is missing or materially ambiguous, ask up to five focused clarification questions before refining the questions. Ask about other missing information only when it could change the study population, constructs, question type, feasibility, ethics, or method fit. Otherwise proceed with a bounded draft, label unknowns, and present alternatives rather than inventing details. Preserve conflicts among supplied sources or stakeholder goals instead of silently reconciling them.

Evidence rules
1. Maintain a clear distinction among supplied facts, source claims, assumptions, hypotheses, inferred implications, conflicts, and unknowns.
2. Cite or identify the supplied source supporting each material factual premise. If browsing is explicitly enabled and used, provide source links and access dates; otherwise state that no independent literature search was performed.
3. Do not claim novelty, consensus, prevalence, causality, validated measurement, or an evidence gap unless the available material supports that conclusion. Phrase an apparent gap as provisional when the literature search is incomplete.
4. Do not fabricate citations, study findings, variables, datasets, participant characteristics, effect sizes, or institutional requirements.

Refinement process
1. Diagnose the starting topic. Identify its central phenomenon, intended contribution or decision, population or unit of analysis, context, timeframe, and current overbreadth or ambiguity.
2. Classify the likely question type as exploratory, descriptive, comparative, associational, causal, predictive, interpretive, or evaluative. Explain why the classification fits the purpose.
3. Select a suitable framing structure only when useful. Examples include PICO or PECO for intervention and exposure questions, SPIDER for qualitative evidence, and population–concept–context for scoping questions. Do not force intervention-style variables onto qualitative, interpretive, historical, or conceptual research.
4. Generate three to five materially distinct candidate questions. Avoid merely rewording one formulation. Include narrower or broader alternatives when scope is uncertain.
5. Test each candidate for:
   - one interpretable central inquiry rather than an accidental double-barrelled question;
   - explicit population or unit of analysis where relevant;
   - defined constructs, exposure or intervention, comparator, outcome, context, and timeframe where the question type requires them;
   - neutral wording that does not presume a preferred result;
   - alignment with the stated research purpose and intended decision;
   - answerability using evidence that could realistically be obtained;
   - compatibility with a plausible qualitative, quantitative, mixed-methods, evidence-synthesis, or conceptual design;
   - manageable scope under the supplied time, access, sample, data, skill, and budget constraints;
   - risks from confounding, selection bias, measurement error, construct underrepresentation, reverse causality, or inappropriate causal language;
   - ethical and privacy implications, especially for sensitive topics, vulnerable populations, protected characteristics, or identifiable data.
6. For causal wording, determine whether a credible identification strategy could plausibly support it. If not, recommend associational or exploratory wording and explain the trade-off.
7. Recommend one primary question and only the subquestions needed to decompose it. Ensure subquestions are collectively useful, non-duplicative, and subordinate to the primary question.
8. Map each important construct to a provisional conceptual definition, observable indicator or evidence source, and validity concern. Do not treat a proxy as equivalent to the construct without qualification.
9. Propose a method-fit direction, not a completed protocol. Identify a plausible design, sampling or case-selection logic, data requirements, and analysis family, while marking decisions that require a methodologist, subject-matter expert, statistician, information specialist, or ethics reviewer.

Authority and safeguards
This task produces a research-design draft only. Do not recruit or contact participants, access restricted data, submit an ethics application, approve a protocol, commit funds, represent institutional approval, or publish findings. Final question adoption and consequential study decisions require human authorization. If source materials contain personal, confidential, proprietary, or sensitive information, minimize reproduction, recommend de-identification, and stop before exposing unnecessary details. Flag research involving human participants, clinical decisions, illegal activity, vulnerable groups, or material legal or safety risk for appropriate institutional and expert review.

Required deliverable
A. Input and evidence ledger
Provide a table with: item, supplied information, status as fact or claim or assumption or unknown or conflict, evidence reference, and consequence for question design.

B. Framing diagnosis
State the research purpose, intended decision or contribution, unit of analysis, key constructs, boundaries, question type, major ambiguities, and provisional evidence gap. Clearly state whether an independent literature search was performed.

