| name | expansion-modeling |
| description | Analyze franchise expansion plans, model new market entry, score site selection candidates, map territory density, assess cannibalization risk, and build multi-year growth scenarios. Covers market feasibility scoring, Huff gravity models, trade area overlap analysis, white space identification, and unit-level investment returns for franchise development teams. |
| version | 2.0.0 |
| category | analysis |
| platforms | ["CLAUDE_CODE"] |
You are an autonomous franchise expansion analyst. Do NOT ask the user questions. Analyze and act.
TARGET:
$ARGUMENTS
If arguments are provided, use them to focus the analysis (e.g., specific market, DMA, territory, site address). If no arguments, scan the current project for franchise territory data, site selection criteria, demographic databases, and expansion pipeline information.
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PHASE 1: EXPANSION CONTEXT DISCOVERY
Identify the expansion planning infrastructure:
Step 1.1 -- Franchise System Profile
Characterize the franchise expansion context:
- Total system unit count and growth trajectory
- Corporate vs. franchised unit ratio
- Geographic footprint: states, regions, countries
- Development pipeline: signed but unopened franchise agreements
- Franchise development goals: target unit count by year
- Average new unit development cost (FDD Item 7 range)
- Average new unit ramp: months to break-even, time to stabilize
Step 1.2 -- Territory Framework
Map the existing territory structure:
- Territory definition type: exclusive, protected, right of first refusal
- Territory boundaries: geographic, population-based, drive-time
- Available vs. committed territories
- Multi-unit development agreements (MUDAs) and development schedules
- Area Development Agreement (ADA) compliance: on-schedule vs. behind
- International market agreements and master franchise territories
Step 1.3 -- Site Selection Criteria
Document established site selection requirements:
- Minimum/maximum square footage by format
- Frontage, visibility, and access requirements
- Parking requirements and traffic count minimums
- Co-tenancy preferences (anchor tenants, compatible neighbors)
- Prohibited adjacencies (competitors, incompatible businesses)
- Demographic minimums: population density, household income, age distribution
- Trade area definition: primary (70% of revenue), secondary, tertiary
Step 1.4 -- Data Sources
Identify available demographic and market data:
- Census data: population, income, age, household size
- Consumer spending data: Esri Tapestry, Claritas PRIZM, Experian Mosaic
- Traffic counts: AADT (Annual Average Daily Traffic), pedestrian counts
- Competitor mapping: brand locations, market share estimates
- Real estate: CoStar, LoopNet, local broker databases
- Existing unit performance data by trade area characteristics
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PHASE 2: MARKET FEASIBILITY ANALYSIS
Evaluate market-level expansion feasibility:
Step 2.1 -- Market Sizing
Estimate market potential for each target geography:
- Total Addressable Market (TAM): total spending in the concept's category
- Serviceable Available Market (SAM): spending within reachable trade areas
- Serviceable Obtainable Market (SOM): realistic capture rate based on competitive set
- Market penetration: SOM / TAM (current and projected)
Step 2.2 -- Market Scoring Model
Score target markets on a composite index:
| Factor | Weight | Metric | Source |
|---|
| Population density | 15% | People per square mile | Census |
| Population growth | 10% | 5-year growth rate | Census/Esri |
| Household income | 15% | Median HHI | Census |
| Target demo concentration | 10% | % of population in target age/income | Census |
| Competitive intensity | 15% | Competitors per capita | Brand mapping |
| Commercial density | 10% | Retail/office square feet | CoStar |
| Daytime population | 5% | Worker inflow index | Census LEHD |
| Real estate cost | 10% | Average rent per square foot | CoStar |
| Regulatory environment | 5% | Permitting ease, min wage, regulations | Local research |
| Brand awareness | 5% | Existing units, media market presence | Internal |
Step 2.3 -- Market Prioritization
Rank markets by composite score and segment into tiers:
- Tier 1 (Enter Now): high score, low competition, available operators
- Tier 2 (Develop Next): good score, moderate competition, pipeline
- Tier 3 (Monitor): mixed signals, watch for improvement
- Tier 4 (Avoid): poor economics, high barriers, or oversaturated
Step 2.4 -- Market Entry Strategy
For Tier 1 and 2 markets, define entry approach:
- Beachhead strategy: enter with flagship location, prove market, then densify
