Assessment Brief – MKT3019



Module Code: MKT3019
Module Title: Data Driven Marketing Decisions
Assessment Type: Individual Report (Module weightage: 50%)
Semester: Semester 2
Academic Year: 2021-22
Hand in Date: Deadline: 4pm 18th May 2022 via Canvas

Module Learning Outcomes


Intended Knowledge Outcomes

1. Demonstrate knowledge and understanding of data types, data handling and
analytics methods essential to data driven marketing decisions
2. Use and apply a range of analytics tools and techniques to develop useful strategic
insights critical to marketing and business operations
3. Understand and apply digital analytics tools and techniques in marketing context
4. Critically evaluate and apply theoretical concepts related to marketing and business
analytics


Intended Skill Outcomes

1. Understand and frame data driven marketing problems
2. Identify the nature of data essential to marketing analytics and decision making
process
3. Develop conceptual and practical understanding of data modelling in marketing
analytics
4. Analyse, resolve and communicate complex business and marketing problems using
data analytics and visualisation tools
5. Apply digital analytics methods in resolving digital marketing problems

Assessment Case Brief


The New-Ark Shoes Ltd.1 is an SME, based in Newcastle Upon Tyne, that operates online
by selling, both, locally produced and imported branded shoes. The business has ambitious
growth plans in rivalling some of the high street shoe stores, and appointed you as a Business
and Marketing Analytics Executive to develop an organisational data driven decision making
culture.

The organisation has received your first descriptive analytics report and wants you to develop
more predictive business related insights into the future. Your next assignment is to produce
a 2000 word (+/-10%) comprehensive analytics report addressing the following:


1. Predictive Business Intelligence: as part of this section you are expected to develop
TWO predictive analytics models and generate key analytical insights using these
models. Your designed predictive models must generate important business insights
related to important marketing mixes or business operations or customer insights. You
must identify and discuss validity and error margins of your decision models and its
implications on your findings. You must use Dataset A (and optionally Dataset B) for
this section. You can also include credible external data to your data models and
analysis in order to enhance the robustness of your analysis. However, using Dataset
B or external data is not mandatory in this section. In addition to the quality of data
models, quality of visualisation will also be taken into account. You should use
appropriate analytics and visualisation software to perform the task. Some of the data
modelling techniques you can consider are as below. [40%]

• Regression modelling
• Classification modelling (binary/non-binary)
• Clustering modelling

2. Digital Marketing: as part of this section you are expected to develop or identify at
least 3 key KPIs (descriptive or predictive analytics) that will help the manager
understand web and digital marketing performance of the company. You must use
Dataset B for this section focused on digital analytics. [15%]

3. Textual/Sentiment Analysis: as part of this section you are expected to conduct
sentiment/textual analysis based on data collected from competitor(s’) social media
platforms. You must focus on generating insights from consumer brand sentiments,
engagement matrix, liked and disliked agendas etc. [15%]




1 Please note that a fictional company name was used to develop a business case and it does not exist in real life. Please do not
associate this company with similar names as otherwise quality of your analysis and recommendations will suffer.

4. Recommendations & Application of Big Data: as part of this section you are
expected to develop strategic recommendations based on your previous analyses. You
must also recommend how the company can improve their data management and
analytics strategy and apply Big Data concept in order to improve their business
performance. [20%]

5. Organisation & Presentation: 10% is dedicated towards overall structure,
organisation and presentation of the report. A clear and organised report structure
along with professional presentation standards will determine the level of mark
awarded under this section. [10%]


Total Mark: 100

Due to lack of technical knowledge your manager cannot give you any specific advice on
what type of predictive models to develop and what type of analysis to carry out. He believes
that as an expert you can make that judgement and present data models that will help him
understand the future of the business. You manager has made historic data available to you
as Dataset A & Dataset B and recommends you carry out comprehensive predictive
business analysis in addition to producing sound strategic recommendations.

A 2000-word report must be submitted via Turnitin link provided on Canvas by 4pm 18/05/22.


Formative Feedback: formative feedbacks will be provided to students based on generic
and individual questions during designated assignment support sessions. There will be
dedicated assignment support sessions in addition to synchronous taught sessions.

What is excluded from the wordcount: Cover Page, Executive summary, Content list,
Reference list, Appendices.


