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Digital Analytics

Objectives

Today, many businesses are digital or somehow connected, turning all the services linked to them into a source of data of an extreme dimension. With multiple touchpoints in a customer journey and more and more automation and AI involved, organizations need to invest in analytics to understand and even predict the behaviour of users on digital platforms. This information is a source of knowledge that can be critical to the organization’s success.

In a quickly changing and dynamic world, having the knowledge and the tools to transform company businesses is critical for success. This theoretical and practical course will guide students through the essential knowledge and ability to use the tools to apply Digital Analytics in organizations and projects of different types.

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

General characterization

Code

400082

Credits

7.5

Responsible teacher

Daniel Filipe Carepo Cachola

Hours

Weekly - Available soon

Total - Available soon

Teaching language

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

Prerequisites

Degree in Business Management, Information Management, Engineering, Economics or Marketing. 

Bibliography

Altamente recomendado:

  • Anil Maheshwari (2021) “Data Analytics Made Accessible: 2021 edition” (Kindle Edition). Amazon Digital Services LLC
  • Google Analytics Breakthrough: From Zero to Business Impact by Feras Alhlou (Goodreads Author), Shiraz Asif, Eric Fettman
  • George S. Nelson (2018) “The Analytics Lifecycle Toolkit”. John Wiley & Sons, Wiley, and SAS Business collection.
  • Hemann, Chuck, and Ken Burbary. Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World. 2nd ed., Pearson FT Press, 2018.


Outros títulos sugeridos:

  • Mobile App Analytics by Wolfgang Beer (2016), Publisher: O'Reilly Media, Inc.
    Hunt, Ben (2011) “Convert!: Designing Web Sites to Increase Traffic and Conversion”. Wiley publishing, inc.
  • Davenport, Thomas H.; Harris, Jeanne G.; Morison, Robert (2010) “Analytics at Work: Smarter Decisions, Better Results”. Harvard Business School Publishing Corporation
  • 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.
  • Avinash Kaushik (2010) “Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity”. Wiley publishing, inc.
  • Brent Dykes (2011) “Web Analytics Action Hero: Using Analysis to Gain Insight and Optimize Your Business”. Peachpit

Teaching method

Theoretical classes to introduce the main concepts of Digital Analytics.
Presentation and demonstration to introduce key concepts and practical situations.
Practical classes to explore and use Digital Analytics and do exercises.
Development of group project to experience a real world assignment.
Final evaluation to validate the knowledge.

Evaluation method

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

 

The group project must be done in groups of 4 students. Each project should have a maximum of 25 pages and 7000 words excluded the 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 represent 40% of the points. To pass a minimum of 9.5 out of 20 points must be obtained in the final exam and in the group project.

 

Final grade calculation (both for 1st and 2nd Period):
a)    50% group project (minimum 9.5)
b)    50% exam (minimum 9.5)
c)    +5% plus for class engagement

 

Class engagement is measured by a mix of class attendance, measuring the attendance both presential and remote, and an intermediate exercise that will be shared and automatically evaluated on Moodle to be released in the trimester break.

Measurement of class attendance will be done using the Moodle attendance form.

Subject matter

  1. Overview of digital analytics
    • 1.1. The evolution of analytics
    • 1.2. Key changes for Analytical mindset
    • 1.3. Digital Analytics in an Era of Data, Privacy and AI
    • 1.4. Change: yes we can!
    • 1.5. The ESE(P) methodology
  2. The awesome world of clickstream analytics
    • 2.1. Main metrics demystified
    • 2.2. Going deep in the data with Dimensions
    • 2.3. Measuring the omnichannel world
    • 2.4. Comparing metrics on different Analytics tools
    • 2.5. Core Technology Concepts and the Web
    • 2.6. Practical applications
  3. Google Analytics as a day-to-day tool
    • 3.1. Evolution of Google Analytics
    • 3.2. Data dimensions and metrics
    • 3.3. Knowing our users
    • 3.4. Acquisition and channels
    • 3.5. Content analysis
    • 3.7. Conversion measurement
    • 3.8. Measuring main outcomes
    • 3.9. Main property configurations
  4. Strategy & Governance
    • 4.1. Analytical Thinking
    • 4.2. The importance of Goals and KPIs
    • 4.3. Data Governance
    • 4.4. Privacy and First-Party Data
    • 4.5. How to build a measurement strategy?
    • 4.6. Reporting and visualization
  5. Advanced usage of analytics tools
    • 5.1. Data Enrichment
    • 5.2. Advanced segmentation
    • 5.3. Funnel analysis
    • 5.4. E-commerce measurement and analysis
    • 5.5. Data Layer and the use of Tag Managers (GTM)
    • 5.6. Leverage AI and automation to power your Analytics
    • 5.7. Experimentation and power of testing with Data
    • 5.8. Measuring the omnichannel world
    • 5.9. Building great Reposts and Dashboards
  6. Power your Marketing with Data and Analytics
    • 6.1. Measuring Marketing Channels
    • 6.2. Funnels and the Customer Journey: from attention to conversion
    • 6.3. Fuelling Advertising and Segmentation with Data
    • 6.4. Attribution Models
    • 6.5. Building user experiences that achieve goals
    • 6.6. Conversion Rate Optimization (CRO)
    • 6.7. Analysis of the client/campaign situation