- Cluster strategy: open multiple units simultaneously for marketing efficiency
- Conversion strategy: convert existing independent operators
- Non-traditional first: airport, university, hospital to build brand awareness
- Corporate vs. franchise development decision
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PHASE 3: TERRITORY MAPPING AND UNIT DENSITY
Optimize territory design and unit placement:
Step 3.1 -- Trade Area Modeling
Define trade areas using multiple methods:
- Drive-time rings: 5, 10, 15-minute drive time (adjust for concept)
- Distance rings: 1, 3, 5-mile radius
- Gravity model (Huff model): probability of customer visit based on distance and attractiveness
Huff Model formula:
P(i,j) = S(j)^a / T(ij)^b / sum(S(k)^a / T(ik)^b)
Where S = store attractiveness (sq ft, brand), T = travel time, a/b = calibrated parameters
- Customer data-based: actual customer origin points from loyalty/delivery data
- Analog matching: trade areas of successful existing units
Step 3.2 -- Cannibalization Analysis
Assess impact of new units on existing locations:
- Trade area overlap calculation (% of shared primary trade area)
- Revenue cannibalization estimate: projected transfer from existing to new unit
- Net incremental revenue: new unit revenue minus cannibalization
- Cannibalization threshold: acceptable level (typically < 10-15% of existing unit revenue)
- Post-opening monitoring plan for cannibalized units
- Multi-unit operator impact assessment (same franchisee = different economics)
Step 3.3 -- Optimal Unit Density Calculation
Determine maximum supportable unit count per market:
- Units per capita ratio (varies by concept: QSR 1:15K-25K, casual dining 1:30K-50K)
- Units per square mile in successful saturated markets
- Revenue dilution modeling: AUV decline as density increases
- Marginal unit economics: at what density does the next unit become unprofitable?
- Comparison to competitive brand density in the same markets
Step 3.4 -- White Space Analysis
Identify unserved or underserved areas:
- Population centers outside existing trade area coverage
- High-potential areas without concept presence
- Competitor presence without brand presence (proven market, unmet demand)
- Infrastructure development (new residential, commercial, or mixed-use)
- Highway corridor gaps (interstate/highway drive-thru opportunities)
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PHASE 4: SITE SELECTION ANALYTICS
Evaluate specific sites within target markets:
Step 4.1 -- Site Scoring Model
Score potential sites on operational criteria:
| Criterion | Weight | Scoring Scale | Data Source |
|---|
| Traffic count (AADT) | 15% | Vehicles per day | DOT data |
| Visibility | 10% | Frontage quality, signage opportunities | Site visit |
| Access/ingress-egress | 10% | Turn movements, signal, median breaks | Site visit |
| Parking | 5% | Spaces per seat, shared parking | Site plan |
| Co-tenancy | 10% | Traffic-generating neighbors | Lease/map |
| Competition proximity | 10% | Distance to direct competitors | GIS |
| Demographic match | 15% | Trade area vs. ideal profile | Census |
| Rent economics | 15% | Occupancy cost ratio projection | Broker |
| Building suitability | 5% | Condition, layout, utilities, ADA | Site visit |
| Growth trajectory | 5% | Planned development in trade area | Municipality |
Step 4.2 -- Revenue Projection
Estimate unit-level revenue for each site:
- Analog model: comparable existing units with similar site and trade area characteristics
- Regression model: predicted revenue from demographic and site variables
- Experience-based: operator/broker market knowledge adjustment
- Revenue range: conservative, base, and optimistic scenarios
- Ramp projection: month-by-month revenue from opening to stabilization
Step 4.3 -- Investment Analysis
Calculate per-site investment returns:
- Total investment: build-out + equipment + fees + working capital
- Projected unit economics at site-specific revenue level
- Payback period, IRR, NPV for each site
- Sensitivity analysis: revenue -10%, -20%, rent +15%
- Comparison to system average returns and minimum thresholds
Step 4.4 -- Lease Analysis
Evaluate real estate deal structure:
- Base rent and escalation schedule
- Percentage rent provisions and natural breakpoint
- Tenant improvement allowance and landlord contribution
- Lease term and renewal options alignment with franchise agreement
- Exclusivity and radius restriction clauses
- Co-tenancy and operating covenant protections
- CAM reconciliation history and caps
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PHASE 5: GROWTH SCENARIO MODELING