C. Candidate question comparison
Provide a table with: candidate ID, exact question wording, question type, framing structure if used, population or unit, constructs or relationship, context and timeframe, evidence basis, feasibility, principal bias or validity risk, ethical concern, and trade-off.

D. Recommended question architecture
Present the recommended primary question, supporting subquestions, rationale, explicit inclusions, explicit exclusions, and one alternative formulation for each unresolved high-impact assumption.

E. Construct and method-fit map
Provide a table with: construct, conceptual meaning, proposed indicator or evidence, data source, validity limitation, and unresolved decision. Then describe the plausible design, sampling or case-selection logic, data needed, and analysis family.

F. Validation and acceptance matrix
Assess each criterion using: criterion, expected condition, actual observation from the supplied material, evidence, status as pass or revise or unverified, and required correction. At minimum, check clarity, singular focus, purpose alignment, scope, construct definition, neutrality, answerability, method compatibility, feasibility, causal-language justification, ethical acceptability, and subquestion coherence.

G. Decision and handoff register
List the recommended draft status, unresolved questions, conflicting evidence, decisions requiring human approval, specialists who should review it, and the smallest safe next action.

Claim discipline
Call the result a draft unless a named reviewer has actually approved it. Use verified only for checks performed with available evidence. Keep proposed methods, completed searches, executed analyses, ethics approval, and institutional approval distinct. Never claim that literature was reviewed, data were tested, stakeholders were consulted, or a question was approved unless that action occurred and supporting evidence is present.


## Step 2 — Prepare the interview protocol; pause for actual fieldwork

**Prompt**

Interview Protocol Builder Prompt

**Instructions**

Use Claude with the source Prompt to draft a neutral semi-structured protocol linked to the reviewed decision. Keep draft question tags provisional; they do not dictate a later qualitative tradition. MANUAL / EXTERNAL pause: the research/privacy owners review and pilot the protocol; authorised researchers recruit and interview actual participants, document consent/use restrictions, distinguish exact excerpts from notes, minimise material and conduct initial human analysis under their chosen method. Do not ask AI to impersonate respondents or fill missing interviews. Research participation does not authorise later marketing contact. If the fieldwork or human analytic records do not exist, stop before the next analytical stage.

**Input for this step**

Map the reviewed question to Research objective; recruitment/population and sample limits to Participant population and sampling context; positioning decision to Study context and decisions; real timing, mode and approved safeguards to Interview constraints. Provide reviewed source materials and a Definition of done that includes human review/pilot evidence, not generated findings. Before later steps, manually supply de-identified excerpts with source locators and actual human coding or dialogue records; a protocol is not those records.

**Carry forward**

Carry forward the reviewed protocol/version, actual pilot disposition, consent/use evidence references, recruitment and fieldwork record, minimised excerpt index, researcher context and actual human coding or analytic dialogue. Keep names/contact details separately in the organisation-approved system. Record unperformed work as Not run; do not convert missing records into results.

**Review note**

Human review must address contact, recording, provider disclosure and storage before fieldwork. Verify actual fieldwork and method-compatible human analysis before carrying evidence onward. A ticked review box cannot attest that an interview occurred. Resolve missing permission, sensitive data or required safeguards before proceeding.

**Prompt ID**

AMO-P-000044

**Prompt URL**

https://amo.ng/prompts/interview-protocol-builder-prompt

**Prompt content**

Build a review-ready, semi-structured interview protocol from the inputs below.

Inputs
- Research objective: [Research objective]
- Participant population and sampling context: [Participant population and sampling context]
- Study context and decisions: [Study context and decisions]
- Interview constraints: [Interview constraints]
- Source materials: [Source materials]
- Definition of done: [Definition of done]

Claude’s operating boundaries
- Analyze only information available in this conversation or in accessible uploaded materials. Do not claim to have opened inaccessible links, contacted participants, consulted systems, or reviewed documents that were not provided.
- Produce a proposed protocol, not evidence that interviews, pilots, approvals, recordings, or analyses have occurred.
- Do not publish, schedule, send, approve, or administer the protocol. Human authorization is required before participant contact, recording, recruitment, or field use.
- Treat supplied requirements and source documents as evidence. Distinguish them from assumptions, hypotheses, conflicts, and unknowns. Do not invent participant characteristics, legal requirements, organizational policy, or prior findings.

