Gains:
- Ability to analyze square meter efficiency, aisle temperature and customer flow with artificial intelligence support and produce layout suggestions
- Ability to draft planogram, cross-selling and category clustering logic with artificial intelligence
- Yapay zeka önerilerinin mağaza gerçeği, güvenlik ve mevzuatla (ör. tütün/alkol teşhir kuralları) doğrulanması gerektiğini kavrayabilme
Square meters of a store mean money. What you put in that square meter, how the customer moves around, which product is at eye level; All of them directly affect sales. There is no “virtual storefront optimization” in the physical store, but there is a similar science: category management and aisle planning. In this unit, you will use artificial intelligence as an analyst generating location recommendations from sales and field data. Again, critical warning: AI provides a blueprint; It's your job to verify with safety, regulations and store reality.
Basic concepts:
Planogram: Bir reyondaki her ürünün hangi rafta, hangi yükseklikte, kaç yüz (facing) ile duracağını gösteren görsel yerleşim planıdır.
Facing: The front-facing surface of a product on the shelf. More faces means more visibility and less stocking.
Square meter efficiency: It is the turnover/profit generated by a department or category per area it covers. "How many TL does each square meter of this shelf generate per month?" is the answer to the question.
Department temperature (hot/cold zones): "Hot" zones where customers visit most and "cold" zones where customers visit less. The hot zone is divided into valuable products, and the cold zone is divided into essential products that do not create traffic.
Basic logic of customer flow
When most customers enter the store, they turn to the right and walk around the perimeter. Therefore:
- Entry zone (decompression): The customer has not yet entered "shopping mode"; The welcome/campaign is placed here, not the critical product.
- Eye level shelf: The most valuable space; high margin or target products here.
- Lower/upper shelves: Less visible; price-oriented or niche products.
- Cashier front: Impulse small products; It is added to the cart while waiting.
- Essential products (milk, bread): Usually at the bottom; the customer sees the entire store as he goes to them.
İpucu: Yapay zekaya mağaza planınızı ve kategori satışlarını verip "hangi kategori sıcak bölgede olmalı, hangisi metrekare verimliliği düşük" diye sorun. But verify any carry he recommends with physical rack size and security.
Cross-selling and adjacency
The basket becomes larger when the products used together are placed side by side: pasta–tomato paste, chips–soda, coffee–filter. Artificial intelligence can find items purchased together (basket analysis) and make neighborhood recommendations. This is called market basket analysis: it finds "who buys this, buys that" patterns from the product combinations on the receipts.
layout tool
What does
Contribution of artificial intelligence
planogram
Plans shelf layout
Draft facing and location proposal according to sales
Basket analysis
Finds what is taken together
Extracts neighborhood pattern from receipt data
Square meter efficiency
Measures profit per field
Marks low yield aisles
Heatmap (flow)
Shows customer density
Hot/cold zone comment and suggestion
Regulatory and security: non-negotiable
Some layout rules come from law and the AI may not know them:
- Tobacco products: Storefront/open display is prohibited in most countries/locations; It is kept in a closed cabinet.
- Alcohol: Subject to sales hours and display restrictions.
- Age-restricted products: Do not place in places accessible to children.
- Food safety: Cold chain products are not placed outside the cabinet, and heavy products are not placed on high shelves (risk of falling).
- Fire/evacuation: Corridor width and emergency exits must remain clear.
Attention: Even if the artificial intelligence's recommendation to "put this product at the entrance, at eye level" makes commercial sense, it cannot be implemented if it is against the legislation. Filter each planogram with local rules.
Step by step layout analysis
- Collect data: Category/SKU sales, shelf length occupied, margin, cart associations, store map.
- Calculate efficiency: Which category produces less profit according to its field?
- Get neighborhood recommendations: Cross-selling opportunities from cart analysis.
- Generate draft planogram: Hot zone, eye level, facing suggestions.