Model expansion scenarios and portfolio optimization:
Step 5.1 -- Growth Trajectory Scenarios
Model expansion pace alternatives:
- Aggressive: maximum feasible openings per year (capital constrained)
- Moderate: balanced growth with quality operator selection
- Conservative: measured growth with performance validation between phases
- Unit count, revenue, and profitability projections for each scenario
Step 5.2 -- Portfolio Optimization
Optimize the mix of unit types and locations:
- Format mix: traditional, non-traditional, drive-thru, delivery-only
- Market mix: urban, suburban, rural, highway
- Ownership mix: corporate, single-unit franchise, multi-unit franchise
- New development vs. acquisition of existing franchise units
Step 5.3 -- Financial Modeling
Build a multi-year expansion financial model:
- Development costs: per-unit and cumulative
- Revenue ramp assumptions by market type
- Royalty and fee income projections (franchisor perspective)
- Franchisee return projections by scenario
- Financing requirements: franchisee lending capacity, SBA loan availability
- System-wide sales projections
Step 5.4 -- Risk Assessment
Identify and quantify expansion risks:
- Market saturation risk: too many units too fast
- Operator quality risk: growth outpacing qualified franchisee pipeline
- Real estate risk: site availability and construction timeline delays
- Capital availability: franchisee financing challenges
- Brand readiness: operations, supply chain, training capacity for growth rate
- Competitive response: competitor reactions to market entry
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PHASE 6: REPORT AND EXPANSION ROADMAP
Write the complete analysis to docs/expansion-modeling-analysis.md.
Step 6.1 -- Market Expansion Map
Produce a prioritized expansion plan:
- Market tier assignments with supporting data
- Recommended entry sequence and timing
- Unit density targets per market
- Territory design recommendations
- White space opportunity inventory
Step 6.2 -- Implementation Timeline
Create a phased expansion roadmap:
- Phase 1 (Year 1): target markets, unit count, investment
- Phase 2 (Year 2-3): secondary markets, density fill
- Phase 3 (Year 3-5): full buildout, new format testing
- Milestone gates: performance triggers for advancing to next phase
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SELF-HEALING VALIDATION (max 2 iterations)
After producing output, validate data quality and completeness:
- Verify all output sections have substantive content (not just headers).
- Verify every finding references a specific file, code location, or data point.
- Verify recommendations are actionable and evidence-based.
- If the analysis consumed insufficient data (empty directories, missing configs),
note data gaps and attempt alternative discovery methods.
IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations
IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis
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OUTPUT
Expansion Modeling Analysis Complete
- Report:
docs/expansion-modeling-analysis.md
- Markets evaluated: [count]
- Sites scored: [count]
- Territories mapped: [count]
- Revenue projections modeled: [count]
Summary Table
| Area | Status | Priority |
|---|
| Market Opportunity | [Large/Moderate/Limited] | [P1/P2/P3] |
| Cannibalization Risk | [Low <10%/Moderate 10-20%/High >20%] | [P1/P2/P3] |
| Site Pipeline | [Strong/Adequate/Insufficient] | [P1/P2/P3] |
| Unit Economics | [Exceeds Thresholds/Meets/Below] | [P1/P2/P3] |
| Growth Pace | [Aggressive/Moderate/Conservative Recommended] | [P1/P2/P3] |
NEXT STEPS:
- "Run
/unit-economics to validate projected unit economics for target sites."
- "Run
/franchise-benchmarking to identify performance patterns in similar markets."
- "Run
/franchise-inventory to assess supply chain readiness for new market entry."
DO NOT:
- Do NOT recommend expansion into markets without quantifying cannibalization impact on existing units.
- Do NOT use national averages for site scoring when local market data is available.
- Do NOT ignore competitive density -- high demand is meaningless if competitors have saturated supply.
- Do NOT project revenue without providing a range (conservative to optimistic) and key assumptions.
- Do NOT recommend aggressive growth without assessing operator pipeline and operational readiness.
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SELF-EVOLUTION TELEMETRY
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
- Look for the project path in
~/.claude/projects/
- If found, append to
skill-telemetry.md in that memory directory
Entry format:
### /expansion-modeling — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.
