Predictive analytics in retail – a lever for growth in margins and corporate profitability.

An example that always comes to mind relates to one of humanity’s most important resources – water. While working at a fuel company, we had a seemingly “simple” task – to predict the weather. Why? Because weather drives demand for bottled water at gas stations. Sales on sunny days were practically twice as high as on rainy days, when sweetened and carbonated beverages dominated. This might seem like a minor detail, but when storage space at a station is limited, you need to use it as efficiently as possible – and that’s when it’s worth analyzing the deciding factor: weather.

This is exactly what predictive analytics is – an attempt to forecast future events and behaviors as precisely as possible to drive results. It not only enables forecasting future trends but also actively identifies opportunities to increase margins.

Predictive analytics is an advanced technology that leverages historical data, external factors, and seasonality through machine learning algorithms and statistical models. Key applications of predictive analytics in retail include: demand forecasting combined with inventory management, customer segmentation to implement personalized marketing strategies, and price optimization to maximize profits.

Start by collecting historical data such as: sales, inventory movements, pricing, seasonality, and weather data. Next, identify key margin areas, such as products with high storage costs or low turnover rates. With this information, we can help you create a planning model that leverages our experience and tools.

We’ll begin with one area or one product category to gradually analyze implementation and the results our model will achieve.

In 2025, predictive analytics is no longer optional – it’s essential to meet customer expectations and increase margins. With OneStream, you have the opportunity to boost profitability by discovering factors that will give you a fresh perspective on business planning.