Analysis of 100 industrial purchase questions highlights technical filtering, part-number search and compatibility data as key sources of buyer friction

A new review from Optimum7 found that 72 of 100 industrial purchase questions required buyers to identify the right product before reaching a product page, highlighting the importance of product discovery in industrial ecommerce conversion.

The review analyzed 100 industrial purchase questions across ChatGPT, Gemini, Perplexity and Grok, generating 400 responses. The questions covered technical specifications, part numbers, replacement parts, compatibility, inventory, pricing, ordering and product documentation across three industrial buyer roles.

The remaining 28 questions focused on information typically needed after product identification, including availability, pricing, ordering and documentation.

Technical Filtering Emerges as a Major Friction Point

Technical product filtering represented the largest category in the review. Many buyer questions combined multiple specifications, such as bore size and stroke length, rather than simply requesting a broad product category.

For industrial manufacturers and distributors, this means ecommerce platforms need structured product attributes that allow buyers to narrow large catalogs based on the specifications they already know.

When product families lack consistent filter structures, buyers can be left with irrelevant results or no effective way to identify the appropriate SKU.

“An industrial buyer arrives with a part number or a bore size, and the site has to turn that into the right product,” said Duran Inci, CEO of Ecommerce.com and Optimum7. “Search can only return what the product data behind it allows, so on a technical catalog our conversion work starts with that data.”

Part-Number and Cross-Reference Search Create Additional Challenges

Part-number searches were another recurring issue, particularly in maintenance, repair and operations scenarios where buyers already knew an SKU or manufacturer part number.

Search systems can struggle with identifiers that contain option codes, configuration suffixes or formatting differences. Trade names, abbreviations and alternate representations can create similar problems when they are not connected to the corresponding product record.

Replacement-part searches add another layer of complexity. Buyers looking for alternatives to discontinued, older or competitor products may depend on cross-reference data linking those identifiers to current products with equivalent dimensions or functions.

Without this information, customers may need to contact a sales representative or leave the ecommerce site to complete the search.

Compatibility Data Can Help Buyers Find the Right Product

Compatibility-related questions typically began with existing equipment, including machine models and motors, and asked which products would work with those systems.

When fitment information is disconnected from the product catalog, buyers may have to compare specifications manually. Structured compatibility data can instead narrow search results to products that match the equipment already in use.

Category architecture also plays a role for buyers who do not start with a part number. Clear product categories and relevant accessory groupings can provide another path to the correct product without requiring customers to rely solely on broad keyword searches.

Conversion Optimization Starts Before the Product Page

The review's findings suggest that industrial ecommerce conversion optimization extends beyond product-page design.

Stock information, pricing, ordering options and documentation remain important once a buyer reaches the correct product. However, improving those elements cannot resolve a search or catalog problem that prevents the buyer from reaching the product in the first place.

For manufacturers and distributors, zero-result searches, filter usage, part-number queries and customer search terminology can provide useful signals about where catalog structure and product data are creating friction.

Addressing those issues earlier in the buying journey can help make product discovery more efficient and create a stronger path from technical requirements to product selection, quote requests and purchases.