Product Data Enrichment in 2026: What to Enrich First and Why

Product data enrichment in 2026: a new baseline for ecommerce

Product data enrichment has shifted from an optional cleanup task to a foundational requirement. In 2026, the bar has moved: enrichment is no longer just about filling empty fields or rewriting descriptions. It is about making every product record complete, consistent, and structured enough to perform across your own storefront, marketplace channels, paid feeds, and an entirely new layer of AI-powered discovery.

The catalyst behind this shift is the rise of agentic commerce. AI shopping agents now compare, recommend, and even purchase products on behalf of consumers by parsing structured product data. According to McKinsey, AI agents could mediate $3 to $5 trillion in global consumer commerce by 2030, and 2026 is widely considered the tipping point. If your product data is not machine-readable and richly attributed, your catalog becomes invisible to these systems.

That said, the core challenge has not changed: most teams still receive supplier files with inconsistent naming, missing identifiers, mixed units, and sparse attributes. Trying to fix everything simultaneously leads to a stalled project. The smarter approach is to treat enrichment as a prioritized system with clear layers, measurable outcomes, and repeatable processes. If your team is still working through foundational data issues, the product data quality checklist is a practical place to start.

What to enrich first: high-impact fields by priority layer

The most effective enrichment strategies work in layers. Each layer builds on the one before it, so skipping ahead rarely pays off.

Layer 1: eligibility-critical fields

These fields determine whether your products can be listed, matched, and trusted by channel systems at all:

• Core identity: stable SKU or product ID, brand name, GTIN or MPN where applicable.
• Commercial state: price, availability, condition, and shipping metadata.
• Category and product type: a consistent classification path mapped to each channel's taxonomy.
• Structured title: a canonical naming pattern that disambiguates variants and communicates intent clearly.

If any of these are weak or missing, further enrichment has limited impact. You cannot improve discoverability for a product that fails validation. For a deeper dive into identifier gaps, see our guide on missing EAN and GTIN in listings.

Layer 2: discoverability and filtering fields

Once eligibility is solid, the next priority is making products findable for specific queries:

• Variant attributes: size, color, material, capacity, pattern.
• Compatibility data: model fitment, included accessories, use-case context.
• Technical specifications: dimensions, weight, power rating, fabric composition, certifications.
• Granular product type values that power faceted navigation and on-site filtering.

This layer is where most teams unlock the biggest gains in search performance and long-tail traffic. A well-structured product taxonomy is essential for this to work.

Layer 3: conversion confidence fields

With the first two layers stable, focus on attributes that reduce purchase uncertainty:

• A complete, consistent image set covering all variants.
• Benefit-led descriptions grounded in verified product attributes.
• Warranty details, return policies, and trust signals.
• Structured merchandising labels for campaign targeting and margin-based workflows.

A simple rule for backlog grooming: if a field affects listing approval, fix it immediately. If it affects search retrieval, prioritize it next. If it affects buyer confidence, address it once the foundation is stable.

Agentic commerce and the new data requirements it demands

The most consequential shift in ecommerce this year is the emergence of AI shopping agents. Google, Microsoft, and others are rolling out tools that allow consumers to delegate product research, comparison, and even purchasing to autonomous agents. This is not theoretical; it is happening in production.

For product data teams, this creates a new category of requirements:

1. Machine-readable structure over marketing copy. Agents do not browse product pages the way humans do. They parse structured attributes, schema markup, and standardized values. A well-written description still matters for human shoppers, but without structured data behind it, agents will skip your products entirely.

2. Answer-oriented content. Agents resolve queries by matching intent to attributes. Products with comprehensive FAQ content, compatibility tables, and solution-oriented descriptions get matched more often. This means enrichment needs to anticipate what a shopping agent might ask, not just what a human might search.

3. Consistent identifiers across channels. Agents operate cross-platform. If your GTIN maps to one product on your storefront and a slightly different variant on a marketplace, the agent loses confidence. Identifier hygiene is now a discovery issue, not just a compliance checkbox.

