ULMS Electronic Module Catalogue

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Marketing and Digital Analytics
Code ULMS893
Coordinator Dr M Guenther
Marketing (ULMS)
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ Second Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):


Modules for which this module is a pre-requisite:


Programme(s) (including Year of Study) to which this module is available on a required basis:


Programme(s) (including Year of Study) to which this module is available on an optional basis:


Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 10



Timetable (if known)              
Private Study 125


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Group presentation. There is a resit opportunity. Standard UoL penalty applies for late submission. This is not an anonymous assessment.  15    30       
Individual learning portfolio. There is a resit opportunity. Standard UoL penalty applies for late submission. This is an anonymous assessment.    70       


This module aims to:

Provide students with cutting-edge knowledge of digital analytics by familiarising them with key metrics and analytical methods used to assess customers’/consumers’ response to a firm’s online presence and marketing as well as to obtain customer insights;

Develop students’ knowledge of web, online ad and social media analytics, with a particular focus on the latter. Where appropriate, lectures will also cover cutting-edge research on the topics;

Develop students’ analytical and critical thinking skills by asking students to recognise and solve problems and to demonstrate their commercial awareness overall;

Develop students’ IT skills (Excel, PowerPoint) and, for those who wish to, learn the required coding to conduct the analyses (although codes will be fully supplied). The latter is of particular relevance to students who are attracted to analytics and may seek a role in this growing field upon g raduation;

Embody an approach to learning that is authentic, e.g. through the work with real-life data and hands-on in order to promote skill and knowledge development.

Learning Outcomes

(LO1) Students will be able to demonstrate a critical appraisal of the purpose, meaning, value as well as limitations of the different analytical methods and metrics taught in the module.

(LO2) Students will be able to demonstrate basic knowledge of handling, manipulating and analysing data in relevant analytical software.

(LO3) Students will be able to summarise and evaluate analysis findings as well as support hypotheses/data interpretations through purposeful enquiry and logic argumentation.

(LO4) Students will be able to interpret their findings for company management, considering company context and activities.

(LO5) Students will be able to present analysis findings in an easily comprehensible, concise and professional manner.

(S1) Problem solving
Students will be able to identify potential marketing problems from the data and derive recommendations to solve them.

(S2) Commercial awareness
Students will need to consider the company context/ activities when investigating problems and proposing solutions.

(S3) Adaptability
Real-life data can be messy and may have differing degrees of analytical usefulness, i.e. there may be more implications that can be derived from some data and less from others. Students need to be able to keep an open mind and work with each data set as it comes.

(S4) Numeracy
Students will be able to interpret cost and web traffic data as well as basic statistics, e.g. frequencies, sets.

(S5) IT skills
Students will need to work with Excel, PowerPoint, R Studio as well as the VLE and library resources in this module. The Office applications are used widely in industry and R Studio experience is valuable for analytics roles in particular.

(S6) Communication skills
Students need to demonstrate critical thinking when analysing and evaluating data and show that they can argue their point logically and concisely as well as support it with evidence when writing up their findings for the learning portfolio tasks. When presenting tasks in teams during seminars, students will also need to demonstrate that they can make their point in a convincing way, i.e. same rules as for written argumentation apply, and support it in a visually appealing manner, e.g. PowerPoint.

(S7) Team work
The group presentation will require students to successfully work in a team.

(S8) Leadership
Students will demonstrate their leadership skills in the group work. Moreover, students will lead by providing their own independent data analyses and by deriving hands-on recommendations for real-life business in the learning portfolio tasks.

Teaching and Learning Strategies

2 hour lecture x 5 weeks
2 hour seminar x 5 weeks
1 hour asynchronous learning x 5 weeks
125 hours self-directed learning



For students with a marketing background, this module follows on from modules such as ULMS889 Market Research and ULMS855 Digital Marketing by introducing students to state-of-the-art analyses of customers’/ consumers’ public discourse about a brand/ firm, e.g. identifying topics and the sentiment of the discourse, as well as key metrics. For students without prior marketing knowledge, this module follows on from foundational marketing modules taught on their programme, including ULMS766 Marketing Management and ULMS855 Digital Marketing.

Content will be available mainly through CANVAS, where students will find all lecture and seminar materials including data sets and codes to work with for the seminar and assessment tasks. CANVAS will also include the reading list for this module with required/ optional readings for each week detailed in the syllabus and module handbook. Students will be expected to stay up to date with lecture materials and readings as well as attend seminars and prepare tasks before coming to the seminar. Doing so will facilitate the timely completion of learning portfolio tasks and allow for a sound understanding of the contents covered.

An indicative list of key topics covered is provided below:

Defining digital analytics and understanding its purpose for marketing;

Web analytics;

Online ad analytics;

Introduction to R Studio and social media analytics;

Topic modelling;

Sentiment analysis;

Social network analysis.

Recommended Texts

Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.