The holiday season is upon us and regardless of what holidays you celebrate the season is likely to affect your business. In the US, some e-commerce companies will do more than half of their annual business in the fourth quarter. For companies that work in the ski industry, their entire annual revenue is earned between November and March!
Seasonality refers to the systemic changes in your business based on the time of year. It is something you need to take into account every time you analyze and interpret your metrics, since it can be a hidden driver of changes to your metrics. It is easy to forget that seasonality is a factor when there are much more immediate and apparent causes, such as competitive actions, product updates and advertising shifts, which means it can often be overlooked. However, seasonal shifts are like the tides and can have a huge impact.
Note that the season itself is not why your metrics change, it is because customer behaviors change with the season. This means that the way seasonality affects your business depends on who your customers are and how their behavior changes. Companies that sell to students see their seasonality tied to the school year calendar, while tourism businesses follow the weather.
This week we’ll discuss ways to account for seasonality in your data, and how to make effective predictions taking seasonality into account. Specifically we will cover:
- Part 2 – Identifying Seasonality
- Part 3 – Predicting Seasonality – from history
- Part 4 – Predicting Seasonality – without history
- Part 5 – Dealing with Bad Data
While I won’t be able to tell you what kind of seasonality affects your business, by the end of the week I’m hopeful you’ll be able to figure that out by yourself!
Quote of the Day: “Winter is coming.” – George R.R. Martin, A Game of Thrones