What the Multichannel product-feed and data-consistency audit prompt does

Act as a commerce-feed architect and product-data quality auditor.

The prompt will, at minimum:

  • Validate the supplied datasets, definitions, time window, market scope and source-of-truth ownership before assessing multichannel product-feed and data-consistency audit
  • Examine source-of-truth ownership, identifiers, variants, required attributes, taxonomy mapping, title and description rules, price and availability synchronisation, image compliance, diagnostics and update latency; retain original record identifiers and show how each finding was derived
  • Segment results only where the data supports the split; expose missingness, sample bias, seasonality, policy changes, promotions, migrations and other confounders rather than hiding them in averages
  • Recompute every material metric from supplied values, disclose formulas, denominators, exclusions and scenario assumptions, and never invent benchmarks or competitor performance
  • Turn the evidence into a channel-by-channel feed remediation and governance plan; assign owner, priority, dependency, expected signal, verification method and human-approval point to each action

Who it is for

Gökhan Güzel's e-commerce prompt for ChatGPT users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Executive summary and data-quality report
  • Multichannel product-feed and data-consistency audit methodology and evidence ledger
  • Segmented findings, calculations and scoring
  • Prioritised action backlog with owners and validation criteria
  • Sources, limitations, confidence and QA report

Variables

PlaceholderPurpose
{{attribute_dictionary}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{brand_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{business_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{channel_feeds}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{channel_list}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{diagnostics_exports}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{identifier_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{price_inventory_sources}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{source_catalog}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{success_metrics}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{target_market}}Target market
{{update_frequency}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{variant_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new ChatGPT conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run Multichannel product-feed and data-consistency audit in ChatGPT

Open a new ChatGPT chat, paste the filled-in Multichannel product-feed and data-consistency audit prompt and answer the short question gate. ChatGPT then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: