Marketing analytics applies causal analysis and AI/ML predictive models to customer behavior, text/sentiment/topic/network analyses to user, firm and AI-generated content, and conjoint analysis to preference, examples address CLV, churn…
University of Illinois Urbana-Champaign · Coursera
Listed level: Intermediate — explicitly published in the course metadata
Marketing analytics applies causal analysis and AI/ML predictive models to customer behavior, text/sentiment/topic/network analyses to user, firm and AI-generated content, and conjoint analysis to preference. Examples address CLV, churn and campaign incrementality. Python demos and a peer-reviewed content-analysis assignment are listed
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Selected requirements in the publisher course metadata: “A basic familiarity with Python is recommended”. Current accounts, software and full prerequisites require the provider page
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Marketing text-mining curriculum applies sentiment, topic modeling, NLP, named-entity recognition, classification, topic clustering and predictive analysis to customer reviews, social posts, feedback and news. Named uses include…
12 modules and 36 assignments. Coursera estimates two weeks at ten hours per week. The public outline does not establish coding or learner-built modelsCheck current priceAsk about teaching language
Python/JSON practicals apply unsupervised text analysis to YikYak sentiment segmentation and Amazon reviews, with TF-IDF/topic models, BERTopic and text networks
Five modules. Coursera estimates one week at ten hours. Two assignmentsCheck current priceAsk about teaching language
Build marketing data foundations, interpret predictive churn/CLV/propensity models, design experiments and incrementality tests, connect scores to lifecycle journeys and govern optimization decisions
9 sections • 54 lectures • 3h 42m total lengthCheck current priceEnglish
