Sales Projections
Sales Projections forecast the revenue potential of a site based on analog modeling—comparing the location you're evaluating against the performance of your existing stores. Rather than relying on generic market data, projections are calibrated to your brand's actual results in similar trade areas.
How Analog Modeling Works
Analog-based forecasting is a foundational methodology in retail site selection. The core principle: a new site will perform similarly to existing stores that share its key characteristics.
The Process
Profile the proposed site: The platform analyzes the searched location's trade area demographics, competitive landscape, traffic patterns, and site attributes.
Identify comparable stores: Your existing locations are evaluated for similarity across the variables that most strongly correlate with your brand's revenue performance.
Weight by similarity: Stores that more closely match the proposed site receive higher weight in the projection; less similar stores contribute less.
Generate the forecast: The weighted performance of analog stores produces a projected revenue range for the new location.
Why Analogs Matter
Generic market data tells you what average retailers do in an area. Analog modeling tells you what your brand is likely to do, based on how you've actually performed in comparable situations. This brand-specific calibration is what makes the projection actionable.
Reading the Sales Projection Card
When you search a site, the Sales Projection card displays in the results panel with several key components.
Model Indicator
The top of the card shows which model is generating the projection:
GrowthFactor Model: [Model Name]
Your organization may have multiple models configured (e.g., separate models for different store formats, regions, or concepts). The model name confirms which is being applied, ensuring you're using the right comparison set.
Projection Views
Toggle between two views using the Total and PSF buttons:
Sales PSF Estimate (Per Square Foot) Revenue projected per square foot of store space. This normalizes performance across different store sizes, making it easier to compare sites regardless of available square footage.
Total Sales Estimate Absolute revenue projection for the location. Requires entering the square footage of the site you're evaluating.
Projection Values
The card displays:
Midpoint
The central projection—your expected case based on weighted analog performance
Lower
Conservative end of the confidence range
Upper
Optimistic end of the confidence range
Lowest Sales Among Comparable Stores
The actual sales of your weakest-performing analog
Highest Sales Among Comparable Stores
The actual sales of your strongest-performing analog
Interpreting the Range
The Lower-to-Upper range represents the realistic performance envelope based on analog variability. A narrow range indicates high consistency among comparable stores; a wide range suggests more variability in how your brand performs in similar conditions.
The Lowest/Highest comparable values provide absolute anchors—showing the full spread of actual results, including outliers that may fall outside the projected range.
Entering Square Footage
To see Total Sales projections, enter the square footage of the site you're evaluating:
Locate the Square Footage field below the projection values
Enter the expected or available square footage (e.g., "10,000 sq ft")
The Total Sales projection updates automatically
Square footage flows through to deal records if you add the site to your pipeline, and appears in generated reports.
Why Square Footage Matters
Two sites with identical Sales PSF projections can have very different total revenue potential:
Site A: $150 PSF × 8,000 sq ft = $1.2M
Site B: $150 PSF × 15,000 sq ft = $2.25M
Total Sales projections help you evaluate whether a site can generate the absolute revenue needed to meet your investment thresholds.
Filters and Analog Selection
The stores used for analog comparison respect any filters you've applied. This allows you to control the comparison set for more relevant projections.
How Filters Affect Projections
When you apply filters (via the Filters button in the top bar):
Only stores matching your filter criteria are used as analogs
The projection recalculates based on this refined comparison set
The card updates to reflect the filtered model
Example Use Cases
Regional filtering: Compare a Texas site only against your other Texas stores if regional factors significantly impact performance.
Format filtering: If you operate multiple store formats, filter to only compare against the same format.
Excluding outliers: Use tags to exclude stores with unusual circumstances (e.g., temporarily impacted by construction) from the analog set.
Viewing Filtered Stores
When filters are active, Filtered Stores appear as purple pins on the map, showing exactly which locations are contributing to the projection.
Cannibalization Adjustments
Projections are based on uncannibalized sales—the revenue the site would generate in isolation, without accounting for overlap with existing stores.
Why Uncannibalized?
Presenting uncannibalized projections keeps the sales forecast and cannibalization analysis separate, allowing you to:
Evaluate the site's standalone potential
Assess cannibalization impact independently
Make a combined decision factoring in both
Accounting for Cannibalization
To estimate net new revenue, review the Cannibalization section in the results panel. If a site shows 20% overlap with an existing store, your net revenue contribution is approximately 80% of the projected sales.
See Cannibalization for details on interpreting overlap percentages.
Analog Details
Understanding which stores are driving your projection adds context to the numbers.
Viewing Analogs
The projection card may display or link to the list of comparable stores used in the model. For each analog, you can see:
Store address
Similarity score (how closely it matches the searched site)
Actual sales performance
Key attributes driving the similarity
What Drives Similarity
Analog similarity is calculated based on the variables that most strongly correlate with your brand's revenue. Common factors include:
Trade area demographics (income, population density, age distribution)
Competitive density
Traffic and accessibility
Co-tenancy and retail mix
Site characteristics (visibility, parking, format)
The specific variables and their weights are calibrated to your brand during model setup.
Interpreting Analog Matches
High similarity scores: The proposed site closely resembles stores with known performance—higher confidence in the projection.
Mixed similarity: The site combines characteristics from different store profiles—projection may draw from diverse analogs with varying performance.
Few close matches: If no existing stores closely resemble the proposed site, the projection has higher uncertainty. This might indicate an expansion into a new market type for your brand.
Using Projections in Decisions
Screening
Sales projections enable rapid go/no-go decisions during initial screening. If a site's projected revenue falls below your minimum threshold, you can quickly move on without deeper analysis.
Prioritization
When evaluating multiple opportunities, projections help rank sites by potential. Sort your pipeline by projected sales to focus energy on the highest-upside opportunities.
Financial Modeling
Projections feed into pro forma development:
Midpoint for base case scenarios
Lower range for downside/stress testing
Upper range for upside scenarios
Always validate projections with your finance team's assumptions about rent, build-out costs, and operating expenses.
Committee Presentations
GrowthFactor projections provide defensible, data-backed revenue estimates for real estate committee review. The methodology (analog-based, brand-calibrated) and the specific analog stores can be presented alongside the numbers.
Projection Accuracy
GrowthFactor projections are designed to be realistic, not optimistic. The methodology is validated against actual store openings.
What Accuracy Means
When we report that recommended sites hit or exceed targets 99.8% of the time:
"Target" refers to the projection range (between Lower and Upper bounds)
Sites performing within or above this range are counted as hitting target
The model is calibrated to produce achievable projections, not aspirational ones
Improving Accuracy Over Time
Projection accuracy improves as:
Your store portfolio grows, providing more analogs
Models are recalibrated with recent performance data
Your team provides feedback on projection vs. actual results
Work with your GrowthFactor team to periodically review model performance and adjust calibration as your brand evolves.
Best Practices
Check your filters. Ensure the analog set makes sense for the site you're evaluating. Comparing an urban site against suburban stores (or vice versa) can produce misleading projections.
Use projections as one input. Sales forecasts are powerful but don't capture every factor. Lease terms, build-out costs, strategic value, and qualitative observations from site visits all matter.
Track actuals against projections. After opening new locations, compare actual performance to the original projection. This feedback loop builds institutional knowledge about model reliability.
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