The Ultimate Guide to Mastering Seasonality and Boosting Business Results

This post discusses the importance of media mix modeling and how it can be used to maximize the business impact of advertising. It also discusses the impact of seasonality on media advertising and how media mix modeling can be used to minimize the impact of seasonality on business outcomes.

The Ultimate Guide to Mastering Seasonality and Boosting Business Results
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Businesses spend billions annually on advertising to increase their product awareness and interest among consumers leading to more purchases. Targeted advertisements or campaigns are launched to reach a broader audience in order to acquire new customers for their products. Advertisements are broadcasted through multiple media, it can be broadcasted on television, radio, magazines, online, through social media, and even in stores to reach a wide audience. Due to the abundance of options and the imperative to maximize return on investment (ROI), efficient allocation of advertising resources presents a significant challenge. This is where media mix modeling becomes paramount for businesses to optimize their advertisement spending and maximize their RoI. By estimating how much money should be spent on certain media channels to achieve specific goals (such as increased sales or brand awareness), media mix modeling is a useful tool for businesses. This allows them to compare various channels' performance and identify where there are opportunities for improvement, and spend resources sensibly. Data-driven insights allow businesses to fine-tune their marketing approaches, increase the value of their spending, and accelerate the process of reaching their business objectives. Media mix modeling is a statistical analysis technique where one analyzes historical advertisement data including spend, ad impressions or clicks delivered, product sales, new customers acquired, etc. in order to understand the impact of different media channels on business outcomes. This allows businesses to perform their advertisement budget portfolio optimization and increase their RoI.

In addition to the impact of media channels, media mix modeling also takes into account external factors such as if the business ran any promotions, holidays, or any special event which might have impacted the sales. This is done in order to avoid any form of overestimation of the impact of the media channels' performance.

But one of the most critical factors that businesses need to address and incorporate into their media mix models is Seasonality.


What is Seasonality and Why is it Important for Media Advertising


In simple terms, we can define seasonality as a characteristic of time series data in which we can observe predictable and regular behavior that gets repeated annually. Thus, we can say that any behavioral fluctuation that is predictable and recurs every year is a seasonal behavior.

But, how does seasonality differ from cyclical effect? Cyclical effects are the ones that can span over varying time periods. They may last longer or shorter than one year such as boosted sales of water filtration devices in a region due to some fault that might have occurred in the water pipeline. This kind of effect isn’t regular or predictable and might not repeat every year. Whereas, a boost in sales of flu medications due to flu season can be characterized as seasonal since it repeats every year from December to February and can be predicted.

But why is seasonality important for media advertising? Seasonality primarily affects media mix modeling in two ways. Firstly, a change in media consumption patterns, and secondly a shift in advertising effectiveness is observed. As we discussed above how media mix modeling can help marketers understand the impact of various media channels on their sales or other key performance indicators such as new customer acquisitions. The incorporation of seasonality into these models enables advertisers to more accurately reflect the shifts in advertising performance that occur throughout the year. For instance, during the holiday seasons, various media channels may observe increased viewership or higher engagement, which makes them more successful in reaching their respective target audiences. Advertisers are able to maximize the effectiveness of their media allocation plans when they acknowledge and take into account seasonality. They are able to modify their advertising budgets, campaigns, and channel selection in order to align themselves with seasonal trends and the demand of consumers. This guarantees that marketing efforts are concentrated during times when they are most likely to generate maximum impact, hence optimizing the return on advertising investments.


What does Seasonality look like in Time Series Data?


We can incorporate seasonality into media mix models by using seasonal factors or dummy variables to represent specific seasonal events such as holidays. These factors capture the influence of different time periods on media response and help adjust the model's predictions accordingly. These variables capture the impact of different time periods on media response and aid in the model's prediction.

In Python, we have a Statsmodel library known as seasonal_decompose, that can help generate seasonality variables for us. The library splits a time series into three components namely, trend, seasonality, and the residuals. Seasonality can be represented by two kinds of models, either additive or multiplicative. 

