Stanford Continuing Studies course "Data-Driven Marketing" by Angel Evan — Customer Insights and Analytics Consultant, January 16 - March 2, 2018 (7 weeks)

BUS 139 is a course for those interested in developing a set of foundational skills in the use of marketing-related data. It is expressly designed for students without math,quantitative or statistical backgrounds and requires no expensive third-party software of hardware—only a Mac or PC version of Microsoft Excel 2013 or later. Each session deals with a core data concept and is supplemented with “how-to” videos and exercises on a variety of tasks marketers commonly perform.

Tableau Public is an incredibly powerful business intelligence tool used extensively in analytics. In fact, it's what Angel Evan uses at his agency.

Week 1: Introduction and Goal Setting

• Cursory overview of the class and its goals
• Examining the delta between the promise of data and marketers’ ability to act
• Overview of various marketing data types and how they differ
• Three simple rules for dealing with data
• Qualitative vs. quantitative data, including when to use each
• Examples of how data can be used to make better business decisions
• Setting Goals – determining what a real goal is and what’s important to track

Week 2: Collection and Preparation

• The four inputs – a review of the various sources of data and how they can be gathered
• Determining the best sources of data – which data is trustworthy
• Customer attributes – determining which truly matter to your business, e.g.,demographics, psychographics, purchasing behavior
• What to do if your data isn’t perfect, e.g., dealing with missing values and how to deal with noisy data

Week 3: Analysis and Interpretation

• Using summary statistics to create immediate insights
• Creating simple formulas that lead to big insights
• Seeing through the lies in data
• Identifying trends, e.g., seasonal trends and customer lifecycle trends
• Segmentation and correlation (positive and negative)
• Sorting, ranking, binning, and filtering
• Using visualization techniques to improve understanding
• Co-mingling data from different sources, e.g., website and social media

Week 4: Decision

• Using data to measure the success of marketing outcomes
• The anatomy of a marketing strategy
• Making data ladder up to real business objectives
• Separating decisions from outcomes
• Placing bets – determining which marketing tactics and channels to invest in

Week 5: Visualization

• Overview of visualization basics
• Examples of good and bad data visualization
• Determining the best format for visualizing your information
• Seven basic types of charts
• Charts vs. infographics vs. data visualization
• Deciding which patterns are worth highlighting and what to emphasize

Week 6: Presentation

• Determining what story you want your data to tell and how best to bring it to life
• The power of narrative
• Three types of presentations for delivering a forceful argument

Week 7: Prediction and Forecasting

• Overview of various predictive analytics and forecasting methodologies
• Forecasting vs. predicting
• Linear vs. logistic regression
• Introduction to basic Artificial Intelligence (AI) models and machine learning
• The basics of predictive algorithms and how they can be used in marketing