Whether you loved your math, statistics and data science classes in college or dreaded them, their growing usefulness in everyday marketing is undeniable. All of us marketers could benefit from upping our data-driven marketing prowess or risk becoming irrelevant. Because everywhere we look, information is being captured, quantified, and used to make business decisions.
And doing it well makes a difference.
According to HBR’s Andrew McAfee and Erik Brynjolfsson back in 2012, “Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”
More recently, in The Rise of the New Marketing Organization Forbes Insights reports that “68% of marketing leaders (defined as those who always use analytics for making and evaluating decisions) say it’s given them competitive advantage when it comes to increasing revenue.”
No, you don’t have to run back to school for a data science degree. But you should familiarize yourself with the basics of introductory statistics and quantitative analysis. When we find them, we’ll point out our favorite resources.
Here’s a great article that uses “plain English” to cover the basics of analytics and statistical methods. It also provides a nice list of resources for deeper dives into the more complex subjects. HBR’s An Introduction to Data-Driven Decisions for Managers Who Don’t Like Math is a great primer for getting started. It outlines:
• why data matters (see quotes above).
• the importance of picking the right metrics (see Beckon’s Guide to Integrated Marketing Analytics—The Metrics that Matter).
• the difference between analytics and experiments.
• vetting analytical conclusions by asking questions of your data (what was the source? what assumptions were made? what variables were not considered?)
• the differences between cause and effect and correlation (“correlation is not causation”).
• the basics of data visualization (“don’t forget to tell a good story”).
• how to get a passing understanding of introductory statistics (“get your hands dirty with the data”).