4-weekahead
Demand forecasts with calibrated confidence bands
Demand Forecasting
ML models that predict service demand by location, time, and customer segment — using real foot-traffic data, not surveys.
Predictive Analytics
Predict demand. Optimize operations.
Forecasting and automation for logistics, retail, and field-service teams that need to plan ahead, not react — built on real foot-traffic data, weather, service history, and location context.
Demand heatmapsOperational outputsData-to-decision workflow
The status quo
Why operations teams need predictive analytics
- You're reacting to demand instead of anticipating it — overstaffing some days, understaffing others
- Spreadsheet-based forecasting can't account for spatial patterns, weather, or seasonal shifts
- Your competitors are already using foot-traffic signals, weather, and service history to plan territory coverage — every month you wait, the gap grows
What we deliver
What we build
02
Spatial Analytics
Geographic clustering, hotspot analysis, and territory optimization for field operations.
03
Process Automation
Automated scheduling, resource allocation, and dispatch based on predicted demand.
04
Performance Dashboards
Real-time KPI tracking with predictive indicators and anomaly detection.
05
Territory Optimization
Drive-time polygons and trade-area analysis to minimize overlap and maximize coverage.
Proof-led positioning
What this page needs to make obvious
Predictive analytics automation, demand forecasting operations, and territory optimization.
Demand heatmaps
Combine location signals, seasonality, weather, and service history into territory pressure views.
Operational outputs
Staffing, dispatch, territory, and expansion recommendations tied to real places.
Data-to-decision workflow
Inputs, model assumptions, evidence notes, and recommended next action stay visible.
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
Territories, service history, target geography, candidate sites, staffing constraints, and decision deadline.
Review surface
Location signals, weather, seasonality, customer history, and competitive context are turned into a decision surface.
Evidence
Source signals, assumptions, confidence, and operator review points stay attached to recommendations.
Output
Dashboard, ranked territory table, staffing view, dispatch plan, CSV, or report.
Source and limits
Technical trust should stay visible.
Confidence
Forecasts need transparent source signals and limitation notes.
Caveat
Predictions should guide review, not replace operator judgment.
Source
Foot traffic, Overture Places, weather, demographics, service history, and competitor locations.
QA boundary
Source notes, confidence language, drift checks, and operator review.
Export path
Dashboard, ranked territory table, staffing view, dispatch plan, CSV, or report.
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.
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01
Data intake
Your service history (12+ months ideal), territory boundaries, and any seasonal context. We assess data quality and recommend baselines.
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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
- Python, PostGIS, Mapbox, TensorFlow, GeoPandas, and custom ML forecasting models
- Operated by Geospatial Solutions LLC — Washington, DC
Questions teams ask before they engage us
Common questions, answered honestly
What does 'predictive analytics automation' actually mean?
It means the prediction is not a report someone reads — it's an input to an automated decision. Demand forecast triggers staffing recommendations, route optimization, and dispatch updates without human-in-the-loop for routine cases.
How accurate are the predictions?
For service demand at 4-week horizon, 75-85% MAPE at aggregate territory level, 65-75% at individual zone. We share backtest results and a model card before any production deployment.
Do you use generic models or build custom?
Custom, every time. Generic foot-traffic models miss your service-area context, your seasonality, and your competitor mix. We start from your service history and layer in spatial signals.
How does this integrate with our existing dispatch and CRM?
REST API with documented schema. Your dispatcher dashboard, CRM, or scheduling tool consumes predictions in the format they already understand — we don't make you adopt a new UI.
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Book a free analytics consultation
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.
See my territory forecast