5 Reasons to Use R Programming for Marketing Analytics

The R programming language provides a great toolset to analyze data. I prefer using the R ecosystem to analyze marketing data for the following reasons:

1. All-In-One Tool

R Studio is an integrated development environment (IDE) that has everything you need in one place. The IDE is great because it provides a self-contained environment where you can do all of your analysis in one place. There’s no need to worry about using a bunch of different tools to get your work done.

Packages – easily load and update new libraries:

Environment Variables – easily visualize variables:

Folder Structure – easily access files and folders:

Plotting – easily visualize data:

And much more…

2. Easy to Understand

The tidyverse collection of libraries has made R more programmer-friendly and easy to understand. Tidyverse brings together several libraries to facilitate an analytics workflow, create reproducible work and communicate results.

The dplyr (part of tidyverse) library, in particular, makes R more approachable because it offers a standardized way to execute common coding tasks. The ability to use R piping %>% to string actions together along with the verb-structure of dplyr makes writing and reading code really enjoyable.

Common dplyr functions:

Example: Piping Dplyr Commands:

customer_data %>%
  select(-campaign_code) %>%
  arrange(transaction_amount)

Takes customer data, removes the campaign code and orders the data by the transaction amount.

3. Marketing Libraries

R has countless libraries to use for different problems and has great libraries for marketing. The two libraries I use on a weekly basis are googleAnalyticsR and searchConsoleR, which allow API access to Google Analytics data and Google Search Console data, respectively. Thanks to Mark Edmondson (data engineer and developer), both libraries are kept up to date and simple to use.

Having the ability to export Google data and integrate with other marketing data can be useful for tasks like attribution, segmentation, and channel analysis.

Learn more about Marketing Libraries in R.

4. Reporting

RMarkdown allows you to create reproducible reports in different formats: HTML, PDF, Word, and more. It is easy to learn, can be done right in R Studio, and allows you to publish your work for others to see.

RMarkdown is great for telling data stories and allows you to re-publish analytics changes with the push of a button.

5. Create Marketing Apps

Shiny is a package that makes it easy to build web apps right in R Studio. Shiny enables you to deliver your work for clients or stakeholders to access.

Although Shiny takes some time to learn, it has everything you need to design, program, and integrate analysis into your app.

Example: Customer Data Platform With Shiny

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