Pricing FAQ

Yes. MPS is also designed to be adopted by simpler organizations. The vocabulary and indicators remain those of the client. The quality of the algorithms is the same as in larger structures.

Yes. MPS is designed for large retail accounts with multi-country, multi-store, multi-category management and centralized governance while maintaining local flexibility.

BOOPER projects show a rapid ROI thanks to reduced investment in pricing, increased volumes, inventory optimization, reduced time spent on manual forecasting, and overall improvement in price image. The first gains are seen immediately.

Yes. With the AI-assisted simulator, users can test different business scenarios (promotions, price variations, changes in product range) and measure their projected impact on volumes, revenue, and margins.

By more accurately anticipating future demand, MPS makes it possible to adjust order volumes, reduce shortages and overstocking, improve service levels, and limit the financial immobilization associated with inventory.

Traditional methods rely on averages and past trends. Machine learning integrates hundreds of variables simultaneously, detects non-linear relationships, and automatically adapts to changes in consumer behavior.

MPS primarily uses historical sales, prices, promotions, commercial calendars, store data, and external drivers (weather, events, competition). The richer the data, the more accurate the models. We recommend a minimum of one year of historical data.

AI analyzes large volumes of historical and contextual data to identify patterns that are invisible to human analysis. For example, it takes into account seasonality, promotions, prices, weather, and competition to produce dynamic and continuously adjusted forecasts.

Absolutely. Our missions are based on your existing tools and data and can be enhanced by BOOPER's analytical and artificial intelligence solutions.

The main results observed are a measurable improvement in margins, greater consistency in pricing, increased promotional performance, and a reduction in uncontrolled pricing decisions.

Yes. Our consultants work across all product categories in the retail sector, both food and non-food.

The duration varies depending on the scope: from a few weeks for a targeted diagnosis to several months for comprehensive strategic and operational support.

BOOPER consulting provides human, methodological, and business expertise that complements the tools. It helps structure strategy, interpret data, and support change within teams.

One task involves analyzing your pricing strategy, data, and processes in order to formulate concrete and directly applicable recommendations for improving your pricing performance.

If you have any questions or spontaneous initiatives, please write to talent@booper.fr or visit our "Contact" page.

If you have any questions or spontaneous initiatives, please write to talent@booper.fr or visit our "Contact" page.

At BOOPER, career progression is achieved through professional goals, training, and exposure to cross-functional projects.

Yes. We welcome interns and recent graduates for challenging assignments, with structured support and opportunities for extension or employment.

After reviewing your application, you will be invited to meet with a member of the HR team, followed by one or more interviews with the operational teams to assess your professional and cultural skills.

We are looking for technical profiles (data, development, AI), pricing consultants, project managers, and sales and marketing support talent. Job openings are regularly updated in our career section.

Deployment is rapid thanks to a modular architecture and Big Data technologies. The first results can be seen within a few weeks.

Yes. BOOPER is compatible with all major IT environments (SAP, Oracle, Google Cloud) and integrates easily with existing tools.

Customers see improved margins, increased revenue, and a return on investment in less than six months.

BOOPER's AI is explainable and designed for pricing professionals. It combines predictive models, business rules, and scenario simulations to secure business decisions.

BOOPER is aimed at retailers, store chains, and brands with large volumes of price, sales, and promotion data.

BOOPER is a software publisher specializing in optimizing pricing and commercial performance for retailers using artificial intelligence.

Absolutely. Our missions are based on your tools and can be enhanced by BOOPER's analytical and AI solutions.

A measurable improvement in margins, greater consistency in pricing, more confident decisions, and a more mature pricing organization.

Yes. Our missions cover all product categories and distribution formats.

From a few weeks for a diagnosis and strategic recommendations, to several months for a complete transformation of pricing governance.

Strategic consulting focuses on the decision-making framework (vision, principles, pricing architecture), while operational consulting concentrates on day-to-day execution (pricing, promotions, reporting).

It aims to define or overhaul your overall pricing strategy: positioning, governance, decision-making rules, and indicators, in order to align your pricing choices with your business objectives.

Yes. BOOPER favors an Agile approach that allows for gradual deployment, continuous adaptation, and strong team involvement throughout the project.

Through indicators such as tool adoption rates, decision quality, business performance, and user satisfaction.

Pricing, Marketing, Purchasing, Finance, and Category Management departments, as well as managers and operational users.

The duration depends on the maturity of the organization and the scope of the project. It can range from a few weeks to several months, depending on the transformation objectives.

