Wouldn’t it be great if you could exactly predict market demand for new products? That way, when customer orders surge, you’d already have a fully stocked warehouse. And when the rapid growth subsides, you’d be prepared in such a way that you aren’t stuck with shelves of dead stock.
No one has a crystal ball, but there are ways to make informed decisions about demand. To do this, you need to obtain data for trend projection models, which can give objective forecasts to help you navigate market fluctuations. Here’s how trend projection works and the methods you can use for forecasting.
What is trend projection?
Trend projection is a forecasting method used to predict future values of a variable based on its historical data. Also known as trend analysis or time series analysis, it’s frequently used in business forecasting and can aid in demand forecasting, sales forecasting, and predicting seasonal variations in consumer behavior.
The goal is to identify and model the underlying pattern of data points to make predictions about future values. The assumption is that historical trends will likely continue into the future—at least in the short to medium term. From there, you can make educated predictions about future trends related to your business.
How trend projection methods work
Businesses typically take a five-step approach when using trend projections to forecast sales:
1. Data collection. The first step in sales trend projection is to gather past sales records. Collect sales data at regular intervals, such as daily, monthly, or yearly. A data management tool can simplify this process.
2. Data analysis. Once you collect the data, organize it in a time series data format, where data points are recorded at successive points in time. Examine the statistical data for patterns, fluctuations, and trends.
3. Trend identification. The simplest statistical method for trend analysis involves charting data on a graph and looking for a linear trend, be it upward (positive trend line), downward (negative trend line), or flat (no trend line). More sophisticated trend projection methods include moving averages, linear regression, and exponential smoothing.
4. Projection. Once you identify a trend, extrapolate it into the future to make predictions. For example, if you spot past trends about holiday shopping, you may be able to forecast seasonal customer demand for upcoming holidays.
5. Preparation and adjustment. Adjust your strategies to capitalize on observed trends. For instance, if your data reveals people buy more footballs during the September-to-January NFL season, start stocking up on footballs in July in anticipation of future sales.
Types of trend projection methods
- Graphical method
- Least square method
- Box-Jenkins method
- Judgmental forecasting
- Machine learning models
Whether you’re conducting market research, measuring seasonal fluctuations in sales, or doing warehouse planning, use trend projections to analyze data patterns and make accurate forecasts. Here are five forecasting methods used in predictive analysis:
The graphical method is a simple and intuitive way to identify and project data trends. It involves plotting the historical data points on a graph and visually inspecting the data to identify trends or patterns.
You might use the graphical method as part of your sales analytics strategy. Visually identify trends by plotting monthly or annual sales data on a graph. You may observe monthly or seasonal variations, like increased sales during the holiday season. Data points may also reveal the same trend over prolonged periods, such as consistent growth over several years.
You could also use the graphical method to assess the impact of marketing campaigns or promotions. By comparing sales before and after a promotion, you can gauge effectiveness and develop projections for future campaigns.
Least square method
The least square method—also known as the fitting trend equation—helps you find trends in data by fitting a line or curve to a scatter plot of data points. This type of regression analysis produces a line as close to all data points as possible, allowing you to identify trends.
You might use the least square method to create a sales forecasting model for a financial quarter, allowing you to estimate future sales based on an established trend. This can help you predict demand and stock your shelves accordingly. If inventory forecasting suggests an increase in sales due to data from past years, you can order more of a particular product to meet customer demand.
The Box-Jenkins method—also known as time series analysis—is a statistical method used in trend projection. It allows you to analyze and forecast reliable time series data by considering different factors behind a trend, such as monthly or seasonal variations in customer behavior.
When properly collected and analyzed, time series data reveals many trends within a company’s operations. For instance, you might apply time series models, such as ARIMA (autoregressive integrated moving average) or GARCH (generalized autoregressive conditional heteroskedasticity), to forecast total sales. This is especially useful for predicting demand for products with complex sales patterns or seasonality.
Judgmental forecasting involves making forecasts based on your domain knowledge and intuition. This approach is valuable when historical data is limited or unreliable and external factors play a significant role. However, as a less formal technique, it’s susceptible to errors caused by an analyst’s personal bias.
Machine learning models
Companies are increasingly using artificial intelligence (AI) and machine learning (ML) to optimize projections. Machine learning techniques, including neural networks, random forests, and support vector machines, can capture complex patterns in data. They are particularly useful when such data doesn’t adhere to simple linear relationships and trends don’t continue at a constant rate.
Trend projection method FAQ
What is the trend projection method of forecasting?
The trend projection method of forecasting involves using historical data and statistical techniques to identify and extrapolate trends, patterns, or relationships in the data to predict future values or outcomes.
Why use the trend projection method?
Businesses rely on trend projection methods to forecast future values using historical data, enabling them to prepare for future developments, make data-driven decisions, and manage resources effectively. This is especially valuable for seasonal forecasting.
Does trend forecasting lead to higher sales?
Trend forecasting can increase sales for businesses seeking to align product strategy with consumer preferences and market demand. However, success hinges on accurate forecasting, effective implementation, and market dynamics.