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MKT3019 Data Driven Marketing Decisions
创建日期：2024-04-11 17:50:15
所属分类：市场营销Marketing
标签： MKT3019
Data Driven Marketing Decisions

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Module Code: MKT3019

Module Title: Data Driven Marketing Decisions
Assessment Type: Individual Report (Module weightage: 50%)
Semester: Semester 2
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 n 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%]
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.
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.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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