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Text Marketing Analytics

University of Colorado Boulder via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Beginner

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

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This three-course sequence develops applied marketing-text analytics skills across supervised classification, unsupervised topic and network analysis, and auditable large-language-model classification. Learners move from defensible human labels and regularized classifiers to topic models and text-derived networks, then to strict LLM label contracts, batch inference, fine-tuning decisions, and class-level evaluation.

Syllabus

Course 1: Supervised Text Classification for Marketing Analytics
Course 2: Unsupervised Text Classification for Marketing Analytics
Course 3: Network Analysis for Marketing Analytics

Courses

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Build reliable supervised classifiers from marketing text and defensible human labels. Learners create coding rules, reconcile coders into gold-standard labels, transform text into predictive features, train a regularized elastic-net model, separate training from validation evidence, and use errors and learning curves to judge model quality and the value of collecting more labeled data.
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Discover patterns in marketing text without predefined outcome labels by combining topic modeling with text-derived network analysis. Learners build and inspect TF-IDF representations, select topic counts and membership assumptions, evaluate and interpret topic solutions, and construct directed or weighted networks from word co-occurrences and user mentions to identify central and bridging structures.
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Use generative AI and large language models as auditable classifiers for marketing text. Learners connect language-model training and contextual embeddings to classification, design strict prompts and machine-readable label contracts, validate outputs before batch inference, compare prompting with fine-tuning, and evaluate results against audited gold-standard labels using accuracy, macro F1, and class-level errors.