Why is it important for businesses to understand seasonality and demand trends?
Modern machine learning models can predict demand more accurately than classic statistical methods. This is especially important for retail, restaurants, logistics, and D2C brands, where forecast errors directly translate into losses. This article breaks down how AI identifies seasonality, trends, and hidden patterns, as well as which data critically impacts accuracy.
How do neural networks analyze seasonality?
Seasonality is a repeating pattern in data related to time: days of the week, months, holidays, climate, customer behavior. For a model to learn these cycles, it requires at least 12-24 months of historical data.
Key methods for detecting seasonality:
- Time series decomposition. The neural network separates data into trend, seasonality, and noise, allowing for more accurate identification of stable changes.
- Recurrent models (LSTM/GRU). Capture long dependencies – for example, an annual sales spike before the New Year.
- Temporal Convolutional Networks. Work faster than RNNs and are good at recognizing repeating patterns over long periods.
- Transformers for time series. Use the attention mechanism to find connections between events far apart in time.
Real-world cases of AI application in recognizing seasonality and demand trends
Case #1: Walmart – a global retail retailer
→ Walmart implemented a machine learning and AI-based inventory management and demand forecasting system: analysis of historical sales, accounting for seasonality, holiday sales, weather, local events, and external factors. The system forecasts demand, helps plan purchases, allocate inventory to stores, and ensure timely delivery.
Result:
The company reported that thanks to AI forecasting, it managed to significantly reduce excess inventory and shortages, improve purchase planning accuracy, and enhance ‘on-shelf availability’ – meaning products were more often available on shelves.
Case #2: JD.com – a Chinese e-commerce and logistics giant
→ A recent study describes how JD.com uses a data-driven approach to assortment planning and inventory distribution: forecasting algorithms + optimization of goods distribution between distribution centers. This allows for assessment of future demand, seasonal and trend fluctuations, and advance allocation of inventory so that goods are readily available for consumers.
Result:
After implementing the JD.com approach, they noticed an increase in the efficiency of local order fulfillment (FDC – front-center), a reduction in logistics costs, and improved customer satisfaction due to timely delivery, even during peak demand periods.
How does AI understand trends and changing customer behavior?
A trend is a long-term upward or downward movement in demand. Unlike seasonality, it doesn't necessarily repeat. Neural networks need to consider many factors:- Changes in audience interests. For example, the growth in demand for lactose-free products.
- Economic fluctuations. Inflation, exchange rates, declining purchasing power.
- Marketing activities. Promotions, collaborations, launch of new product lines.
- External news. Bloggers, media, influencer recommendations.
Neural networks analyze not only historical sales but also additional indicators: competitor price dynamics, ERP data, weather, search queries, and market growth rates. Thanks to this, algorithms can predict increasing demand in advance. For example, one e-commerce brand noticed a “boom in interest” for a certain category of goods 6 weeks before the peak and managed to scale up purchases.
Practical benefits for business
Companies implementing AI-based demand forecasting receive specific, measurable effects:
- reduction of excess inventory by 20-40%;
- reduction of write-offs and losses in retail;
- increased purchase accuracy and optimization of logistics;
- timely response to demand spikes and drops;
- revenue growth due to reduced out-of-stock.
If a business plans to implement such a system, it is important to assess the volume of data, the complexity of the task, and the budget. This is easy to do by → using the cost calculator for developing a neural network EasyByte.
For complex cases, it is better to discuss the architecture and variations of models in advance,
→ by signing up for a free consultation with an EasyByte expert.
What is needed for accurate forecasts?
To ensure that a neural network consistently recognizes seasonality and trends, three conditions must be met:
- Clean, accurate data. Missing values, duplicates, spikes due to promotions, and human errors can distort the algorithm's conclusions.
- Sufficient historical depth. The longer a business collects data, the better the model sees seasonal cycles.
- Regular retraining. The market changes, so the model should be updated every 2-8 weeks.
This creates a “living” tool: it not only analyzes statistics but also helps businesses make confident decisions based on data, not intuition.
📌FAQ: frequently asked questions about recognizing seasonality and demand trends
Question: What data is needed to train a model?
Answer: Typically, sales history, price data, inventory levels, promotions, weather, and events affecting demand are required.
Question: Can a model be trained with a small amount of data?
Answer: Yes, but the forecast quality will be lower. A hybrid approach is often used – part of the signals are taken from external sources.
Question: Is AI suitable for small businesses?
Answer: Yes: even small stores and cafes benefit significantly, especially in purchasing and staffing planning.
Question: How often should the model be retrained?
Answer: On average, once every 1-2 months, but the frequency depends on market dynamics and the amount of new data.
Question: Can weather and external factors be taken into account?
Answer: Yes, this increases forecast accuracy, especially in retail, FMCG, and delivery.
Question: How to understand that the model is working correctly?
Answer: This is evident from metrics such as MAPE, RMSE, and a reduction in write-offs and out-of-stock.