Which retailers use Booper to manage their pricing?

Yes, with two distinct approaches. In B2C, the challenge is to shift from reactive management to a predictive, measurable, and data-driven pricing strategy, often for broad and highly competitive product lines.

In B2B, the challenge takes on a different nature: structuring multi-segment pricing grids—which are often negotiated on a case-by-case basis—and securing margins on a per-customer basis rather than per product on the shelf.

These two worlds share neither the same input data nor the same governance rules: a wholesaler thinks in terms of commercial terms per customer, while a food retailer thinks in terms of retail prices per store. Booper adapts its settings—business rules, scenarios, and margin safeguards—to the logic specific to each model.

For a pricing department that oversees both a distribution network and a wholesale business, this distinction prevents the application of a B2C approach to decisions that, in reality, are made at the customer contract level.

Booper serves six major sectors: food retail (GSA), home improvement and gardening (GSB), specialty retail (GSS—discount stores, variety stores, automotive, cosmetics), pharmacies, fast-moving consumer goods (FMCG) brands and manufacturers, and B2B retail through wholesalers and purchasing groups.

Each sector has its own decision-making timeline: daily adjustments in e-commerce; weekly or biweekly adjustments for sensitive SKUs in food retail; and monthly adjustments in most non-food sectors. Booper’s settings follow this pace rather than imposing a single rhythm.

What remains consistent across all sectors is the underlying logic: combining business rules and predictive models to objectify a decision that is often made today based on instinct or by mechanically following the competition.

For pricing strategies in a sector not yet listed here, the criterion remains the same: a sufficient number of SKUs and stores to ensure that the recommendations have statistical significance.

Booper's customers operate in France, Poland, Vietnam, and Thailand—markets with very different competitive and regulatory dynamics, which led to the platform being designed to operate across multiple countries from the outset rather than as a later addition.

In practical terms, this means centralized pricing governance at the group level, with local rules that can be adjusted on a country-by-country basis: tax considerations, local competition, seasonality, and price sensitivity are not managed the same way in Bangkok as they are in Rennes.

For a retail group operating in multiple countries, this architecture avoids having to duplicate the tool for each market: configuration is done at the group level, with local variations, rather than rebuilding the pricing management system at each location.

The initial scoping and configuration take just a few weeks: auditing existing data and processes, prioritizing an initial scope (a department, a channel, or a region), connecting to the data, and defining business rules and margin safeguards.

AI-assisted management begins as soon as the system is put into service within this initial scope—teams receive daily recommendations that they approve, rather than waiting for a full rollout before seeing the first visible results.

Expansion into new departments, countries, or channels then takes place gradually, at the retailer’s own pace, with cross-functional governance shared among the relevant departments.

For management that is hesitant to get started for fear of a lengthy project, this approach of defining a narrow initial scope reduces both project risk and the time to the first measurable profit.

Our clients manage thousands of SKUs across networks ranging from a few dozen to several hundred stores, using a structured pricing governance model rather than manual management.

The architecture is designed to scale without requiring a rebuild: an initial limited scope (a radius, a zone) can later be expanded to the entire network, to new countries, or to new channels, without changing the tools or governance framework.

It is this ability to scale up gradually—rather than the initial volume—that distinguishes industrialized pricing management from spreadsheet-based pricing management: the latter reaches its limits long before it has covered an entire network.

For a data or IT department evaluating a solution's scalability, the key metric is therefore not just the current number of records, but the network's growth trajectory over the next two to three years.

No. The deciding factor is not the group's revenue, but the complexity of the network to be managed: the number of SKUs, stores, and channels, and the point at which manual management using spreadsheets becomes unworkable.

A medium-sized company with multiple stores and a wide product assortment often gets more value out of Booper than a large corporation with a limited, already standardized catalog, because it is the combination of volume and complexity that gives the recommendations real statistical value.

Each retailer follows its own pricing maturity path: some start on a limited scale to validate the approach, while others roll it out across multiple departments or countries from the outset.

For the pricing department of a mid-sized retailer—which often has fewer in-house data science resources than very large companies—this accessibility is a game-changer: Booper handles the technical complexity, while business decisions remain in the hands of the teams who know the field.