Store network decisions are among the highest-stakes choices a retailer makes — a single location commitment typically locks in 10-15 years of lease obligations and millions in capital expenditure. Based on our analysis of retail case prompts across MBB and Big Four interviews, network and location questions appear in roughly 1 in 4 retail cases, often as a secondary layer within broader growth or profitability scenarios.
When You’ll Face Network Cases
Interviewers use store network cases to test spatial reasoning, quantitative modeling, and your ability to balance growth ambition against cannibalization risk. You’ll typically encounter these in three forms:
| Case Prompt Type | What You’re Really Being Asked | Example Prompt |
|---|---|---|
| Expansion planning | Where to put the next 50 stores | “Our grocery client wants to grow from 200 to 300 stores in 3 years — where should they expand?” |
| Underperformance diagnosis | Why certain locations fail | “15% of stores consistently miss targets — should the client close them?” |
| Format strategy | Which store format fits which market | “The client operates hypermarkets but considers launching convenience stores — how should they decide?” |
The Location Decision Framework
Every store location case ultimately reduces to one question: will this site generate enough incremental profit to justify its cost, including the sales it steals from existing locations?
flowchart TD
A[Location Decision] --> B[Market Attractiveness]
A --> C[Site-Specific Factors]
A --> D[Network Effects]
B --> B1[Population density & demographics]
B --> B2[Household income & spending power]
B --> B3[Competitor saturation]
C --> C1[Visibility & accessibility]
C --> C2[Rent & build-out cost]
C --> C3[Parking & footfall patterns]
D --> D1[Cannibalization of own stores]
D --> D2[Supply chain reach]
D --> D3[Brand awareness spillover]
In our experience working with candidates, the most common failure mode is analyzing each potential site in isolation without modeling the network-level impact. Strong candidates immediately ask: “What happens to the two existing stores within a 5-kilometer radius?”
Key Metrics for Store Network Cases
Fluency with these metrics signals that you understand how retailers actually evaluate their real estate portfolio:
| Metric | Definition | Why It Matters |
|---|---|---|
| Revenue per square meter | Annual revenue ÷ selling area | Primary efficiency benchmark across formats |
| Trade area overlap | % of catchment areas shared between own stores | Quantifies cannibalization risk |
| 4-wall EBITDA | Store-level profit before corporate allocation | Determines whether a location is self-sustaining |
| Payback period | Total investment ÷ annual store-level profit | Drives go/no-go decisions (typically 3-5 years target) |
| Sales transfer rate | % of closed store’s revenue retained by nearby stores | Critical for closure decisions |
| Drive-time coverage | % of target population within X-minute drive | Measures network completeness |
Cannibalization: The Concept Interviewers Test Most
Cannibalization is where most candidates stumble. When a retailer opens a new store, some portion of its revenue comes from customers who would have shopped at an existing store. In our experience coaching retail case preparation, roughly 60% of store network case prompts include a cannibalization dimension — either explicitly or as a trap for candidates who ignore it.
The cannibalization calculation:
Incremental Revenue = New Store Revenue − (Cannibalized Sales from Store A + Store B + ...)
Incremental Profit = Incremental Revenue × Margin − New Store Fixed Costs
Typical cannibalization rates by format:
| Scenario | Expected Cannibalization | Implication |
|---|---|---|
| New store 1-2 km from existing | 20-40% of new store revenue | Proceed only if market is growing fast enough |
| New store 5-10 km from existing | 5-15% | Usually acceptable |
| New format (e.g., convenience near hypermarket) | 10-25% on overlapping categories | Offset by incremental occasions captured |
| Different brand/banner | 5-10% | Lower awareness crossover |
Format Strategy: Matching Store Type to Market
Format selection cases test whether you can match retail economics to local demand characteristics — a skill that also applies in market entry cases when evaluating geographic expansion. The key insight interviewers reward: format choice is a hypothesis about what trade-offs local consumers will accept.
mindmap
root((Format Selection))
Hypermarket
Large catchment (15-20 km)
Weekly destination shop
Low margin, high volume
Requires cheap land / suburban
Supermarket
Medium catchment (3-5 km)
2-3 visits per week
Balanced margin/volume
Urban/suburban locations
Convenience
Small catchment (500m-1km)
Daily top-up missions
High margin, low volume
Dense urban / transit hubs
Dark Store
Delivery-only
No foot traffic needed
Requires order density
Industrial / low-rent areas
Worked Example: Network Expansion Prioritization
Prompt: “Your client is a mid-market fashion retailer with 120 stores in Western Europe. They have budget to open 20 new stores over two years. How would you prioritize markets?”
