This course is designed to equip you with the skills necessary to effectively utilize marketing data and reports, enabling you to make informed and critical decisions based on that data. The instructor will guide students on a journey of data exploration, beginning with data collection, visualization, and analysis, and concluding with the application of new methods (such as machine learning) and the utilization of diverse data types (including unstructured big data, such as text data) to address various marketing challenges faced by firms.

For more information about this course, please look at the syllabus.

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• Monday, Aug. 25: slides
  • Install R/Rstudio
• Wednesday, Aug. 27:
  • Slides
  • Required readings:
    • Chapter 3 of R for Data Science
    • The Groupon Effect on Yelp Ratings: A Root Cause Analysis
  • Optional readings:
    • Chapter 1 of Data Visualization: A practical introduction
    • Lecture 5 of Data Storytelling for Marketers
  • Code
    • Chart types
    • Beautify plot

• Wednesday, Sept. 3:
  • Slides
  • Required readings:
    • Chapter 7 of R for Data Science
  • Optional readings:
    • Chapters 3, 4, 5 of R for Marketing Analytics
  • Code:
    • Replicate slides' figures: code
    • Simulate marketing dataset: code
    • Variation case: html and R Markdown, data

• Monday & Wednesday, Sept. 8, 10
  • Slides
  • Required readings:
    • Chapter 7 of R for Data Science
  • Optional readings:
    • Chapters 3, 4, 5 of R for Marketing Analytics
• Code:
  • Replicate slides' figures: code
  • RateBeer case: html and R Markdown, dataset, partial solution/helper, full solution

• Monday & Wednesday, Sept. 15, 17
  • Slides OLS
  • Slides Logit
  • Required readings:
    • Chapters 3.4 and 3.6 of R for Marketing Students
  • Optional readings:
    • Lecture 6 of Data Storytelling for Marketers
    • • Chapters 7, 9.2 of R for Marketing Analytics
  • Code:
    • In class Airbnb analysis: OLS, Logit
    • Airbnb exercise: html and .Rmd, solution html and R Markdown

• Monday & Wednesday, Sept 22, 24
  • Slides
    • Clustering
    • Recommender systems
  • Required readings:
    • Chapters 5, 6, and 7 of R for Marketing Students
    • Netflix Billion Dollar Secret
    • Marketing Automation: Recommendation Systems
  • Optional readings:
    • Chapter 11.1–11.3 of R for Marketing Analytics
    • Two decades of recommender systems at Amazon
  • Code:
    • PCA in-class exercise: code and data
    • Clustering in-class exercise: code and data
    • Streaming platforms in-class discussion, solution

• Monday, Sept 29
  • Guest speaker: Jonathan Elliot, Slides
• Wednesday, Oct 1
  • In-class exercise (clustering analysis): html, R Markdown, data

• Monday, Oct 6
  • Work on group project (slides)
• Wednesday, Oct 8
  • No class (Fall recess)

• Mid-term project proposal presentations

• Monday, Oct 20
  • Slides
  • Optional readings:
    • Chapter 11.4–11.6 of R for Marketing Analytics
• Wednesday, Oct 22
  • Exercise: Predicting Click-Through-Rate with Logistic Regression: R Markdown, HTML, click data, R Markdown solution, HTML solution

• Monday, Oct 27
  • Slides
    • Basics of tex analysis
    • Regularized Logit
  • Readings:
    • Chapter 8 of Introduction to R for Data Science
    • Text analysis in R: Word embeddings
    • GMLNET R Package
    • Reputation Systems
    • HBR: How fake Customer Reviews do-and don't-work
    • Detecting Fake Review Buyers Using Network Structure: Direct Evidence from Amazon
  • Optional readings:
    • Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
    • Using Traditional Text Analysis and Large Language Models in Service Failure and Recovery
• Wednesday, Oct. 29
  • Replica of the Harry Potter books analysis from Chapter 8 of Introduction to R for Data Science: R Markdown, HTML
  • Cleaning online review text and computing Tf-Idf by hand: R Markdown, HTML
  • Predict online reviews sentiment using Tf-Idf
  • Comparing a classifier using Tf-Idf vs Word2Vec: R script, Pre-trained W2V model (needed to compute document vectors using W2V)

• Monday, Nov 3
  • Slides, alternative link
  • Readings:
    • Measuring advertising incrementality using Ghost Ads
    • What is the difference between attribution vs. incrementality?
  • Optional readings:
    • Retail Media ROAS Demystified
• Wednesday, Nov 5
  • Guest speaker: Yang Wang, Principal Economist at Amazon

• Monday, Nov 10
  • Slides
  • Readings:
    • HBR: Research: Products Labeled as Sustainable Sell Better
    • The impact of sustainability programs on consumer purchase behavior: Evidence from Amazon
  • Optional readings:
    • Difference-in-Differences
    • Matching methods
• Wednesday, Nov 12
  • Causal inference exercise (selection)
  • Matching

• Monday, Nov 17
  • Guest speaker: Giovanni Marano, Analytics Senior Director at FanDuel
• Wednesday, Nov 19
  • Slides
  • Readings:
    • Study: Generative AI results depend on user prompts as much as models
    • Forget What You Know About Search. Optimize Your Brand for LLMs
    • Using LLM for Market Research
    • Applying Large Language Models to Sponsored Search Advertising

• Monday, Dec 1
  • Presentation order (12 - 1:20 pm):
    • Group 9: Spotify
    • Group 2 Netflix
    • Group 3: Brazilian e-commerce
    • Group 6: Megamart
  • Presentation order (2 - 3:20 pm):
    • Group 5: Spotify
    • Group 2: Fake reviews
    • Group 10: Adidas
    • Group 4: Marvel
    • Group 6: Google Apps
• Wednesday, Dec 3
  • Presentation order (12 - 1:20 pm):
    • Group 7: Spotify
    • Group 5: Customer shopping
    • Group 8: Chewy-Pet Smart
    • Group 10: Housing
  • Presentation order (2 - 3:20 pm):
    • Group 1: Spotify
    • Group 3: Airlines satisfaction
    • Group 7: Amazon
    • Group 9: Interstellar
    • Group 8: Spotify