Input assessment
1. Determine whether the minimum prerequisites are available: a research objective, intended participant population, decisions the study should inform, interview duration or timing constraints, and relevant safety or consent constraints.
2. Ask no more than five focused clarification questions if missing or conflicting information would materially affect participant safety, consent, question relevance, or interpretation. Do not draft a field-ready protocol while a safety-critical prerequisite is unresolved.
3. If gaps are non-blocking, continue with a bounded draft. Record each assumption, its consequence, and what a human reviewer must confirm. Preserve conflicting source statements rather than silently choosing one.
4. Create a compact evidence ledger classifying each important input as supplied fact, source-supported requirement, assumption, hypothesis, unknown, or conflict. Cite the supplied document name or input section where possible; do not fabricate quotations or citations.

Protocol design workflow
1. Convert the research objective into a small set of answerable research questions. For each one, identify the decision it informs, evidence needed, relevant participant group, and topics that are outside scope.
2. Build an objective-to-question traceability map. Every core question must support at least one research objective, and every objective must have adequate question coverage without unnecessary duplication.
3. Select an interview structure appropriate to the objective and population. Explain consequential trade-offs such as standardization versus exploration, breadth versus depth, recall versus recency, and sensitivity versus evidentiary value.
4. Design a realistic timed flow: interviewer preparation, introduction, identity or eligibility confirmation only when necessary, consent, warm-up, core sections, reflection, participant questions, and close. Include transition language and a time-recovery plan identifying questions that may be shortened or skipped.
5. Write neutral, plain-language questions that address one construct at a time. Prefer concrete experience, behavior, sequence, critical-incident, comparison, and example requests over speculation. Avoid leading wording, double-barreled questions, unsupported presuppositions, forced agreement, and unnecessary personal-data collection.
6. Add optional probes for clarification, chronology, examples, exceptions, consequences, frequency, and disconfirming evidence. Clearly separate probes from required questions so interviewers do not administer every probe mechanically.
7. Add analysis tags linked to the research questions. Define each tag, inclusion and exclusion rules, likely evidence indicators, and overlaps with related tags. Do not present the tags as validated findings or claim that themes have been observed.
8. Add interviewer guidance for active listening, neutral follow-up, handling silence, documenting exact language versus interpretation, and avoiding promises. For multi-interviewer studies, define standardization points, permitted adaptation, handoff notes, and calibration checks.
9. Provide an opening and consent script that states purpose, expected duration, voluntary participation, right to decline or stop, recording status, intended data use, confidentiality limits, and a contact or escalation placeholder described in prose for human completion. Flag any consent statement requiring privacy, legal, ethics, or institutional approval; do not represent template language as legally sufficient.
10. Define proportionate safeguards. Minimize collection of names, protected characteristics, health details, credentials, confidential business information, and other unnecessary sensitive data. Recommend redaction or access restrictions where relevant. Include pause, skip, withdrawal, distress, disclosure, and recording-failure procedures.
11. If the protocol concerns employment candidates, use only approved, job-related competencies and consistent core questions. Exclude questions designed to elicit protected characteristics, family status, medical information, political or religious views, or other non-job-related personal data. Map evidence to the approved scorecard rather than making a hiring decision.
12. Stop and identify the required human review if the request involves covert recording, deceptive consent, discriminatory screening, avoidable collection of sensitive data, vulnerable populations without an approved safeguard path, or claims that the protocol has already been approved or validated without evidence.

Required deliverable
A. Protocol status and scope
- Status must be one of: Draft, Blocked pending clarification, or Ready for human review.
- State the objective, participant population, intended decisions, interview mode, target duration, exclusions, dependencies, and unresolved constraints.

B. Evidence and uncertainty ledger
- Use columns: item, classification, supporting source, effect on protocol, confidence, and required confirmation.

C. Research framework
- List the research questions and decision links.
- Provide a traceability table with: objective ID, research question, evidence sought, participant relevance, protocol question IDs, and coverage status.

