Merchandising & Buying
Markdown trigger and depth analysis
Analyse sell-through rates, weeks of cover, and competitor pricing to surface markdown recommendations. Merchandiser reviews and approves timing and depth before action.
8–15%
reduction in end-of-season markdown rate
Opportunity assessment
Significant impact. Material effect on profitability, revenue, or cost base — visible at business level.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Peak AI's SKU-level markdown optimisation delivered a 300 basis point increase in profit margins for a luxury fashion retailer within 12 months (Peak AI). BCG found advanced analytics markdown approaches boosted gross margins by 10–20% across 20+ fashion retailers (BCG).
Requires a unified view of live stock levels, sales velocity, and forward order cover — if these sit in separate systems, the integration work is the first project.
Markdown decisions are partly science, partly trading instinct. The AI surfaces the trigger signal; the merchandiser applies judgment on timing, competitor activity, and open-to-buy implications.
Start with categories where sell-through data is cleanest (e.g. a single non-seasonal category) before extending to fashion or seasonal ranges where the analysis is more complex.
Beware of training the system to trigger markdowns too early — this is a real risk if the model is optimised purely on stock clearance without weighing full-price sell-through opportunity.
Experiment starter: At the start of the next clearance window, export sell-through and cover data for one category and prompt an LLM to flag lines where a markdown intervention is recommended, with suggested depth. Compare the AI's flags against what the team would have actioned manually and track end-of-season clearance rates for both groups.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support 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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