These standards are a living document. MAC will revise them as we publish, learn, receive feedback, and improve our process. The current version applies to work published after this page is adopted, with older work reviewed and updated over time where practical.
Why these standards exist
Marketing shapes decisions, expectations, trust, and the customer experience.
It also relies on claims, measurements, research, stories, and systems that are often difficult for the public, clients, or even marketers themselves to inspect.
MAC exists to ask better questions about those conditions. We also believe that a publication asking others to show their work should be prepared to show its own.
Marketing accountability begins when a claim can be explained, evidenced, and defended to the people affected by it.
What MAC publishes
Labels that tell readers what kind of work they're encountering.
We use labels to help readers understand the nature and evidentiary basis of a piece. A label is not a quality guarantee. It is a statement of the kind of work the reader is encountering.
| Type | What it means |
|---|---|
| Commentary | An author's argument, reflection, or professional observation. Material factual claims are sourced, and the article does not present opinion as established fact. |
| Analysis | An interpretation of evidence, policy, data, cases, or industry patterns. The article distinguishes documented facts from MAC's interpretation. |
| Case Study | An examination of a defined company, campaign, platform, product, event, or practice. It identifies scope, source material, relevant limitations, and context. |
| Investigation | A substantial evidence-based examination or original reporting on consequential conduct. It includes a methodology statement, documented sources, and an opportunity for material subjects to respond where feasible. |
| Framework or Tool | A reusable MAC method, diagnostic, checklist, or standard. It explains intended use, limitations, and version status. |
| Satire or Experimental Work | Creative or satirical material clearly labeled to reduce the likelihood that a reasonable reader mistakes it for reporting, analysis, or advice. |
How we handle evidence
Credible, relevant, and proportionate to the claim.
MAC aims to use evidence that is credible, relevant, and proportionate to the claim being made. We prioritize original or primary material when it is available and meaningful, including official policies, filings, court records, documented methodologies, campaign materials, direct interviews, and source data.
Independent research and reputable journalism can provide important context and corroboration. Vendor reports, platform studies, sponsored research, and first-party claims can be useful evidence, especially when the underlying data and method are available. They are not automatically independent proof.
We will identify meaningful limitations, including incomplete evidence, gaps in public data, uncertain causality, small or unrepresentative samples, methodology constraints, conflicts of interest, and changes in circumstances over time.
Source principle
A claim repeated by several secondary sources does not become stronger than the underlying evidence. Where practical, MAC will trace consequential claims to their originating source and assess whether that source supports the conclusion being repeated.
| Evidence type | How MAC uses it |
|---|---|
| Primary evidence | Used for core factual claims where possible. Examples include official documents, original data, documented methods, original campaign materials, and direct interviews. |
| Independent research and reporting | Used for context, corroboration, and comparative analysis. We consider the source's methods, funding, scope, and relevance. |
| Industry and vendor claims | Used with context about who produced the claim, how the research was conducted, and who may benefit from the conclusion. |
| Anecdotes and social content | Used as illustration or leads for further inquiry. They do not independently establish a broad pattern. |
| AI-generated output | Never treated as a source. Claims suggested by AI require verification against underlying evidence. |
Fact, interpretation, and uncertainty
A forceful conclusion should not obscure a weak evidentiary basis.
MAC will distinguish between what can be documented, what can be reasonably inferred, and what remains uncertain. We will avoid assigning motives or internal states to people or organizations without supporting evidence. When discussing incentives, organizational pressures, or likely consequences, MAC will make clear when it is offering a reasoned interpretation rather than a documented fact.
| What a reader may encounter | How we will treat it |
|---|---|
| Documented fact | Supported by a source or clearly identified primary material. |
| Observed pattern | Explained with relevant scope, method, and limitations. |
| Interpretation | Clearly presented as MAC's reading of the available facts, context, incentives, or consequences. |
| Opinion | Identified through the article label and supported where it includes material factual claims. |
| Uncertainty or dispute | Named directly when the evidence is incomplete, conflicting, unavailable, or reasonably open to more than one interpretation. |
How we evaluate marketing claims
The MAC Evaluation Standard.
For case studies, editorial reviews, investigations, and other work evaluating an external claim or practice, MAC may use the MAC Evaluation Standard. The framework helps readers follow the basis of an assessment.
| Question | What MAC will examine |
|---|---|
| What is the claim? | The specific statement, implication, promise, metric, or offer at issue. |
| Who receives it, and in what context? | The intended audience, channel, timing, product, service, or decision environment. |
| What evidence supports it? | The relevance, quality, completeness, and limitations of available substantiation. |
| What happens in practice? | Relevant policy, product behavior, pricing, data practice, customer experience, or operating evidence. |
| Who benefits, controls, and bears risk? | Who produces the measurement, benefits from the framing, controls key conditions, and bears the consequences if the claim fails. |
| What does the evidence establish? | What is substantiated, partially supported, unsupported, contradicted, unresolved, or in need of further evidence. |
| What response was offered? | Relevant context, correction, evidence, or disagreement from a materially affected subject where outreach was appropriate and feasible. |
| What changed? | Updates, corrections, clarifications, policy changes, or unresolved issues after publication. |
MAC's Evaluation Standard is an editorial framework. It does not replace legal, regulatory, scientific, or formal adjudicatory processes. Where legal or regulatory findings are relevant, MAC will attribute them to the responsible authority.