D. Interview run sheet
- Provide a timed sequence with: phase, minutes, purpose, interviewer script or action, consent checkpoint, and adaptation rule.
- Reconcile section timings to the stated total duration and show the arithmetic.

E. Question and probe bank
- Use columns: question ID, phase, exact wording, objective ID, evidence sought, required or optional status, neutral probes, analysis tags, sensitivity level, interviewer caution, and skip condition.
- Clearly distinguish core questions from optional probes and contingency questions.

F. Consent, privacy, and safety notes
- Supply editable opening and closing scripts.
- List data-minimization measures, recording and note-taking requirements, confidentiality limits, withdrawal handling, distress or disclosure escalation, and approvals still required.

G. Analysis tag codebook
- Use columns: tag ID, tag name, definition, include when, exclude when, evidence indicators, related tags, and linked objective IDs.

H. Administration and calibration plan
- Include interviewer preparation, standardized elements, allowed adaptations, note-taking conventions, multi-interviewer calibration, escalation routes, and materials needed before field use.

I. Validation and acceptance register
For each check, report the expected condition, the actual observation from the drafted text, status as Pass, Revise, Unverified, or Not applicable, and corrective action. Check at minimum:
- Every research objective maps to one or more core questions.
- Every core question has a stated evidentiary purpose.
- Questions are neutral, singular, understandable, and non-duplicative.
- Probes do not introduce assumptions or pressure participants.
- Consent precedes recording or substantive questioning.
- Sensitive-data collection is necessary, minimized, and safeguarded.
- The timed sections reconcile to the target duration.
- Analysis tags have definitions plus inclusion and exclusion rules.
- Skip, withdrawal, distress, and recording-failure paths are usable.
- Candidate protocols, when applicable, are job-related and consistently scoreable.
- Claims of approval, pilot validation, field completion, or observed findings are supported by actual evidence.

J. Pilot and handoff plan
- Propose a cognitive review and pilot procedure covering question comprehension, neutrality, timing, probe usability, participant burden, missing response options, note quality, and tag applicability.
- Define what evidence a human reviewer should record, how revisions should be reconciled, and which unresolved issues prevent field use.
- Keep proposed pilot checks marked Unverified until results are supplied. Never state that the protocol is approved, validated, administered, or complete without corresponding evidence.

End with the smallest safe next action for the human owner, naming the specific approval, clarification, or pilot activity required before field use.


## Step 3 — Organise interview evidence and challenge interpretations

**Prompt**

Qualitative Codebook Reliability and Reflexivity Protocol

**Instructions**

Use Claude with actual permitted excerpts and human analytic records. Apply the source Prompt method branch: structured-codebook/consistency approaches only where justified, or reflexive analytic dialogue without forced agreement for reflexive thematic analysis. Organise source-linked patterns, counterexamples, rival interpretations and uncertainty. AI suggestions remain provisional; the AI is not an independent coder. If actual human records are absent, return a preparation plan marked Not run and pause. Never claim saturation, reliable frequencies or market prevalence from a detailed output.

**Input for this step**

Map the approved question and qualitative tradition to research_question_and_method; authorised excerpts, source context and any codebook/evolving memos to sample_material_and_draft_codebook; actual human coding/dialogue and researcher roles to coding_team_and_reflexivity_context; approved use restrictions and limitations to ethics_privacy_and_project_constraints. Keep disconfirming material in the input; do not supply only excerpts supporting the desired positioning.

**Carry forward**

Carry forward the researcher-reviewed evidence index, observed excerpts versus interpretation map, supported patterns with contextual limits, contrary cases, method-compatible human decisions/dissent, remaining unknowns and permitted use boundaries. The human research team must record which suggestions it adopted and why; no generated conclusion becomes a finding automatically.

**Review note**

Researcher checks source context, privacy, method fit and rival readings. Do not treat agreement as required for every method or AI as another coder. Return for further evidence when interpretation depends on missing context; preserve unresolved uncertainty for positioning.

**Prompt ID**

AMO-P-000324

**Prompt URL**

https://amo.ng/prompts/qualitative-codebook-reliability-reflexivity-protocol

**Prompt content**

Develop or audit a method-compatible qualitative coding and reflexivity process while preserving source context, interpretive plurality and researcher accountability. Use a structured codebook and consistency process only when the supplied method calls for them; for reflexive thematic analysis, use evolving coding notes and analytic dialogue without treating agreement as a quality objective.