For simplicity, let us assume we are dealing with an additive model. An additive model can be used when the variance of the time series doesn't change over different values of the time series. Mathematically we can represent an additive model as follows where the individual components of trend, seasonality, and residual are added together.


The Ultimate Guide to Mastering Seasonality and Boosting Business Results


The Ultimate Guide to Mastering Seasonality and Boosting Business Results
Figure 1: Seasonality decomposition of revenue over 8 months


Trend Factor


The trend component describes the change in the time series that occurs over a longer period of time and is more systematic. It reflects the fundamental increase or decrease in the series and provides an indication of the overall trend as well as the magnitude of the data collected over a prolonged time period. It is helpful in determining the underlying pattern of the data as well as the directionality of the data. In Figure 1, we have the seasonality decomposition of revenue over 8 months, and if we look at the trend we observe that there is a decline in revenue during the summer months of the year. This insight can be crucial for advertisers as they can devise a change in strategy or their spending pattern.


Seasonality Factor


The seasonality factor refers to recurrent patterns that take place over shorter periods of time, often within the span of one year. It is a representation of the frequent oscillations that occur as a result of external influences such as the weather, holidays, or other cultural events. The recurrent peaks and valleys that are characteristic of seasonality are a reflection of the regularities that can be anticipated within the data. In Figure 1 above we can see that there are peaks every alternate month which can help guide businesses to identify some external influences having an impact on revenue.


Residual Factor


The random and unexplained variations that cannot be attributed to the trend or seasonality are represented by the residual component, which is also known as the error or noise component. It takes into account any fluctuations or anomalies that are still present after the trend and seasonality components have been taken into consideration. The fraction of the data that is unpredictable and lacks a systematic pattern is denoted by the residual component.


Challenges faced in seasonality analysis


  1. Multiple seasonalities: In certain time series data one can observe multiple seasonality patterns at a daily, weekly as well as monthly level which is difficult to capture with a simple seasonal decomposition and may require more complex processes.
  2. Data sparsity: If we do not have evenly distributed data over a period of time i.e., if we have infrequently sampled data or very few data points then it might impact the seasonality estimation. Hence, it is recommended to have a daily or weekly level dataset for at least 2 years for better quality seasonality estimation.
  3. Non-stationarity: In case the time series data has a changing variance then will impact seasonality estimation.

    Limited or sparse data points within a particular season can hinder the accurate estimation of seasonal effects, especially when dealing with shorter time series or infrequently sampled data.

  4. Irregularities: Often times we have outliers in data due to some external factors which can distort the seasonality analysis. It is advised to perform data screening prior to performing seasonality analysis such as outlier detection and removal.

Finally, we saw how seasonality influences media mix modeling and drives strategic business decisions. Marketers can optimize their advertising tactics and budget allocation based on swings in consumer behavior and market dynamics throughout the year by including seasonality in media mix models. Understanding seasonal patterns allows firms to target the correct demographic, choose the best media channels, and time their advertising campaigns for maximum impact. Companies may improve the efficiency and efficacy of their advertising activities, increase customer engagement, generate sales, and ultimately improve their return on investments by employing seasonality analysis information. Seasonality enables firms to adjust and tailor their marketing tactics to correspond with shifting consumer demands and preferences, giving them a competitive advantage in a volatile environment.
Mayukh Maitra is a Data Scientist at Walmart working in the media mix modeling space with more than 5 years of industry experience. From building Markov process based outcomes research models for healthcare to performing genetic algorithm based media mix modeling, I've been involved in not only making an impact in the lives of people but also taking businesses to the next level through meaningful insights. Prior to joining Walmart, I've had the opportunity to work as a Data Science Manager in GroupM in the ad tech space, Senior Associate of Decision Science in Axtria working in the domain of health economics and outcomes research, and as a Technology Analyst in ZS Associates. In addition to my professional roles, I’ve been part of jury and technical committee for multiple peer reviewed conferences, have had the opportunity to judge multiple tech awards and hackathons as well.