Applying the Vector Auto Regressive (VAR) model to isolate and analyse its effects using R. What is the effect of each marketing medium? How do these marketing media interact with each other?
Marketing is not as straightforward as it used to be. In this digital age, where we are exposed to many different channels and media platforms, the typical customer journey does not follow the simple linear route anymore. For marketers, this poses a problem. How do we know which marketing medium is attributable to sales? What is the effect of each marketing medium? How do these marketing media interact with each other? We can answer those questions above by using the Vector Auto Regressive (VAR) model which captures the different inter-dependencies between the different marketing spend modeled in a time-series. An example can be seen as below. The data set used is a time series with 14 attributes and 191 weeks worth of data which covers weekly marketing spend by marketing medium, the metrics according to marketing funnel stages both for online and offline, as well as revenue. However, for this purpose, we will only be using the marketing spend attributes and the revenue. To work with time series and the VAR model, we would need to install the libraries below. library(tseries) library(vars) Then we read the data.
data <- read_excel("dataset_var.xls") kable(head(data))%>% kable_styling() Image for post Image by author First, we set the data as time series objects. Since there are 191 weeks, that would be more than 3.5 years of data. Therefore, I have set the frequency to 52, representing 52 weeks in a year.
Applying the ARIMA model to forecast time series dataThe notion of stationarity of a series is important for applying statistical forecasting models since.
TS may look like a simple data object and easy to deal with, but the reality is that for someone new it can be a daunting task just to prepare the dataset before the actual fun stuff can begin.
I knew I wanted to do two things in the process of writing my bachelor’s thesis: improve my programming skills and work with time-series data prediction.
Learn the Fundamental Rule of Time Series Analysis: Stationarity is an important concept in the field of time series analysis with tremendous influence on how the data is perceived and predicted.
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