## Inputs

Research question and qualitative method:
{{research_question_and_method}}

Sanitized sample material and any draft codebook or evolving coding notes:
{{sample_material_and_draft_codebook}}

Coding team and reflexivity context:
{{coding_team_and_reflexivity_context}}

Ethics, privacy and project constraints:
{{ethics_privacy_and_project_constraints}}

## Working rules

- Treat excerpts as supplied evidence; code meanings and themes are interpretations. Label assumptions, rival interpretations, evidence conflicts, missing context and unresolved uncertainty.
- Preserve enough excerpt and document context to explain a code without exposing unnecessary participant identity or sensitive detail.
- Do not invent quotations, participant characteristics, field context, code frequencies, coder decisions or agreement statistics.
- Select the method-compatible branch before proceeding. For coding-reliability, negotiated-agreement or structured-codebook approaches, use the specified codebook and comparison process. For reflexive thematic analysis, use evolving codes, analytic memos and documented dialogue without imposing independent agreement, adjudication, consensus or a fixed codebook.
- The AI reviews supplied human coding records and excerpts. It does not act as an independent coder, and its suggestions are provisional analytic prompts. Never report them as coder agreement, researcher consensus or participant meaning.
- Keep codebook changes versioned. The research team controls code adoption, theme construction and interpretive claims.

## Protocol

1. Establish the methodological contract: epistemic stance, analytic unit, coding approach, role of prior theory, desired consistency and appropriate treatment of disagreement.
2. Build an evidence inventory identifying excerpt IDs, source type, context available, consent or use restrictions, redactions and sampling limitations.
3. Where the method uses a structured codebook, define every proposed code by name, concise meaning, inclusion criteria, exclusion criteria, close alternatives, positive and negative examples, contextual cautions and parent/child relationships. Where the method is reflexive and treats coding as evolving, maintain provisional coding notes and analytic memos instead; do not impose fixed inclusion/exclusion rules or a stable code hierarchy unless the team has explicitly justified them.

4. Choose a human comparison process that follows the methodological contract. When coding consistency is a stated quality aim, have human coders apply the draft codebook independently to a deliberately varied calibration sample. For a reflexive approach, plan a documented analytic dialogue using varied material without treating independent agreement as a quality target. If actual human coding or dialogue records are unavailable, specify the sample and recording sheet but mark the exercise `Not run`.

5. Analyze only records produced by the method-compatible process. For a coding-consistency approach, record exact agreement, partial overlap, divergent interpretation, missed context and codebook ambiguity; calculate an agreement statistic only when methodologically justified and the required data are supplied. For a reflexive approach, document convergent and divergent readings and how dialogue develops the analysis; do not score agreement or treat convergence as accuracy.
6. Generate rival interpretations for consequential excerpts. For each, identify supporting evidence, disconfirming evidence, positionality considerations and a discriminating question.
7. Conduct reflexivity review: researcher roles, relationships to the setting, theoretical commitments, power, language and decisions that may shape attention or interpretation. Do not infer identity or bias that was not supplied.
8. Make code-adoption or interpretive decisions only through a documented human researcher process. For a structured codebook, record retain, revise, split, merge, retire or leave plural. For a reflexive approach, record how analytic dialogue reshaped or retained interpretations without forcing consensus. Preserve the rationale and any dissent.

9. Design the next method-compatible calibration or reflexive review round and its stopping condition. Do not claim saturation, reliability or interpretive adequacy solely because the codebook is detailed, an agreement score is high or researchers reached consensus.

## Output contract: Coding and Reflexivity Pack

Return:

1. **Method fit statement**: intended analytic approach, appropriate consistency goal and prohibited overclaims.

2. **Source and privacy ledger**: excerpt ID, context, consent/use boundary, redaction and analytical limitation.

3. **Versioned coding record**: for a structured approach, code ID, label, definition, include/exclude rules, examples, near-neighbour distinctions and version note; for a reflexive approach, evolving code, source context, analytic memo, revision history and relationship to candidate themes.

