Merchandising & Buying
Range review analysis
Summarise sales performance, markdown rates, and return data by category to surface underperformers and inform ranging decisions.
40–60%
reduction in range review preparation time
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Very easy to implement. Ready-to-use tools exist. Can be up and running in days or weeks with minimal technical resource.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Very low risk. Fully internal use. A person checks everything before it goes further. Worst case is a minor internal inconvenience.
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
The quality of the analysis depends entirely on data quality — if sales, markdown, and return data live in separate systems, the data pull is the main complexity.
AI identifies patterns; the ranging decision still requires commercial judgment. Position this clearly as decision support, not a recommendation engine.
Define the output format in advance (what does a useful range review summary look like?) so the AI output feeds directly into your existing meeting format.
The most common failure mode is asking too broad a question. Narrow it: 'which lines have a return rate above 20% and a margin below 40%?' produces something actionable. 'What's wrong with the range?' does not.
Experiment starter: Take last season's data for one category and prompt the AI to identify the bottom 10% of lines by a composite of sell-through, margin, and returns. Compare the output against the buyer's instinct. Strong agreement validates the analytical approach before building anything automated.
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
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Buyer brief generation
Draft structured buying briefs from season parameters, trend references, and last-year performance data. Reduces prep time before supplier meetings.
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