Digital Marketing Analytics
Coursera
Businesses today have access to an increasingly large amount of detailed customer data, and this influx of “big data” is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business. This valuable data may include in-store and online customer transactions, customer surveys, web analytics, as well as prices and advertising. You’ll also learn how to assess critical managerial problems, develop relevant hypotheses, analyze data and, most importantly, draw inferences to create convincing narratives which yield actionable results. Artificial intelligence and machine learning will be explored as tools to deepen analytical skills and acumen and hone decision making. This comprehensive exploration into digital marketing analytics tools and techniques is critical knowledge for any marketing influencers, digital marketing analysts and product and brand decision makers within small and medium businesses as well as larger organizations with international reach.
More resources on Marketing Analytics
Analytics Mania
Julius Fedorovicius's tutorial site on Google Tag Manager and GA4, with step-by-step guides on tags, triggers, variables, and dataLayer. Prepares you to implement and debug event tracking on a real site.
Occam's Razor
Avinash Kaushik's long-running blog on digital analytics strategy, covering measurement frameworks, attribution, segmentation, and reporting that drives decisions. Reading it teaches you to pick metrics tied to business outcomes rather than vanity dashboards.
Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment
eBay ran large-scale randomized experiments switching off paid search ads across matched markets. Returns to branded keyword advertising were near zero because those clicks cannibalized free organic traffic, and non-experimental estimates overstated effectiveness by a wide margin. The single most consequential empirical result in paid search, and the reason it survives despite the pre-2022 caution: this is not a tactical guide tied to a UI, it is a causal finding about the economics of search advertising that has not been overturned.
A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook
Compares standard attribution methods against randomized controlled trials using 12 Facebook lift studies covering 435 million user-study observations. Observational approaches, including matching on thousands of behavioral variables, frequently failed to recover experimental results, often overstating advertising effects substantially.
Marketing Analytics
Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. This course, developed at the Darden School of Business at the University of Virginia, gives you the tools to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns. You'll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources. You can follow my posts in Twitter, @rajkumarvenk, and on linkedin: https://www.linkedin.com/in/education-marketing. Thanks, Raj Professor of Business Administration at Darden
Marketing Analytics
Wayne Winston works through marketing problems in Excel: pricing and demand curves, conjoint analysis, customer lifetime value, market segmentation, and forecasting. You finish able to build these models yourself in spreadsheets with the supplied data.