B2B Ecommerce for Distributors: Why Product Data Is the Real Growth Bottleneck

B2B ecommerce for distributors has moved beyond the question of whether buyers will use a digital channel. The more important question is whether a distributor can make enough of its assortment usable online to meet that demand.

MarketScale’s analysis of B2B digital commerce captures the shift through Watsco, where digital channels now represent 37% of revenue. That is not an experimental channel sitting at the edge of the business. It is a core revenue engine.

The benchmark also points to a less visible constraint. As digital revenue grows, distributors must publish broader assortments, onboard supplier ranges faster, and make technical products easy to find and compare. The bottleneck moves down the stack—from launching an ecommerce site to preparing the product information that makes the site work.

B2B ecommerce has crossed the adoption threshold

For years, distributors could treat ecommerce as another ordering option: useful for repeat purchases, but secondary to branches, phone orders, and sales representatives. That framing breaks down when a meaningful share of revenue passes through digital channels.

At that point, the online catalog has to carry more of the commercial workload. It must help a buyer identify the right product, filter by technical requirements, understand availability, compare alternatives, find a substitute, and order confidently without asking a salesperson to interpret every record.

That changes the unit of competition. A storefront launch is a project. Maintaining a complete, searchable, trustworthy catalog is a continuing operating capability. The second is harder because the raw material arrives from many organizations that do not share a data model.

The ecommerce platform is only the visible layer

A polished product detail page can make digital commerce look like a presentation problem. But a product cannot become reliably searchable and purchasable until several less visible decisions have been made:

1. Identity: Is the supplier row a net-new product, a duplicate, a new pack size, or another supplier’s offer for an item already in the catalog?
2. Classification: Which category and attribute schema apply to it?
3. Normalization: Do units, names, values, and formats follow the distributor’s conventions?
4. Enrichment: Are the specifications buyers need present, or trapped in a PDF, datasheet, image, or description?
5. Validation: Does the record meet the requirements for publication, and can important values be traced to a source?
6. Distribution: Can the approved record move consistently into the ERP, PIM, ecommerce platform, marketplace feed, and search index?

The ecommerce application renders the answer to those questions. It does not, by itself, resolve them. A better site cannot infer that two supplier codes describe the same manufacturer part, recover a missing voltage from a technical document with guaranteed accuracy, or decide whether conflicting dimensions should overwrite an existing record.

This is why B2B catalog management is becoming an ecommerce growth discipline rather than an administrative task.

More suppliers create compounding catalog complexity

Assortment growth is rarely a clean multiplication of rows. Every supplier introduces a new interpretation problem.

One sends a spreadsheet with one product per row; another uses a workbook per family. One supplies manufacturer part numbers with punctuation; another removes it. A third puts dimensions in a PDF and uses marketing names where the distributor expects technical categories. The same item may appear under a supplier SKU, manufacturer part number, GTIN, legacy ERP code, and marketplace identifier—with none consistently populated.

The result is not merely more data. It is more possible conflicts among data:

Adding the tenth supplier is therefore not simply repeating the work required for the first. Each new range must be reconciled with the catalog that already exists. Without durable matching, mapping, and validation rules, the number of manual comparisons expands with the assortment.

A repeatable supplier onboarding process and explicit attribute-mapping workflow turn that complexity into controlled exceptions instead of another spreadsheet project.

Poor product data directly limits ecommerce growth

Product data quality can sound like an internal housekeeping metric. In digital distribution it governs revenue-producing customer experiences.

• Supplier onboarding speed: A signed supplier agreement does not expand the sellable assortment until the products are live.
• Time to publish: Manual extraction and category mapping delay the return on assortment investments.
• Search and filtering: Missing or inconsistent technical attributes cause relevant products to disappear from results.
• Product comparison: Buyers cannot evaluate alternatives when comparable specifications occupy different fields or units.
• Recommendations and cross-sell: Models trained on weak identity and relationships recommend irrelevant accessories or duplicate items.
• Substitution: A credible substitute requires structured evidence that form, fit, function, and constraints are compatible.
• Customer confidence: Incomplete dimensions, unexplained conflicts, and duplicated pages make buyers verify information elsewhere—or buy elsewhere.

