SAD Marketing
PredictOps Retail

PredictOps
Science at the service
of commerce

The first Machine Learning algorithm specifically trained on 45 years of French transactional and socio-demographic data to model the forecast Turnover of your points of sale, validated by systematic backtesting.

Access to study via PredictOps platform: POA.

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How it works
±5%
Turnover Accuracy
Validated systematic backtesting
45 years
Historical Data
Unmatched proprietary base (since 1981)
Emergency Services
Proven Model
Technology born from Fire Services
FEMTO-ST
AI Research
CNRS Laboratory
The Story

Born from emergencies. Perfected for retail.

PredictOps was first an emergency services technology. This ensures its robustness for turnover estimation.

01

Genesis: SDIS 25

Faced with the challenge of optimizing human and material resources against increasingly unpredictable crises, the Doubs Fire and Rescue Service (SDIS 25) partnered with SAD Marketing to develop a predictive model for spatial interventions.

02

Academic Rigor: FEMTO-ST

The FEMTO-ST laboratory (CNRS/UFC/ENSMM) provided scientific AI validation. The models integrate complex spatial variables to detect emerging risks.

03

Transposition to Retail

The same mathematical modeling and geospatial prediction techniques are applied to commercial turnover issues. If it's accurate enough to save lives, it's accurate enough for your expansion.

Methodology

How PredictOps calculates your Turnover?

1

Multi-source ingestion

INSEE census data, aggregated mobile flows, historical transactional sales databases since 1981, business registration and competitive flows.

2

Real catchment area modeling

Generation of dynamic isochrones that take into account physical obstacles, urban attractors, real travel times, and psychological boundaries.

3

Consumption potential calculation

Application of the modified Huff law: estimation of capturable demand against point-of-sale attractiveness, concept, prices, and competition.

4

History-based adjustment

The machine learning model refines the final prediction from hundreds of reconciled purchase behaviors to deliver your turnover forecast irrevocably.

PredictOps Retail FAQ

Tout ce que vous devez savoir sur nos solutions.

What is the actual accuracy of PredictOps Retail?
Our models are validated by systematic backtesting processes on more than 45 years of real historical data (since 1981) to estimate the forecast turnover of your future stores.

What data is used by the algorithm?
PredictOps ingests over 200 quantitative and qualitative variables: mobile population flows (telephony), census data (CSP, income, age), competitive attractiveness, business registration flows, road traffic, and our own proprietary historical database.

How does PredictOps Retail differ from classic geomarketing tools?
Most software just displays isochrones and counts population (raw data). PredictOps is decision-making intelligence: the algorithm processes this data and delivers direct Turnover forecasts.

How much does a PredictOps study cost?
PredictOps is available upon request. The access cost depends on the number of locations to analyze, the complexity of your sector, and your needs for integrating your own data. Contact us for a personalized estimate.

How long does it take to get a study?
Thanks to our automated algorithmic infrastructure, coupled with expert human verification, we deliver a complete analysis within 5 working days after the mission is validated and your elements are received.

Does the model adapt to my sector of activity (fashion, food, etc.)?
Yes. We calibrate the algorithm for your specific concept. Whether you are in fast food, clothing, or DIY, PredictOps learns the performance drivers of your market.

Do you take into account inflation and economic conditions?
Absolutely. Our neural networks are fed with macro-economic variables (inflation, locally estimated purchasing power) in order to anchor the forecast turnover in current economic reality.

Can PredictOps Retail estimate cannibalization?
Yes. The algorithm integrates the notion of commercial cannibalization: it simulates the impact of a new opening on your existing points of sale in the same consumption basin.

Do we need to provide you with our sales data?
It is recommended but not mandatory. Applying the model to your past real data allows for training and considerably refining algorithmic accuracy for your future openings.

Is your geomarketing data secure and GDPR compliant?
All our processing, particularly on mobile flows, relies on aggregated and anonymized data in accordance with European legislation. Your proprietary data remains strictly partitioned.

Test PredictOps on your next location

Our experts analyze your variables and provide an estimate within 5 working days.

Submit a study request