Merchandising & Buying
Own-brand and branded margin mix analysis
Analyse where own-brand lines could substitute branded volume without losing customers, and where own-brand is cannibalising higher-margin sales, at category and store cluster level.
1–3%
improvement in category gross margin
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.
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.
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
Substitution behaviour differs sharply by category. Customers trade to own-brand readily in staples and resist strongly in categories tied to identity or gifting — model per category rather than assuming one elasticity.
Branded ranging is often tied to supplier funding. Include promotional income in the margin comparison or you will recommend a switch that loses more in rebate than it gains in margin.
Watch the trip, not just the basket. A customer who cannot find their brand may shop the whole trip elsewhere, and basket-level analysis will not see that.
Experiment starter: Model one category where own-brand penetration is below your average. Test the recommended range change in a matched store group for eight weeks and measure category margin and category footfall together.
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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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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