MRT297 - Predictive Analytics for Marketing

Outline information
Semester
Schools offering this subject
Last revision date May 25, 2020 1:45:55 AM
Last review date Aug 3, 2020 12:15:12 AM

Subject Title
Predictive Analytics for Marketing

Subject Description
This course provides a comprehensive review of common predictive analytic techniques as they relate to marketing. Students explore techniques presented under three main categories of predictive analytics: time series, regression and machine learning. The examination of these techniques will support making marketing forecasts, performing diagnostics, building predictive models, and using machine-learning techniques to profitably impact the customer journey. The emphasis of this course is on application rather than theoretical foundations of the techniques.

Credit Status
1 credit (3 units)
Required for BMRK – Honours Bachelor of Commerce - Marketing

Learning Outcomes
Upon successful completion of this subject the student will be able to:

  1. Analyze time series data to make forecasts.
  2. Evaluate common measurements of error to assess the adequacy of time series forecasts.
  3. Build linear regression models to predict marketing-related outcomes.
  4. Conduct statistical inference on the parameters of the regression model to evaluate their significance.
  5. Apply model diagnostic procedures to evaluate the validity of linear regression models.
  6. Describe the characteristics of common machine-learning techniques.

Essential Employability Skills
Respond to written, spoken, or visual messages in a manner that ensures effective communication.

Apply a systematic approach to solve problems.

Use a variety of thinking skills to anticipate and solve problems.

Analyze, evaluate, and apply relevant information from a variety of sources.

Show respect for diverse opinions, values, belief systems, and contributions of others.

Interact with others in groups or teams in ways that contribute to effective working relationships and the achievement of goals.

Manage the use of time and other resources to complete projects.

Take responsibility for one's own actions, decisions, and consequences.

Academic Integrity
Seneca upholds a learning community that values academic integrity, honesty, fairness, trust, respect, responsibility and courage. These values enhance Seneca's commitment to deliver high-quality education and teaching excellence, while supporting a positive learning environment. Ensure that you are aware of Seneca's Academic Integrity Policy which can be found at: http://www.senecacollege.ca/about/policies/academic-integrity-policy.html Review section 2 of the policy for details regarding approaches to supporting integrity. Section 2.3 and Appendix B of the policy describe various sanctions that can be applied, if there is suspected academic misconduct (e.g., contract cheating, cheating, falsification, impersonation or plagiarism).

Please visit the Academic Integrity website http://open2.senecac.on.ca/sites/academic-integrity/for-students to understand and learn more about how to prepare and submit work so that it supports academic integrity, and to avoid academic misconduct.

Discrimination/Harassment
All students and employees have the right to study and work in an environment that is free from discrimination and/or harassment. Language or activities that defeat this objective violate the College Policy on Discrimination/Harassment and shall not be tolerated. Information and assistance are available from the Student Conduct Office at student.conduct@senecacollege.ca.

Accommodation for Students with Disabilities
The College will provide reasonable accommodation to students with disabilities in order to promote academic success. If you require accommodation, contact the Counselling and Accessibility Services Office at ext. 22900 to initiate the process for documenting, assessing and implementing your individual accommodation needs.