Modular Pricing Solution

A modular pricing solution is a platform that combines several complementary building blocks to manage a retailer’s pricing performance. It centralizes pr Pricing Optimization Software, sales forecasting, product matching, promotion management, markdowns, clearance sales, and price monitoring within a single environment to improve the consistency of commercial decisions. The modular approach addresses a reality in retail: not all retailers have the same priorities or the same level of pricing maturity at any given time. A monolithic platform would impose the same scope on everyone, whereas a modular architecture allows retailers to first activate the highest-priority modules—such as competitive matching and performance analysis—before gradually expanding to sales forecasting or advanced promotion management. This modularity also facilitates integration with the existing ecosystem: each module can interface with tools already in place (ERP, PIM, BI) without requiring a complete overhaul of the information system right from the initial deployment. For a retailer, this approach avoids two common pitfalls in pricing projects: an overly ambitious rollout from the start that fails due to a lack of buy-in, or a proliferation of specialized tools that aren’t interconnected, which fragments data and pricing decisions.

It is the observation and recording of a product's displayed price at a given time across one or more retailers. This can be done manually (in-store, online browsing) or automatically via web scraping.

A retail pricing platform like BOOPER MPS is used to structure, streamline, and secure pricing decisions. It helps retailers manage their prices, promotions, price positioning, markdowns, and profitability using data, artificial intelligence, and advanced analytical models. Specifically, the platform relies on several complementary modules that cover the entire decision-making cycle: from analyzing price and margin performance to price optimization, including promotion management, competitive pricing, and inventory clearance. This modular approach allows each retailer to activate the modules relevant to its specific needs rather than adopting a rigid, monolithic system. The goal is not to replace the business expertise of pricing teams with an automated system, but to empower them to make decisions faster and with greater reliability: impact simulations before deployment, alerts on pricing inconsistencies, and well-reasoned recommendations rather than prices imposed without explanation. For a retailer, this standardization of pricing management becomes particularly critical as the number of SKUs, stores, or countries to manage increases—manual management via spreadsheets quickly reaches its limits when faced with the complexity of a modern multi-store network.

Price monitoring is the objective (knowing what price a product is sold at); web scraping is a method to achieve this at scale—an automated process that extracts prices from public web pages and structures them into actionable data without repetitive human intervention.

A pricing platform enables you to analyze price and margin performance by product, category, or store; simulate different pricing scenarios; measure the potential impact on volumes, revenue, and profitability; and then help teams make decisions faster using reliable, shared metrics. This optimization begins with visibility: many retailers manage their margins on an aggregated basis, without sufficient detail by SKU, category, or store to pinpoint exactly where performance gaps lie. A platform like BOOPER MPS drills down to this level of granularity to reveal the actual drivers of performance, rather than averages that mask widely varying situations across different scopes. Once these drivers are identified, scenario simulation allows retailers to estimate the impact of a price change before implementing it—on expected volumes, revenue, and margin—which transforms pricing decisions from a gamble into an informed trade-off, based on data rather than intuition. For a retailer, this combination of analytical granularity and simulation tangibly changes the speed and reliability of decision-making: teams make decisions based on shared metrics, which also limits differences in interpretation among business units regarding the same pricing situation.

Yes, in principle: collecting public data is not prohibited. The applicable framework depends on the nature of the data (personal or not), compliance with the terms of service of the scraped website, and the load imposed on the server. In June 2025, the CNIL published precise criteria to ground this type of collection on legitimate interest.

Yes. The platform incorporates an AI-powered sales forecasting engine to anticipate demand, better manage supply, adjust prices, prepare sales campaigns, and validate decisions prior to deployment. This component is particularly useful for improving the quality of trade-offs between volume, inventory, margin, and sales performance. This forecast is based on the retailer’s historical sales data, cross-referenced with factors that influence demand—seasonality, past sales campaigns, and previously observed price changes—to project expected sales for the coming weeks under various management scenarios. The value of this module extends beyond mere forecasting: it directly feeds into the platform’s other modules, particularly to simulate the impact of a price change or promotion on sales and inventory before it is actually implemented, rather than discovering the effect only after the campaign has launched. For a retailer, this forecasting capability reduces the risk associated with every pricing or promotional decision: it allows them to weigh various scenarios in advance, with an objective estimate of their impact on revenue, margin, and inventory levels, rather than relying on reactive management that is corrected after the fact.

This depends on the category: daily or even multiple times a day for references with high promotional volatility, and weekly for stable-priced references. Applying a uniform frequency across an entire catalog wastes collection capacity without increasing relevance.

