Assessment Brief – MKT3019
Module Title: Data Driven Marketing Decisions
Assessment Type: Individual Report (Module weightage: 50%)
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%]
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.
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.
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
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
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.
no business centric knowledge.
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.