4. Richer attribute depth. Google has announced dozens of new Merchant Center attributes designed for conversational and agentic commerce, including compatible accessories, substitutes, and answers to common product questions. Enriching these fields now is a way to get ahead of competitors who have not adapted.

Teams using Lasso can automate much of this work: normalizing supplier inputs, filling attribute gaps with AI, and validating outputs against channel-specific schemas in a single workflow, so enrichment keeps pace with these evolving requirements.

The 80/20 enrichment sprint: a four-week rollout plan

Catalog-wide rewrites almost always stall. A more effective approach is to pick a high-impact slice of your catalog and run a focused sprint.

Week 1: scope and baseline

• Identify one or two categories with strong revenue and frequent data issues.
• Audit field completeness and value consistency for those categories.
• Define your priority field list (10 to 15 fields from Layers 1 and 2).
• Record baseline KPIs: feed error rate, zero-result search rate, listing CTR, time-to-publish.

Weeks 2 and 3: normalize and enrich

• Standardize naming conventions: units, abbreviations, casing, synonym resolution.
• Fill missing identifiers and map categories to channel taxonomies.
• Enrich high-intent attributes: size, material, compatibility, dimensions.
• Validate variant integrity so parent-child relationships are clean.

This phase is where automation has the largest effect. Instead of manually patching records SKU by SKU, teams that use Lasso run mapping, normalization, and gap-filling as a single automated pipeline. The difference is especially visible in catalogs with inconsistent product titles or data merged from multiple supplier sources.

Week 4: publish, measure, expand

• Push enriched data to storefront and feed endpoints.
• Re-check platform diagnostics and listing quality alerts.
• Compare baseline versus post-sprint KPIs.
• Document what worked and convert it into a reusable category template.

After one successful sprint, scale by applying the same template to the next category. This is how enrichment becomes a repeatable operating rhythm rather than a one-off cleanup.

Measuring enrichment: the KPIs that actually matter

Enrichment without measurement is just busywork. The goal is to track both leading indicators (data quality improvements) and lagging indicators (business outcomes).

Leading indicators to track monthly:

• Attribute completeness rate across your priority field set.
• Value normalization score: reduction in duplicate, inconsistent, or invalid values.
• Feed issue rate: errors and warnings per 1,000 SKUs.
• Time-to-publish: how quickly new products go from supplier file to live listing.

Lagging indicators to track quarterly:

• Conversion rate lift in enriched categories versus control groups.
• Zero-result search rate reduction on your storefront.
• Return rate changes for specification-sensitive products.
• Revenue per session in categories that received enrichment.

Industry benchmarks suggest that companies implementing structured enrichment programs see measurable improvements in feed approval rates and on-site findability within the first sprint cycle. The compounding effect becomes significant over two to three quarters as enrichment coverage expands across the catalog.

A practical governance cadence:

• Weekly: fix the highest-risk attribute gaps in active categories.
• Monthly: refresh controlled vocabularies and update mapping rules.
• Quarterly: retire unused fields, raise quality thresholds, and audit taxonomy drift.

For teams that want to automate the measurement layer alongside enrichment, Lasso's pricing includes built-in validation and QA checkpoints that surface issues before they reach your channels.

Getting started: your first move this quarter

If your catalog has data quality issues, momentum is more valuable than perfection. Here is a pragmatic starting sequence:

1. Pick one category where missing or inconsistent attributes are causing visible feed errors or search friction.
2. Lock in the 10 to 15 highest-impact fields using the layer framework above.
3. Run the four-week 80/20 sprint.
4. Prove KPI improvement to stakeholders.
5. Apply the same template to the next category.

For teams dealing with data from multiple suppliers, the process of standardizing supplier product data is often the prerequisite step that makes everything else easier. And if your product descriptions need work alongside the structured data, our guide on product descriptions that sell covers how to write copy grounded in enriched attributes rather than guesswork.

The enrichment landscape in 2026 rewards teams that build repeatable systems, not those that chase one-time perfection. Start small, measure rigorously, and scale what works.