Trend forecasting is a complicated but useful way to look at past sales or market growth, determine possible trends from that data and use the information to extrapolate what could happen in the future. Marketing experts typically utilize trend forecasting to help determine potential future sales growth. Many areas of a business can use forecasting, and examining the concept as it relates to sales can help you gain an understanding of this tool.
Time Series and Trends
Trend forecasting is quantitative forecasting, meaning its forecasting is based on tangible, concrete numbers from the past. It uses time series data, which is data where the numerical value is known over different points in time. Typically, this numerical data is plotted on a graph, with the horizontal x-axis being used to plot time, such as the year, and the y-data being used to plot the information you are trying to predict, such as sales amounts or number of people. There are several different types of patterns that tend to appear on a time-series graph.
When looking at sales numbers, for example, a constant trend is seen when there is no net increase or decrease in sales over time. The sales may increase or decrease at specific dates, but the overall average stays the same. However, even if the average results are the same within a year, there still can be seasonal changes. For example, sales levels may be consistently greater in the summer and lower in the winter, although the average is the same in the entire year.
A linear pattern is a steady decrease or increase in numbers over time. On a graph, this appears as a straight line angled diagonally up or down. If someone looked at sales of VCRs, for example, they might see a diagonal line angled downward, indicating that sales of VCRs are decreasing steadily over time.
An exponential pattern is simpler than it may sound. Rather than a slow, steady increase over time, an exponential pattern indicates that data is rising at an increasing rate over time. Instead of a straight line pointing diagonally up, this graph shows a curved line where the last point in later years is higher than the first year, if the rate is increasing. An exponential trend for sales might indicate that sales were very slow in early years, but the product has been growing increasingly popular with each year as more people are interested in purchasing it.
More Complicated Patterns
Trend forecasting also may deal with patterns that are much more complicated than constant, linear or exponential graphs. For example, a damped trend may show there was an overall increase in sales for a number of years and then the sales suddenly stopped. A polynomial trend might show a gradual increase, then stagnation in sales over time and then a decrease in sales.
Forecasting Using Patterns
Looking at data over a number of years and finding patterns, you can use this information to extrapolate future patterns. A trend means that the same series of events is happening over and over. For example, if there is a trend of constant sales each year with a decrease of sales in winter that is offset by an increase in the summer, a person might extrapolate this to predict that sales will continue to be low in the winter. A store manager might use this information to offer additional products in the winter to help hedge against a drop in sales that time of year. However, forecasting isn't done quickly by just looking at a graph. Forecasters may translate the patterns of a graph into a formula to better predict what will happen in the future. They also may use spreadsheet software, which typically comes with built-in trend forecasting tools.
Trend forecasting is scientific, but it is also uncertain. The longer into the future a forecast is applied, the more uncertain the results become. Unexpected events can happen that will disrupt a steady pattern. The more complicated a pattern appears to be, the more uncertain a trend forecast is.
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