MKT3019 Data Driven Marketing Decisions Semester 2 2023-24R

Java Python Assessment Brief – MKT3019

Module Code:	MKT3019
Module Title:	Data Driven Marketing Decisions
Assessment Type:	Individual Report (Module weightage: 50%)
Semester:	Semester 2
Academic Year:	2023-24
Hand in Date:	Deadline: 4pm on Friday May 10th, 2024.

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. 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 busines MKT3019 Data Driven Marketing Decisions Semester 2 2023-24R s-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 (decision 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 of your predictive analytics models (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 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 argument construction coupled with visualisation will also be important considerations. Appropriate analytics and visualisation software must be utilised to perform. this task. [40%]

2. Digital Marketing: as part of this section, you are expected to develop or identify at least 4 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. [20%]

3. Textual/Sentiment Analysis: as part of this section you are expected to conduct sentiment/textual analysis based on data collected from a competitors social media platform. You must focus on generating insights from consumer brand sentiments that could inform. strategic thinking for New-Ark Shoes Ltd. [10%]

4. Recommendations: as part of this section, you are expected to develop strategic recommendations based on all your previous analyses (in parts 1,2 & 3 above). You must also include recommendations on how the company can improve their data management and analytics strategy and apply Big Data concepts 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.

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