Case Study

Optimized eCommerce Taxonomy Design | Worldwide Industrial Supplier



This global supplier of material handling and industrial products' large and diverse product offering experienced taxonomy design driven by internal product managers rather than enhanced for customer experience. Under the influence of many players product attribution data was complex and non-normalized. Additionally, fixed-depth hierarchy and mixed organizing principles resulted in buried product categories and convoluted site navigation. All of these factors contributed to a shopping experience for customers that was less than optimal.


EIS conducted competitive analysis & benchmarking of site functionality to inform taxonomy & attribution schema and website redesign. Our team redesigned a Primary Display Hierarchy (by Category) and created an Alternate Display Category (by Industry) to support customer navigation & product findability.

We performed user navigation testing to evaluate strength of current state and proposed future state primary hierarchy design. Finally, working closely with the client's team, we streamlined product data management processes by restructuring & optimizing product attribution schema to support attribute inheritance and navigation facets.


EIS performed a comparison of competitor product data functionalities and provided an overview and definitions for key data entities and data model consideration. This helped business sponsors understand the value and context of recommended data model changes so they could prioritize & gain buy-in from IT teams. 

Efficiencies and user experience improvements were impacted by these accomplishments:

  • Primary hierarchy redesigned from fixed, 3-level model to variable-depth model to expose buried product categories and increase accuracy of customer navigation.
  • 99.99% of products classified to redesigned primary taxonomy.
  • 75% Reduction in attributes achieved by normalizing & merging duplicative values.
  • Style guidelines and recommendations delivered to support sustainable, accurate product data creation and data fill operations.

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