The Product Data Audit: A Step-by-Step Template for Ecommerce Teams
A product data audit is the essential first step in any enrichment project, any channel launch, and any commercial performance investigation where product data may be a contributing factor. Without one, you are enriching blind, unable to prioritize correctly, unable to measure improvement, and unable to distinguish data-caused performance problems from other causes. This guide provides a complete, repeatable audit template that any ecommerce team can execute in a week.
Before You Start: Define Your Audit Scope
A product data audit can be as narrow, one category, one channel, or as broad, full catalog, all channels, as your resources allow. Before beginning:
- Define the catalog scope, which categories and how many SKUs will you audit? For a first audit, start with your top-3 revenue categories. They represent the highest commercial impact for any gaps you find.
- Define the channel scope, which channels are in scope? At minimum: your DTC website, Google Shopping, and, if applicable, Amazon. Each has different data requirements.
- Define the standard, what does “complete” look like for this audit? You need a reference for what fields should be populated, at what quality standard, for each category and channel. If you do not have this documented, part of your audit is creating it.
Do not start broad by default
A first audit that is too broad often stalls before it produces useful action. Starting with the top-3 revenue categories gives you enough coverage to expose systemic issues while keeping the work operationally realistic.
The 7-Component Audit Framework
What the audit covers end to end
Completeness
Which fields exist, and where the blank-rate is already costing visibility.
Precision
Whether populated values are actually queryable and normalized.
Identifiers + channels
GTINs, schema, and feed diagnostics that govern eligibility and trust.
Commercial outcomes
Titles, returns, and suppression signals that tie data quality to revenue impact.
Component 1: Attribute Completeness Audit
This is the most commercially impactful audit component. For each category in scope, identify the required and recommended attributes for each channel and measure what percentage of products have each attribute populated.
How to execute: export your full product catalog from your data source, PIM, ecommerce platform, or spreadsheet. For each category, list the target attributes, from your channel requirements documentation, or from the channel’s own attribute requirement pages. For each attribute, use a COUNTIF or equivalent formula to count products with a non-blank value. Divide by total product count to get completeness percentage.
Output: an attribute completeness matrix, categories on one axis, attributes on the other, completeness rates in each cell. Any cell below 80% is an enrichment priority. Any cell below 50% is a critical gap.
Component 2: Value Precision Audit
Completeness does not guarantee quality. This component identifies attributes that are populated but with imprecise, descriptive, or non-machine-queryable values.
How to execute: for each of your top-10 numeric or categorical attributes, pull the unique values across your catalog. Sort by frequency. Identify: (a) descriptive values used where numeric values are required (“lightweight” in a weight field); (b) variant expressions where canonical values should be used (“navy,” “midnight,” “dark blue” all in a single color attribute field); (c) unit-ambiguous values, numbers without units in a field where units are required.
Output: a precision issues list, attribute, problem type (descriptive/variant/unit-missing), frequency, and sample values. This becomes your normalization work order.
Component 3: Title Quality Audit
Evaluate your current titles against the channel-specific formula standards for each category.
How to execute: export titles and evaluate each against a scoring rubric: (1) does the title contain the primary category keyword? (2) is the keyword in the first 70 characters? (3) does the title follow the channel-specific formula? (4) are any prohibited terms present (promotional language, special characters, all-caps words)? (5) is the title within the optimal character count range for the channel? Score 1 point per yes, 0 per no. A score of 4–5 is a passing title. Below 4 is a rewrite priority.
Component 4: GTIN and Identifier Audit
Evaluate GTIN coverage and validity across your catalog.
How to execute: export the GTIN field from your product feed. For each product: (a) is the GTIN field populated? (b) does the GTIN pass GS1 check digit validation? (c) does the GTIN correspond to a real GS1-registered product, query the GS1 registry for a sample? (d) for products without a GTIN, is this because it is a private-label product, or because a GTIN exists but was never sourced?
Output: GTIN gap count, validation error count, private-label product count needing GS1 registration. Total GTIN coverage percentage across the catalog in scope.
Component 5: Schema Markup Audit
Evaluate schema.org implementation on your DTC product pages.