Because a technology project without human support carries a high risk of non-adoption. Change management secures ROI and the overall performance of the project.

Change management in pricing aims to support teams in adopting new methods, tools, and pricing decision-making processes in order to ensure their effectiveness and sustainability.

Absolutely. Our missions are based on your existing tools and data and can be enhanced by BOOPER's analytical and artificial intelligence solutions.

The main results observed are a measurable improvement in margins, greater consistency in pricing, increased promotional performance, and a reduction in uncontrolled pricing decisions.

Yes. Our consultants work across all product categories in the retail sector, both food and non-food.

The duration varies depending on the scope: from a few weeks for a targeted diagnosis to several months for comprehensive strategic and operational support.

BOOPER consulting provides human, methodological, and business expertise that complements the tools. It helps structure strategy, interpret data, and support change within teams.

One task involves analyzing your pricing strategy, data, and processes in order to formulate concrete and directly applicable recommendations for improving your pricing performance.

BOOPER projects deliver rapid ROI by identifying quick wins, improving pricing consistency, and optimizing competitive positioning. Initial gains are typically seen within three months.

Yes. BOOPER is designed for complex organizations with analyses by store, cluster, region, and country, while ensuring centralized governance of decisions.

Yes. Each recommendation is accompanied by a quantitative and qualitative estimate of its effects on margin, revenue, and price positioning.

The BOOPER diagnosis combines retail expertise, artificial intelligence, and advanced analytics (ABC, clustering, price mapping). It goes beyond simple observation to offer quantified scenarios and quick wins that can be implemented immediately.

BOOPER relies primarily on your sales data, price lists, promotional history, store data, and competitor price reports. The richer and more reliable the data, the more accurate the recommendations.

A pricing diagnosis is a comprehensive analysis of your pricing policy aimed at assessing its consistency, effectiveness, and positioning in relation to the market. It identifies concrete levers for optimization in terms of shelf space and promotions.

Benefits are quickly realized thanks to reduced manual tasks, increased decision reliability, and improved competitive responsiveness. ROI is typically achieved in less than three months.

The collected data feeds into BOOPER's simulation, optimization, and pricing governance modules. It enables arbitration between competitiveness, margin, and price positioning.

BOOPER is designed for large retail accounts with multi-brand, multi-country, and multi-category management. The platform centralizes data while maintaining local granularity by store or zone.

Yes. BOOPER archives all readings to analyze trends, measure price variations, and identify competing strategies over time.

The platform applies automatic controls: anomaly detection, data cleansing, format harmonization, and business validation. Decisions are therefore based on reliable, audited data.

BOOPER integrates data from web scraping, panelists, field store surveys, and internal surveys. This multi-source approach ensures a comprehensive view of the competition.

Web scraping involves automatically collecting prices displayed on competing e-commerce sites and marketplaces. BOOPER transforms this data into actionable indicators to guide pricing strategy.

Yes, for our B2B customers or franchise and independent networks, MPS manages and simulates transfer prices just as easily as consumer sales prices. This gives you an overview of the wholesale margin, retail margin, and consolidated margin.

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.

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.

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, 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, and 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. 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: retrieving the necessary data (prices, costs, sales, inventory, repositories), processing and optimizing it in the solution, and returning price recommendations to your business tools. This approach limits the impact on your IT organization and allows for gradual deployment, even in constrained technical contexts.

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, alignment or deviation rules in relation to 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.

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.

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.

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.

Yes. BOOPER is designed for large retail accounts with multi-country, multi-store, and multi-category management, while maintaining centralized governance and local flexibility.

BOOPER projects deliver a rapid ROI by reducing excessive markdowns, improving inventory turnover, and reducing the time spent on manual decisions. Initial gains are typically seen in less than three months.

The solution calculates markdowns at a granular level (SKU, store, region) in order to tailor the discount to actual sales potential and avoid unnecessary discounts.

Yes. BOOPER anticipates the periods when markdowns have the greatest impact on demand and avoids reductions that are too early or too late.

Manual markdown is based on generic rules and intuition. AI-driven markdown relies on predictive models that simulate the actual impact of each reduction level per product and per store.

BOOPER primarily uses historical sales, inventory, prices, promotional calendars, store data, and external factors such as weather and competition. The richer the data, the more accurate the recommendations.

AI analyzes sales history, inventory levels, seasonality, prices, and customer behavior to predict the actual impact of markdowns. It identifies the right level of discount at the right time to maximize sales while protecting margins.

