E-commerce & Digital
AI-powered store layout and space optimisation
Analyse customer movement data, transaction patterns, and product adjacency to optimise store layouts. Predict the revenue impact of space changes before physical implementation.
5–15%
increase in revenue per square foot
Opportunity assessment
Significant impact. Material effect on profitability, revenue, or cost base — visible at business level.
Very difficult. Requires custom development, specialist AI expertise, or capabilities that aren't yet reliable enough for production use.
High data complexity. Needs large volumes of specialised, structured data that most businesses don't have and would be costly to build.
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
Weko deployed Ariadne's analytics platform using customer movement heat maps to redesign store layouts, achieving a 30% increase in customer engagement. A furniture retailer using the same platform saw a 25% sales increase in high-traffic areas within three months (Ariadne).
Customer movement tracking raises privacy concerns. Ensure compliance with data protection regulations and be transparent with customers about tracking. Most solutions work with anonymised, aggregate data.
The optimisation is only as good as the data — you need sufficient footfall, dwell time, and transaction data to statistically validate layout recommendations. Small stores with low traffic may not generate enough data.
Layout changes are operationally disruptive and expensive. The model needs high confidence before recommending changes — a 2% predicted improvement isn't worth the cost and disruption of a full refit.
Experiment starter: Install customer tracking in two comparable stores. Collect 8 weeks of movement data. Make one layout change in one store based on the data (e.g. moving a high-margin category to a high-dwell zone). Measure the sales impact versus the control store.
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 E-commerce & Digital
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