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Module

MKT3019 : Data Driven Marketing Decisions

  • Offered for Year: 2020/21
  • Module Leader(s): Dr Ronnie Das
  • Owning School: Newcastle University Business School
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
Semester 2 Credit Value: 10
ECTS Credits: 10.0

Aims

This module aims to develop theoretical knowledge and practical skills essential to tackle data driven marketing decisions in traditional and contemporary digital marketing practice. The module will introduce data identification, cleaning and handling techniques in addition to appropriate analysis and visualisation methods key to business and marketing decision making. Teaching delivery will focus on delivering computer based analytics software literacy in addition to conceptual data model development process in solving complex business and marketing problems.

A number of industry standard analytics tools will be used, in addition to problem based case studies, to enhance knowledge and understanding of marketing analytics in real life scenario.

The module aims to equip students with advanced analytical skills that may offer supportive complementary knowledge and skills to Stage 3 capstone modules such as MKT3097 (Marketing Consultancy Project) and MKT3096 (Contemporary Marketing Dissertation).

Guided Independent Study will help students widen their knowledge and understanding of the subject area through a range of learning activities including extended reading, reflection, research, and problem based exercise practice.

Outline Of Syllabus

Indicative topics include:

•       Understanding and framing data driven marketing problems
•       Advanced data analytics for marketing and business decision making
•       Introduction to data types, data wrangling, data storage architecture and data retrieval process
•       Importance of Big Data
•       Descriptive and predictive analytics
•       Data visualisation, reporting and communication techniques
•       Digital Marketing data analytics
•       Social Media data analysis

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Structured Guided LearningLecture materials182:0036:00Lecture Materials (asynchronous)
Guided Independent StudyAssessment preparation and completion130:0030:00N/A
Guided Independent StudyDirected research and reading148:0048:00N/A
Structured Guided LearningStructured research and reading activities181:0018:00On line activities (asynchronous)
Scheduled Learning And Teaching ActivitiesSmall group teaching91:009:00Synchronous
Scheduled Learning And Teaching ActivitiesDrop-in/surgery91:009:00Synchronous
Guided Independent StudyIndependent study150:0050:00N/A
Total200:00
Teaching Rationale And Relationship

Lectures are used to present the underlying theory and concepts related to business and marketing analytics. Taught sessions will focus on teaching important analytical methods in addition to delivering knowledge and skills essential in identifying data types, data wangling process, data importation methods, data analysis and visualisation techniques in relation to specific business and marketing needs.
Practical sessions will allow students to apply theory to real world business and marketing problems using sophisticated analytics tools. Besides theorical knowledge, the analytical skills developed in computer-based workshops, structuring, cleaning, manipulating and analysing raw business/marketing data, will provide valuable knowledge and skills essential to contemporary industry practice.

Guided independent study provides students with the opportunity to consolidate their understanding of material presented in lectures and practical sessions, and to prepare for formative and summative assessments.

Both assessments will test the listed learning outcomes.

There may be a limit to the number of students who can enrol into the module.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report1A502000 words
Report2A502000 words
Formative Assessments
Description Semester When Set Comment
Lab exercise1MFormative assessment and feedback will be ongoing based on case based exercises.
Written exercise1MAssessment briefing and draft review
Lab exercise2MFormative assessment and feedback will be ongoing based on case based exercises.
Written exercise2MAssessment briefing and draft review.
Assessment Rationale And Relationship

The individual report based assessments will help to evaluate achievement of learning outcomes, both, knowledge and skill based. Students will be asked to conceptualise marketing analytics problems based on given case scenario. They should handle, clean, analyse, visualise and report strategic insights from raw data using appropriate analytical tools, while making appropriate recommendations for business development and process improvement.

Semester 1 assessment will focus more on traditional marketing and descriptive analytics.
Semester 2 assessment will focus on advanced analytics problems including digital analytics, social media sentiment analysis, and predictive analytics.

The assessments will provide the opportunity to demonstrate theoretical and technical knowledge in addition to computer based analytics software literacy.

Reading Lists

Timetable