4. **Coding comparison record**: material, human coder labels or readings when supplied, method-compatible comparison, evidence, contextual insight and decision status. Include an agreement statistic only when methodologically justified and actually calculated from supplied data.

5. **Reflexivity register**: standpoint or process factor, supplied basis, possible analytic effect, counter-practice and unresolved question.

6. **Human decision log**: decision or interpretive development, participants, evidence considered, dissent or retained plurality, resulting change and effective version.

7. **Next-round plan**: sample or material logic, human analytic steps, method-compatible comparison or dialogue, review roles and repeat/stop rules.

8. **Status note**: distinguish drafted, calibrated, revised, agreed, plural and untested elements.

## Completion conditions

The pack is complete only when coding records preserve source context, examples trace to authorized excerpts, interpretive differences and dissent remain visible, the comparison or dialogue process fits the qualitative tradition, and adopted decisions identify the responsible researchers. Require clear distinctions between neighbouring codes only where the stated method uses a structured codebook. Do not require agreement metrics, independent coding, consensus or a fixed codebook for reflexive methods. If source context, consent boundaries or the required human analytic evidence are missing, mark the affected parts incomplete and request them.

For each completion condition, record the expected evidence, the actual supplied evidence, `Met`, `Not met`, or `Blocked` status, and any unresolved researcher action.

Do not certify thematic saturation, intercoder reliability, validity, consensus or ethical compliance unless the named procedure was actually completed and evidenced.


## Step 4 — Compare positioning options and record the validation decision

**Prompt**

Offer Positioning Workshop Prompt

**Instructions**

Use ChatGPT with the source Prompt to translate reviewed research into bounded positioning options. Tie audience conditions and proof to evidence; preserve the sample limits and disconfirming interviews. Treat scoring as a discussion aid, not proof or approval. MANUAL / EXTERNAL final gate: authorised humans perform any agreed validation, supply its actual design/results, challenge the proposed choice and decide whether to adopt, revise, hold or seek more evidence. Validation not performed stays Not run. No customer contact, campaign, pricing or claim publication occurs in this Workflow.

**Input for this step**

Map offer facts and decision to Offer description and Business objective; permitted market evidence to Target market evidence; researcher-reviewed excerpts/patterns/contradictions to Customer research. Supply actual Competitive alternatives and Proof assets or mark them Unknown. State Commercial constraints, Brand and compliance constraints and Success criteria including evidence traceability and human validation. Approval of research does not authorise reuse for targeting or marketing contact.

**Carry forward**

Final handoff: evidence-linked option comparison, draft messages clearly marked Draft, unsupported claims and research limits, proposed validation plan, actual validation result or Not run, reviewer dissent, human decision or Pending, scope/conditions and smallest authorised next step. Pass to campaign planning only after the responsible owner accepts the evidence and unresolved risks. A hold decision is useful; it is not a completed positioning validation.

**Review note**

Marketing/research/product owners review implications and claim substantiation; privacy/other specialists review their scope where needed. Record actual human decision and validation evidence separately. Stop unsupported claims, sensitive targeting and unapproved release. Do not call the work validated, approved, launched or representative without the corresponding evidence.

**Prompt ID**

AMO-P-000021

**Prompt URL**

https://amo.ng/prompts/offer-positioning-workshop-prompt

**Prompt content**

Run an evidence-led offer positioning workshop using the following inputs.

Inputs
- Offer description: [Offer description]
- Business objective: [Business objective]
- Target market evidence: [Target market evidence]
- Customer research: [Customer research]
- Competitive alternatives: [Competitive alternatives]
- Proof assets: [Proof assets]
- Commercial constraints: [Commercial constraints]
- Brand and compliance constraints: [Brand and compliance constraints]
- Success criteria: [Success criteria]

Input gate
The minimum prerequisites are a sufficiently specific offer description, the business decision this positioning must support, and some basis for identifying a potential buyer or market. Useful supporting material includes interview transcripts, survey results, sales-call notes, win-loss findings, reviews, funnel data, pricing, testimonials, case studies, competitor pages, brand guidance, and applicable claim restrictions.

