Merchandising & Buying
Product attribute enrichment
Automatically extract and assign searchable product attributes — colour family, material, occasion, fit, aesthetic — from descriptions and images. Improves findability and filter performance on site.
75–90%
reduction in manual attribute tagging time
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Very easy to absorb. Runs quietly in the background or gives people a helpful new input. Makes everyday work easier with almost no friction.
Tooling required
Things to consider
Walmart used generative AI to enrich 850M+ product attributes — CEO Doug McMillon stated it would have required 'nearly 100 times the current headcount' without AI (Modern Retail).
The attribute taxonomy must be agreed and locked before building — the AI assigns to your taxonomy, and changing it mid-project means reprocessing everything.
Image-based attribute extraction (colour, silhouette, styling) requires a computer vision model or multimodal LLM. Text-based extraction from descriptions is simpler and a good starting point.
Human QA on a sample of outputs each month catches taxonomy drift and keeps confidence levels honest — especially important where attribute accuracy drives filter conversion.
Poor attribute tagging directly suppresses filter usage and conversion. The downstream impact on search and navigation performance is often larger than teams realise, which makes this worth investing in properly.
Experiment starter: Select 100 products currently lacking full attribute coverage. Run automated attribute extraction and have the merchandising team QA the outputs. If 85%+ of attributes are correct without editing, proceed to automate for new product onboarding. Measure filter usage and conversion on those 100 products after re-tagging.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in Merchandising & Buying
Product description writing
Generate first-draft product descriptions from attributes, imagery, and brand guidelines. Buying team reviews, edits, and approves before publish.
Supplier spec sheet extraction
Extract structured product data from unstructured supplier spec sheets, PDFs, and email attachments. Outputs clean records ready for system upload.
Range review analysis
Summarise sales performance, markdown rates, and return data by category to surface underperformers and inform ranging decisions.
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