How we treat subjects fairly
Critical work is more useful when it is specific, evidence-led, and fair.
MAC may write critically about claims, practices, industries, institutions, and systems. When a post makes a material critique of a named company, platform, agency, organization, or person, MAC will seek an opportunity for the subject to provide relevant context, evidence, correction, or disagreement when doing so is feasible and appropriate to the work. We will represent material responses accurately and proportionately.
A subject's response does not automatically resolve an issue. It may add context, correct the record, identify missing information, or clarify a disagreement. MAC may maintain its conclusion after considering the response and will explain why when appropriate.
How we use AI
AI can assist work. It cannot carry responsibility for a published claim.
MAC may use AI-assisted tools for research organization, transcription, idea development, outlining, editing, coding, visualization, and administrative work. AI can help a small team organize and evaluate complex material. It can also produce plausible language and unreliable claims at speed.
Human contributors remain responsible for the thesis, source selection, factual accuracy, context, interpretation, fairness, and final editorial judgment of every published piece. AI-generated output is not treated as a source.
When AI materially contributes to analysis, reporting, data processing, visualization, or draft development, MAC will disclose the tool's role and the human review performed. If AI-assisted work contributes to a factual error, unsupported claim, fabricated source, or misleading presentation, MAC will correct the record visibly and review the process that permitted the error.
Corrections and updates
A correction is part of the work.
MAC expects its work to improve through challenge, correction, and new evidence. We will correct material factual errors visibly. Corrections and substantial updates will identify what changed and when practical, why it changed. When new evidence, a subject response, or changed circumstances materially affect an earlier conclusion, MAC will update the original work or publish a linked follow-up.
MAC will maintain a public corrections and updates record as this practice develops. We also plan to periodically review our own work and identify conclusions, predictions, or methods that require reconsideration.
Relationships and conflicts
Disclose what could reasonably affect a reader's understanding.
MAC will disclose sponsorships, paid relationships, barter arrangements, affiliate relationships, material professional affiliations, and other conflicts that could reasonably affect a reader's understanding of a piece. Contributors will identify relevant relationships connected to the subject of their work.
Having experience in an industry, company, platform, agency, or discipline does not disqualify a contributor from writing about it. It may provide valuable context. Relevant experience should be disclosed when it could reasonably shape the reader's assessment of the work.
Reader challenges and research ideas
We welcome disagreement that engages the evidence and reasoning.
Readers can challenge MAC's work, provide additional evidence, request corrections, suggest an issue for follow-up, or propose a research question.
To submit a correction, challenge, source, or research idea, use the form below or contact us at Jay@jaymandel.com. For a useful submission, include the question or claim, relevant context, available source material, and any information that would help MAC investigate it fairly. Anonymous submissions may be accepted, although MAC may be limited in what it can verify or publish without corroboration.
Help shape the work
Use the submission tool on this page to check a post against these standards, or email a correction, challenge, source, or research idea.
Research reports
Transparent inquiry rather than predetermined conclusions.
MAC is developing research reports around narrow, answerable questions that matter to marketers, customers, and the broader public.
Where relevant and feasible, a MAC research report will identify its research question, scope, source materials, data collection or selection method, assumptions, limitations, AI-assisted role, findings, interpretation, competing explanations, relevant subject responses, and updates.
Potential areas of inquiry include the sources behind widely repeated marketing statistics and infographics; AI claims about productivity, accuracy, and personalization; platform measurement; data consent; agency-client reporting; subscription design; and gaps between public brand values and operating practice.
A living standard
We will earn credibility through the quality of our questions.
MAC is still building its public body of work. These standards are intended to establish the habits we want to be known for: careful evidence handling, clear labels, fair evaluation, transparent AI use, meaningful correction, and a willingness to learn in public.
We are experienced marketers with beliefs, blind spots, and a conviction that the profession can do better. We will earn credibility through the quality of our questions, the transparency of our methods, the fairness of our evaluations, and the willingness to revise the record when better evidence requires it.
MAC's credibility will come from being as transparent about its own work as it asks others to be about theirs.
Version 0.1 · Draft for review · Last updated August 30, 2026
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