Merchandising & Buying
Price architecture and entry point analysis
Analyse where price points sit within each category against customer willingness to pay and competitor ranging, and identify gaps in the good-better-best structure that are costing volume or margin.
2–5%
improvement in category margin mix
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Price architecture is usually inherited rather than designed. Most categories accumulate price points through individual buying decisions, and nobody steps back to look at whether the ladder makes sense as a whole.
The entry price point does disproportionate work — it anchors the customer's perception of the whole category. Test changes there with far more care than changes in the middle of the range.
Elasticity estimated from historical price changes is confounded by the promotions that usually accompanied them. Separate promotional and everyday price effects or you will overstate how price-sensitive the category is.
Distinguish gaps worth filling from gaps that exist for good reason. A missing price point may reflect an absence of demand rather than a missed opportunity, and the model cannot tell the difference on its own.
Experiment starter: Map the current price ladder for two categories against volume and margin at each point, alongside the equivalent competitor ladder. Take the three clearest gaps to the buying team and ask whether each is a deliberate choice or an accident.
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.
AI Transformation Playbook
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