The AI pricing platform that makes your prices more reliable and boosts your margins

Booper is designed for large retail chains and mid-sized companies with a significant volume of product SKUs and transactional data: food and non-food retail, home improvement, beauty, specialty equipment, as well as B2B networks. The platform is designed for multi-store or multi-channel organizations seeking to structure their pricing governance and manage their profitability in a systematic and measurable way.

This positioning stems from the nature of the BOOPER MPS engine: it combines business rules and predictive models, which requires a sufficient volume of data (sales history, product catalogs, store data) for the forecasts and recommendations to have real statistical value. A retailer with a broad product assortment or a network of several dozen stores benefits more from this approach than an organization with a very limited product catalog.

This sectoral diversity is reflected in the organizations Booper already supports, ranging from large national chains—such as one of our clients in the food sector (with over 1,700 stores)—to regionally based multi-brand groups like the Barbotteau Group in the French Caribbean, as well as B2B players with specific transfer pricing strategies. Each is progressing along its own pricing maturity trajectory, moving from a process that is still largely manual toward predictive and automated pricing management.

For a sales or pricing department evaluating Booper, the key factor is therefore not so much the industry as the complexity of the network to be managed (number of stores, channels, and SKUs) and the need to transition from reactive pricing—often managed in spreadsheets—to a structured and measurable pricing governance framework.

Unlike tools based on static rules or spreadsheets, Booper combines artificial intelligence, predictive models, and retail industry expertise. The platform continuously analyzes sales, competition, seasonality, and purchasing behavior to generate explainable recommendations, rather than rigid rules applied without regard for context.

This difference primarily lies in the nature of the decision made: a traditional tool applies a predefined rule (for example, systematically aligning prices with the lowest market price) without measuring its actual impact on demand or margin. Booper simulates this impact before implementation, using the specific price elasticities for each product, thereby transforming a generic rule into a decision based on quantified projections.

The other difference lies in the scale at which decisions can be made: spreadsheet-based management quickly reaches its limits when dealing with more than a few hundred SKUs or a few dozen stores, whereas the Booper platform is designed for complex, multi-store, or multi-country networks, with centralized governance and local flexibility. It thus enables a shift from manual, reactive pricing to strategic, predictive, and traceable management of pricing decisions.

For a pricing department, this shift changes the nature of the teams’ work: less time spent calculating and verifying prices product by product, and more time devoted to strategic decision-making (margin, competitiveness, price-image) based on recommendations that have already been simulated and documented.

Yes. Booper is designed for business users (pricing, category management, marketing, finance) and not for data scientists. Each recommendation is accompanied by clear metrics: expected impact on margin, volume, and competitiveness; justification based on the data used; and alternative scenarios.

This transparency addresses a practical challenge: a price recommendation generated by an algorithm, without any explanation, is difficult to validate and even harder to justify to sales or finance management. By outlining the factors that led to a given recommendation (changes in demand, competitive positioning, margin constraints), Booper enables teams to understand the reasoning behind it rather than simply applying a result.

This clarity is also supported by the alternative scenarios provided: rather than a single solution, teams can compare several options (for example, a defensive versus an offensive approach toward a competitor) and choose the one that best aligns with their current business strategy, with a clear understanding of the trade-offs between margin, volume, and price-image for each option.

For a pricing team, this clarity is key to ensuring that recommendations are actually adopted over the long term: teams that understand and can justify their pricing decisions are more likely to stand by them when facing senior management, which strengthens the brand’s overall pricing governance rather than undermining it through reliance on a tool perceived as opaque.

Booper's predictive models are based on proven data science methods and are trained using real historical data (sales, prices, promotions, competition) specific to each client. They are regularly recalibrated to account for changes in customer behavior and market trends, and consistency checks and performance metrics are used to continuously measure the reliability of the forecasts.

This continuous recalibration addresses a reality in retail: a model trained once on static historical data gradually becomes less accurate as purchasing behavior, competition, or the economic environment change. By regularly incorporating recent sales data, Booper models adjust their forecasts in real time rather than extending a trend that has become obsolete.

Reliability also depends on explainability: the recommendations and forecasts generated by the platform are accompanied by the factors that explain them (seasonality, promotions, recent trends, contextual factors), which allows business teams to understand, verify, and, if necessary, adjust a recommendation rather than simply following a “black box.” This transparency is what has won over clients such as one of our clients in the food industry, where the ability to balance automation with business teams’ control over decisions was a key factor in their decision to choose our platform.

For a pricing or data team, this combination of continuous recalibration and explainability is key to ensuring that teams actually adopt the recommendations: a reliable but opaque model is difficult to follow over the long term, whereas a reliable and understandable model becomes a lasting part of decision-making processes.

