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. 

Analysis Tasks

1- Construct a model to predict customer churn using binary classification trees (C&R Tree) 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 charti.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. 

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.

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