Seasonality

What is seasonality?

Seasonality is a time series characteristic of predictable data changes that repeat annually or seasonally. Understanding seasonality helps inform business decisions. Many companies use time series intelligence software to better understand their insights and trends from time series data.

Types of seasonality

Some examples of seasonal variations and cycles to review in time series data include:

• Time of day
• Daily
• Weekly 
• Monthly 
• Quarterly
• Yearly 

Benefits of tracking seasonality

Businesses must track and understand their seasonality so they can make well-informed decisions. Companies that follow their seasonality may experience the following benefits:

• Better planning and inventory counts: If a business determines that it sells the highest number of products every March and April based on seasonality data, the company can use this information to plan to ensure enough inventory is in stock to meet demand. 

• Informed staffing decisions: During the fourth quarter of the year leading up to the holidays, many retailers in the United States open seasonal positions as they anticipate needing more staff on schedule. This is a great example of how businesses and industries use seasonality data to make hiring decisions that fit the needs of their business without putting themselves at risk. 

• Tailored marketing campaigns: Marketing teams can plan advertising campaigns around seasonal changes to create consumer excitement. Businesses can set themselves apart from the competition by starting their campaigns before competitors and launching exclusive seasonal deals.

• Potential to allure investors: Investors want to understand the seasonal nature of businesses and economic trends. Awareness of business earnings and expected fluctuations provides investors with the valuable information they need to make investment decisions.

Seasonality examples

Many instances of seasonality occur throughout the year. Two examples are described below, along with ways businesses can approach the busy season.

A company that sells sunscreen in the United States will see a spike in the summer months between May and September. The same company will see a significant drop in sales operation and revenue during the winter months. Understanding their time series data allows the sunscreen company to predict how much inventory they need to get through the busy summer season.

A Colorado-based company specializing in winter jackets and apparel predicts a spike in sales starting in October based on its time series data. The peak in demand lasts through March and slows during the summer months. While demand decreases for winter jackets, the company also creates summer outdoor gear and shifts to its large summer inventory from April through September. 

Seasonality vs. cyclicality

Seasonality and cyclicality might seem interchangeable, but there is an important distinguishing factor to acknowledge. Seasonality occurs within one calendar year during fixed and known periods. Cyclicality can span periods shorter or longer than one calendar year and is not necessarily fixed.