A question I’m often asked is, “what should I learn next as a marketer?” And its variation, “what’s next for marketers that I should be studying?”
Let’s assume you’ve mastered the basics, such as:
- Basic marketing strategy
- Analytics and reporting with common tools like Google Analytics
- Principles of digital advertising
- Content creation
- Conversation and community building
- Budgeting and marketing finance
Where should a marketer go next after mastering the basics?
As of early to mid 2017, the unequivocal answer is either Python or R, with a data science mindset.
Why Data Science
The data science mindset – by which I mean becoming a data scientist – is essential to the modern marketer. We must be able to:
- Ask intelligent questions of data
- Define variables and locate supplementary data sets
- Formulate correct hypotheses
- Design statistically valid tests
- Gather and analyze our data well
- Refine a hypothesis
- Create a general theory
This is, unsurprisingly, the scientific method:
However, when people chase the idea of becoming a data scientist, more often than not, they’re learning to simply become a more sophisticated analyst, rather than a full scientist. They spend so much time learning the tools which address only the analysis portion that they neglect the rest of the scientific method – and only the method in its entirety can properly be called data science.
Why is the scientific method important? In order to move beyond mere speculation, we must generate proof of our hypotheses that is measurable, repeatable, and reviewable by others. Any claim we make under the scientific method can be validated by our peers (peer review) and improved upon. Contrast this with how many marketers operate – on guesswork and experience – and it’s easy to see how data science will improve marketing if learned and done properly.
Why Python and R
The reason I recommend either R or Python for marketers to learn as part of becoming a data scientist is these two languages are the current gold standards for data analysis and machine learning. Both have thousands of sample applications, usually open-source, available to jump start a marketer’s programs. With just a bit of search, we can find the answers to most common problems from thousands of forum posts online, helping speed us on our way to working solutions.
If I had to choose, I’d pick R over Python for the average non-technical marketer because R Studio is slightly more user-friendly than the pure command line.
Both require some technical skill, but the curious and driven marketer will pick up R faster and be more productive with it sooner.
Embrace Marketing Data Science!
The value of technology-averse marketers continues to rapidly decline. The marketer who adamantly refuses to use technology, data, and science to inform their marketing programs is an endangered species. The wave of data science sweeping over marketing is an extinction-level event, wiping out resistance to data and “the way we’ve always done things”.
I applaud and encourage the marketers who ask questions like what we began with – what to learn next, where to go, how to grow. These are the traits of the most valuable marketers we will employ in the days to come.
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