SALES FORECAST
Definition
Sales forecasting (or demand forecasting) involves predicting future sales volumes for a product or category over a given period (week, month, season)
It is based on an analysis of historical sales data, market trends, seasonality, planned promotions, and external events (weather, holidays, social trends)
Forecasts inform purchasing, pricing, and logistics decisions.
Why it's important
- Optimizing inventory: Reliable forecasts help prevent overstocking (storage costs, markdowns) and stockouts (lost sales, dissatisfied customers).
- Managing prices: Anticipating high demand allows you to raise prices (dynamic pricing), while anticipating a decline allows you to plan promotions.
- Improving profit margins: buying the right quantity at the right time reduces purchasing costs (through volume discounts) and minimizes end-of-season markdowns.
A concrete example
A toy distributor is using a forecasting model to estimate Christmas sales
The algorithm analyzes sales data from the past three years, incorporates TikTok trends (viral products), the weather forecast (harsh winter = more indoor games), and planned promotions
It predicts a 25% increase in board games and stable sales for high-tech toys
The distributor adjusts its orders accordingly: +30% for board games, stable inventory for high-tech items
Result: stockouts avoided for games, no overstocking of high-tech items, and optimized margins.
Methods
Traditional statistical models: moving averages, exponential smoothing. Simple but not very accurate for complex product assortments.
Machine Learning: algorithms (Random Forest, XGBoost, neural networks) that learn complex patterns and incorporate dozens of variables (prices, weather, competitor promotions, events, web trends)
Significantly higher accuracy.
Hybrid approaches: combine statistical models (for the baseline) and machine learning (to refine forecasts for key products).
Common Mistakes
- Ignoring external factors: A forecast that doesn’t take into account the weather, school holidays, or viral trends misses major fluctuations.
- A single template for all products: a bestseller and a niche product don’t follow the same patterns. Segment your templates by category or sales profile.
- Don’t update the models: a model trained on 2020 data won’t capture post-COVID changes. Retrain them regularly using recent data.
Learn more
- Research & Data: Price analysis to review your sales history and improve the quality of your forecasting data.
- Solutions: Sales forecasting and AI to deploy high-performance forecasting models and automate the forecasting process.
- Tip: Operational pricing to align your forecasting, procurement, and pricing processes.
- Resources: Check out our case studies on the implementation of AI-powered forecasting in retail.
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