A growing priority for businesses
In an economic environment marked by uncertainty and fluctuating demand, the ability to anticipate sales is a decisive competitive advantage. Better stock management, optimized staffing, and more effective marketing campaigns: sales forecasting boosts operational efficiency, responsiveness, and profitability.
It was with this goal in mind that Idealis carried out a proof of concept (POC) in collaboration with one of its clients, to explore how artificial intelligence can be used to predict sales and revenue based on existing data.
What most companies still do and why it’s no longer enough
Many companies still rely on Excel-based averages, intuition, field experience, extrapolations from the previous year, or reactive adjustments based on weather or current events.
However, these methods fall short in effectively factoring in multiple influential variables like seasonality, events or promotions. They lack predictive capacity, cannot prevent stockouts or overstocking, and do not allow for optimal resource allocation. In contrast, AI offers a robust alternative by leveraging structured internal and external data to deliver accurate, actionable and tailored forecasts.
POC objective: deliver reliable, actionable sales forecasts
The aim of the POC was to demonstrate that data from the client’s information systems (past sales, calendar events, promotional activity) could feed a predictive model capable of answering key strategic questions such as:
How many units of Product X are likely to be sold in Store Y next week?
What revenue can be expected over the next four weeks, by store?
Two types of predictions were tested: product-level sales forecasts by time period, and consolidated revenue estimates by store or segment. These forecasts can be generated at various or monthly, depending on operational needs.
Methodology: smart use of available data
The model implemented relies on the analysis of historical sales data by product, store and period, along with public holidays, school vacations and local events. It also takes into account contextual information specific to each store, such as location, foot traffic and past marketing activities.
This data was enriched and integrated into a prediction engine powered by machine learning algorithms, enabling the automatic identification of recurring patterns and the generation of realistic forecasts.
Observed results: measurable operational benefits
The observed results highlight tangible operational benefits, notably the optimization of stock management and the anticipation of order volumes, which helps reduce stockouts and overstocking while improving product turnover.
Workforce planning is also streamlined by adjusting staffing levels according to activity forecasts, which helps improve in-store service quality. Additionally, marketing effectiveness is enhanced through more precise campaign targeting during high-potential periods, as well as the deployment of specific offers 10 to 15 days in advance based on the established projections.
Example: real-world application in a retail chain
In a network of cosmetics stores, historical data revealed a recurring sales peak for lip gloss ahead of the summer holidays. Thanks to the predictive model:
Thanks to the predictive model, stock levels were locally adjusted based on the identified potential, teams were reinforced during peak periods, and a promotional campaign was launched one week before the expected peak.
Result: improved stock turnover, better-aligned workforce coverage, and a clear increase in revenue during the period.
Why choose Idealis for this type of project?
Proven business and technical expertise
Idealis has a dedicated data science department with specific expertise in data from the Odoo ERP system.
A results-oriented approach
Each project is structured to generate concrete operational decisions: our deliverables are ready for immediate use by your sales, logistics, or marketing teams.
Comprehensive support before and after the POC
We manage the entire process: data structuring, modeling, clear reporting, team training, and gradual integration into your business tools.
A progressive and scalable approach
This POC represents the first step toward data-driven management. It can be enhanced with new factors (weather, web traffic, customer behavior), automated, and connected to your CRM or BI systems.
What you should know before getting started
Do your data need to be perfect?
No. Even partial historical data can be enough to reveal significant trends.
What is the average duration of a POC of this type?
Typically between 3 and 5 weeks, depending on data quality and scope of analysis.
Is it useful for medium-sized businesses?
Yes. Even with a limited number of stores, performance gaps clearly justify adopting a forecasting approach.
What happens after the POC?
We continue to provide support through phased deployment, automation, documentation, team assistance, and performance tracking.
A first step toward predictive commercial management
This POC is not an isolated project. It marks the beginning of a more agile and strategic management approach, where decisions are based not on intuition but on reliable forecasts.
In the long term, this approach can be extended to other areas such as purchase planning, traffic forecasting, and market trend detection.
Would you like to assess the potential of your data?
Contact Idealis Solutions to benefit from a personalized, no-obligation assessment.
In less than a month, we help you build an initial predictive approach tailored to your challenges and pace. Contact Idealis Solutions at : solutions@idealisconsulting.com
Anticipating sales with artificial intelligence: a strategic lever for driving commercial performance