Omni-Channel Marketing: How Can We Evaluate Its Impact?

Omni-Channel Marketing: How Can We Evaluate Its Impact?

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.

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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.

time-series-analysis sales marketing business data analysis

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