Our clients are looking for explainable AI—not a “black box”—combined with advanced business rules to anticipate demand and make concrete recommendations. Not just a promise: measurable results from retailers who were already managing their pricing before we got involved.
Pricing exists to drive profit margins and growth, not for its own sake.
02
Industry expertise and local presence
Teams with retail expertise, available throughout the project.
03
A Culture of Continuous Innovation
The models evolve based on our customers' data; they are not static.
04
Data Security and Governance
All data remains traceable, auditable, and under the customer's control.
Customer Case Studies
How Booper Has Made a Difference for Our Customers
Sector
Supermarket
Overseas Territories · Caribbean
Price Image
Not the lowest price: the right price, plus a little extra margin.
The company used to apply a single " Markup " to the entire store. Now, each category has its own focus: foot traffic, sales, or profit margin. Complaints about prices being too high have disappeared, and competitors are raising their prices.
Issue
Rebuilding a tarnished brand image in an insular market where everyone compares prices.
Change
One role per category, a rating scale from A to E, and a three-month trial period before expansion.
+1 point
Markup s on the products tested, over the course of a quarter.
Consistent pricing across the entire network, without losing control.
The challenge wasn't simply to have yet another tool. We needed to maintain national pricing consistency across millions of prices, simulate each scenario by category, and leave the final decision to the business teams.
Issue
Balancing competitiveness, profit margins, and pricing tiers across a national network.
Change
Forecasts, cross-elasticities, and validation workflows all on a single platform.
1,700
retail locations governed by a single, centralized pricing structure.
Competitive prices that help you make decisions, not just sit on a shelf.
Competitors' prices are imported directly into the tool and matched to the retailer's products by cloning and linking SKUs. The price indices are up to date, and each price alignment is simulated before being approved by the team.
Issue
Track national competitors across a very extensive product catalog, store by store.
Change
Price indices, cloning and chaining matching, and centrally validated recommendations.
99.95%
platform availability in 2025, with the platform having been in production since 2022.
Competitive Intelligence DivisionNational home improvement chain
Sector
Food
Southeast Asia
Testing & Measurement
Each price change is measured against a control group.
The new prices were first rolled out in a few pilot stores and compared to control stores. The results are reviewed weekly and supplemented by an in-store price perception survey.
Issue
Move away from a robust but manual method using Excel across multiple retail chains.
Change
Pilot program with a control group, benchmark indices, and capped price fluctuations.
818
customers surveyed in-store to gauge their perception of the pilot's price.
An analysis that shows where the profit margin really lies.
The retailer thought it knew its market positioning. The analysis compared its prices to the market, weighted by sales volume, and revealed the margin gap between products exposed to competition and the others.
Issue
Assess price positioning relative to the market and identify the " Markup."
Change
An analysis of sales history, a weighted index, and then data-driven management.
10%
These products account for 80% of sales across the entire network.
One price per store group, revised every six weeks.
The group used to manage several retail chains using Excel and manual processes. Now, prices are linked, simulated in batches, and applied by store group. Each pricing cycle starts from its own baseline.
Issue
Manage multiple franchise locations, each with its own pricing.
Change
Reference linking, batch simulations, suggested prices by store cluster.
6 weeks
by price cycle, simulated in batches across all retail chains.
Network & Franchise ManagementMulti-brand franchise group
Sector
Hypermarket
Freelancer
Competitive Alignment
Saved, compared, and replayable price scenarios.
The store's strategy: to be the cheapest in the area. Booper serves as a benchmark for comparing prices with competitors, and each scenario is recorded: what was decided, why, and the ability to replay it.
Issue
Hold the cheapest position in the zone without losing sight of the margin.
Change
Comprehensive competitive benchmarking and a complete history of pricing scenarios.
2
Users are enough to drive the hypermarket's price alignment.
Finally find out which products are really driving the profit margin.
While organizing the product assortment, the team noticed that a small number of items accounted for the bulk of sales and profit. Before discontinuing a product, they first test a price reduction and monitor the impact on sales.
Issue
An overwhelmingly large selection, with no clear understanding of how each item contributes.
Change
Price segmentation also helps reduce the product assortment and tied-up inventory.
200,000 euros
the targeted cash flow, from 20,000 to 12,000 items (ongoing goal).
Case studies written by Booper based on real client projects. Clients are anonymous; the avatars are illustrations. See our case studies on the "Our Clients" page.
A BI tool provides metrics. It shows what has happened. Booper goes a step further: it recommends an action, a price, or a change date, based on predictive models and business rules specific to each retailer.
This difference changes the nature of the pricing teams' work: they no longer start with a dashboard that they have to interpret, but with a recommendation that has already been justified, which they then validate or adjust.
Each recommendation is accompanied by the factors that explain it: elasticity, competitive position, margin constraints, and seasonality. Nothing is presented as a decision made solely by the machine.
For a pricing team that must justify its decisions to a committee, this traceability is often the deciding factor. A figure that cannot be explained cannot be defended.
AI-assisted management begins as soon as the system is put into operation, initially within a limited scope—such as a department or zone. The first effects on profit margins or stockouts are generally visible within the first few months of implementation.
This limited initial scope makes it possible to measure the actual impact before expanding the tool to the entire network, rather than waiting for full deployment to see the first results.
For support. The recommendations are suggestions, not requirements: teams retain control over validation, with configurable margin safeguards based on their own rules.
The goal is not to replace business judgment but to relieve it of repetitive work (reviewing each reference one by one in a spreadsheet) so that it can focus on the decisions that matter.
Barbotteau, a retail group in the French Caribbean, saw a 1-point increase in Markup s over the course of one quarter for the products tested in its supermarket, thanks to BOOPER.
Until then, the group had been using a single " Markup " managed in Excel. It switched directly to a hybrid pricing model based on AI and business rules.
The method: three optimal prices per product (volume, revenue, margin), selected based on the role of each category.
The perception of pricing improved at the same time: there were fewer comments in stores that the products were “too expensive.” The initiative was then expanded to all four of the group’s retail chains.
This isn't about prices that change constantly, but rather about better-adjusted prices. Across our entire customer base, the gain ranges from +0.5 to +3 margin points in just a few months.
Roles and approval processes are clarified from the outset, with workflows that track who decides what. All data remains under the client’s control and can be audited at any time.
For an IT or compliance department evaluating the tool, this explicit governance is what distinguishes a standardized approach from the informal use of AI.
Most often because the existing tool is still a general-purpose solution (reporting, ERP, basic pricing module) that lacks the predictive capabilities and detailed business rules needed for day-to-day pricing decisions.
Booper is designed 100% for pricing—not just as one module among many: it is this specialization, combined with the ability to get it up and running in just a few weeks, that makes it a compelling reason to replace existing systems rather than simply add yet another tool.
The deciding factor is not the size of the team but the volume of listings and stores that need to be managed. It is this volume that makes manual management unfeasible and justifies the use of a dedicated tool.
A small pricing team often sees immediate benefits from Booper: the technical complexity is handled by the tool, while business decisions remain in their hands—without the need to hire data scientists.
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What about your profit margins?
BOOPER isn't just a technological solution. It's a structured framework for securing, standardizing, and scaling your pricing strategy.