If a prerequisite is missing or contradictory, ask no more than five blocking questions and do not issue a final positioning recommendation. If optional evidence is absent, continue only with bounded hypotheses, identify what remains unknown, and reduce the confidence assigned to affected conclusions. Never fill an evidence gap with invented customer behavior, competitor facts, market statistics, testimonials, or performance results.

Tool and action boundaries
Use ChatGPT to organize the supplied material, identify patterns, facilitate positioning decisions, compare alternatives, draft options, and design validation tests. Do not imply that ChatGPT has interviewed customers, inspected private systems, accessed links, browsed current market information, or run experiments unless that capability was explicitly available and the resulting evidence is present. When browsing is explicitly enabled, cite the page URL, publisher, access date, and relevant observation; distinguish third-party claims from verified facts.

This workshop produces recommendations and draft marketing language only. Do not publish, contact customers, change campaigns, alter pricing, approve regulated claims, or represent the positioning as organizationally approved. Flag claims involving health, finance, legal outcomes, safety, guaranteed results, comparative superiority, sensitive traits, or named competitors for qualified human review. Exclude unnecessary personal or confidential data. Refuse deceptive positioning, fabricated scarcity, fake social proof, or messaging that disguises material limitations.

Evidence rules
1. Create stable evidence identifiers such as E1 and E2 for material used in the analysis.
2. Classify each material proposition as supplied fact, direct observation, reported customer statement, assumption, hypothesis, unknown, conflict, or unsupported claim.
3. Record the source, recency, segment relevance, and confidence for each important proposition. Do not convert repeated opinions into measured facts.
4. Preserve conflicting signals. Explain whether a conflict may result from segment, buying stage, customer maturity, geography, channel, sample bias, or stale evidence.
5. Tie every recommended promise, differentiator, objection response, and proof point to evidence identifiers or mark it explicitly as a hypothesis requiring validation.
6. Use words such as validated, proven, measured, approved, tested, or completed only when the supplied record shows the relevant action, method, result, and approval. Otherwise use proposed, drafted, unverified, unavailable, or blocked.

Workshop workflow
1. Frame the decision. Restate the offer, intended commercial outcome, decision deadline if supplied, non-negotiable constraints, stakeholders whose approval is required, and what this workshop will not decide.
2. Assess readiness. Determine whether the evidence supports a positioning recommendation, only provisional options, or discovery planning. Report the limiting gaps before continuing.
3. Segment the market by meaningful buying conditions rather than demographics alone. Compare buyer role, user role, triggering event, job to be done, current workaround, urgency, ability to buy, desired outcome, switching friction, and disqualifying conditions. Recommend one primary segment only when the evidence supports prioritization.
4. Model the problem. Separate symptoms from the underlying problem, quantify consequences only where evidence permits, identify emotional and operational stakes, and test whether the pain is urgent, costly, frequent, recognized, and addressable by this offer.
5. Map the competitive frame. Include direct competitors, internal solutions, manual workarounds, delay, and doing nothing. For each alternative, identify why buyers choose it, its strengths, its trade-offs, and the credible reason to switch. Do not manufacture competitor weaknesses.
6. Build the value logic. Connect offer capabilities to buyer outcomes through an explicit capability-to-benefit-to-outcome chain. Identify the mechanism that makes the promise plausible, expected time to value, dependencies, customer effort, exclusions, and conditions under which the offer is a poor fit.
7. Audit proof and claims. Match each potential claim to available proof such as demonstrations, product data, case studies, testimonials, credentials, methodology, guarantees, or third-party evidence. Flag weak attribution, tiny samples, outdated results, atypical outcomes, missing consent, and claims that require legal or compliance review.
8. Generate three materially different positioning territories. Each must specify the primary buyer, category or frame of reference, urgent problem, promised outcome, mechanism, differentiation, proof, principal trade-off, likely objection, and situations where the territory should not be used.
9. Score the territories from one to five on buyer relevance, urgency, distinctiveness, believability, evidence strength, defensibility, commercial fit, delivery fit, and compliance risk. Show the rationale and evidence identifiers behind each score. Keep score precision separate from evidence confidence and do not let an unsupported total determine the recommendation.
10. Make the decision. Recommend one territory, or state that no territory is supportable. Explain rejected alternatives, strategic trade-offs, dependencies, dissent or unresolved conflicts, and the human approval required before adoption.
11. Draft the positioning system. Produce a concise internal positioning statement, value proposition, short offer description, three message pillars, supporting proof, objection responses, disqualifier language, and claim caveats. Keep internal strategic language distinct from customer-facing copy.
12. Design validation. Convert the riskiest assumptions into prioritized tests using customer interviews, message testing, landing-page experiments, sales-call tagging, win-loss review, willingness-to-pay research, or campaign tests as appropriate. Specify the hypothesis, target segment, method, sample limitations, metric, decision threshold, duration or stopping rule, owner, required approval, and interpretation risks. Do not report tests as run.
13. Prepare the handoff. Identify which website, sales, product-marketing, enablement, advertising, and onboarding artifacts would need revision if the recommendation is approved. Separate draft work from authorized implementation and name the smallest reversible next step.

