Pricing Analytics brings together all statistical, econometric, and artificial intelligence methods used to measure the impact of prices on demand, margins, and commercial performance. It draws on sales histories, competitive data, customer signals, and contextual variables (seasonality, weather, catchment area) to produce actionable indicators: elasticity, cannibalization, threshold effects, and performance scenarios.

Pricing Analytics provides analytical insights (elasticity models, store segmentation, inter-product relationships), while Price Optimization transforms these analyses into operational recommendations for optimal prices according to a defined strategy (margin, volume, price image, inventory). BOOPER unifies these two dimensions in a single decision-making engine.

Dynamic pricing involves continuously adjusting prices based on various internal and external factors: changes in demand, inventory levels, seasonality, competitive positioning, price elasticity, margin targets, and commercial constraints. Unlike a static approach (one-off price review), dynamic pricing is based on business rule scenarios and optimization algorithms that enable companies to offer the "right price, at the right time, on the right product," while remaining aligned with the brand's commercial strategy and price image.

No, the solution works in the form of scenarios combining an unlimited number of business rules. Each customer can model their own constraints and decision-making logic:• margin thresholds,• rounding rules,• product hierarchies,• price positioning by universe or by brand,• promotional constraints,• rules for alignment or deviation from certain competitors. The scenario-based approach allows you to test several strategies in parallel (e.g., defensive vs. offensive strategy) and measure their impact before going live. This ensures flexibility, risk control, and consistency with your pricing policy.

Yes. The solution integrates with existing IT environments thanks to a Data Loader that can adapt to your current information flows (flat files, ERP exports, databases, APIs). The goal is not to transform your IT system, but to connect to it in a pragmatic way: • retrieval of necessary data (prices, costs, sales, inventory, repositories), • processing and optimization within the solution, • return of price recommendations to your business tools. This approach limits the impact on your IT organization and allows for gradual deployment, even in technically constrained contexts.

Yes, competitor data is not mandatory. When competitor data is missing or incomplete, the solution relies on: • sales history, • price sensitivity (elasticity), • costs and profitability targets, • product cycles and seasonality, • business expertise gained from comparable projects. This allows us to set prices that are consistent with the market while meeting your margin and volume targets. When competitive data becomes available, it can be integrated as an additional building block to refine scenarios and enhance the accuracy of recommendations.

Yes. BOOPER is designed for multi-store and multi-channel environments. It integrates geo-pricing, point-of-sale clustering, and local data mining to set differentiated prices by area while ensuring overall pricing strategy consistency. Some customers use it both at headquarters and at the point of sale to optimize pricing decisions and improve responsiveness to local competition.

Yes. The module enables ex-ante simulations based on your historical store and e-commerce data. You can compare several scenarios (price increases, competitive alignment, local strategy) and measure their projected impact on volumes, revenue, and margin before going live.

BOOPER structures the decision-making process through complete traceability of assumptions, simulations, and recommendations. Each decision is documented, shared between pricing, sales, and management teams, and validated according to defined workflows. Pricing becomes a measurable, auditable, and data-driven process.