Gain an Introduction to MarketingLearn how to use Tableau to analyze marketing performance and drive improvement. In this interactive course, you’ll learn the basics of modern marketing, like the difficult challenge of marketing attribution and the unique elements of B2B vs. B2C marketing. You’ll also get hands-on practice with Tableau functions, like FIXED, DATEPARSE, and CONTAINS statements which you’ll frequently use when working with marketing data.
Use Tableau Functions for Marketing AnalysisYou’ll use these functions to conduct a series of analyses, like trending performance over time, and looking at important 2x2 charts, that marketing analysts conduct regularly. You will conduct these analyses across a series of different types of marketing, including ads you see as you surf the internet or emails you find in your inbox. By the end of the course, you’ll work with data from each of these popular marketing methods and gain a deeper understanding of each.
Learn Key Marketing Benchmarks
You’ll learn key questions to drive your analytics, how to benchmark performance using standard metrics like LTV/CAC and engagement rates, and methods to identify areas for improvement.
Web Traffic, Journeys, and AttributionFree
In chapter one, you will gain a thorough understanding of the basics of marketing data and analytics, starting with how data is generated and stored. You will cover marketing channels, journeys, and understand how they are defined. By the end of this chapter, you will be able to restructure data to identify those journeys and assign credit to interactions via different attribution models.Marketing analytics in Tableau50 xpCookie functionality50 xpWeb data example50 xpIdentifiable traffic100 xpCustomer segmentation100 xpUser purchase flag100 xpTouches, journeys, and attribution50 xpAttribution models50 xpData offsetting50 xpPrior touch100 xpNext touch100 xpTouch classification100 xpFirst vs. last touch100 xp
Email and Paid Social Marketing
In chapter two, you will dive deeper into two specific marketing channels: email and paid social marketing. You will cover the strategies behind each channel, typical use cases, and understand the components that make up each type of marketing. By the end of this chapter, you will be able to analyze performance across several metrics unique to each channel and understand how to use data to identify areas for improvement.Marketing funnels50 xpClick-through rate change50 xpCalculated field aggregation50 xpClick-through rate100 xpOpen rate100 xpEmail diagnostic100 xpPaid social and A/B testing50 xpA/B testing50 xpAnalytics operations50 xpClick-back rate100 xpEngagement and purchase rates100 xpCreative100 xpFollower metrics100 xp
Paid Search and Organic
In chapter three, you will build on analytics learned in chapter two to better understand two other heavily-used channels; paid search and organic marketing. You’ll learn how each of these channels works, their similarities and differences, and the importance of web pages and digital strategy. Additionally, you’ll examine relationships between data variables and how to manipulate data (both qualitative and quantitative) to perform analyses. By the end of this chapter, you will be able to analyze performance across various metrics unique to each channel and understand how changes in one channel may impact another.Paid search50 xpPaid search review100 xpString operations50 xpKeyword spend100 xpMobile vs. desktop100 xpClick-through rate100 xpWhat is organic traffic50 xpOrganic search ranking50 xpP-values, R-squared values, and moving averages50 xpEngagement rate100 xpURL group100 xpMetric correlation100 xpChannel correlation100 xp
In chapter four, you will combine what you’ve learned in previous chapters to calculate and analyze LTV, CAC, and their ratio. You’ll learn how to build these metrics, such as average order size, age, and lifespan, from their component parts and examine changes in them. Returning to earlier work on paid marketing, you’ll analyze changes in CAC over time through a unioned data set, gaining exposure to connived data sets in Tableau. You’ll then wrap up the course by calculating LTV/CAC.
DatasetsWorkbooks and Datasources
Mariam IbrahimSee More
MIT graduate student
Mariam is currently a graduate student at MIT, where she researches the use of neural networks to predict quality concerns in metal 3D printing. Prior to graduate school, she worked at IBM for four years where she led the development of a product prediction model, the re-vamp of IBM’s cloud marketing execution, and competed internationally in a marketing data hackathon. Her passions include using data to drive operational outcomes, storytelling, and good chocolate croissants.
Maarten Van den BroeckSee More
Senior Content Developer at DataCamp
Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. He is also a certified Power BI and Tableau data analyst. After his career as a PhD researcher at KU Leuven, he wished that he had discovered DataCamp sooner. He loves to combine education and data science to develop DataCamp courses. In his spare time, he runs a symphonic orchestra.