Universidade Nova de Lisboa

Universidade Nova de Lisboa

NOVA Information Management School

Digital Analytics

Code

400082

Academic unit

NOVA Information Management School

Credits

7.5

Teacher in charge

Bruno Filipe Santos Amaral

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Objectives

Today, many businesses are digital or trying to be, turning all the services that are linked to them a source of data of an extreme dimension. With multiple touchpoints in a customer journey, it?s vital for organizations to invest in analytics to understand and even predict the behaviour of users in digital platforms. This information is a source of knowledge that can be a critical factor for the organizations success.

The main objective of this course is the application of quantitative methodologies to the data generated and its integration with other sources of data by websites, web applications, mobile applications and other digital platforms. Furthermore, to explore how these analyses and knowledge can be incorporated in the decision processes to growth revenue and ROI.

Prerequisites

Students shall have a backgound in Marketing, Management, Mathematics or Informatics.

Subject matter

  1. Overview of analytics
    1. The old paradigm of web analytics 1.0
    2. Key change in web analytics 2.0
    3. Digital Analytics in a Era of Digital Transformation
    4. Predictive analytics
    5. Change: yes we can!
  2. The awesome world of clickstream analytics: metrics
    1. Standard metrics revisited: eight critical web metrics:
      1. Visits and visitors
      2. Time on page and time on site
      3. Bounce rate
      4. Exit rate
      5. Conversion rate
      6. Engagement
    2. Web metrics demystified
    3. Going into an omni-channel world
    4. Practical applications
    5. Metrics solutions
  3. Online advertising metrics and Funnel Conversion
    1. Definition of cost models (CPC, CPM, CPA?)
    2. Evolution of pricing models
    3. Advantages and disadvantages of cost models
    4. Practical examples and demonstration of comparative tables
    5. Funnel: from attention to conversion
    6. The role of landing pages
    7. Anatomy of a landing page and importance for conversion
    8. Analysis of the client / campaign situation 
  4. Analytics Framework
    1. Analytics Thinking
    2. Objectives definition
    3. Practical applications
  5. Google Analytics as a day-to-day tool
    1. Introduction to Google Analytics
      1. How Google Analytics works
      2. Navigation in Google Analytics
      3. Metrics revision ? the essential
      4. The main areas of Google Analytics reports
    2. Audience reports
      1. Geographic and language information
      2. Visitor behaviour
      3. Technical reports
      4. Benchmarking
    3. Acquisition reports
      1. Direct, referring and Search traffic
      2. Search Engine Optimization
      3. Campaigns
    4. Content reports
      1. Exit pages and arrival pages
      2. Event tracking and AdSense
      3. Site Search
    5. Account and property configuration
      1. Practical configuration of a Google Analytics account
      2. Accounts, profiles, views and users
      3. Main setup tips
    6. Advanced Segments and Filters
      1. The importance of segmentation
      2. Segments vs. Profiles
    7.  Site objectives
      1. Defining objectives, according to application goals
      2. Conversion funnels
      3. Objectives reports
      4. Dashboards configuration for different management areas
    8. A/B Testing (Experiments)
    9. Reports Personalization: practical applications
    10. Multi-channel Funnels
      1. Direct and Indirect Conversions
      2. Attribution Models
    11. Practical applications
  6. Group project

Bibliography

  • Hunt, Ben (2011) ?Convert!: Designing Web Sites to Increase Traffic and Conversion?. Wiley publishing, inc.
  • Brent Dykes (2011) ?Web Analytics Action Hero: Using Analysis to Gain Insight and Optimize Your Business?. Peachpit
  • Davenport, Thomas H.; Harris, Jeanne G.; Morison, Robert (2010) ?Analytics at Work: Smarter Decisions, Better Results?. Harvard Business School Publishing Corporation
  • Avinash Kaushik (2010) ?Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity?. Wiley publishing, inc.
  • Brian Clifton (2012) ?Advanced Web Metrics with Google Analytics, 3nd Edition?. John Wiley & Sons
  • Alistair Croll and Benjamin Yoskovitz (2013) ?Lean Analytics: Use Data to Build a Better Startup Faster?. O?Reilly
  • Eric Siegel (2016) ?Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die?. Wiley publishing, inc.
  • Anil Maheshwari (2018) ?Data Analytics Made Accessible?. Amazon Digital Services LLC

Teaching method

Theoretical classes for introducing the basic concepts of Digital Analytics.

Presentation and discussion of practical situations.

Practical classes with exercises. 

Development of group project.

Evaluation.

Evaluation method

The evaluation will be based on the class participation and attendance, a group project and also a formal final examination.

The group project must be done in groups of 4 or 5 students. Each project should have the maximum of 20 pages and 5000 words excluded appendix.

The formal final examination will include questions covering all subjects addressed during the term. It will include theoretical questions that represent about 60% and practical ones that represents 40% of the points. To pass a minimum of 9.5 out of 20 points must be obtained in the final exam.

Final grade calculation (both for 1st and 2nd Period):

  • 50% Group Project
  • 50% Exam
  • +5% plus for class participation and attendance

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