How to execute: run a sample of 20 product URLs through Google’s Rich Results Test. For each URL, record: Product schema present (yes/no); Offer fields complete (yes/no); aggregateRating present (yes/no); additionalProperty fields present (yes/no); any validation errors listed. Also check feed-schema agreement: compare the schema price and availability against your Merchant Center feed for the same products.
Output: schema completeness score (average across sampled pages), list of validation errors, count of feed-schema conflicts.
Component 6: Channel Feed Diagnostic Audit
Evaluate your current channel performance data for data-quality signals.
How to execute: Google Merchant Center, pull the active Diagnostics tab. Count products with errors by error type (price mismatch, missing GTIN, policy violation, etc.). Calculate total disapproval rate (disapproved ÷ total) and express as a monthly ad spend waste figure (disapproval rate × monthly Shopping spend). Amazon Seller Central, open the Listing Quality Dashboard. Count ASINs by quality score range (below 60, 60–70, 70–80, above 80). Calculate the percentage below the suppression threshold.
Component 7: Return Rate by Reason Audit
Identify data accuracy problems revealed by returns data.
How to execute: pull your returns dashboard filtered for the top-20 returned SKUs. For each, record the primary return reason. Calculate what percentage of returns are attributed to “not as described,” “wrong size,” “different from image,” or “not as expected.” These are all data accuracy or completeness failures. For each data-attributable return reason, identify the specific attribute or field that was missing or inaccurate.
Output: return rate attributable to data problems (expressed as a cost), list of specific data gaps identified through return reasons.
Consolidating Audit Findings into a Prioritized Action Plan
The audit produces findings across 7 components. The prioritization framework for converting findings into an action plan:
| Finding Type | Priority Logic | Typical First Action |
|---|---|---|
| Attribute completeness < 50% for Tier 1 filter attribute | Critical, complete query exclusion for every filter user of that attribute. Highest commercial impact. | AI enrichment sprint for that attribute across all products in the category, this week. |
| Google Merchant Center disapproval rate > 5% | High, active ad spend producing zero impressions. Immediately quantifiable waste. | Diagnose top error type in Merchant Center; fix data issue causing most disapprovals first. |
| Amazon ASINs below quality score 65 | High, products invisible in organic search; paid campaigns dragging below efficient threshold. | Listing quality improvement sprint for suppressed ASINs; attribute completion and image compliance first. |
| GTIN coverage below 70% | Medium-High, entity matching not achieved for significant share of catalog; compounding Shopping Graph disadvantage. | GTIN sourcing project; supplier data request; GS1 registration for private-label products. |
| Schema feed-schema conflicts | Medium, active Shopping Graph trust penalty; potential Merchant Center policy issue. | Audit schema generation logic; switch to dynamic schema if static; fix conflicts this sprint. |
| Title quality score below 3/5 for top-revenue SKUs | Medium, organic rank and CTR below potential on high-value products. | Title rewrite for top-50 revenue SKUs; generate channel-specific variants per formula. |
| Return rate “not as described” above category benchmark | Medium, ongoing data accuracy cost; Amazon listing quality risk. | Identify specific inaccurate attributes from return feedback; fix attribute values for top-returned SKUs. |
How findings turn into action
Measure
Collect baseline data across completeness, precision, identifiers, schema, channels, and returns.
Rank by impact
Prioritize gaps by exclusion risk, wasted spend, suppression, and revenue leakage.
Assign sprint actions
Translate each finding into a specific remediation work order with ownership.
Re-audit
Use the same template to verify improvement and reset priorities each cycle.
Visibility loss first
Filter exclusion, disapprovals, and suppression usually outrank every other issue in commercial urgency.
Then structural trust
GTIN, schema, and feed conflicts shape entity confidence and long-run discoverability.
Then conversion friction
Titles and return-causing data issues matter next once exclusion issues are under control.
How Velou Runs Catalog Audits
Commerce-1’s catalog intelligence function automates every component of this audit, attribute completeness rates per category, value precision scoring, GTIN coverage and validation, title quality scoring, schema completeness evaluation, and return-rate analysis, producing a unified catalog health report with commercial impact estimates for each finding.
The manual audit template in this article is the approach for teams without AI enrichment tooling. For teams who want the same analysis automated across their full catalog in hours rather than a week, this is what we do.
Get a full catalog audit in hours, not a week
Commerce-1 automates all 7 audit components across your full catalog with commercial impact estimates.
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