Niels Bohr once said: “Prediction is very difficult, especially if it’s about the future.” In this article, I will show how companies can improve their sales forecasting. Predicting future sales performance is a significant challenge, especially for businesses operating in a dynamically changing market.
Historical Data Is Not Enough
Companies often base their forecasts solely on historical data, which frequently leads to errors and costly mistakes. Many assume that if sales were high in previous years, they will remain at a similar level in the future. However, markets are dynamic, and past trends do not necessarily repeat themselves. Take the electric car market in Europe as an example – in January, sales dropped by 45% compared to the same period a year earlier. This demonstrates how rapidly changing conditions can dramatically impact results.
Effective forecasting requires analyzing real market factors rather than relying solely on past data. Events such as changes in trade policies, aggressive marketing campaigns by competitors, new product launches, or shifts in consumer budgets have a tangible impact on sales and must be considered in forecasts.
Forecasts Should Consider Causes, Not Just Effects
How can we determine how temperature increases will affect beverage sales? According to labor standards, every worker performing outdoor duties in hot weather should consume an additional liter of fluids per hour. Even if this assumption were entirely accurate, it does not apply to a representative portion of our customers and may not reflect reality.
To understand the relationship between weather and beverage sales, consumer behavior must be studied. Such analyses help identify key factors influencing sales. Most consumers do not drink more beverages simply because their bodies need extra fluids; rather, they spend more time outdoors and engage in social activities. Even an exceptionally warm April will not generate the same sales as May or June. The reason is not the temperature itself but the higher number of outdoor events. Beverage consumption is influenced not only by weather conditions but also by the number of festivals, concerts, sports events, and other gatherings that attract people and encourage consumption.
Factors Influencing Sales Forecasts May Have Different Weights Over Time
Warm weather in May is highly significant, whereas in April, it may not be as important. But how can we determine which factors matter and when?
Systematic data collection is crucial for understanding the real dependencies affecting sales. Data on temperature, precipitation, event calendars, public holidays, competitor actions, and price fluctuations can provide valuable insights into how demand is shaped. Additionally, gathering transactional data and analyzing purchasing patterns over different periods is essential.
A sufficiently broad dataset enables not only an understanding of the impact of individual factors on sales but also the adaptation of business strategies to changing market conditions.
Using Machine Learning for Sales Forecasting
Machine learning can help determine what and when influences beverage sales by analyzing large datasets and detecting hidden relationships. Algorithms can consider various factors such as temperature, precipitation levels, day of the week, time of day, event calendars, and historical sales data. This allows for identifying patterns that manual analysis might overlook.
Machine learning models, such as linear regression, random forests, or neural networks, can predict how specific variables affect sales under different circumstances. For example, they may indicate that temperatures above 25°C increase beverage sales but only when mass events take place. On cooler days, other factors—such as public holidays or promotional campaigns—may have a more substantial impact on sales.
Machine learning also allows for continuous forecast improvement. Models can adjust to changing conditions by analyzing new data and updating predictions. This enables companies to not only plan inventory more effectively but also optimize marketing efforts and tailor their offerings to actual demand.
The Critical Role of Data
For machine learning models to be effective, high-quality data is essential. Companies should focus on systematically collecting information that may impact sales. For the beverage sales forecast example, in addition to basic historical data, it is valuable to include:
- Weather variables (temperature, precipitation, pressure)
- Event and holiday schedules
- Sales location characteristics (urban, tourist, residential)
- Marketing activities and promotions
- Price changes and competitors’ pricing strategies
- Customer feedback and preferences
Only with a comprehensive dataset can companies effectively train forecasting models and avoid misleading assumptions.
Key Takeaways
- Historical data is not enough – Relying solely on past performance can lead to inaccurate forecasts. The market is dynamic, and trends can shift due to various factors.
- Understanding causes is essential – Sales are shaped by real events and influencing factors such as weather, event calendars, competitor actions, and consumer budgets. Past statistics alone are insufficient without considering the mechanisms that drive demand.
- Collecting data is crucial – Gathering diverse data points enables businesses to leverage machine learning to determine the true impact of various factors on sales, leading to more precise forecasts.
