Situation
A rapidly growing fitness studio chain in the U.S. was evaluating aggressive physical expansion but lacked a standardized, data-driven approach to assess new locations. Site feasibility assessments were dependent on real estate consultants and varied widely in accuracy, speed, and cost.
The company needed a scalable model to project long-term site performance, account for cannibalization risk, and support capital planning with consistent financial metrics that leadership and the board could rely on across every expansion decision.
Objective
We were asked to build a location assessment capability that could replace the consultant-dependent process with an internal, repeatable tool. Specifically:
- Use demographic data and historical store performance to forecast revenue potential for new sites.
- Identify peer stores based on demographic similarity and model projected attendance and sales at each proposed location.
- Estimate cannibalization impact by analyzing population overlap between new and nearby existing stores.
- Develop a detailed 10-year pro forma including cash flows, EBITDA, IRR, and payback period across multiple lease scenarios.
- Package results into a dynamic, board-ready dashboard with override functionality and scenario toggles for rent, ramp-up, and capex inputs.
Approach
We built the model in layered stages, starting with data collection and moving through revenue forecasting, financial modeling, and dashboard delivery.
Demographic profiling and site matchingWe collected third-party demographic and market indicators for each proposed studio location and matched potential sites to existing high-performing locations with similar demographic profiles. This peer-matching approach grounded revenue forecasts in observed performance rather than generic market assumptions.
Revenue forecastingWe studied historical studio data to uncover correlations between local demographics and revenue performance, then designed forecasting models tailored to each site’s demographic profile. Model accuracy was validated by comparing projections against actual results from a sample of existing studios. Revenue estimates were then refined to account for cannibalization by analyzing trade area overlap with nearby locations.
Financial modelingWe reviewed historical cost structures across existing studio locations to model location-specific expense profiles. A detailed pro forma was built incorporating lease terms, capex, and tenant improvement allowances. Key financial metrics (ROI, IRR, NPV, EBITDA, and payback period) were calculated and benchmarked against internal investment thresholds and target return profiles.
Board-ready dashboardAll outputs were packaged into a dynamic dashboard with scenario toggles for rent, ramp-up timeline, and capex assumptions, plus override functionality so the team could stress-test assumptions in real time during board reviews.
Figure 1Trade area demographic profile for a proposed studio location: residential population, workplace population, business landscape, and unemployment rate benchmarked against the urban average.
Figure 2Monthly revenue estimate for a new studio across the first six months, broken down by revenue stream, showing both gross and GAAP-adjusted figures across ramp-up.
Figure 310-year cash basis income statement (top) and comparable studio analysis (bottom): capital investment breakdown, financial performance metrics (ROI, payback, IRR, EBITDA), and Year 3 operating benchmarks versus targets.
Figure 4Executive summary card: 4-wall EBITDA, payback period, cash-on-cash return, and three-year revenue progression alongside DMA context (population, rank, and existing studio count).
Figure 5Peer studio benchmarking: the proposed location compared against four demographically similar studios across demographics, rent, build-out costs, and Year 3 revenue.
Impact
The model cut site evaluation turnaround time by over 40%, enabling the expansion team to assess more locations in less time without adding headcount or relying on external consultants for every decision.
Back-testing validated model accuracy: when run against existing studios, the model correctly identified underperforming locations in the portfolio, giving leadership confidence that projections for new sites were grounded in real performance patterns, not assumptions.
The board gained a consistent framework for comparing investment opportunities across regions. Capital allocation decisions that previously rested on variable consultant outputs now sit on a standardized, auditable model with clear financial metrics. The scalable internal capability supports the chain’s long-term growth with better financial control at every expansion step.