Merchandising & Buying
Assortment localisation by store cluster
Cluster stores by demographic, sales, and space characteristics, and recommend which lines each cluster should carry rather than applying a single national range to every location.
3–7%
increase in like-for-like sales in localised categories
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.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
High people impact. Daily workflows change materially for a number of people. Training, communication, and active change management needed.
Tooling required
Things to consider
Localisation gains are real but capped by your supply chain. If the distribution model cannot pick store-specific ranges economically, the analysis will produce a plan you cannot execute.
Cluster count is a commercial decision, not a statistical one. Twenty clusters may fit the data best and be unmanageable for the buying team — start with four to six and prove the value.
Sales history is censored by what you chose to stock. A cluster that never received a line cannot demonstrate demand for it, so blend in external demographic or market data.
Experiment starter: Cluster your estate and localise one category across two clusters for a full season, holding the rest of the estate on the national range. Compare like-for-like sales and terminal stock at season end.
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.
AI Transformation Playbook
Ready to assess your own opportunities?
The playbook gives you the full 150-opportunity directory, scoring tools, and 100+ templates for every stage of an AI transformation programme.