Promotion Management: Manage Your Campaigns with Precision Using AI

Promotion management involves designing, simulating, implementing, and then measuring a company’s promotional campaigns (promotional plan, mechanics, discount levels, budget) so that they serve a specific objective: traffic, volume, margin, or inventory turnover. It is complex to manage because the actual effect of a promotion is not immediately apparent: we see the volume sold, but not the lost margin or sales that have simply been shifted.

A single promotion can have several effects at once: increased volume, cannibalization of non-promoted items, sales that are merely brought forward, a halo effect on related purchases, and pressure on inventory levels. Added to this are operational constraints: a budget to be adhered to on a category-by-category basis; a consistent approach across stores, the web, marketplaces, and B2B catalogs; multiple functions to coordinate (management control, marketing, category management, supply chain); and, in France, a regulatory framework to comply with (Egalim promotional caps, thresholds for selling at a loss, and reference prices for discount advertisements).

That is why decisions are based on incremental margin, not volume. For a step-by-step example, see how to simulate a promotion before launching it.

AI analyzes past sales, promotional strategies, pricing, seasonality, and customer behavior to predict the actual impact of promotions. It helps select the best promotional strategies, the right discount level, and the right targeting to maximize ROI.

Specifically, this analysis draws on the history of past campaigns to identify recurring patterns: which mechanisms (direct discounts, multi-purchase offers, loyalty cards) work best by category, which discount levels generate sufficient uplift without eroding margins, and which external factors (seasonality, events, competitive pressure) influence the outcome of a campaign.

BOOPER MPS translates these analyses into concrete recommendations before a campaign is launched and offers a “What If” simulation module to test different promotional scenarios and measure their projected impact on volume, revenue, and margin before any budget commitments are made.

For a retailer, this approach changes the way promotional plans are developed: instead of repeating the same strategies out of habit, decisions are based on an objective impact assessment, which limits low-return campaigns and focuses the promotional budget on what actually works.

MPS uses historical sales data, promotional schedules, prices, margins, store data, inventory levels, and external factors (seasonality, events, competition) to manage promotions.

Each type of data plays a specific role: historical sales data and past promotional schedules help identify the strategies that actually work for each category; inventory data prevents the promotion of items at risk of running out of stock or, conversely, helps clear out excess inventory; external factors refine the impact forecast by taking into account the actual context of the campaign rather than an average historical data set.

The promotional calendar can be created directly within the tool or imported from a third-party solution via API, and updated collaboratively by various business units based on their level of responsibility: management control for objectives, marketing for scope definition, category management for products, and supply chain for orders and sell-through rates.

For a retailer, the richness and reliability of this data directly determine the accuracy of recommendations: a promotional plan based on incomplete inventory or sales data remains unreliable, no matter how sophisticated the models used to analyze it may be.

A promotion management tool must cover the entire lifecycle of a campaign: developing the promotional plan, simulating each option before launching it, rolling out the selected strategy across all channels, and then measuring the actual ROI to fine-tune the next campaign.

Specifically, six features make all the difference: a promotional calendar built into the tool or imported via API and populated by various business units; targets and a budget tracked by category; a “What If” simulator that projects volumes, cannibalization, halo effect, inventory, and incremental margin; compliance with French regulatory constraints (Egalim caps, resale-at-a-loss threshold, 30-day reference price); an ROI metric that compares actual sales to expected sales without promotions; and the identification of low-profit promotions, with suggested alternatives.

A tool that shares the same data as base price, sales forecasts, and markdowns prevents promotions from being managed in silos. On this point, see why it’s important to consolidate price, promotions, and markdowns into a single tool.

Yes. The “What If” simulation module allows you to test different promotional scenarios and measure their projected impact on volume, revenue, and margin before any actual launch.

In practical terms, this module uses historical sales data and proven strategies to project the likely outcome of a new initiative: the proposed discount level, the chosen strategy, and the scope of products or stores involved. It allows you to compare several scenarios side by side before selecting the most favorable configuration.

This simulation does not replace actual measurement once the promotion has launched, but it significantly reduces the risk of discovering after the fact that a promotion mechanism was poorly calibrated: a discount that is too generous, targeting that is too broad, or a poorly chosen time frame can be adjusted in advance rather than identified after the fact, once the budget has been committed.

