What Are Seasonal Trends?
Seasonal trends are regular patterns of demand that happen at certain times of the year. These up-and-down changes in demand are often linked to holidays, weather, and other yearly events.
For example, there is higher demand for warm winter clothes in the colder months, but higher demand for summer clothes in the warmer months.
Stores plan what products to stock and what sales to have based on these seasonal demand patterns. This helps them sell more and make more profit.
Seasonal trends don't just affect consumer products like clothing. They also impact industries like tourism, farming, and construction.
The tourism industry gets a lot more customers in the summer when people tend to travel more. The construction industry is busier in spring and summer when the weather is nice for buildings.
Understanding when demand goes up and down is important for businesses. It helps them make good decisions about what to produce, hiring employees, and advertising. Knowing the seasonal trends allows companies to operate better.
Why does seasonality affect search results pages?
It's all about the time of the year when certain topics or keywords trend and search results page rank them around the same time.
For example, around the holidays, people search more for gifts, decorations, and recipes. So search engines try to show more results related to those topics.
In the summer, there are more searches for travel, outdoor activities, and events happening that season. The search results will reflect those popular summertime searches.
Search engines want to show people the most relevant information based on when they are searching. So the results can change depending on the season.
However, search engines also look at other factors like your location, what you've searched before, and the specific words used in your search.
All of this helps determine the relevance and ranking of the results that are most useful to show you.
But seasonality is one important reason why you may get different search results for the same topic at different times of the year. The engines are trying to match what people commonly look for during each season.
How to identify seasonal trends?
There are several methods to identify seasonal trends:
Time Series Analysis
One common way is to analyze data over time using time series analysis.
Time series analysis looks at past data month-by-month, quarter-by-quarter, or year-by-year to spot repeating patterns or trends that happen during specific seasons.
Organizations can examine their past sales, website traffic, labour needs, etc. to see when there are upward or downward seasonal changes that occur annually.
Using charts and graphs makes it easier to visually identify these seasonal ups and downs in the data over time.
Modern data analysis tools don't just provide basic line graphs either. They have advanced visualizations that give an even clearer picture of when seasonal fluctuations tend to happen.
Looking at data this way helps organizations not only identify seasonal patterns from the past but also predict when they are likely to occur again in the future.
This predictive ability allows businesses to plan ahead by anticipating periods of higher or lower demand based on the typical seasonal cycles in their data.
This way, systematically analyzing data over long time periods makes it much easier to pinpoint seasonal fluctuation patterns that can then inform business decisions.
Seasonal Decomposition of Time Series
Seasonal decomposition is a way to break down data tracked over time into different pieces or components. This makes it easier to see seasonal patterns.
Essentially, it separates the data into three main parts:
a linear trend component
a cyclic (seasonal) component
residual variations.
This technique is useful when data is impacted by periodic seasonal factors like holidays, weather, production cycles, etc. that cause predictable ups and downs each year.
Visualization
Plotting your data on a graph or chart can help you see seasonal patterns just by looking at the visuals.
Many different types of graphs and visualizations can be used, ranging from basic bar charts to fancy interactive graphs. But they all have the same main goal - to make data easier to understand just by looking at it.
Box Plots
A box plot is a type of graph that shows you how data is distributed or spread out. It can be used to spot seasonal patterns by comparing the distribution across different seasons.
Box plots are a simple way to show the key points of a data set in one diagram.
For each data set, the box plot shows the median value (the middle number), the quartiles (the values that split the data into fourths), and any outlier values that are unusually large or small compared to the rest.
Note: While box plots and visualizations are both used to represent data, they serve different purposes and are used in different contexts. Box plots are best suited for showing the distribution of values in a dataset, while visualizations are best for presenting data in a visually appealing and easily understandable manner.
Autocorrelation
Autocorrelation looks at how closely related a data point is to other data points from earlier time periods.
Specifically for seasonality, it checks if there is a strong relationship between a data value and the values from the same season in previous years.
For example, if this month's sales numbers are very similar to the sales from the same month last year and the year before, that's a sign of a potential seasonal pattern.
Seasonal ARIMA models
ARIMA (Auto Regressive Integrated Moving Average) models are statistical techniques used to forecast trends in data that cycle over time periods.
The "seasonal" version of ARIMA models is designed specifically to account for repeating seasonal patterns in the data, like yearly, monthly, or weekly cycles.
These seasonal ARIMA models analyze the data to identify the seasonal component - the ups and downs that repeat every season or cycle. They then use this seasonal pattern to predict future seasonal fluctuations.
To identify if your important search terms driving website traffic have seasonal trends, a few key things are recommended:
First, check if those search terms historically show repeating spikes or dips during certain seasons or time periods each year.
If they do, this indicates the terms may have seasonal influences affecting their popularity.
Next, make sure you have a way to categorize and monitor those potentially seasonal search terms separately from non-seasonal terms.
Then closely track their performance during the peak and valley seasonal periods you identified.
Using methods like those above can expose if certain high-value search terms follow seasonal cycles. This lets you forecast their popularity and adjust your content strategy accordingly.
Are seasonal trends and seasonal SEO the same?
Seasonal trends refer to how people's interests and what they search for change during different times of the year.
For example, people tend to search more for things like pumpkin recipes in the fall and swimsuits in the summer. These are seasonal trends in what becomes popular.
Seasonal SEO is what businesses do to their websites and online content to get found more easily when those seasonal interests peak.
It means optimizing your site with the right keywords, content, etc. to show up higher in search results during the seasons when demand is higher for your products/services.
So if you sell swimsuits, you would do seasonal SEO in the spring/summer to help your site rank better when everyone starts searching for swimsuits during those months.