FMCG data platform
Synthetic data generation for national product performance
- Challenge
- Product performance was well covered in the regions with established retail reporting and thin or absent everywhere else. Planning national launches from that data meant treating the reported regions as if they represented the country, which they did not.
- Approach
- We trained generative models on the regions with dense history, conditioned on the attributes that actually differ between markets: store format, seasonality, price tier and category mix. We used them to synthesise plausible performance for the gaps. The output carries confidence bands, and the model is validated by holding out a well-covered region and checking whether it reconstructs it.
3×
improvementmore markets modelled in launch planning
- Python
- PyTorch
- Databricks
- MLflow
- Spark
- Azure ML
Outcome
Planners can now model a national launch across every market rather than extrapolating from the reported ones, with the synthesised regions clearly marked as modelled and carrying their own uncertainty.
Sector: FMCG