Learn about regression analysis.
- Have you ever noticed how one thing can somehow influence another? In other words, two items or two events can be correlated. For example, ice cream sales go up as the temperature goes up. Remember however that correlation does not imply causation. For example, there's a strong positive correlation between per capita consumption of mozzarella cheese and the number of civil engineering doctorates awarded, but that doesn't mean that cheese consumption affects the affinity for postgrad academia in civil engineering. Regression analysis is a primary statistical technique in understanding the relationship between things.
In our case, those things are marketing output and business outcomes. In this course, we'll focus specifically on linear regression. In linear regression analysis, we assess at least two data points, an independent variable and a dependent variable. Here's a quick definition for each type. A dependent variable is a thing that may be influenced by some other thing. A rainy day often means umbrellas are being used. In this case, the use of the umbrella is the dependent variable. Its use depends on it raining.
An independent variable, on the other hand, is a thing that might influence some other thing. It does the influencing. So in the previous analogy with the rainy day, the rain is the independent variable. Think of it this way, it's going to rain whether folks choose to use an umbrella or not. It's independent. In the marketing world, there's a similar dynamic at play. The more people that experience your marketing, see a television commercial, for example, the more interest and the more engagement there is with your brand. So in this case, the amount of marketing is the independent variable and consumers' attention is the dependent variable.
Now, let's get to work. Imagine that I'm walking through a casino in Vegas with one of my clients. We're on our way to the MAGIC show, which is a biannual event where all the apparel brands go to show their fashions for the upcoming season. So you've got brands from Nike to Quiksilver and they're all there, making deals with their current customers and creating new ones. This is how most clothing makes its way onto the clothing rack in your favorite store. My buddy stops at the roulette table, drops $100 on red, and it's the equivalent of a coin toss. It's 50 50 odds.
The wheel spins, it lands on red, we have a winner. Luck is on our side. And this win has my client feeling bullish and as we continue to make our way to the exhibit hall, we discuss plans for a Times Square takeover campaign. This is a big investment, but it's a competitive business, and the winners have to make big bets to build consumer perception that drives demand. It's our job to make sure those are safe bets as well. Our client in this branded lifestyle space has made similar investments in the past and we have some really good data on hand to analyze, data that will help us to see what sort of response we have achieved using different channels, like broadcast media, out-of-home billboard, for example.
Our assignment is to leverage that data to clarify the impact on those investments. Now, regression analysis is really going to be our friend here. What it allows for us to do is to find those relationships between those marketing outputs and the business outcomes that we need. So, what we're going to do in the next few videos is look at how to model this data. As a result, we'll be able to provide advice to our client to help them make the safe bets to drive returns without having to just get lucky. Let's face it, marketing shouldn't be about gambling.
We have the data to ascertain luck.
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment