This chapter positions the book at the intersection of data science and marketing and makes the case for text analytics as a core capability for modern marketers. It explains how user-generated content—reviews, social posts, chats, and surveys—forms a vast stream of unstructured text that, with the right tools, reveals sentiment, preferences, and emerging trends. Readers get oriented to the R/RStudio workflow and key packages (tidyverse, ggplot2, magrittr), including practical guidance on installing and maintaining packages and why piping simplifies analysis. The chapter introduces the companion dataset (Amazon tablet reviews) and a support website with code, data, and exercises. It also outlines the book’s path: preprocessing and regex basics; obtaining data via files, scraping, and APIs; text representation (Bag-of-Words, tf-idf) and visualization; clustering and classification; topic modeling; sentiment analysis; named entity recognition and summarization; and advanced techniques like word embeddings and transformer models. Throughout, the emphasis is hands-on, marketing-focused, and accessible to beginners while remaining useful to practitioners.