For a retailer, this simulation capability changes the way promotional plans are developed: trade-offs between commercial appeal and profitability are made based on quantitative projections, which helps ensure that decisions are sound before deployment rather than having to be corrected in a rush during the campaign.

MPS identifies unprofitable promotions, measures their actual ROI, and proposes more effective alternatives. Decisions are based on objective data, not just on past performance or intuition.

Over-promotion is a common pitfall in retail: promotional strategies that are repeated year after year simply because they have become routine, without their actual profitability being systematically reassessed. By comparing actual sales to expected sales excluding promotions, MPS identifies campaigns that primarily result in markdowns without sufficient uplift to offset them.

The “What If” simulation module complements this preliminary analysis: it allows you to test different scenarios before launching a campaign, thereby limiting the risk of over-promotion right from the planning stage, rather than realizing it after the fact once the budget has been committed.

For a retailer, avoiding excessive promotions has a direct impact on margins: every euro in discounts granted without a sufficient uplift represents a pure erosion of profitability—one that often goes unnoticed unless it is measured on a transaction-by-transaction basis rather than at the overall level of the promotional plan.

Yes. MPS is designed for large retail accounts, offering multi-country, multi-store, and multi-category management with centralized governance while maintaining local flexibility.

This multi-level management approach addresses a common need in complex networks: to develop a coherent promotional plan at the central level while giving local teams the flexibility to address market-specific factors, such as varying price sensitivity across countries, local sales calendars, and regulatory constraints specific to certain regions.

MPS enables collaborative management of the promotional calendar: objectives can be defined by financial planning, the framework established by marketing, the relevant products proposed by category management, and orders and sell-through rates monitored by the supply chain, with each department contributing according to its level of responsibility. Data can also be imported from a third-party solution via API or created directly within the tool.

For a multinational chain, this organizational structure avoids two opposing pitfalls: overly centralized management that ignores local realities, or overly decentralized management that results in a loss of consistency in branding, pricing, and governance at the group level.

MPS compares actual sales to expected sales excluding promotions in order to isolate the uplift generated by the campaign. ROI is calculated by factoring in margin, costs, and additional volumes.

This metric is based on a model of what sales would have been in the absence of the promotion: a counterfactual benchmark derived from historical data and contextual factors (seasonality, underlying category trends). The difference between actual sales and expected sales represents the uplift that can actually be attributed to the promotion, rather than a natural sales trend that would otherwise be mistakenly attributed to the promotion.

Once this uplift has been isolated, the ROI calculation incorporates all of the transaction’s economic parameters—the margin reduced by the discount granted, the additional costs associated with the promotion, and the additional sales volume generated—to provide a comprehensive measure of profitability rather than a simple markdown rate.

For a retailer, this measurement method is a game-changer compared to a purely volume- or revenue-based analysis: a promotion that generates a lot of sales may turn out to be unprofitable once margins and costs are taken into account, and vice versa—it is this actual profitability that MPS makes it possible to objectively assess.

BOOPER projects deliver a rapid return on investment (ROI) through the optimization of promotional budgets, the reduction of stockouts, and improved margins, with initial gains typically seen within a few months.

This rapid turnaround can be explained by the nature of the measures taken: identifying unprofitable promotions and correcting or discontinuing them has an immediate effect on the margin, without having to wait for a full production cycle to end. Similarly, better anticipation of the promotional impact allows for more accurate inventory planning in advance, which limits both stockouts during the promotion and residual excess inventory once the promotion is over.

However, this ROI depends on the quality of the available data and the teams’ ability to implement the recommendations generated by the AI: a solution that correctly identifies promotions that need to be corrected will have no effect if the decisions are not reflected in the promotional plan that is actually implemented.

For a retailer, this quick return on the promotional budget (one of the most significant and least carefully managed expense categories in many chains) is often the first visible result, even before a broader transformation of pricing management takes place.

By projecting, prior to launch, five effects for each option: volume growth, cannibalization of non-promoted SKUs, the halo effect on related purchases, in-store Stock coverage , and incremental margin compared to a period without promotion.

The mechanisms selected are those with a positive incremental margin, or those that the retailer treats as an investment in traffic, knowing their cost.