I know for sure that human behavior could be predicted with data science and machine learning. People lie—data does not. Taking a look at human behavior from a sales data analysis perspective, we can get more valuable insights than from social surveys.
In this article, I want to show how machine learning approaches can help with customer demand forecasting. Since I have experience in building forecasting models for retail field products, I’ll use a retail business as an example.
Moreover, considering uncertainties related to the COVID-19 pandemic, I’ll also describe how to enhance forecasting accuracy.
And don’t worry if your business’s focus isn’t on retail. The main goal of this article is to describe the logic of how machine learning can be applied in demand forecasting both in a stable environment and in crisis.
Updated 4/20/2020: COVID-19 as an Anomaly: How to Forecast Demand in Crisis
What Is Demand Forecasting in Machine Learning?
Machine learning techniques allow predicting the amount of products/services to be purchased during a defined future period. In this case, a software system can learn from data for improved analysis. Compared to traditional demand forecasting methods, machine learning:
- Accelerates data processing speed
- Provides a more accurate forecast
- Automates forecast updates based on the recent data
- Analyzes more data
- Identifies hidden patterns in data
- Creates a robust system
- Increases adaptability to changes
According to technology trends in the retail sphere, demand forecasting is often aimed to improve the following processes:
• Supplier relationship management. By having the prediction of customer demand in numbers, it’s possible to calculate how many products to order, making it easy for you to decide whether you need new supply chains or to reduce the number of suppliers.
• Customer relationship management. Customers planning to buy something expect the products they want to be available immediately. Demand forecasting allows you to predict which categories of products need to be purchased in the next period from a specific store location. This improves customer satisfaction and commitment to your brand.
• Order fulfillment and logistics. Demand forecasting features optimize supply chains. This means that at the time of order, the product will be more likely to be in stock, and unsold goods won’t occupy prime retail space.
• Marketing campaigns. Forecasting is often used to adjust ads and marketing campaigns and can influence the number of sales. Sophisticated machine learning forecasting models can take marketing data into account as well.
• Manufacturing flow management. Being part of the ERP, time series-based demand forecasting predicts production needs based on how many goods will eventually be sold.