Yes. MPS is also designed to be adopted by simpler organizations. The vocabulary and indicators remain those of the client. The quality of the algorithms is the same as in larger structures.

Yes. MPS is designed for large retail accounts with multi-country, multi-store, multi-category management and centralized governance while maintaining local flexibility.

BOOPER projects show a rapid ROI thanks to reduced investment in pricing, increased volumes, inventory optimization, reduced time spent on manual forecasting, and overall improvement in price image. The first gains are seen immediately.

Yes. With the AI-assisted simulator, users can test different business scenarios (promotions, price variations, changes in product range) and measure their projected impact on volumes, revenue, and margins.

By more accurately anticipating future demand, MPS makes it possible to adjust order volumes, reduce shortages and overstocking, improve service levels, and limit the financial immobilization associated with inventory.

Traditional methods rely on averages and past trends. Machine learning integrates hundreds of variables simultaneously, detects non-linear relationships, and automatically adapts to changes in consumer behavior.

MPS primarily uses historical sales, prices, promotions, commercial calendars, store data, and external drivers (weather, events, competition). The richer the data, the more accurate the models. We recommend a minimum of one year of historical data.

AI analyzes large volumes of historical and contextual data to identify patterns that are invisible to human analysis. For example, it takes into account seasonality, promotions, prices, weather, and competition to produce dynamic and continuously adjusted forecasts.

BOOPER projects deliver a rapid ROI by optimizing promotional budgets, reducing stockouts, and improving margins. Initial gains are typically seen in less than three months.

MPS compares actual sales to expected sales excluding promotions in order to isolate the uplift generated by the campaign. ROI is calculated by incorporating margin, costs, and additional volumes.

Yes. MPS is designed for large retail accounts with multi-country, multi-store, multi-category management and centralized governance while maintaining local flexibility.

MPS identifies unprofitable promotions, measures their actual ROI, and proposes more effective alternatives. Decisions are based on objective data, not just history or intuition.

Yes. The "What If" simulation module allows you to test different promotional scenarios and measure their projected impact on volumes, revenue, and margin.

MPS uses historical sales, promotional calendars, prices, margins, store data, inventory, and external factors (seasonality, events, competition).

AI analyzes past sales, promotional mechanics, prices, seasonality, and customer behavior to predict the actual impact of promotions. It allows you to choose the best mechanics, the right discount level, and the right targeting to maximize ROI.

The gains come from reduced manual time, reliable price comparisons, improved business decisions, and overall competitive performance. The first benefits are usually seen in less than three months.

MPS uses product databases, descriptions, technical attributes, price histories, and competitive data. The richer the data, the more accurate the association proposal.

No, although recommendations are becoming increasingly intelligent and automatic, the association requires human validation in order to train the algorithms.

BOOPER identifies substitutable products based on their functional attributes and price positioning in order to structure MN/MDD/PPx chains and analyze the effects of cannibalization and upmarket positioning.

When there is no direct association by EAN, cloning takes over and links the retailer's products with those of its competitors; this applies to both private labels and national brands. The other type of complementary relationship is chaining: the retailer's products are grouped together to establish links in terms of hierarchy, coefficients, and historical models.

AI simultaneously analyzes labels, attributes, formats, brands, and price behaviors to detect similarities invisible to human analysis. It continuously learns from user validations to improve its accuracy. When available, AI also takes image recognition into account.

Product matching, or matching in certain situations, consists of automatically identifying equivalent products between different brands or ranges in order to compare their prices, performance, and competitive positioning on a consistent basis.

The main benefits observed are: measurable improvement in margins, greater pricing consistency between stores, reduced time spent on manual calculations, the ability to anticipate the impact of decisions, and strengthened pricing governance. The solution also helps secure promotional campaigns and improve competitiveness against rivals.

No. Booper is designed to be used directly by business teams. Artificial intelligence is encapsulated in simple, educational interfaces. Methodological support is provided to help users interpret results and structure their pricing decision-making processes, without relying on a dedicated data team.

Yes. The platform is designed to interface with the main retail IT environments: ERP, BI tools, PIM, POS systems, and existing pricing solutions. Integration is based on standard connectors and secure APIs. Booper fits into the existing ecosystem without requiring a complete overhaul of the architecture.

Booper's predictive models are based on proven data science methods and are trained using real historical data (sales, prices, promotions, competition). They are regularly recalibrated to incorporate changes in customer behavior and markets. Consistency checks and performance indicators are used to continuously measure the reliability of forecasts.