Merchandising & Buying
Markdown trigger and depth optimisation
Use AI to recommend when to markdown, how deep to go, and which products to prioritise based on rate of sale, weeks of cover, and margin targets.
5–15%
improvement in markdown margin recovery
Opportunity assessment
Significant impact. Material effect on profitability, revenue, or cost base — visible at business level.
Challenging. Significant technical work required. Likely needs an AI engineer or specialist vendor. Not a quick win.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
High people impact. Daily workflows change materially for a number of people. Training, communication, and active change management needed.
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).
This requires clean, integrated data across sales, stock, and margin at SKU-location level — most retailers have it but in separate systems that need joining.
The biggest risk is over-reliance on historical patterns. Markdown models trained on normal trading will misfire during unusual periods (weather events, competitor closures, viral moments).
Human override must be simple and fast — if the buyer can't easily reject or modify a recommendation, they'll stop using the system.
Start with clearance markdown (where the objective is simple: clear stock) before attempting in-season promotional markdown, which involves more complex trade-offs.
Experiment starter: For one end-of-season clearance event, run the AI recommendations alongside your normal markdown decisions but don't act on them. Compare what the model would have done against what you actually did, and calculate the margin difference retrospectively.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — Change Management, Scaling, and AdoptionThis opportunity scores low on change complexity — the change management section covers how to land it with the team.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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