For companies with large, highly technical catalogs, product discovery can quickly become one of the hardest problems to solve. When you’re dealing with hundreds of thousands of SKUs — many with subtle differences in compatibility, specifications, or use cases — traditional approaches to site navigation and search often fall short.
The challenge is compounded when product data is sparse. Manufacturer-supplied information is often limited to part numbers, leaving customers without the context they need to identify the right item. Images may be missing or generic, and complex compatibility rules aren’t always captured in the catalog structure.
For customers, this creates friction at the exact moment when clarity matters most. For the business, it results in missed conversions, lower satisfaction, and catalogs that are difficult to maintain or scale.
The good news: these challenges can be turned into opportunities by rethinking how catalogs are structured and how customers interact with them.
DHS Success Story
DHS is a retailer focused on parts and equipment for small engine repair — serving customers who maintain or fix equipment such as lawnmowers, chainsaws, and power tools. While they do sell full machines, the majority of their business revolves around replacement parts. Their business model depends on helping customers quickly locate the precise components needed to repair existing equipment.
With a catalog of nearly 400,000 products, many of which are technically similar but vary in machine compatibility, the ability to efficiently and accurately find the right part is essential to both user satisfaction and conversion.
The Challenge: Sparse Product Data at Scale
Managing such a vast and technical catalog presented a number of challenges:
- Limited metadata: Most product data was sourced from manufacturers and tended to be minimal. Product titles and descriptions were often nothing more than part numbers.
- Poor imagery: Many items had no image at all or used a generic DHS-branded placeholder logo.
- Unsearchable catalog: Without descriptive data, users had to know exact manufacturer part numbers to find items. This made browsing or filtering nearly impossible.
- Weak SEO performance: Sparse content severely limited DHS’s ability to appear in organic search results.
- Complex fitment relationships: A single part could fit multiple machines — and not all parts were universally compatible. Understanding which parts fit which machines was often a guessing game for users.
Solutions and Improvements
To tackle the product data issue, we worked with DHS to implement several key improvements:
- Deduplication: We identified large clusters of duplicate listings created for different fitment mappings and consolidated them into a single canonical product record.
- Fitment metadata: We added machine fitment relationships to relevant products so customers could understand compatibility without needing external references.
- Image substitution scripting: To meet Google Merchant Center’s product data requirements, we developed a script that replaced placeholder logos with basic visual stand-ins: a box mockup featuring the manufacturer's logo and part number — providing minimal visual differentiation and meeting feed compliance standards.
- Metadata standards: We introduced a standardized set of recommended attributes, including:
- Is OEM? (vs aftermarket)
- Manufacturer
- Manufacturer Part Number
- Product Type (Machine, Part, Parts Manual, Kit)
- Local Pickup Availability
- Fitment (for parts)
- Component SKUs (for kits)
While this was not an exhaustive attribute model, it provided DHS with a high-leverage, scalable foundation to enrich their catalog and improve the shopping experience.
A Breakthrough: The Parts Finder Experience
The most transformative improvement came through a rethinking of how users typically search for parts.
Traditionally, customers repairing machines refer to printed parts manuals to identify part numbers. We digitized and modernized this experience through a custom product template built specifically for machine products.
The Parts Finder Component
On machine PDPs, users are presented with an interactive “Parts Finder” that mimics the logic of a parts manual:
- The customer navigates to their specific machine’s product page.
- There, a visual breakdown shows each parts diagram from that machine’s manual.
- Clicking a diagram opens an indexed map of numbered components.
- Each component is cross-linked to the corresponding part PDP.
- Users can add individual parts to their cart directly from the breakdown view.
This system radically improves part discovery by aligning with the natural behavior of DHS’s core customers, while working within the limits of DHS’s product data quality.
Supporting Thin PDPs
In parallel, we developed a simplified PDP layout for generic part products — tailored to work well even when product data is extremely limited. This allowed for a consistent and functional user experience, even in cases where enhancements hadn’t yet been implemented.
Ongoing Recommendations
While we made substantial improvements to product discovery and data structure, we continue to recommend expanding and enriching DHS’s product metadata. We suggested using a third-party enrichment tool like DataX.ai to accelerate the process, but DHS expressed concern about the reliability of AI-generated product data.
Instead, they chose to pursue manual enrichment using low-cost offshore data entry teams. While this approach offers more control over accuracy, it may also extend the timeline for achieving comprehensive catalog improvements.
Turn Your Catalog Complexity Into Opportunity
DHS’s story is just one example of a challenge shared by many organizations with large, technical catalogs: limited product data, complicated compatibility, and customer journeys that break down under the weight of complexity. What their experience shows is that you don’t have to wait for “perfect” data to create meaningful improvements.
By rethinking product discovery around how customers actually search, navigate, and decide — and by introducing scalable structures for enrichment and UX — even the thinnest catalog can be transformed into a driver of loyalty and conversion.
For any business facing similar challenges, the lesson is clear: start with the customer journey, not just the data. When you design around real-world workflows, catalog complexity becomes less of a barrier and more of a competitive advantage.
