4-weekahead
Demand forecasts with calibrated confidence bands
CRE Site Selection
Corner activity, competitor proximity, weekday vs weekend patterns, and accessibility scoring.
Retail Analytics
Rank every business in a neighborhood by real visitor activity
Not surveys. Not estimates. Real foot traffic — built for CRE teams, retail strategists, and franchise operators tired of making million-dollar location decisions on vibes.
Trade-area mapsRanked locationsExpansion scenarios
The status quo
Why retail site selection needs spatial AI
- A bad location decision costs $500K+ in buildout and 3-5 years of underperformance
- Demographic reports don't capture foot-traffic patterns, competitor proximity, or accessibility
- Your real estate team evaluates sites by instinct — competitors evaluate sites with data
What we deliver
What we analyze
02
Trade Area Analysis
Primary, secondary, and tertiary trade areas using drive-time polygons and visitor flow data.
03
Revenue Forecasting
ML models that predict location-level revenue from spatial features and comparable sites.
04
Market Territory Optimization
Optimize franchise territories and sales regions to minimize overlap and maximize coverage.
05
Underserved Category Analysis
Find anchor traffic, underserved retail categories, and dead zones for expansion strategy.
Proof-led positioning
What this page needs to make obvious
Retail predictive analytics, retail site selection, foot traffic analysis, and trade area analysis.
Trade-area maps
400m, 800m, and custom-radius views around sites, corridors, or competitors.
Ranked locations
POIs and corridors ranked by observed visitor activity and surrounding context.
Expansion scenarios
Compare candidates for franchise, QSR, medical retail, fitness, grocery, and service retail.
Proof workflow
Input, review, evidence, output.
Modeled on the live Geospatial Solutions demos: the page should show what the buyer sends, what they review, what evidence stays visible, and what they receive.
Input
Candidate sites, competitor list, target customer geography, trade-area radius, and expansion criteria.
Review surface
Foot-traffic signals, POI context, competitor density, demographics, and access patterns are compared by site.
Evidence
Rankings show source signals, radius assumptions, competitor context, and confidence limits.
Output
Ranked table, site comparison, trade-area report, CSV, or dashboard.
Source and limits
Technical trust should stay visible.
Confidence
Retail recommendations should show source signals and ranked evidence.
Caveat
Foot traffic is a decision input, not a guaranteed revenue forecast.
Source
Foursquare signals, Overture Places, POIs, competitor data, trade areas, and demographics.
QA boundary
Source notes, radius assumptions, competitor context, and candidate review.
Export path
Ranked table, site comparison, trade-area report, CSV, or dashboard.
Before the first call
What you send · What you get
No vague discovery phase. You bring four or five things, we return a specific plan you can evaluate.
→ What you send
- 112+ months of service history (CRM export or equivalent)
- 2Territory boundaries and any current scheduling logic
- 3CRM/dispatch platform name for integration scoping
- 4Seasonal or geographic patterns you have noticed
← What you get back
- 1Data quality assessment — what is usable and what needs cleanup
- 2Baseline forecast accuracy on your data (held-out backtest)
- 3Model architecture recommendation with reasoning
- 4Dashboard wireframe showing recommended actions
- 5Retraining schedule with automatic drift detection plan
Deliverables
What you walk away with
- Demand forecast dashboard with 4-week ahead service predictions
- Territory and site selection scoring tied to real foot-traffic signals
- Model card and backtest results so you can audit accuracy
- Monthly retraining pipeline with automatic drift detection
How we work
A scoped path from sample data to running system
No open-ended retainers. No "discovery phases" that bill for months without producing anything you can evaluate.
-
01
Data intake
Your service history (12+ months ideal), territory boundaries, and any seasonal context. We assess data quality and recommend baselines.
-
02
Model build
Foot-traffic signals from Foursquare + Overture, weather, demographic, and competitor data combined with your service history. Tuned to your geography.
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03
Dashboard
Action-oriented view: recommended staffing levels, route assignments, expansion candidates. Not a raw model output — a decision surface.
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04
Retrain
Monthly retraining on accumulated data. Model drift surfaced automatically. We can transfer the pipeline or keep operating it under SLA.
Live on geospatialsolutions.co
Click into the actual work
These open the real, interactive demos on our main site — not screenshots, not videos. Click around before you decide to talk to us.
Why teams trust us
- Foursquare popularity + Overture Places — millions of US POIs, regularly refreshed
- PostGIS, Mapbox, Python, React, TensorFlow, GeoPandas, and custom ML models
- Operated by Geospatial Solutions LLC — Washington, DC
Questions teams ask before they engage us
Common questions, answered honestly
What kind of retail does this work for?
Strongest for service-driven retail with high location dependency: restaurants, gyms, salons, convenience, c-store, fast casual, and franchise concepts. Less useful for e-commerce or destination retail with weak local trade areas.
How accurate is foot-traffic data vs surveys or estimates?
We use real foot-traffic signals from Foursquare and Overture Places — millions of US POIs with hourly visitor patterns. This is observed behavior, not survey response. Accuracy is what behavior actually shows, not what people say they do.
Can you score multiple sites against each other?
Yes — that's the typical engagement. You provide 5-20 candidate sites, we return ranked scoring on foot traffic, demographics, competitor proximity, accessibility, and projected revenue with a confidence band on each.
How does this differ from Esri Business Analyst or Buxton?
Both are excellent for demographic and category analysis. We add real foot-traffic behavior data (Esri/Buxton mostly use estimates) and pricing transparency (no $40k annual license for a single-project decision).
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Book a free retail analytics briefing
Drop a pin. We will show you the foot traffic and demand forecast live.
Bring a territory or trade area. We will pull real foot-traffic data and a demand forecast on the call so you can evaluate the signal before any engagement.
Compare two retail sites