The commercial effect compounds. A product that is present in the ERP but absent from a category filter is technically listed and practically invisible. A range that takes twelve weeks to onboard misses twelve weeks of demand. A duplicate record does not just make the catalog untidy; it divides reviews, inventory signals, content quality, and search authority.

That makes product data quality and product matching leading indicators of ecommerce performance, not back-office cleanup activities.

Agentic commerce raises the stakes

AI does not remove the need to structure a distributor catalog. It increases it.

If an AI agent is expected to search a catalog, answer a technical question, compare specifications, recommend a product, find an approved substitute, or eventually place an order, it needs three foundations:

• Reliable product identity so it knows which records represent the same product, a variant, an accessory, or an alternative.
• Structured attributes so it can compare like with like rather than guess from descriptions.
• Provenance so important facts can be checked against a supplier document or other authoritative source.

Without those foundations, automation simply allows incorrect catalog decisions to happen faster. An agent can confidently compare a millimetre value with an inch value, recommend an incompatible substitute, or treat two offers as two distinct products. Fluent output does not repair the record underneath it.

The important distinction is between data that a machine can read and data on which a machine can safely act. JSON fields and schema markup can make a catalog machine-readable. Deduplicated identities, normalized units, validated attributes, confidence thresholds, and source evidence make it operationally trustworthy. Our deeper analysis of agentic commerce and machine-readable product data explains why maintaining that trust is a continuous job.

Product data becomes infrastructure

This argument does not require distributors to replace every core system. Those systems have different jobs:

The architectural gap is between incoming information and the systems expected to use it. Supplier files do not arrive ready for an ERP. PDFs do not arrive as PIM attributes. A collection of plausible matches does not arrive as approved catalog identity.

Claro is validating an execution layer for that gap. The aim is not to become another system of record. It is to do the recurring work between fragmented supplier information and the distributor’s existing ERP, PIM, ecommerce platforms, marketplaces, and internal catalogs: ingest, match, classify, enrich, validate, retain provenance, route exceptions to people, and write trusted results back.

That is one architectural response, not a reason to start with a software purchase. Distributors should first identify where supplier information stops flowing, which decisions are repeatedly made by hand, and which errors have the highest commercial consequence. The right execution model is the one that improves those workflows while preserving governance in the systems teams already operate.

What distributors should measure

Revenue share shows that digital commerce matters. Operational measures show whether the catalog can support its next stage of growth.

These metrics should be segmented. An average completeness score can hide an important category with no voltage values or a strategic supplier whose records require manual review. Measure by supplier, category, attribute, and commercial priority. Then connect operational improvements—such as lower intervention rates or faster classification—to time to publish, search conversion, and digital revenue.

The goal is not a perfect catalog frozen in time. It is a controlled system that processes change quickly, publishes trustworthy records, and makes uncertainty visible before it reaches a buyer or an agent.

The next ecommerce advantage sits below the storefront

MarketScale’s Watsco example is a useful signal because 37% of revenue through digital channels changes what counts as infrastructure. The catalog is no longer supporting the channel from the back office. It is part of the channel.

The next phase of B2B ecommerce competition will not only be about who has the best storefront, personalization engine, or AI assistant. It will be about who can turn supplier information into usable digital catalog data fastest—and keep it usable as suppliers, products, specifications, and channels change.

For distributors, that is the practical progression: more ecommerce creates pressure for a larger assortment; a larger assortment creates supplier-data complexity; and that complexity makes product data management a growth capability. Fixing the layer underneath the storefront is how digital ambition becomes a scalable catalog rather than a growing onboarding queue.

Source and further reading