Digital Marketing and E-commerce Case Study

Digital Marketing and E-commerce Data Case Study

Course Arrangement

The two central ideas of modern marketing are: “Digital marketing of customer value-centric” and ” Strategic planning of data analysis-based”. Therefore, in addition to using data cases, this course allows students to practice customer value management and digital marketing tools. Also, We plan to cooperate with domestic e-commerce companies, and ask the company to provide front-end (customer behavior data collected from websites, traffic analysis tools and various digital media) and back-end (customer basic data and transaction records) data to cooperate with us through online Tools and web crawlers collect external data from the Internet (text content of social media or review sites, or website traffic, keyword research, competitive analysis data, etc.), and integrate them into a combination of front-end, back-end and external data, and at the same time A huge relational data set with structured and unstructured data, used to do the Capstone Project of the entire digital marketing and e-commerce micro-learning process; imitating the most common situation encountered by the planners of ordinary companies, the Capstone Project will only to provide information without specifying the target of analysis, students need to comprehensively use the analysis tools and methods they have learned during the course of study:

  1.  Use a big data analysis platform to organize structured and unstructured data
  2.  Use exploratory analysis methods and data visualization tools to see the structure and trends hidden in the data
  3.  Ask key questions from the structure and trends,
  4.  Use predictive and diagnostic methods as well as simulation and optimization tools to propose feasible countermeasures to key issues.

 

Course Syllabus

PART-I Digital Media And Online Tool Operation Practice
  • Digital Marketing
  • The Business Implication of Big Data
  • Company WebSite
  • Web Analytics by Google Analytics
  • Keyword Research & Search Engine Optimization
  • Keyword and Display Advertisment
  • Content and Virus Marketing
  • Affiliate Marketing and Celebraties
  • Social Media & Social Marketing
  • Mobile Marketing
  • Integrated Marketing and Communications
PART-II Case Study of Digital marketing

PART-III  Capstone Project of Digital marketing

  • Data Content
    • Front-end data: customer behavior data collected from websites, traffic analysis tools and various digital media
    • Background information: customer basic information and transaction records
    • External data: external data collected from the Internet through online tools and web crawlers, such as the text content of social media or review sites, or website traffic comparison, keyword research, competitive analysis data, etc.
  • Steps of Analytics：
    • Use big data analysis platform to organize structured and unstructured data,
    • Use exploratory analysis methods and data visualization tools to see the structure and trends hidden in the data,
    • Ask key questions from the structure and trends,
    • Use predictive and diagnostic methods to make models,
    • Use simulation and optimization tools to propose feasible countermeasures to key issues,
    • Prepare final presentation

Self-Made Materials

This course does not use textbooks, we will build on past self-made teaching materials, according to the needs of the curriculum re-preparation of teaching materials. The teaching materials include:   

  • Course Website：Integrate self-made teaching materials and online resources to promote mutual learning (HTML)
  • Preview Video：To help students understand online preview content
  • Material：PowerPoint slide of every unit 
  • In-class Note：In-class R-Notebook of every unit
  • Homework Note：Homework R-Notebook of every unit
  • Online Simulate Program：Help students understand more complex concepts in an interactive simulation (R: Shiny)
  • Class Video： It is convenient for students to review or make up course. (YouTube)

Business Analytics Platform from College of Management, NSYSU

This course will use more than 300 million data set. Students can use the big data computing resources in Business Analytics Platform from College of Management, NSYSU via the student account, including:

  • All large data sets will be arranged in the Hadoop network file system in advance
  • Big data can be loaded into the Spark Integrated Analysis Engine to process the data by the data frame interface,
  • Big data can also be loaded into the GreenPlum distributed database and processed by the SQL interface
  • In addition to the data frame and SQL interface, the Integrated Analysis Platform also provides data uploading and exploration functions.
  • Through the platform, you can use a high-end interface (Keras) to use a GPU-equipped deep computing host to create artificial intelligence models
  • When the user logs in for the first time, user can use the above resources through the demo program under the root directory (exercise)
  • Users can upload and analyze corpus data in a semi-automated manner through the Text Analysis platform. Recently, the text platform has stored text data on most pages of the PTT website in the past 10 years. Users can directly use keywords and dates to filter PO Text content and analysis.

Online Course

This course will also cooperate with the “Digital Marketing and Website Data Analysis Online Course Self-study Map” of College of Management, National Sun Yat-Sen University and make full use of the online course content of well-known foreign universities. We take the following online courses (MOOC’s) as references:

We will choose appropriate teaching units, homework exercise or case study as preview materials or cite it in our teaching materials from those online course.

Open Resource Reference

This course will collect the data sets form the following open data websites to edit and manage data cases:

Online Course Certification

We also plan to help students complete the two free online courses provided by Google Analytics Academy and obtain certification through mutual (self) assisted learning:

Other Online Resource

In addition, we also plan to guide students to use the following free trial online services during the course:

Reference

Here are reference of this course：

  1. Chapman, C., & Feit, E. M. (2015). R for marketing research and analytics. Springer.
  2. James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer.
  3. Kaushik, A. (2009) Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex.
  4. Stokes, Rob. (2013). eMarketing: The essential guide to marketing in a digital world. Quirk eMarketing.