ML Pricing Intelligence

Basic price tracking tells you what competitors charge right now. ML pricing intelligence tells you what they will likely charge tomorrow, how sensitive your demand is to each price point, which competitors actually matter for each product, and what your optimal price should be given your specific business objectives. It transforms raw price data into actionable strategy through predictive modeling, causal inference, and optimization algorithms.

Our ensemble models achieve 94% directional accuracy on 24-hour competitor price predictions and 87% accuracy on 7-day forecasts. Accuracy varies by category — stable categories like industrial supplies see 97%+ accuracy, while volatile categories like consumer electronics are closer to 90%. Every prediction includes confidence intervals so you know the uncertainty range and can set your automation thresholds accordingly.

Our causal inference framework uses techniques like instrumental variables and difference-in-differences to isolate the true price effect from confounders. We control for seasonality, day-of-week effects, promotional activity, competitor actions, inventory changes, and external events. This produces elasticity estimates that reflect genuine price sensitivity rather than correlations driven by other factors that happened to coincide with price changes.

Yes. Our anomaly detection uses multi-signal analysis rather than simple threshold rules. A competitor dropping their price by 40% on Black Friday is expected and scored as normal. The same 40% drop on a random Tuesday with no inventory signal and no category-wide movement is flagged as a probable error. The system considers historical patterns, category norms, calendar events, and corroborating signals before classifying any observation as anomalous.

At minimum, we need your product catalog with SKU identifiers and current prices. For optimal results, we also integrate your historical sales data (to build elasticity models), inventory levels (for demand forecasting), margin data (for profit-aware optimization), and business rules (MAP floors, minimum margins, strategy preferences). We handle all competitor data collection ourselves through our scraping infrastructure.

Competitive monitoring and anomaly detection work immediately — day one. Price prediction models need 2-4 weeks of data to calibrate per competitor. Elasticity models require 4-8 weeks of sales data with natural price variation to produce reliable estimates. Demand forecasting improves continuously but provides directional value within the first 2 weeks. We supplement early-stage models with category-level priors to provide useful guidance even before product-specific models mature.

Absolutely. You can set hard constraints (minimum margin, MAP floors, maximum price change per day), soft constraints (prefer to be within 5% of competitor X), and business rules (never price higher than our DTC site on marketplaces). The ML models optimize within these boundaries. You can also configure approval workflows where high-impact recommendations require human sign-off before execution.

For new products, we use transfer learning from similar products in your catalog and category-level models trained on thousands of product launches. The system identifies comparable products based on category, price range, brand tier, and attributes, then uses their historical demand patterns as a prior. As the new product accumulates its own data, the model gradually transitions from the category prior to a product-specific model.

Our ML pipeline generates repricing signals within 30 seconds of detecting a relevant market change. For auto-execute workflows, prices are submitted to marketplace APIs within 60 seconds of signal generation. End-to-end latency from competitor price change to your price update is typically under 5 minutes, depending on the marketplace's own processing time. For human-approval workflows, signals are queued with full context for rapid review.