Learn who in a marketing organizations needs the skills covered in this course.
- There's an old saying in data architecture, garbage in, garbage out. To ensure the output from our data analysis provides quality insights, we need to ensure quality data. Here are four criteria that should help. Your data must be reliable. If you don't trust the source of the data, find another source. Second, you want your data in raw format. Data with a lot of preprocessed calculations can limit your ability to understand the full picture and can limit your options for analysis. That's not to say you don't want your data source to perform some level of preparation, but be careful that those steps ahead of your data procurement don't limit what you can do with the data.
Third, you want data to be well documented. In your organization, you want to specify that you have the right metadata in place, which is a set of information that describes the data itself. Similarly, we want our data to be well organized. Chances are, you're going to need to perform some amount of data organization once you have the raw data, and that's okay. It should be expected. The more organized it can be kept in the initial collection stage though, the better. A useful acronym to know and to use is ETL.
This stands for extract, transform, and load. It's the process you go through to procure and prepare your data to work with. So, where can you extract your data from? Just about any system you use, be it your CRM platform, your marketing automation platform, your paid search campaign platform. Now, these days, many of these platforms, they're going to offer an API. That's an application programming interface, which can provide you with the programmatic access to that data. That way, much of what you extract can be automated.
Other platforms are lagging behind this API revolution, so just be prepared to account for some manual processes as well. The right process will follow three steps. Step one, define. Define what data you need, how much, and how often. Step two, procure. Determine what data you can obtain that aligns with that definition and put a process in place for procurement. Then step three, store. Establish the necessary infrastructure to redundantly house your data.
Now, in this course, you won't have to worry about this process, because I've already gone through these steps for us, but it's important to know how to tackle this stuff when you're ready.
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