Product matching, cloning, and chaining are used to ensure the reliability of competitive comparisons and to correctly link equivalent products across retailers, formats, or channels. This helps improve the quality of price monitoring, avoid analytical errors, and make more accurate pricing decisions based on truly comparable products. Using AI, product matching automatically identifies equivalent products between a retailer’s assortment and those of competitors, even when product names differ from one retailer to another. When there is no direct association via EAN, cloning takes over and links the retailer’s products to those of competitors, whether they are private-label or national brands. Chaining, on the other hand, groups the retailer’s products together to establish relationships based on hierarchy, coefficients, and historical patterns. Without these mechanisms, competitive price comparisons rely on approximate matches, which can lead to biased pricing decisions: aligning with a competitor who is actually poorly positioned in comparison, or overlooking a real price gap masked by a poor product match. For a retailer, ensuring the reliability of the product database is a foundational element that is often underestimated: it determines the quality of all competitive analyses and pricing decisions that subsequently rely on these comparisons, within a framework of centralized pricing governance.

No, the two sources are complementary. Retailer panels aggregate actual sales data with a time lag, which is useful for validating underlying trends. Scraping provides near real-time freshness, product by product, suited for daily pricing management.

Yes. BOOPER MPS covers promotion management, simulation of promotional effects, forecasting of promotional sales, as well as markdown and inventory clearance strategies. The goal is to help teams better target promotional strategies, limit value destruction, and accelerate inventory turnover while protecting margins. These three areas are interconnected in a retailer’s day-to-day operations: a poorly calibrated promotion can create excess inventory that must then be sold off through markdowns, while a well-planned inventory clearance strategy reduces the need for emergency corrective promotions. Addressing these issues within a single environment, with shared data, prevents siloed decisions that are out of sync with one another. The “What If” simulation module allows retailers to test the impact of a campaign before its launch, while markdown strategies rely on the analysis of inventory and residual demand to determine the right discount level at the right time, rather than applying a uniform markdown by default. For a retailer, this expanded scope changes the nature of management: instead of treating promotions and clearance sales as separate issues managed by different teams, the platform integrates them into a single approach focused on margin protection and product lifecycle management.

Because collecting a price is only valuable if you are certain it corresponds to your exact reference. Approximate matching produces price discrepancies that appear real but are not, which silently distorts all decisions based upon them.

Yes. BOOPER presents MPS as a platform capable of integrating into a company’s existing ecosystem and leveraging data useful for pricing decisions. This includes, in particular, interoperability with ERP systems, BI tools, PIMs, and other data feeds necessary for sales and pricing management. This integration capability is a prerequisite for most retailers: sales, inventory, and product catalog data already exist in existing systems (ERP, PIM), and a pricing platform that would require manual re-entry or a overhaul of these systems would rarely be feasible in practice. MPS is designed to connect to these existing sources rather than duplicate them. This integration works both ways: data flows from the existing ecosystem to fuel analyses and recommendations, and decisions made in MPS can, depending on the configuration, flow back down to the retailer’s operational systems (point-of-sale, e-commerce) to be applied without re-entry. For a retailer, this interoperability determines the speed and cost of project deployment: the more structured and accessible the existing data ecosystem is, the faster the integration—which in turn accelerates the delivery of initial results regarding margins and pricing consistency.

A pricing platform like BOOPER MPS is designed for teams in pricing, category management, procurement, marketing and product offerings, IT and data, finance, and executive management. The goal is to provide each function with a shared view of pricing performance, scenarios, and business impacts, while strengthening pricing governance and coordination across teams. Each function uses the platform differently: pricing teams drive daily analyses and recommendations; category management and procurement rely on performance data to make decisions regarding product assortment and supplier negotiations; marketing monitors the impact on price perception; finance measures the effect on profitability; and executive management has a consolidated view for strategic steering. This shared view mitigates a common problem in retail organizations: different business units working with different data or metrics on the same pricing issue, which leads to disagreements that are difficult to resolve due to the lack of a common reference point. For a retailer, having a platform used by multiple departments—rather than a tool reserved solely for the pricing team—strengthens overall governance: pricing decisions are better understood and more readily accepted when the relevant departments share the same data and scenarios from the outset.

BOOPER is more than just pricing software. The website also offers services in price analysis, pricing strategy development, operational pricing consulting, change management, and pricing training. This combination of technology, industry expertise, data, and support aligns with BOOPER MPS’s positioning as a comprehensive platform. This dual approach addresses a real-world observation: pricing software alone—no matter how effective it may be—has no impact if it is not backed by a clear pricing strategy, defined governance, and teams that are trained and supported as they adopt new practices. Conversely, methodological support without a tool to scale it up remains limited in its ability to handle large volumes of data and decisions. In practice, these offerings are combined as needed: a company can start with a pricing assessment to identify its priorities, follow up with a strategic or operational consulting engagement, and then deploy MPS with change management support and a training plan for its teams. For a retailer, the benefit of this integrated approach is that it avoids having to coordinate multiple service providers with different methodologies on the same pricing project—which carries the risk of conflicting recommendations between the tool, the strategy, and human guidance.