There are many different applications for data analysis in business, but the general goal of all of them is to inform future decisions. We look at data to learn from past cases and use this knowledge for future strategies. However, we also need to have an idea about what we can expect in the future in order to plan ahead. Therefore, we also use data analysis for future modeling and forecasting. Trend analysis forecasting is one of the methods to do it. Let us look at what it is and how businesses benefit from it.
Seeing the patterns
A trend is a kind of pattern, a feature that is in some sort of repetition. One way to look at trends is a synchronic or cross-sectional analysis, which is looking at what is trending at a particular interval of time. For example, we can ask what kind of hairstyles are popular right now. In this case, the pattern that we see is the choice of hairstyles over a population across a given time.
Another way is to look at trends in reference to time series. This is what we would do if we looked at how the popularity of a particular hairstyle changed from the beginning of the century until now. The pattern that we would look for in this case is the change over time. This is how we look at trends when we talk about trend analysis forecasting, as what interests us is the changes we can expect in the future.
Such trend analysis is done by looking at historical numerical data over time. This means that we look at data points that describe how something has been changing at a specified period of time. We can analyze the trends in regard to all kinds of features from profit to size in employees just so long as we can express those features in numbers.
The results of the analysis will generally be presented in a graph with the horizontal axis plotting time and the vertical axis plotting the numerical values describing the feature that is being investigated for example sales numbers.
A few kinds of trends can usually manifest on such a time-series graph, the most basic of which is constant and linear. A constant pattern or a horizontal trend is a line in the graph that goes more or less parallel to the time axis, indicating that the thing being analyzed neither decreases nor increases, it is stable over time. Linear pattern, conversely, is either an upward trend or downward trend, representing increase or decrease respectively.
Use cases and benefits
Now that we have the basic picture of trend analysis forecasting, let us look at how it is used in business. Among the most common of the various purposes and benefits of trend analysis are the following.
- Recognizing opportunities. Knowing the direction in which something is moving means being able to see potential opportunities of some course of action. Investors use trend analysis to recognize such opportunities by analyzing the growth trends of companies. Not only traditional financial data but also various types of firmographic data can be used for such analysis. From web traffic data to information regarding hiring activities, different kinds of alternative data can show trends that are very indicative of a firm’s future prospects.
- Comparing companies. Trend analysis is a very convenient method of comparison. Two or more curves representing different companies can fit very well on the graph, thus allowing to see the difference immediately. This can be used by investors to measure firms against each other, as well as businesses comparing their competitors or potential partners. Additionally, firms can compare themselves with other companies, thus identifying where they lead and where fall behind in their industry landscape.
- Wide applicability. As mentioned, trend analysis forecasting can be done in regard to anything that can be translated into numerical values. And businesses certainly deal with a lot of quantifiable information, which means that this method can be applied to forecast many relevant changes. Sales revenue, employee turnaround and various operational costs are just a few forecastable things that can enhance decision-making across departments.
- Varying levels of complexity. The basic trend forecasting is quite simple as compared to other methods of data analysis. There are just two variables one of which is time and the other is represented by the raw data being analyzed. However, trend analysis can be enriched and made more sophisticated by employing various AI tools to compare different trends and their relations. Furthermore, machine learning can be utilized for automated modeling. For this, of course, one first needs a lot of traditional and alternative data to train the algorithms with.
Something to keep in mind
It is important to remember that even though trend analysis aims at predicting the future, all the data that we ever have describes only the past. There is no guarantee that the future will follow the current pattern.
There may be changes in the structure of the thing being analysed or other upcoming fluctuations that we do not know about. Or it may be that the current trend had an unrecognized cause in the past that will not be repeated in the future. And, of course, there is always a chance of a black swan market event that is by definition unpredictable.
All these and many other variables can potentially distort the results of forecasting. The good news is that we can try to take some of them in consideration. As long as we can get additional data about what else can be expected, we can use it to adjust our trend curve and come up with a potentially more accurate forecast.
With trend analysis forecasting we look at the data of the past to see the changes of the future. What we see is progress or regress, growth, decline or stability. Equipped with this information we are better prepared to plan for the future, arrive at the right decisions and take relevant measures in time to make them count.