Blog: Pricing & AI—Retail Analytics and Strategies

A pricing assessment is an audit of a company's pricing policy based on its actual data: sales, margins, pricing schedules, promotions, and competitors' prices.

It examines four key areas—price consistency, market positioning, price image, and promotions—to determine which discrepancies warrant action and what the expected return is.

The most common issues include inconsistent pricing tiers, overpriced benchmark products subsidized by non-comparable products sold at excessively low prices, promotions that are extended despite being unprofitable, and exceptions that have become the rule.

Each recommendation is costed and presented in a roadmap that distinguishes between “quick wins” that can be implemented immediately and structural initiatives.

A pricing proof of concept (POC) does not require perfect data, but rather data that is appropriate for what it needs to demonstrate, with known limitations. The product catalog, sales data, prices and promotions, purchase costs, and inventory levels form the foundation to be examined; their depth and level of detail depend on the objective, and competitor data becomes essential as soon as the test focuses on price positioning.

Before calculating profit, the recalculated revenue, volumes, and margin must be reconciled with those from management accounting, using aligned definitions and jointly established tolerances. To compare multiple software vendors, the same data and the same protocol are required.

A pricing proof of concept (POC) is a test that is limited in both time and scope, conducted using a retailer’s actual data, which compares the pricing decisions recommended by a solution to a baseline scenario measured in the same way. It provides evidence when five elements are established before the test begins: the scope, the baseline scenario, the metrics, the duration, and the resources allocated.

The method described here distinguishes between three categories of indicators that are too often conflated (model performance, economic gains, and team usage), explains how to test multiple vendors using the same dataset, and explains why a proof of concept (POC) should be conducted without altering the existing price-dissemination tool.

A SaaS pricing management platform centralizes a retailer’s prices, rules, competitor data, and sales forecasts in a browser-based tool—with no servers to install or versions to maintain. For the retail industry, its benefits include the ability to respond quickly to market changes, measurable margin gains, built-in compliance with French regulations, a predictable subscription cost, and scalability across thousands of retail locations.

Booper, a French pricing software provider since 2014, exemplifies this model with a platform that manages more than 4,000 retail locations for chains such as Magasins U, E.Leclerc, and Castorama, and aims to increase profit margins by 0.5 to 3 percentage points within a few months.

A franchisor cannot impose a resale price: the law prohibits it from doing so; it can only recommend a price or set a maximum. A franchisee’s margin is therefore governed by a documented target, defined by network segment rather than on a store-by-store basis, and applied uniformly to all retail locations within the same segment. Three tiers are usually sufficient: aggressive pricing online, geopricing in-store, and an optimized margin for the franchisee.

Reversibility must be negotiated before the contract is signed—never after. Once the contract is signed and the system is live, the balance of power has completely shifted. Four assets are at stake: your raw data, your rule set, your trading history, and your competitive matching table. Only the first is generally covered by contracts.

The gap between perception and reality is well documented: 89% of executives believe they can switch suppliers in less than a month, but among those who have tried, 58% report that the effort failed or took much longer than expected (Zapier, 2026). The goal isn’t to leave, but to have the option to do so: that’s what maintains a healthy supplier relationship throughout the contract term.

Pricing decisions account for only a small portion of the workweek in most organizations. The rest is divided among preparation, coordination, formatting, and justification. This time is not wasted: justifying a price to a buyer is real work. The problem is that it takes the place of what creates value.

The measurement is based on a reconstructed typical week, not on self-reported estimates: the discrepancy between the two is often a factor of two. The correction then follows a specific order: eliminate, then automate, then delegate. Automating a useless task is tantamount to making it permanent.

The model is the easy part. A recommendation engine can be up and running in a few months; what takes years is the knowledge base, competitive matching, the rule set, explainability, and compliance. According to Exclaimer (2025), 71% of in-house developments are eventually abandoned, and 83% in regulated sectors: the breaking point is almost never deployment—it’s maintenance.

The hidden cost is a cost of continuity: an in-house tool relies on two or three people, and if they leave, the asset becomes a liability. The build remains useful within a narrow and stable scope, as a complement to a solution rather than a replacement for it. The deciding factor isn’t the team’s expertise; it’s their ability to last ten years.

Data and context are two distinct assets: data describes what happened, while context explains what we are allowed to do and what would be absurd. This context is implicit by nature: it has never been written down because there was never a need to do so as long as decisions were made by humans who shared it.

Four categories cover the essentials: price benchmarks, relational constraints, unwritten rules, and commercial objectives by category. Formalizing them provides lasting value, because the context outlasts the models: algorithms are replaced every two or three years, but a written context remains a company asset.

The accumulation of rules is a cumulative process, not an isolated incident: it grows with every legitimate decision made in isolation, even though no single decision is open to criticism. Five forms predominate: the exception that has become permanent; the seasonal rule that is never suspended; rules that contradict one another; the orphaned rule whose author has left; and the redundant rule.

The cost isn't what you might think: before it eats into profit margins, debt erodes trust. A team that no longer understands why the system suggests a certain price will stop following it. The solution is a consistent routine rather than a major cleanup effort every three years: a quarterly portfolio review, four questions per rule, and an expiration date set at the time of creation.

A business case for a pricing solution combines two types of return on investment that are completely different. Friction ROI refers to the hours saved: easy to measure, but capped. Decision ROI refers to the margin points gained from better-set prices: difficult to measure, and unlimited.

Confusing the two explains the gap identified by IBM in 2025: 66% of executives report productivity gains from AI, but only about one in five has met their return on investment (ROI) goals. The ROI of a decision isn’t measured by comparing “before” and “after,” but rather against a control group; otherwise, seasonal factors, inflation, or a competitor’s actions will be credited to the tool. The test to apply to a business case: if you remove all the “time saved” lines, is the project still profitable?

Optimix merged with Maxxing in July 2025, and PricingHUB was acquired by Akeneo in June 2026. In less than a year, two of the three software providers most frequently used by French retailers underwent changes in ownership, raising the question of alternatives for their customers.

This comparison evaluates Optimix XPA, PricingHUB, Mercio, Yieldigo, and BOOPER based on what truly sets a pricing solution apart in France: native support for French regulations, pricing by store or by zone, the approval process before a price changes at the register, and roadmap independence.

A manufacturer that raises its list price does not set the price on the shelf. Between the negotiated list price and the displayed price lie the retailer, its markup, and its promotions. What is measured is the price elasticity of each product, retailer by retailer.

Target-based pricing starts with a target set by the market or the company (selling price, Markup, price index) and works backward from there, rather than starting with the cost.

Its traditional form, target costing, is calculated as follows: target cost = target selling price (excluding tax) × (1 − target Markup ).

In retail, the method is applied at two levels: product listing (especially for private-label products) and department management, with margin or price index targets set by category role.

The volume sold during a promotion does not indicate whether it created value: some of it comes from similar products (cannibalization), and some from purchases that were simply brought forward.

Each promotional scenario must be costed out prior to launch using the same metrics: base sales, actual incremental sales, cannibalization, carryover, halo effect, net margin for the category, and cost per unit actually gained.

A margin-volume arbitrage strategy can then be explained: a stated objective, visible forecasting factors, constraints adhered to, and a documented validation process.