Required output
A. Decision frame and readiness status
- Decision to be made
- Commercial objective
- Scope and exclusions
- Required approvers
- Readiness: recommendation-ready, provisional, or blocked
- Blocking gaps and non-blocking limitations

B. Evidence and uncertainty register
Provide a table with: evidence ID, proposition or observation, classification, source, recency, segment relevance, confidence, conflict or limitation, and where it is used.

C. Buyer and problem model
Provide a segment comparison table and a primary-segment decision. Include buyer, user, trigger, job to be done, problem, consequence, desired outcome, purchase barriers, switching friction, and poor-fit criteria.

D. Competitive frame
Provide a table covering each alternative, why it wins, its credible strengths, trade-offs, switching trigger, and evidence status.

E. Claim and proof matrix
Provide: proposed claim, capability or mechanism, buyer outcome, evidence IDs, substantiation strength, caveat, prohibited or risky wording, and review owner.

F. Positioning territory scorecard
Compare three territories across all specified criteria. Show individual scores, evidence confidence, rationale, risks, and the meaningful strategic trade-off represented by each option.

G. Positioning decision
State the recommended territory or no-supportable-decision outcome. Include the internal positioning statement, value proposition, category frame, differentiation, reasons to believe, exclusions, rejected alternatives, unresolved dissent, confidence level, and approval status.

H. Messaging and objection framework
Provide three message pillars with supporting proof, a short offer description, the five most consequential objections with evidence-based responses, claim caveats, and language that identifies poor-fit buyers. Mark every item as draft.

I. Validation backlog
Provide a prioritized table with: assumption, risk if wrong, hypothesis, target segment, method, metric, acceptance threshold, stopping rule, owner, required approval, privacy or compliance control, current state, and interpretation risk.

J. Acceptance and verification record
Evaluate each success criterion using: criterion, expected evidence, actual supplied observation, evidence IDs, status as met, unmet, unknown, or conflicting, and action needed. Also verify that the recommendation names one priority buyer, addresses a recognized problem, distinguishes the offer from the full alternative set, uses supportable claims, acknowledges trade-offs, states poor-fit conditions, and can be tested. Do not mark acceptance complete without corresponding evidence.

K. Approval and implementation handoff
List proposed artifact changes, responsible owner, dependencies, review gates, reversible first action, and current state. End with the smallest safe next action; do not characterize any change as published, approved, tested, or implemented unless supplied evidence demonstrates that state.


## Completion criteria

Researcher has reviewed question, sample/method fit and interview suitability; unsuitable questions are routed elsewhere
Protocol has actual human review/pilot evidence before field use, or remains Not run / Blocked
Fieldwork and analysis are MANUAL / EXTERNAL; actual evidence and permission references are supplied before interpretation is treated as completed
Every important interpretation and positioning option traces to evidence, contradictory observations and explicit limits
No qualitative sample frequency is presented as market prevalence; no AI suggestion is counted as an independent human coder
Validation is MANUAL / EXTERNAL: actual result or Not run, and an accountable human decision, are recorded
Unresolved gates remain open; a completed AI response or Workflow review checkbox is not proof that fieldwork, validation or approval occurred