Pricing at the heart of

performance

BOOPER is designed for retailers, store chains, and brands that have large volumes of data on prices, sales, and promotions. This positioning stems from the very nature of the platform: the analytics, forecasting, and recommendation modules derive their value from the volume and richness of the available data. A retailer with a limited number of SKUs or stores will benefit less from highly automated pricing management than a multi-store or multi-country network facing thousands of simultaneous pricing decisions. In practice, BOOPER supports both food and non-food retailers across various formats—hypermarkets, neighborhood stores, and e-commerce—whenever the complexity of pricing management (number of SKUs, points of sale, and channels) warrants a dedicated platform rather than spreadsheet-based management. For a company unsure of whether such a solution is right for them, the most decisive factor remains the volume and actual complexity of the pricing decisions to be managed: the higher the number of daily pricing decisions, the more value automation provides compared to manual management.

Yes. BOOPER is compatible with major IT environments (SAP, Oracle, Google Cloud) and integrates easily with existing tools. This compatibility addresses a key challenge for most retailers: sales, inventory, and product catalog data already reside in existing systems—often the result of years of investment—and a pricing platform that requires duplicating or manually re-entering this data would rarely be feasible in practice. The integration works both ways: data is pulled from existing systems to fuel BOOPER’s analyses and recommendations, and—depending on the chosen configuration—decisions made on the platform can be pushed back to the retailer’s operational systems to be applied without manual re-entry. For a retailer, this compatibility directly determines the speed of deployment and the actual cost of the project: the more standardized and well-structured the existing ecosystem is, the faster and more reliable the integration—which in turn accelerates the delivery of initial results in pricing management.

BOOPER can be deployed quickly thanks to its modular architecture and Big Data technologies. Initial results are visible within a few weeks. This speed is primarily due to the platform’s modular approach: rather than requiring a full deployment of all modules from day one, it’s possible to prioritize the most urgent components for the retailer—competitive matching, price performance analysis—before gradually expanding the scope as needed. However, the actual timeline depends heavily on the quality and accessibility of the company’s data: a well-structured information system, with reliable product databases and data feeds that are already integrable (ERP, PIM, BI), significantly speeds up integration, whereas fragmented or poor-quality data requires preliminary work to ensure reliability. For a retailer, this speed of implementation is a key factor in choosing a pricing platform: it allows the company to begin seeing concrete results—such as quick wins in margins and improved pricing consistency—even before the full scope of the intended functionality has been deployed.

Customers are seeing improved margins, revenue growth, and a rapid return on investment. These results stem from the levers activated by the platform: improved pricing consistency limits margin losses caused by uncontrolled inconsistencies; more refined promotion management reduces low-profit operations; and more accurate demand forecasting ensures decisions are sound before implementation rather than requiring corrections afterward. The magnitude of these results varies depending on the scope of deployment, the quality of available data, and the organization’s ability to truly adopt new decision-making practices—which is why BOOPER systematically combines technical deployment with change management support and, if necessary, skills development for teams. Cases such as that of one of our clients in the food industry—which manages several million prices across more than 1,700 stores—illustrate this shift from reactive pricing to predictive pricing, featuring automated recommendations, pre-execution simulation, and centralized decision governance—rather than a fragmented and time-consuming management process.