Seasonality

Seasonality refers to a predictable pattern of changes in a business or economy that unfolds throughout the year. They may be calendar years or commercial seasons. It helps in keeping track of trends in the economy and stock analysis.

Fluctuations analyzed over time aid in gaining a clear understanding of business performance. Through performance analysis, an accurate depiction of how products and services are affected by these timelines can be reached to adjust sales data. With the help of the data, businesses can plan their stocks according to seasonal benchmarks.

• Seasonality describes variations in an organization's sales revenue brought on by outside forces that happen on a predictable timetable and happen at the same time(s) each year. 
• Knowing how seasonality influences business sales can help owners understand what they can and cannot manage. 
• Sales will fluctuate up and down from time to time according to seasons. Businesses can adjust those numbers in accordance with the data from the industry with keen observation and analysis. 
• Forecast is important as it informs budgetary and operational decisions in purchasing, production, marketing, advertising, etc.  

Seasonality in business is the repeating and expected patterns of change in a particular activity, occurrence, or trend over specific time intervals, such as daily, monthly, or yearly. These variations occur on a predictable timetable, consistently occurring at the same time(s) each year. It's a perfectly normal and unavoidable component of doing business.

The demand for goods and services is nevertheless impacted by seasonality, even if a business isn't considered seasonal. Understanding them is important as they are frequently subjected to sales fluctuations that are otherwise unaccounted for. A sales downturn that lasts longer than two months may be misdiagnosed by those who are unaware of the natural cycle of a given business.

Knowing how seasonality influences business sales can help owners understand what they can and cannot manage. This is perhaps the biggest advantage. Sales will fluctuate up and down from time to time according to seasons. Businesses can adjust those numbers in accordance with the data from the industry with keen observation and analysis. Through this, they will be able to determine whether those changes are under their control or not.

Hence, these companies can focus their efforts and resources on the areas where they will have the greatest impact. For most profit-maximizing businesses, choosing and implementing an appropriate sales forecast method is crucial to planning and control. The accuracy of this forecast is vital, as it guides decisions in budgeting and operations, including purchasing, production, marketing, and advertising.

Let us look into a few examples:

Example #1

Different seasons offer different types of vegetables, known as vegetable seasonality. Summer squashes are one such example. Their availability in certain seasons creates demand for them at that particular time.

Pumpkin is another popular product, often considered a vegetable. It is, in fact, a fruit, but it has an undeniable presence in the fall season. Pumpkins are considered to be fall produce because they are typically available from mid-September to November. In the U.S., people celebrate it throughout the season, be it for food or as decoration for Halloween; this short availability and profound use will inevitably push their demands during that season.

Example #2

Another example could be hotels near tourist destinations. When the season opens up for tourist places, they buzz with activity from people coming to spend their vacations. In the summer, one such example can be found on Gotland, a large Swedish island. At other times of the year, the Scandinavian region is covered with ice, which becomes pleasant in the summer. In these times, people who otherwise cannot explore cold regions can experience the best views of the land.

The hotels around the area will earn multiples during the destination's peak season. When the season passes, there may be little or no activity, giving the hotels periods of loss. Businesses that operate seasonally have a target market they concentrate on during and after their peak time. A seasonal company can optimize marketing and outreach by focusing on a smaller customer base and customizing its goods, services, and communications offerings. Hotels can offer discounts during such times to maintain some level of income. 

Example #3

Around the holidays, U.S. stocks often witness a rising phenomenon known as the "Santa Claus rally." The period includes seven business days: the final five of the current year and the first two of the following year. Over the past 20 years, the S&P 500 Index, a measure of U.S. market performance, has grown by 0.7% annually on average. It is mostly attributed to optimism for the New Year, holiday spending, and stock traders on vacation. The market sees a high activity since it is a collective feeling of the majority. 

Example #4

Winter clothing manufacturing companies are another great example of seasonality. When thick woolen clothes are discarded in summer, the demands for such clothes tend to reduce. The companies can plan for such seasons in advance; the raw materials, such as wool, can be cheaper in summer than in winter. Buying them in advance or manufacturing them in the summer can reduce the costs associated with manufacturing. The work pressure that will be endured in the winter time will also be reduced. This way, the labor cost can be kept in check. 

A data set that monitors a sample over time is a time series. One prominent feature of time series is seasonality. There are two variations of it: additive and multiplicative.

Given below are the differences between the three, which are deterministic components of the time series:

• Seasonality: Series experiences consistent seasonal fluctuations (e.g., every month, quarter, or year). Every season has a well-defined time frame.
• Trend: A long-term increase or decrease in the data, which might not be linear, is referred to as a trend component. Trends may change directions over time.
• Cyclicality: Data shows rises and falls with no discernible pattern. A cyclical component's average duration is shorter than a cycle's average length. The cyclical component is considered part of the trend. Trend cycle is a term used to collectively describe the trend and periodic elements.

This article has been a guide to Seasonality & its meaning. We explain comparison between additive & multiplicative seasonality & with cyclicality, & examples. You may also take a look at the useful articles below –

• Periodicity Assumption
• Time Series Decomposition
• Empirical Analysis