Pricing software to Manage your Product Offer – Boost your Margins – Predict Sales Demand

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 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 catalog. This sectoral diversity is reflected in the organizations already supported by Booper, 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 curve, moving from a process that is still largely manual toward predictive and automated management. For a sales or pricing team evaluating Booper, the key factor is therefore not so much the industry sector 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 via spreadsheets, to structured and measurable pricing governance.

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 to context. This difference is most evident in the nature of the decision produced: a traditional tool applies a predefined rule (for example, systematically matching the lowest price on the market) without measuring its actual impact on demand or margin. Booper simulates this impact before implementation, drawing on the specific price elasticities of each product, which transforms 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 beyond 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, and price perception—based on recommendations that have already been simulated and documented.

Yes. Booper is designed for business users—pricing, category management, marketing, finance—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 real-world challenge: a price recommendation generated by an algorithm, without explanation, is difficult to validate and even harder to defend to sales or finance leadership. By laying out the factors that led to a given recommendation—changes in demand, competitive positioning, margin constraints—Booper enables teams to understand the reasoning behind the recommendation rather than simply applying a result. This clarity is also supported by the proposed alternative scenarios: rather than a single answer, teams can compare several options (for example, a defensive versus an offensive stance against a competitor) and choose the one that best aligns with their current sales strategy, with a clear understanding of the trade-offs between margin, volume, and price perception for each option. For a pricing department, 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 presenting to 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, and competition—specific to each client. They are regularly recalibrated to account for changes in customer behavior and market conditions, and consistency checks and performance metrics are used to continuously measure the reliability of the forecasts. This continuous recalibration addresses a key reality in retail: a model trained once on static historical data gradually loses accuracy as purchasing behaviors, competition, or the economic environment change. By regularly incorporating recent sales data, Booper’s 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 blindly 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 decisive factor in their choice. For a pricing or data department, this combination of continuous recalibration and explainability determines whether teams will 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 feeds—plain text 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, product catalogs), processing it within the platform, and then feeding the recommendations back into 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 of integration rather than replacement reduces project risk and accelerates go-live, without having to wait for an IT system modernization project—which often takes several years to complete.

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 encapsulated in simple, intuitive interfaces, and methodological guidance helps users interpret the results and structure their pricing decision-making processes. This accessibility stems from how the 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, commercial constraints), while the AI engine handles the underlying calculations. Methodological support complements this system: it helps organizations new to AI-assisted decision-making structure their decision-making processes—who approves what, based on which criteria—rather than leaving them to navigate a technical tool on their own. This is particularly relevant for mid-sized companies, which often have fewer in-house data science resources than very large enterprises but face the same challenges regarding the reliability of pricing decisions. For a sales department, 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 the business decision—where to apply a given strategy and with what priorities—remains in the hands of the teams who know the field.

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, data-driven pricing: every decision—whether a price adjustment, markdown, or competitive alignment—is made based on quantitative projections rather than intuition, thereby limiting 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 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 leadership, the challenge goes beyond mere one-time margin gains: 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.