- Filter and apply: Verify with legislation, security, physical reality and customer experience, apply with the store team.
mini cases
Case 1 — Square meter efficiency: In a grocery store, the delicatessen section occupies 12% of the sales area but generates only 5% of the turnover. Artificial intelligence marks this in its efficiency analysis. The aisle is narrowed to 8% and high-margin snacks come into the vacated hot space. Category square meter efficiency increases by 22% in three months; Delicatessen sales remain almost constant despite the contraction.
Case 2 — Cross-selling neighborhood: A DIY store finds in its basket analysis that 38% of those who bought drills also bought drill bits in the same receipt, but the two products are in different aisles. Artificial intelligence puts the bits next to the drill with a neighborhood suggestion. Nib sales increase by 27% in 6 weeks; additional turnover becomes permanent.
Case 3 — Legislation saves the filter: A grocery store tells artificial intelligence to "put the most profitable products at the entrance, at eye level." Cigarettes also appear on the recommendation list. The responsible person catches this in the legislative filter and removes it from the proposal; Violation of the ban on open display and its punishment are prevented.
Weak prompt / Strong prompt
Weak prompt:
How should I organize my store?
No data, no sketches and no restrictions; It turns out to be a common cliché.
Powerful prompt:
Your role: category and aisle planning specialist. Data: Below are the monthly turnover, margin and shelf meter coverage of the categories. In addition, product pairs taken together from the basket analysis. Circulation from the right from the store entrance to the cash register. Task: (1) mark 3 categories with low square meter efficiency, (2) suggest 2 categories that should be moved to the hot zone, (3) suggest 2 cross-selling neighborhoods. Constraint: Do not make suggestions against tobacco and alcohol display rules; do not recommend cold chain products outside the cabinet. Make a table with the reasons for the suggestions. Data: [paste]
Copiable prompt templates
1) Square meter efficiency analysis
Calculate your square meter efficiency using the turnover, margin and shelving meters occupied by the following categories. List the 3 most efficient and 3 least productive categories. Give table. Data: [paste]
2) Cross-selling neighborhood
Department adjacency suggestions emerge from the following pairs of products purchased together (basket analysis). Give the 5 strongest associations and suggested placement. Data: [paste]
3) Draft planogram
Suggest a draft shelf layout for the following category: which product should be at eye level, which should be top/bottom, what should be the facing distribution?Base on margin and sales velocity. [specify regulatory restrictions].Product list: [paste]
4) Regulatory/security filter
Check the following layout suggestions for legislation and safety: tobacco/alcohol display, age limit, cold chain, corridor and emergency exit. Tick those that are not applicable. Suggestion: [paste]
Common mistakes
- Just looking at the turnover: The category that takes up a lot of space and earns little is inefficient; Look at the square meter efficiency.
- Relying on AI to legislate: Tobacco/alcohol/age rules should be filtered by humans.
- Ignoring physical reality: Shelf size, product weight, aisle width determine the feasibility of the suggestion.
- Forcing cross-selling: Placing unrelated products next to each other confuses the customer; rely on data.
- Forgetting the customer experience: Over-optimization can create a maze-like store and reduce satisfaction.
- Set it once and forget it: The layout should be reviewed regularly with season and sales.
In summary
Store layout is a science of space-efficiency and customer-flow. AI generates efficiency, adjacency and planogram outlines from sales, margin and basket data. But tobacco/alcohol display, security and physical reality are non-negotiable; Filter each suggestion with legislation and store reality and implement it.
Application task
Mağazanızdaki kategorilerin aylık cirosunu, marjını ve kapladığı raf metresini bir tabloya alın. "1) Metrekare verimliliği analizi" ile en verimsiz kategorileri bulun, sepet verinizle "2) Çapraz satış komşuluğu" önerileri çıkarın. Son olarak tüm önerileri "4) Mevzuat/güvenlik süzgeci"nden geçirin.
checklist
- [ ] Kategori ciro, marj ve alan verisini topladım.
- [ ] I calculated the square meter efficiency.
- [ ] Sepet analizinden çapraz satış komşuluğu çıkardım.
- [ ] Tütün/alkol/yaş ve güvenlik kurallarını süzdüm.
- [ ] I verified the physical shelf reality.
- [ ] Yerleşimi düzenli gözden geçirme planı kurdum.