Strong structuring approach:
Define selection criteria (weighted scoring):
- Market size: target demographic population × average fashion spend
- Competitive intensity: number of direct competitors per 100K population
- Operational fit: proximity to existing distribution centers
- Real estate availability: suitable sites at acceptable rent-to-revenue ratios
Screen and rank markets:
- Apply minimum thresholds (e.g., population >150K, competitor density below national average)
- Score surviving markets on weighted criteria
- Overlay with cannibalization risk from existing stores
Sequence the rollout:
- Phase 1: Markets with existing brand awareness (near current stores) for faster ramp-up
- Phase 2: New territories with proven demographic fit but requiring marketing investment
Quantify the investment case:
- Average build-out cost × 20 stores = total capex
- Expected revenue ramp: Year 1 at 60-70%, Year 2 at 85-90%, Year 3 at 100% of mature store
- Portfolio payback: weighted average across all 20 locations
Store Closure Decisions: The Flipside
Closure cases are emotionally harder for candidates — the instinct is to “fix” underperformers. Strong candidates recognize that closing an unprofitable store isn’t losing revenue entirely because of sales transfer: customers shift to nearby alternatives.
Decision framework for closures:
| Factor | Keep Open If… | Close If… |
|---|---|---|
| 4-wall EBITDA | Positive, even if below target | Negative for 6+ consecutive quarters |
| Sales trend | Stabilizing or improving | Declining with no identifiable fix |
| Lease flexibility | Approaching break clause | Locked in for 5+ years at above-market rent |
| Sales transfer | Low transfer rate (<30%) due to isolation | High transfer (>50%) to nearby stores |
| Strategic role | Serves as brand billboard in key market | No strategic value beyond its own P&L |
Common Mistakes in Store Network Cases
Based on our analysis of candidate performance in retail mock interviews:
- Ignoring the time dimension: Store profitability ramps over 2-3 years — a dynamic also covered in our retail profitability guide. Judging a new location on Year 1 results leads to premature closure decisions.
- Treating all square meters equally: A store’s revenue per square meter varies dramatically by zone — front-of-store impulse areas vs. back-of-store staples. Strong candidates ask about sales density distribution.
- Forgetting the exit cost: Closing a store involves lease termination penalties, staff redundancy, inventory liquidation, and brand perception damage. The “just close it” recommendation needs a cost basis.
- Assuming online substitution is complete: When evaluating whether physical stores are needed, candidates overestimate customers’ willingness to shift entirely online. In grocery, 80%+ of revenue still flows through physical stores in most markets.
Key Takeaways
- Store network cases test spatial reasoning and quantitative modeling — interviewers want to see you balance growth ambition against cannibalization risk
- Always model network effects: no store operates in isolation, and adding or removing one location redistributes demand across the network
- The core metrics (revenue per square meter, 4-wall EBITDA, trade area overlap, payback period) should be requested within your first clarifying questions
- Cannibalization rates of 20-40% are common for nearby stores — candidates who ignore this consistently overstate the value of new locations
- Store closures require sales transfer analysis: if 50%+ of revenue migrates to nearby stores, the “loss” is much smaller than the headline number suggests
- Format strategy is fundamentally about matching local demand patterns to the economics of different store models
Build your spatial intuition by browsing retail industry cases that feature network questions, apply growth strategy frameworks to expansion scenarios, and test your approach with an AI Mock Interview that adapts difficulty based on your structuring quality.