Marketers, are we at the point that you're at a competitive disadvantage if you're not using R to crunch data?
If your answer is, "What R you talking about?” it’s time to consult with a data analyst. At least that’s the message Eleanor McDonnell Feit and Christopher Chapman are selling.
R is for marketing, they say, because its open-source nature means it is not tied to any specific data source.
Pulling it Together
So all the data from different platforms that marketers usually need to understand — from transactions to online surveys to CRM databases — all integrate well with R, they claim. And they have heady credentials.
Feit is an assistant professor of marketing at Drexel University and a fellow of the Wharton Customer Analytics Initiative. and Chapman is a senior qualitative experience researcher at Google.
Want to do an association rules analysis to understand why customers buy certain products at the same time — like the clichéd diapers and beers? R can do it.
Want to crunch a conjoint analysis to understand how consumers value different product features, like a smartphone’s screen size versus it weight? R can do it.
R is superstrong in predictive modeling and data visualization too, said Feit and Chapman, so imagine the reports you could create for the C-Suite types.
How it Began
For those who like a little context, here's a little history.
R is a programming language conceived by academics Ross Ihaka and Robert Gentleman in 1996, who then, in the true spirit of the Ivory Tower, released it to the world as open source software.
Its primary purpose is data analytics. By 2009, The New York Times was touting its 250,000 users and how it was challenging commercially available software.
Now in 2015, "it's even hotter now than it was then," said Chapman.
It's the standard system taught in colleges and grad schools for doing data science and statistics. Increasingly other software systems, even commercially available products, are integrating with R, or even wielding R for their core statistical models.
And another benefit tied back to its open-source-ness: users and fans have created more than 6,000 "packages" — plug-ins that empower you to do all that data visualization, predictive modeling, conjoint analyses and more.
In her role as an inquisitive academic, Feit has met with small marketing analytics practices.
Though her observations may be far from statistically relevant, she notes how these groups always seem to have one to two people capable in R. One practice leader mentioned to her that she has one young team member who won't use anything but R.
Feit (who we recently talked to about retail customer analytics) testifies to not having touched Microsoft Excel in at least three years.
At its minimum, that is what R can be: a high-powered substitute to a spreadsheet.
Surely, the many corporations using the programming language value as more than that. (Even as far back as the NYT times piece, Google, Pfizer, Merck, Bank of America, the InterContinental Hotels Group and Shell were cited as using it.)
All You Need to Know
To encourage more marketers to use it, Feit and Chapman wrote a book. Titled “R for Marketing Research and Analytics,” the text is meant to help readers overcome the downside of R — its steep learning curve.
"It's not point and click software," Feit said. It's a programming language.
The first half of the book is meant to be a crash course in how to program in R. The second half discusses the advanced applications that can be done with those 6,000 packages, which when combined, Feit said, allows R to outrival any commercial software on the market.
That said, Chapman offers up an analogy as one last sales pitch: There's a place in the North Cascade mountains that he visits whenever he can. It's an all-day trip up. Buses that help travelers make the trip bear signs that say, "The trip up is free, the trip down is costly."
The meaning: It's very hard to leave once you make it to the top. Likewise for R. It's a tough slog to learn. Once you do, you won't want to use anything else, they claim.