Sales forecasting using AI
Sales forecasting using AI
Retail Sales Forecast: anticipate demand and make more confident pricing decisions with 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.
Sales Forecasting: Anticipate Demand to Better Manage Your Prices
The quality of pricing decisions depends directly on the reliability of your sales forecast. The AI-powered sales forecasting module uses machine learning models capable of incorporating multiple variables to generate detailed and dynamic forecasts at the product and store levels.
Multivariate Forecasts
The model incorporates sales history, seasonality, promotions, and price effects to generate detailed forecasts at the product and store levels.
Reducing stockouts and excess inventory
By better forecasting sales volumes, you can reduce stockouts and avoid costly overstocking.
Tariff Impact Simulation
Measure the impact of a pricing decision on demand before implementing it, to ensure your arbitrage strategies are sound.
Optimized Sales Planning
Reliable and transparent sales forecasts to help you better plan your purchases and business operations.
AI for sales forecasting designed for pricing
Beyond basic calculations, the module adapts to the specific circumstances of each product and continuously improves to remain a reliable tool for your pricing decisions.
Forecasting Demand on a Product-by-Product Basis
The algorithm analyzes the sales history for each SKU, detects seasonality, and adapts to market conditions, rather than setting your prices based on a generic average.
Models that improve over time
Sales forecasts are automatically adjusted based on actual sales, resulting in increasingly reliable pricing decisions.
An algorithm tailored to each product
The engine selects the most relevant statistical or AI model based on the sales profile, rather than applying a single method to the entire catalog.
A shared vision between pricing and sales
Category management, marketing, and pricing teams rely on the same sales forecast to align pricing decisions with sales operations.
Customer testimonials
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.
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.
Yes. With the AI-assisted simulator, users can test different business scenarios (promotions, price variations, changes in product range) and measure their projected impact on volumes, revenue, and margins.
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.
