Reference no: EM132993541

MIS784 Marketing Analytics - Deakin Business School

Learning Outcome 1: To apply analytics models to a wide range of marketing activities
Learning Outcome 2: Use computer software to analyse consumers' data and understand the strength and limitations of each software
Learning Outcome 3: Analyse and interpret the output of a range of Customer analytics models in
order to improve the decision making process
Learning Outcome 4: Demonstrate comprehensive understanding of Customer analytics models
Learning Outcome 5: Demonstrate ability to communicate the findings in writing in a way
that is useful for both academics and practitioners.

Case Study

Tesco PLC is a British multinational supermarket chain headquartered in Welwyn Garden City, Hertfordshire, England, United Kingdom. It is the third largest retailer in the world measured by profits and second-largest retailer in the world measured by revenues. It has stores in 12 countries across Asia and Europe and is the grocery market leader in the UK, Ireland, Hungary, Malaysia, and Thailand. The company currently offers products in nine different categories including Apparel, Bakery, Deli, Dairy, Fresh Produce, General Merchandise, Grocery, Liquor, and Meat.

Tesco launched its customer loyalty scheme, the Tesco Clubcard, in 1995, with two levels (Silver and Gold). It has been cited as a pivotal development in Tesco's progress towards becoming the UK's largest supermarket chain and one that fundamentally changed the country's supermarket business. Cardholders can collect one Clubcard point for every £1 they spend in a Tesco store, or at Tesco.com. This enables the company to collect data on purchase behaviour of customers and utilize it to design customized offers and conduct targeted retention campaigns.

Data
The data of this assessment task relates to a random sample of 30,000 customers from Tesco Clubcard (20,000 training set & 10,000 test set) in a period from 1 January 2015 to 31 December 2015. The 18 variables in the data table are described below:

Analysis Tasks
1- Construct a model to predict customer churn using logistic regression and evaluate the performance of the constructed model on the holdout sample provided (use metrics related to confusion matrix).
2- Evaluate the performance of the constructed model against the RFM method (use lift chart-
i.e. concentration to make the comparison).
Report Tasks
1- Introduction and problem definition
2- Literature review: Use only academic journal articles. APA style should be used for referencing.
3- Methodology and empirical study: this should include a discussion of your analytical techniques, your model evaluation metrics, your working data, and your model building process.
4- Results: evaluate your analysis results to explain how your constructed models perform and also how they are positioned against a base model (random guessing).
5- Conclusion and Recommendations: This should consider the implications of your results and how they may reduce marketing expenditure and contribute to customer retention.

Attachment:- Marketing Analytics.rar