AI Data Scientist for Retail Teams
Retail decision velocity comes down to four predictive engines: demand at SKU × store, the right price under elasticity + competitive constraints, the right inventory level under service-level commitments, and segmentation that drives campaigns. OctOpus owns all four end-to-end. Drop the sales table, point at the target, and ship the model — same week, not next quarter.
Forecasting that respects hierarchy
Retail forecasts have structure — store × SKU × week — and naive flat models underperform. OctOpus picks NeuralForecast (TFT, PatchTST) with hierarchical reconciliation when the panel is dense, falls back to LightGBM with lag/rolling features for noisy long-tail SKUs, and zero-shots Chronos / TiRex on cold-start items. Always validates on a chronological holdout, never a random split.
Pricing that learns elasticity
Build a price-elasticity model per SKU or segment, with monotonicity constraints so the recommended price respects business sense (higher price → lower units). OctOpus adds competitor-price sensitivity when the data's there, and surfaces the optimal price under margin floors and inventory constraints.
Segmentation that drives revenue
Behavioural segmentation from transaction tables: RFM, inter-purchase interval, category preference vectors, lifetime value buckets. Auto-picks clustering family (KMeans for spheroidal segments, DBSCAN for irregular shapes, soft GMM membership). Exports labelled segments back to your CRM / email tool.
Key capabilities
- Demand · pricing · inventory · segmentation — one workflow.
- Hierarchical forecasting (store × SKU × week) with reconciliation.
- Price-elasticity with monotonicity constraints.
- Chronological holdout, never a random split on time series.
- Exports model output to your CRM / merchandising tool.
Frequently asked questions
Can OctOpus forecast SKU-level demand?
Yes. Hierarchical forecasting (store × SKU × week) is the default treatment for retail panel data. OctOpus picks NeuralForecast (TFT/PatchTST) for dense panels, LightGBM with lag features for noisy long-tail SKUs, and zero-shots Chronos for cold-start items. Validation always uses a chronological holdout.
How does OctOpus handle dynamic pricing?
Builds a per-SKU price-elasticity model with monotonicity constraints (higher price → lower units, enforced not just hoped for). Adds competitor-price sensitivity when that data is available. Surfaces the optimal price under business constraints — margin floor, competitive ceiling, inventory pressure.
Does OctOpus handle promotional uplift?
Yes. Promotions are treated as binary or categorical features; the agent fits a base demand model and a promotional lift separately, then composes them for forecast generation. Cross-price elasticity is captured when substitute/complementary SKUs are flagged in the input.
Can I ship segments back to my CRM?
Yes. After segmentation runs, every customer row gets a segment label and a soft-membership probability. Export to CSV or hit the prediction API to score new customers as they enter the CRM.