market-research-reports – skill Dữ liệu & Khoa học cho agent AI

Market Research Reports

Purpose

Create decision-focused market reports whose claims, calculations, assumptions,

and uncertainties can be audited. Match depth and format to the question and

evidence. There is no required length, chapter count, visual count, or output

format.

Do not:

• imitate or imply affiliation with a consulting, analyst, or research brand;
• invent citations, quotes, market shares, or paid-market figures;
• present TAM/SAM/SOM or a forecast as one certain truth;
• treat a framework, chart, or fluent narrative as evidence;
• provide investment, legal, antitrust, tax, accounting, or regulatory advice.

Operating principles

1. Define before sizing. Fix product, customer, geography, channel, period,

measure, unit, denominator, currency/base year, and taxonomy.

2. Map every claim. Every factual or quantitative claim has a claim ID and

exact source IDs.

3. Separate statement types. Distinguish facts, estimates, calculations,

forecasts, opinions, and recommendations.

4. Prefer primary evidence. Use official statistics, regulator records,

filed company disclosures, and transparent original studies before

secondary synthesis.

5. Preserve uncertainty. Retain source conflicts, revisions, scenario

ranges, sensitivity, and limitations.

6. Keep methods reproducible. Use local structured inputs and deterministic

calculations when practical.

7. Collect lawfully and ethically. No deception, PII disclosure, access

circumvention, confidential material, or trade-secret acquisition.

Workflow

1. Establish the research contract

Clarify:

• decision, audience, deadline, and materiality threshold;
• formal market definition and adjacent exclusions;
• buyer, payer, user, transaction, and value-chain level;
• geography and treatment of imports, exports, and channels;
• historical period, forecast period, and retrieval cutoff;
• revenue/expenditure, gross output/value added, units, capacity, users, or

another measure;

• stock/flow, gross/net, taxes, and denominator;
• currency, base year, and nominal/real/current/constant basis;
• industry and product classification with version;
• permitted data sources, primary research, confidentiality, and output format.

Ask a focused question when a missing choice would materially change the

denominator or result. Otherwise state a provisional scope and proceed.

Use references/report_structure_guide.md for modular report design.

2. Build the evidence plan

Route each question to the source closest to the underlying event:

1. primary law, regulator decision, official filing, or official statistic;

2. original company filing or attributable first-party disclosure;

3. transparent survey/study with inspectable methods;

4. institutional or peer-reviewed research using identifiable primary data;

5. industry association data with disclosed coverage;

6. reputable secondary synthesis;

7. lawfully accessed paid estimate with inspectable scope and method;

8. news/commentary for leads or attributable events.

For company data, prefer the official filing system in the relevant

jurisdiction. For industry, labor, prices, population, trade, and national

accounts, prefer the responsible national statistical agency or central bank.

For cross-country work, use harmonized World Bank, IMF, OECD, or Eurostat data

only after checking definitions and original-source lineage.

Read references/official_data_sources.md before using public APIs. API rules

and limits are a dated snapshot: verify current official terms before automated

or high-volume retrieval. Never put an API key in a report or bundled script.

3. Create the source ledger

Assign stable IDs (S-001, S-002, ...). Record:

• title, publisher, URL/persistent ID, source type;
• publication date and retrieval date;
• original producer when accessed through an aggregator;
• geography, covered population, period, and vintage;
• currency, base year, price basis, measure type, unit, and denominator;
• taxonomy and version;
• preliminary/revised/final/current status;
• method, sample, imputation, suppression, and limitations;
• license/terms and lawful local snapshot path.

Use assets/source_ledger_template.csv and validate it:

python3 scripts/validate_evidence_ledger.py data/source_ledger.csv

If publication date is unavailable, record not-stated; do not guess.

4. Maintain a claims ledger

Assign IDs (C-001, ...). Keep the exact claim text, statement type, source

IDs, report location, as-of date, geography, currency/base, measure/unit,

taxonomy, revision status, confidence, calculation ID, and assumption IDs.

Rules:

• one end-of-paragraph citation does not support unrelated sentences;
• split compound claims that rely on different evidence;
• a calculation cites its inputs, not a source that never published the result;
• an aggregator and its original source are not independent corroboration;
• an interview theme is not population prevalence;
• absence of public feature evidence means unknown, not no.

Audit mappings:

python3 scripts/audit_claim_citations.py \
  data/claims.csv data/source_ledger.csv

See references/evidence_model.md.

5. Size the market as scenarios

Measurement guardrails

Give every component a disjoint coverage_key and one shared

denominator_id. Do not add:

• manufacturer revenue to distributor or end-customer spend;
• production, imports, and sales without trade/inventory reconciliation;
• parent and subsidiary revenue;
• bundles and their included components;
• gross output and value added;
• installed-base stock and annual transaction flow;
• overlapping customer or geographic segments.

Use product classifications and supply-use logic when industry codes are too

broad. Preserve an unknown/residual category instead of forcing totals.

Top-down and bottom-up

Compute independently:

TAM_top = sum(disjoint in-scope component values)

TAM_bottom =
  sum(customer_count
      * addressable_fraction
      * annual_quantity_per_customer
      * price_per_unit)

Then apply scenario-specific serviceability and capture assumptions:

SAM_s = TAM * serviceable_fraction_s
SOM_s = SAM_s * obtainable_share_s

Use at least two genuinely different scenarios; a downside/base/upside set is

usually useful. State horizon, constraints, evidence, and assumptions. SOM is

not a guaranteed revenue forecast.

Run the deterministic calculator:

python3 scripts/calculate_market_sizing.py \
  assets/market_sizing_scenarios_template.json

Report both methods, midpoint-relative gap, scope differences, sensitivity, and

unresolved reconciliation. Do not average incompatible methods.

6. Forecast with explicit uncertainty

Separate observed, estimated, and forecast periods. Record series ID,

frequency, units, seasonal adjustment, transformations, taxonomy breaks,

retrieval date, and vintage/revisions.

For each scenario:

• provide an annual rate path or driver equations;
• state demand, price, supply, regulation, competition, capacity, and timing

assumptions;

• list evidence and assumption IDs;
• identify conditions that invalidate the scenario.

Do not call scenario bounds confidence or prediction intervals. Do not assign

probabilities without a validated probabilistic model and diagnostics.

Run:

python3 scripts/forecast_sensitivity.py \
  assets/forecast_sensitivity_template.json

Show the range by year, endpoint sensitivity, influential assumptions, and

switching values. See references/data_analysis_patterns.md.

7. Analyze customers and primary research

For survey evidence, disclose sponsor, target population, frame,

probability/non-probability design, recruitment, mode/language, field dates,

unweighted sample, subgroup bases, weighting, response/participation,

instrument wording, precision, processing, and limitations.

For interviews/focus groups, disclose recruitment, consent, role coverage,

dates/mode, guide, coding, divergent evidence, privacy controls, and limits to

generalization.

Label AI-generated responses as simulations, not human survey respondents or

observed demand. Disclose AI-assisted collection/processing and human validation.

Before reporting a trend across survey waves, compare the exact wording, response

options, question order, target population, recruitment, mode, and weighting.

A changed instrument or sample can create an apparent demand shift. Mark the break,

use an overlap/bridge study when available, or report the waves separately rather

than feeding the difference into a growth forecast. See [AAPOR best practices](https://aapor.org/standards-and-ethics/best-practices/).

Never:

• collect more personal data than necessary;
• place direct identifiers or raw recordings in report artifacts;
• use research as disguised selling or lead generation;
• misrepresent identity/purpose;
• pressure participants to reveal employer/customer s

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