Yes. The Booper platform is designed to integrate with the main retail IT environments: ERP, BI tools, PIM, point-of-sale systems, and pricing solutions already in place. The integration relies on standard connectors and secure APIs, allowing Booper to fit into the existing ecosystem without requiring a complete overhaul of the architecture.

This integration relies on a Data Loader capable of adapting to existing data streams (flat files, ERP exports, databases, APIs) rather than imposing a single format or protocol. The goal is not to transform the client’s information system, but to connect to it in a pragmatic way: retrieving the necessary data (prices, costs, sales, inventory, reference data), processing it within the platform, and then delivering recommendations back to the business tools already used by the teams.

This approach minimizes the impact on the IT organization and allows for a phased rollout: an initial scope can be connected (a category, a channel, a country) and then expanded, rather than requiring full integration from the project’s launch—a key factor for groups whose IT systems have been built in successive layers through acquisitions or organizational changes.

For both an IT department and a pricing department, this approach—which focuses on integration rather than replacement—reduces project risk and speeds up deployment, without having to wait for an information system modernization project, which often takes several years. Details are available on the Integration and Monitoring page.

No. Booper is designed to be used directly by business teams (pricing, category management, marketing, finance) without relying on a dedicated data team. The artificial intelligence is integrated into simple, user-friendly interfaces, and methodological guidance helps users interpret the results and structure their pricing decision-making processes.

This accessibility stems from the way results are presented: each recommendation or forecast is accompanied by clear metrics (expected impact on margin, volume, and competitiveness) and the factors that explain them, rather than simply raw model outputs that only a data team would be able to interpret. Business teams work with scenarios and rules they understand (margin thresholds, sales constraints), while the AI engine handles the underlying calculations.

Methodological support complements this system: it helps organizations new to AI-assisted decision-making to structure their decision-making processes (who approves what, based on what criteria) rather than leaving them to navigate a technical tool on their own. This is particularly relevant for mid-sized networks, which often have fewer in-house data science resources than very large companies but face the same challenges regarding the reliability of pricing decisions.

From a sales management perspective, this lack of technical prerequisites broadens the range of organizations capable of adopting predictive pricing management: the complexity of data science remains the responsibility of the software vendor, while business decisions (such as where to apply a particular strategy and with what priorities) remain in the hands of the teams with on-the-ground expertise.

The main benefits observed with an AI-powered pricing solution like Booper include a measurable improvement in profit margins, greater pricing consistency across stores, a reduction in the time spent on manual calculations, and an increased ability to anticipate the impact of decisions before implementing them.

The improvement in margins stems from the shift from uniform or approximate pricing to differentiated and data-driven pricing: each decision (price revision, markdowns, competitive alignment) is made based on quantitative projections rather than intuition, which limits unnecessary discounts and poorly calibrated price increases. Pricing consistency across stores, in turn, stems from centralized governance that guides local decisions through common business rules, while allowing for the flexibility needed to accommodate the specific characteristics of each region.

The solution also helps ensure the success of promotional campaigns by simulating their impact on volume and margin before launch, and improves competitiveness against rivals through more reliable and responsive market monitoring. These benefits reinforce one another: better governance facilitates analysis, better analysis improves recommendations, and recommendations that are better understood are more readily adopted by teams.

For senior management or sales teams, the challenge goes beyond simply achieving a one-time margin increase: it is the ability to transform pricing into a measurable, data-driven process capable of adapting to a retail market characterized by intense competitive pressure and increasingly price-sensitive customers.

Pricing software like BOOPER continuously optimizes prices: it recalculates its recommendations as sales, inventory, and competitor prices change, rather than setting a fixed pricing schedule that is updated a few times a year.

The engine combines sales forecasts, price elasticity measured on a SKU-by-SKU basis, and your business rules. Each new data point recalibrates the models, and each recommendation is explained before being validated.

The difference from an automated repricing tool lies in governance: prices are not sent to the registers, price tags, and e-commerce platforms until they have gone through the approval process defined by the retailer.

To get an overview of the solutions available on the market, check out our comparison of retail pricing software.

By simulating it and then testing it in a pilot environment, BOOPER quantifies each pricing scenario in terms of volume, margin, inventory, and price index before going live.

The selected scenario is then implemented in a few pilot stores and compared to control stores. The results are analyzed weekly, which isolates the actual effect of the price change from seasonal factors, weather, or promotions.

Once the gain has been measured, the strategy is extended to the rest of the network. The method is detailed in our article on POC pricing and value measurement.