Start Your Growth
How To Forecast Cost and Revenue With Models That Account For Seasonality

Brett Tadlock

VP of Customer Engagement

Schedule Free Consultation

revenue forecasting
  • What is Seasonality?

    Companies need to identify seasonality in their business to predict many factors. It includes staffing and inventories as per the related activities for expected seasonality. Hence, to increase future revenue at the same time by reducing the costs.

    Seasonality also affects individual businesses or industries operated only for a small period. It can be a small business for a particular season to generate maximum profits for that specific time. They are also known as seasonal industries, e.g., a brand that only deals with woolen clothes is activated in the winter and tries to make maximum profits.

    The portfolio and profits of an investor are affected by seasonality as a fundamental approach to analyze the stocks. It can help one to understand how much benefits a company is going to make for a product in a particular period. The more sales, the higher will be the gains as a seasonal measure.

    Seasonality accounts for the analysis of data against a particular time series pattern in an economy or business. It can be predictable or regular patterns or fluctuations of data that repeats or recurs in a specific period, i.e., generally one calendar year. It works best in analyzing both economic and stock trends.

    A company can use it as necessary information to make different decisions like staffing and inventories. They are regular cycles that occur as per a particular season for businesses to know about periodic fluctuations. A season can be related to commercial season like festive or holiday season, or it can be a calendar season like rainy, winter or summer.


    4 Different Kinds of Seasonality

    1) Single or Multiple Periods – It is about forecasting for a certain period, says for a year or month, so it comes under a single. Multiple is when more than one-time series factors need to be considered, like both annual and monthly, to predict the behavior of sales data.

    2) Integer or Non-Integer Periods – It is about forecasting data as per a specific time. E.g., weekly data with quarter seasonality repeats after every like 122/7 = 17.42 weeks.

    3) Periodic and Non-Periodic – It is to either predict the behavior in a certain period, e.g., Christmas Day or non-periodic like Black Friday sales.

    4) Multiple and Nested, Multiple but not Nested – It can be both nested and multiple. It is something like analyzing weekly and daily seasonalities of water demand per hour. Otherwise, in case of No nesting but multiple, it would be something like yearly and weekly seasonalities against the water demand of a day.


    How to choose a Seasonality Model for Forecasting?

    It is both time taking and hard for one to detect and analyze mixed types of seasonality. So Data science can help to use a two-step approach.

    1) Use of simple statistical methods– The purpose here is to do preliminary detection. It is a visualization of partial auto-correlation and auto-correlation plots with real-world phenomenon. It can help one to get a better estimate of seasonality. It can be confirmed by using time-series decomposition.

    2) Using modeling outcomes to do Fine-tuning – A basic seasonal model can be one from among four main categories;

    a) Fourier series models – In this model, the seasonalities are identified using the linear combination of sine and cosine terms. A simple model for dealing with non-integer single seasonalities.

    b) ETS or ExponenTial Smoothing Models – It includes Holt-Winterss models. It can be used to capture single integer seasonalities. It can be used to deal with integer multiple and nested type complex methods of seasonalities.

    c) Regression Models that use Dummy Variables – The variables are binary and can help one in detecting the outcome of a particular season (like a specific day of a calendar event or the week). It can well address non-periodic cycles.

    d) SARIMA or Seasonal ARIMA models.


    Bottom Line

    Forecasting the cost and revenue of a business is not such an easy task. It can be performed with seasonality models taken into consideration. It also needs different data science tactics as a part of its complete process.

    If you are also interested in forecasting the price and profits that your business will be going to generate in a specific period, then reach us. Our team of experts has relevant knowledge and experience about how to choose the best seasonality model. The chosen model must work best for your business and can save your time in financial analysis.

    We at NextBee can help you in doing both descriptive and predictive analysis that best suits your requirement. You can take our help to make the best use of data analytics, machine learning, and data mining to identify particular patterns in massive data sets. Also, we can help with data visualization and more business analytics solutions.

    So, we can also help to strategize better the net cost needed. One can plan well on investment to incur both for the services and products sold. The predictions can be used to strategize about other expenditures on efforts and charges to make the sales.

    Let’s connect today!