Often, distributors who are on an expansion drive find themselves managing multiple taxonomies. This could be the result of acquisitions or from catering to various industries. These taxonomies typically have similar structures and deal with related products, and this makes it all the more difficult to spot duplicates and optimize the crisscrossing class paths.
Consider a typical scenario - a distributor of industrial equipment acquires a small interiors business, and now wants to merge the interiors taxonomy with theirs.
Now the interiors taxonomy might have a paint scraping tool classified under, say,
Chemicals, Lubricants & Paints > Paint Supplies & Accessories > Paint Scraper
However, in the larger scheme of things, the scraper is essentially just a tool, and might be classified under:
Tools > Hand Tools > Cutters & Scrapers > Scrapers
This calls for an examination of the descriptions of these products, an understanding of how the business categorizes their products, and an insight into how their customers might best find what they’re looking for. Once this understanding is in place, we automate the process of identifying nodes to merge and class paths to optimize. Whether we are looking at large taxonomies with tens of thousands of nodes, or a compact one, our process is just as meticulous and efficient.
Trained on annotated data, and given plenty of context, our ML models are able to discern patterns from the descriptions, titles and attributes from the product content. Using these patterns we identify duplicate categories and unwieldy navigation paths. We then rebuild a single optimized taxonomy taking all the necessary elements from the two taxonomies. What is more, we customize the new taxonomy structure to fit your unique business needs - this is not just a copy of a generic taxonomy, but customized to your specifications, and completely aligned with your ecommerce product strategies.
