PredictOps
Science serving
your expansion
The first Machine Learning algorithm specifically trained on 45 years of transactional and sociodemographic data to model the projected turnover of your retail locations, validated by systematic backtesting.
Born for emergencies. Perfected for retail.
PredictOps was first developed as a public safety technology. This is what guarantees its robustness.
The Genesis: SDIS 25
Faced with the challenge of optimizing resources during increasingly unpredictable crises, Fire & Rescue Services (SDIS 25) partnered with SAD Marketing to develop a predictive intervention model.
Academic Rigor: FEMTO-ST
FEMTO-ST lab (CNRS) provided scientific validation. The models integrate complex variables: weather, social data, local events, and web query analysis to detect emerging risks.
Retail Transposition
The same spatial modeling, machine learning, and traffic prediction techniques are applied to commercial revenue challenges. If it's accurate enough to save lives, it's accurate enough for your expansion.
How PredictOps calculates your revenue?
Multi-source Ingestion
Census data, aggregated mobile traffic, transactional sales databases (SAD 1981-2025 history), commercial competition data, and SIRENE business flows.
Real Catchment Area Modeling
Generation of dynamic isochrones (car, pedestrian, transport) accounting for physical obstacles, competing attractors, and psychological boundaries.
Consumer Potential Calculation
Application of a modified Huff's Law: estimation of capturable demand based on surface area, concept, pricing, and competitive pressure.
Proprietary Calibration
The model is finely calibrated on our exclusive database of 500+ studies conducted since 1981, allowing sector-specific corrections (F&B, fashion, food...).
Test PredictOps on your next location
Our experts analyze your case and provide an estimate within 5 business days.