Analytics Concepts Leaders Should Master
Most leaders' day-to-day lives aren't spent deep in the trenches of data analytics. Certainly, there are some who manage analytics programs, but for those who oversee creative, line employees, sales, customer service and other functions, exposure to data and analytics is much more unfamiliar.
With greater emphasis being placed on data-driven decision-making, it's becoming more important that all leaders have some level of grounding in a few analytics basics. In a recent Harvard Business Review article, Amy Gallo outlined four analytic concepts that every manager and leader should understand, which we'll share in this issue of Promotional Consultant Today.
Randomized controlled experiments. This phrase typically makes people think about large clinical trials, but it can be used to reference a variety of corporate uses like deciding if a costlier piece of equipment is more effective than a less expensive one. To brush up on the difference between dependent (a variable whose value depends on something else) and independent variables (a variable whose value is not dependent on something else) and what it means to be a controlled test, Gallo suggests a refresher from data expert Tom Redman.
A/B testing. These are tests that compare two versions of something to determine which performs better. They are typically used to compare things like two different sets of ad copy or two different web designs. Learn more here.
Regression analysis. Once you have the data, there are several ways to analyze it, but linear regression is perhaps one of the most important. This is a way to mathematically analyze data to see if there is a relationship between two different variables. The purpose is to answer questions about which factors matter the most. You don't have to do the math yourself, as there are programs for this, but understanding the process will help guard against misuse of the data, like confusing correlation with causation.
Statistical significance. Like everything in life, specific results could be due to random chance or may have been influenced by external factors. For this reason, it is critical to understand the test for statistical significance, which helps quantify the likelihood that the results observed are meaningful.
Source: Amy Gallo is a contributing editor at Harvard Business Review and the author of the HBR Guide to Dealing with Conflict at Work.