Sales forecasting using AI
Sales forecasting using AI
Anticipate demand and improve your decisions commercial opportunities thanks to AI
BOOPER MPS takes your strategy into account and anticipates your sales. You guide the scenarios and steer your decisions toward growth and profitability without compromising your price image. Reliable, explainable, and immediately actionable forecasts.
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Solutions tailored to each pricing challenge
Why choose BOOPER ?
Customer testimonials
Our customers share their feedback
Discover how our customers leverage BOOPER's artificial intelligence to structure their pricing decisions, secure their margins, and accelerate their commercial performance.
We have made our entire pricing decision-making process more reliable thanks to BOOPER.
The teams now have a clear and shared view of price performance by category and by store, with data-driven recommendations.
The platform allows us to anticipate the impact of our choices on the margin and to justify our decisions to management with concrete and measurable indicators.
The predictive scenarios offered by BOOPER have transformed the way we prepare promotional campaigns.
We can compare several pricing scenarios before launch, measure their effects on volumes and profitability, and secure our business decisions.
This has allowed us to become more responsive while improving the consistency between supply strategy, price image and economic performance.
BOOPER has enabled us to industrialize our pricing approach without losing strategic control.
The teams have common tools to analyze the competition, simulate decisions and align field actions with business objectives.
We have structured a cross-functional governance that improves coordination between sales, marketing and finance while generating tangible results on the margin.
Frequently Asked Questions - BOOPER MPS - Sales forecasting using AI
AI analyzes large volumes of historical and contextual data to identify patterns that are invisible to human analysis. For example, it takes into account seasonality, promotions, prices, weather, and competition to produce dynamic and continuously adjusted forecasts.
MPS primarily uses historical sales, prices, promotions, commercial calendars, store data, and external drivers (weather, events, competition). The richer the data, the more accurate the models. We recommend a minimum of one year of historical data.
Traditional methods rely on averages and past trends. Machine learning integrates hundreds of variables simultaneously, detects non-linear relationships, and automatically adapts to changes in consumer behavior.
By more accurately anticipating future demand, MPS makes it possible to adjust order volumes, reduce shortages and overstocking, improve service levels, and limit the financial immobilization associated with inventory.
BOOPER projects show a rapid ROI thanks to reduced investment in pricing, increased volumes, inventory optimization, reduced time spent on manual forecasting, and overall improvement in price image. The first gains are seen immediately.
Yes. MPS is designed for large retail accounts with multi-country, multi-store, multi-category management and centralized governance while maintaining local flexibility.
Yes. MPS is also designed to be adopted by simpler organizations. The vocabulary and indicators remain those of the client. The quality of the algorithms is the same as in larger structures.
