The success of your business (or any business, for that matter) relies on your ability to make predictions. This might seem unfair, but it’s the reality. For example, you predict what products your customers want and what marketing strategies will be successful. Of course, the decisions you make shouldn’t just be based on hope. The most successful businesses are able to forecast future events and trends based on the data that they have collected. This is called “trend analysis.”
Trend analysis is a technique used to predict future events. This is done by analyzing historical data and identifying past trends where specific actions or events are identified across multiple time periods. This information is plotted and analyzed for patterns that can indicate past trends as well as potential future trends.
Analyzing trends often yield these benefits:
Through trend analysis, you can obtain these kinds of critical information about your business and customers:
By examining your organization’s revenue and cost information for multiple reporting periods using trend analysis, you’ll be able to look for trends and inconsistencies, like identifying inaccuracies in your preliminary financial statements. For example, say you discover a surge in expenses on one income statement followed by a significant decline in the following statement next month. Upon closer examination you might find that there was an expense that was accidentally entered twice in the first month.
Trend analysis can also be used to identify if sales are declining and where, to identify unusual expenditures, to estimate future cost and revenue results for budgetary purposes, and to look for evidence of fraudulent claims.
Trend analysis is commonly used in the financial sector to predict stock prices. Investors will look at how a specific stock performs over a specific period of time. They will examine historical data points as well as factors that may have affected the stock’s performance, such as market conditions, changes in the sector, competition, and more. Using this information, you can potentially predict how investments are going to behave over both the short-term and the long-term.
Besides being able to predict consumer behavior, trend analysis can help you determine what drove past consumer behavior trends. For example, you might find that traffic to your website has been spiking every Friday. While this can help you predict future trends (there’s a good chance it continues spiking on upcoming Fridays), you can also look for other trends happening at the same time to better understand why consumers are flocking to your site on Fridays. While identifying consumer behaviors is important, understanding the reasoning behind it will be much more helpful.
When you perform a trend analysis, any predictions that you make will be based on historical data. If the business environment changes, then the accuracy and reliability of your forecasts will suffer. For example, sales may have been steadily growing over the past two years. You can predict that they will continue to grow; however, you may suddenly have a new direct competitor that can change this trend. Additionally, your historical results may have been affected at times of inflation, making it more difficult to predict future trends.
Another major challenge that can hinder accurate trend analysis is the ability to identify turning points. Some turning points are obvious. For example, if you’ve lowered the pricing on your main product line and it’s resulted in increasing sales ever since. Aberrations can be obvious as well. For example, if you’re a roofer, business may suddenly skyrocket following a hurricane. However, not all turning points are obvious. In some cases, it can be difficult to tell whether a turning point is an aberration or the beginning of a new trend, calling for more data to support your analysis. This can be particularly challenging for newer businesses, which don’t have a lot of historical data available.
There are three basic methods that can be used: qualitative, time series analysis, and causal models. The qualitative method uses qualitative data, such as expert opinions, and information about special events to predict trends. Qualitative methods are often used when data is limited, such as when a new product has just been introduced onto the market. Time series analysis relies entirely on historical data since it focuses on patterns and pattern changes to identify trends. Causal models use very specific information about the relationships between system elements, which means that they depend heavily on historical data to identify trends as well.
There are also many tools available that can be used to perform trend analysis. These tools can analyze your data for trends and visualize those trends in different ways. Some of the more highly recommended trend analysis tools you should consider using include:
Most people are familiar with Microsoft’s Excel spreadsheet program; however, not everyone is familiar with the abundance of features Excel offers. For example, Excel has a basic but effective trend analysis tool simply called the TREND function. The Excel TREND function calculates linear trendlines and visualizes them for you, making it easier to identify past trends.
IBM’s SPSS statistics software was designed for interactive statistical analysis. You can use SPSS to identify potential trends easily by simply inputting the dependent variable in the Dependent List box and then inputting the quantitative factor into the Factor box. After specifying the Degree as Linear, you’ll obtain the results on the One-Way ANOVA screen.
Considering the analytical capabilities of most BI platforms, it’s no surprise that Microsoft’s Power BI solution has a trend analysis feature. Performing a trend analysis can be done through the Analytics pane, which is located in the Visualizations pane. Here, you’ll be able to create numerous dynamic reference lines. You can then choose the color, style, position, and transparency of those lines to visualize your data, making it easier to identify trends. There’s even a Forecast feature that will forecast trends based on your data.
Tableau has a reputation as a powerful data visualization tool. One of its features is the ability to show trend lines in a visualization to highlight existing trends in your data. Tableau allows you to specify how you want your trend lines to look as well. To add trend lines to your visualizations, all you have to do is go to the Analytics pane and drag the Trend Line option into view and drop it on one of the model types, which include power, polynomial, exponential, linear, and logarithmic.
Minitab is a statistics-based software with an excellent trend analysis feature. Minitab lets you choose from linear, quadratic, exponential growth or decay models, as well as S-curve trend models.
R is a software environment developed for statistical computing and graphics. It’s commonly used to develop statistical software as well as data analysis. You can create a time series trend analysis in R; however, you’ll need some familiarity with programming language to do so.
Stata is a well-known data science and statistics solution that provides users everything that they need for data analysis and data management. Using Stata, you can store and manage both small and large sets of data and perform statistical analysis, including trend analysis. However, just like with R, you will need to have some programming knowledge.
Matlab is a programming language developed by MathWorks. A Time Series Analysis and Forecast feature is available that lets you analyze time series and forecast future trends. Like the previous two trend analysis solutions, Matlab does require some programming know-how, although it is easier to learn and use than other programming languages such as Java.
Performing trend analysis can provide you with a lot of insight into trends that are relevant to your business. By identifying past trends that may not have been so obvious before, you can make more informed business decisions. The ability to forecast future trends using historical data can also help to reduce or even avoid some of the risks involved with investing in new business strategies. Considering how easy it is to run and how many tools there are available, there’s no reason that trend analysis shouldn’t be a major part of your data analytics strategy.
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