Marking Scheme

Criteria
Does not meet
Standards
Meets Standards Exceeds Standards
0-39% 40-49% 50-59% 60-69% 70+%


1. Predictive Business
Intelligence [40%]:
Strategic insights
generated from the BI
dataset, along with use
and application of
innovative data models
and visualisation
techniques.
Rationale for analysis
and recommendations
pertaining to the insights
generated from the data.




A poor standard of
data model
development and
analysis that
appears to be a
cursory attempt
and does not
addresses the
requirement.


Inadequate
standard of data
model
development and
analysis that
appears to be
rather simplistic
and presents very
little decision-
making insights.

Visuals are
elementary.

Adequate
standard of data
model
development and
analysis that
appears to be
acceptable and
presents moderate
decision-making
insights
addressing all four
key areas.

Visuals are
acceptable.

Very good data
model
development and
analysis that
appears to be
appropriate to the
degree level.
Innovative
calculations/visuali
sations were used
to generate
creative decision
making insights
addressing key
areas.


Excellent data model
development and
analysis that presents
visionary analytics and
visually appealing
results. Innovative
calculations were used
to generate
professional level
decision making
insights, addressing
key areas.


2. Digital Marketing
[15%]: Strategic insights
generated from the Web
and Digital Analytics
dataset, along with the
use and application of
innovative KPIs and
visualisation techniques.
pertaining to the insights
generated from the data.


Poor standard of
digital marketing
KPI selection and
analysis that
appears to be a
cursory attempt
and does not
addresses the
requirements.

Inadequate
standard of digital
marketing KPI
selection and
analysis that
appears to be
rather simplistic
and presents very
little decision
making insights.

Adequate
standard of digital
marketing KPI
selection and
analysis that
appears to be
acceptable and
presents
moderate decision
making insights.
Very good
standard of digital
marketing KPI
selection and
analysis that
appears to be
appropriate to the
degree level.
Innovative
calculations/visuali
sations were used
to generate
creative decision
making insights.


Excellent standard of
digital marketing KPI
selection and analysis
that presents visionary
analytics and visual
results. Innovative
calculations were used
to generate
professional level
decision making
insights.
3. Textual/Sentiment
Analysis [15%]:
Strategic insights
generated from the
Textual/Sentiment
Analysis, along with
quality of data collection
and application of
engagement matrices.
Poor standard of
data collected and
analysed.
Principles behind
textual/sentiment
analysis not clearly
understood.
Findings present
no business
centric knowledge.

Weak data
collection and
analysis.
Principles behind
textual/sentiment
analysis not
strategically
understood.
Findings present
little business
centric knowledge.

Moderate level of
data collection and
analysis.
Principles behind
textual/sentiment
analysis
understood to a
moderate level.
Findings present
moderate level of
business centric
knowledge.

Good level of data
collection and
analysis. Principles
behind
textual/sentiment
analysis
understood to a
good level.
Findings present
good level of
business centric
knowledge.


Excellent level of data
collection and analysis.
Principles behind
textual/sentiment
analysis understood to
an expert standard.
Findings present
professional business
centric knowledge.

4. Recommendations
[20%]: Ability to generate
strong strategic
recommendations linked
to analysis of primary
data. Use and
understanding of Big
Data concept in
advancing the business.


Little or no
synthesis of
findings. No clear
strategic
recommendations.
No understanding
of Big Data
Concept.


Weak synthesis of
findings. No clear
strategic
recommendations.
Little
understanding and
application of Big
Data Concept.

Moderate
synthesis of
findings. Strategic
recommendations
have weak
business purpose.
Moderate
understanding
and application of
Big Data Concept.


Good synthesis of
findings. Strategic
recommendations
have strong
business purpose.
Good
understanding and
application of Big
Data Concept.


Excellent synthesis of
findings. Strategic
recommendations
have visionary
business purpose.
Creative understanding
and application of Big
Data Concept.


5. Report structure,
organisation, and
presentation
[10%]:
The structure lacks
coherence. There
are many
grammatical
errors, and the
presentation is
visually
unappealing.
The structure has
some coherence,
with some
grammatical
errors.
Presentation lacks
any
professionalism or
impact.
The structure is
coherent with few
errors.
Presentation is
semi-professional
and has some
impact, but lacks
consistency in
places.
The structure is
very coherent and
easy to follow, with
minimal to no
errors.
Presentation is
professional
throughout with a
high level impact.

The structure is
excellent, report is
developed to an
exemplary standard.
High visual impact with
extensive professional
presentation.