This module covers the fundamentals of customer segmentation, including the use of k-means clustering and distance metrics to group consumers based on similarities. Learners will gain an understanding of how segmentation supports effective marketing strategies and customer value management.

This module covers the fundamentals of marketing mix modelling, including data analysis, regression techniques, and interpreting media impact. Learners will gain hands-on experience in assessing marketing effectiveness and budget allocation using R. It also explores practical applications and limitations of these models.

This module explores advanced methods for evaluating the impact of digital touchpoints in marketing, including Markov chain models and Shapley values. Learners will gain insight into how to use R for data visualization and model comparison, and understand the challenges of implementing attribution models in real-world scenarios.

This module explores the collection and analysis of user-generated data to uncover customer insights and support marketing strategies. It covers techniques for extracting information from digital sources, including Google Trends and web crawling, and applies these methods to real-world scenarios like airline choice analysis. Learners will gain practical skills in using UGD for strategic decision-making.

This module explores how marketing professionals measure customer mindset through key metrics like brand awareness, consideration, and liking. Learners will gain insights into evaluating marketing effectiveness and applying R for data analysis. The content emphasizes the importance of long-term brand-building strategies.

This module explores the application of text mining techniques in marketing, focusing on sentiment analysis, topic modeling, and how these methods can extract valuable insights from user-generated data. Learners will understand how to apply algorithms like VADER and LDA to analyze customer feedback and improve marketing strategies.

This module provides an in-depth exploration of supervised learning techniques used in predicting customer churn and marketing classification. Learners will gain hands-on understanding of logistic regression, decision trees, and ensemble methods like boosting and bagging. The course emphasizes model evaluation, tuning, and practical applications in real-world marketing scenarios.

This module introduces learners to essential demand forecasting techniques such as ARIMA, Holt-Winters, and exponential smoothing. It covers how to apply these models in R, evaluate forecast accuracy using RMSE, and analyze seasonal patterns in real-world data. Learners will gain practical skills in building and assessing forecasting models for business decision-making.

This module covers the fundamentals of image analytics, including the use of convolutional neural networks (CNNs) for image classification and the application of these models in marketing. Learners will gain insights into how image data can inform marketing strategies and consumer behavior analysis. The module also explores the practical implementation of image analytics techniques in R.

This module explores the practical aspects of managing data analytics projects, including data storage, quality, computational challenges, and the ethical implications of data analysis. Learners will gain insights into how to effectively implement analytical results in real-world marketing scenarios and understand the limitations of predictive models.