Seasonality

Last Updated :

21 Aug, 2024

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Dheeraj Vaidya

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Seasonality Meaning

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.

Seasonality

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 Explained

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.

Examples

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. 

Additive vs Multiplicative Seasonality 

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.

Key pointsAdditive Seasonality Multiplicative Seasonality 
ConceptAdditive seasonality denotes that the widths or heights of seasonal periods show no changes over time. A multiplicative seasonality shows increasing/decreasing frequency (width) and amplitude (height) of seasonal cycles. 
TrendDenotes a linear (straight-line) trend.Suggests a non-linear trend (curved trend line).
SuitabilitySuitable if the seasonal fluctuations' magnitude does not change with the level of the time series.Suitable if the seasonal fluctuations rise or fall in proportion to changes in the level of the series.
DependencyThe amplitude of the seasonal variation is independent of the time series level.The amplitude of the seasonal variation is connected to multiplicative seasonality.

Seasonality vs Trend vs Cyclicality

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.

Frequently Asked Questions (FAQs)

1. What is the seasonality index?

A seasonality index is a statistical measure that quantifies the fluctuation or variation of a time series data set based on seasonal patterns. It helps identify the impact of different seasons on data points, providing insights into when certain events or trends are likely to occur.

2. What is the importance of seasonality in time series?

Seasonality in time series is crucial for understanding recurring patterns and fluctuations. It aids in predicting when specific events or behaviors are likely to happen, allowing businesses to optimize their strategies, allocate resources effectively, and make informed decisions based on historical data trends.

3. What are the three types of seasonality?

Seasonality manifests at various time scales: daily, weekly, monthly, quarterly, bi-annually, and annually. Daily patterns may reflect consumer behavior, while weekly trends often influence business activities. Monthly seasonality includes billing cycles, and quarterly patterns align with financial reporting. Yearly cycles involve annual events like holidays. These patterns are vital for informed decision-making and resource allocation across industries and sectors.

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 –