Marketing Data Science

Course details

In the era of data-driven decision-making, Marketing Data Science equips students with the ability to harness the power of data in optimizing marketing campaigns and understanding customer behaviour. This course introduces advanced analytical techniques and their applications in marketing.

Start Date

TBD

Target Audience

Undergraduate students venturing into the world of blockchain, with or without a prior technical background.

Duration

14-15 weeks (28 Classes)

Learning Objectives

Recognize the significance of data in modern marketing.
Learn techniques to analyze and interpret vast amounts of marketing data.
Apply machine learning and predictive analytics in marketing decision-making.
Understand customer segmentation, lifetime value prediction, and churn analysis.

Key Features

Classes led by experts in data science and marketing.
Practical sessions with industry-standard tools like Python, R, and Tableau.
Case studies showcasing the impact of data analytics on marketing strategies.

Learning path

Unit 1: Introduction to Marketing Data Science (4)
Unit 2: Customer Analysis and Segmentation (6)
Unit 3: Sales and Revenue Analytics (5)
Unit 4: Digital Campaign Analytics (5)
Unit 5: Predictive Analytics in Marketing (4)
Unit 6: Advanced Topics (4)

Topics covered

Role of Data in Modern Marketing.
Data Sources in Marketing.
Understanding the Marketing Funnel through Data.
Key Marketing Metrics and KPIs.

Behavioural Analytics.
Cohort Analysis.
Lifetime Value Prediction.
Churn Analysis.
Customer Segmentation using Machine Learning.
Persona Creation through Data.

Sales Forecasting.
Price Optimization.
Promotional Analysis.
Cross-selling and Upselling Analytics.
Path to Purchase Analysis.

Tracking Digital Campaigns.
A/B Testing and Multi-armed Bandit.
Multi-channel Attribution.
ROI Analysis of Digital Campaigns.
Social Media Analytics.

Predicting Customer Behavior.
Market Basket Analysis.
Recommendation Systems.
Predicting Campaign Outcomes.

Text Analytics for Customer Feedback.
Sentiment Analysis.
Visual Analytics in Marketing.
Big Data Tools for Marketing.

Grading:

Class Participation: 10%
Weekly Assignments: 30%
Group Data Analysis Projects: 20%
Mid-Term Examination: 20%
Final Examination: 20%

Prerequisites:

Advanced Business Statistics.
Market Research.
Digital Technologies for Business.

Reading Material:

Leading textbooks on marketing analytics and data science.
Access to online datasets for hands-on practice.
Tutorials and case studies from leading marketing data science professionals.

Assignments:

Real-world marketing data analysis tasks.
Predictive modelling assignments.
Visualization and reporting tasks using advanced tools.

Faculty: A renowned academic with a blend of theoretical and practical experience in blockchain technology.

Teaching Instructors: TBD