SDST3613 Marketing analytics (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 50
Course Co-ordinator Dr C W Kwan, SCDS (Department of Statistics and Actuarial Science) < cwkwan@hku.hk >
Teachers Involved (Dr C W Kwan,Statistics & Actuarial Science)
Course Objectives This course is designed to provide an overview and practical application of trends, technology and methodology used in the marketing survey process including problem formulation, survey design, data collection and analysis, and report writing.  Special emphasis will be put on statistical techniques particularly for analysing marketing data including market segmentation, market response models, consumer preference analysis and conjoint analysis.  Students will analyse a variety of marketing case studies.
Course Contents & Topics Marketing decision models, Market response models, Survey research, Statistical methods for segmentation, Statistical methods for positioning, Statistical methods for new product design
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 develop hands-on skills of curve fitting and analyzing data with SAS procedures or R packages
CLO 2 understand marketing decision models
CLO 3 understand cluster analysis, factor analysis, multidimensional scaling, correspondence analysis, conjoint analysis, choice models, confirmatory factor analysis, and discriminant analysis in market segmentation, positioning and new product design
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in BIOL2102 or (ECON1280 and any University level 2 course) or (SDST1601 and any University level 2 course) or (SDST1602 and any University level 2 course) or SDST2601 or (SDST1603 and any University level 2 course) or SDST2901
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2024 Major in Statistics < PLO 1,2,3,4,5,6 >
2023 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2023 Major in Statistics < PLO 1,2,3,4,5,6 >
2022 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2022 Major in Statistics < PLO 1,2,3,4,5,6 >
2021 Bachelor of Arts and Sciences in Applied Artificial Intelligence < PLO 2,3,4 >
2021 Major in Statistics < PLO 1,2,3,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    Examination Dec     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, a class test and a group project) 50.0 1,2,3
Examination One 2-hour written examination 50.0 1,2,3
Required/recommended reading
and online materials		 Lattin J., Carroll J.D. and Green P.E.: Analysing multivariate data (Thomson)
Malhotra, Naresh: Marketing Research: An Applied Orientation (Pearson, 2010, 6th ed.)
Johnson R., Wichern D.: Applied Multivariate Statistical Analysis (Prentice Hall, 5th ed.)
Lilien G.L. and Rangaswamy A.: Marketing Engineering (Prentice Hall, 2003, 2nd ed.)
Course Website http://moodle.hku.hk
Additional Course Information NIL