Strategy & Expansion
Cannibalization Prediction Engine
Predicts the impact of new store openings on existing store performance using spatial analytics and trade area overlap modeling.
What it does
- Models trade area overlap between new and existing locations
- Predicts sales transfer percentage using spatial gravity models
- Identifies which existing stores carry the highest cannibalization risk
- Calculates net incremental revenue after cannibalization adjustment
- Maps customer flow patterns to quantify shared customer base
- Simulates multiple site configurations to minimize self-cannibalization
- Tracks post-opening actual vs. predicted cannibalization rates
- Generates cannibalization-adjusted site approval recommendations
What it takes
- Store-Level Sales Data
- Trade Area Demographic Data
- Customer Address / Loyalty Data
- Site Location Data
- Competitor Location Data
What it returns
- Excel Table
- PDF Report
- Map Export
- JSON Data
Written into the systems your team already runs, not into a second system of record.
How teams run it
Trade Area Overlap Analysis
Map geographic overlap between the proposed site and existing stores.
Sales Transfer Prediction
Predict how much revenue will shift from existing stores to the new location.
Net Incremental Revenue Model
Calculate true incremental value after accounting for cannibalization.
Site Configuration Optimizer
Compare alternative site locations to minimize cannibalization.
Cited, or flagged
Every value is anchored to the clause it came from. Where one is missing, Surfaice says so rather than inventing it.
Read where it lives
Documents are processed for the request and not retained afterwards. Nothing is copied into a second system of record.
Never trained on
Your documents do not train models — yours or anyone else's.
More in Strategy & Expansion
See it run on your own documents.
Fifteen minutes, your files, and the clause behind every answer.