MIS784 Marketing Analytics
MIS784 Marketing Analytics
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.
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:
|ID||Unique ID of customers|
|Purchase||Number of purchases during the observation period1|
|The time gap between customer’s first purchase and last purchase during the|
|The time gap between customer’s first purchase and last day of the|
|Loyalty||A binary variable to show membership level: (0) Silver (1) Gold|
|Service Failure||Number of service failures during the observation period|
|Total Profit||Total profit generate by the customer during the observation period|
|AP.spent||Total spending on Apparel category during the observation period|
|BH.spent||Total spending on Bakery category during the observation period|
|DL.spent||Total spending on Deli category during the observation period|
|DY.spent||Total spending on Dairy category during the observation period|
|FV.spent||Total spending on Fresh Produce category during the observation period|
|Total spending on General Merchandise category during the observation|
|GR.spent||Total spending on Grocery category during the observation period|
|LQ.spent||Total spending on Liquor category during the observation period|
|MT.spent||Total spending on Meat category during the observation period|
|Socio Economic status of the customer on a scale from 1(lowest) to 10|
|A binary variable to show the churn status of the customer in the prediction|
|Churn||period2 (0) non-churner (1) churner|
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 chart-i.e. concentration to make the comparison).
1- Introduction and problem definition
2- Literature review: Use only academic journal articles.
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 the constructed models perform and also how they are positioned against a random model (random guessing).
5- Conclusion and Recommendations: This should consider the implications of your results and how they may contribute to customer retention and reduce marketing expenditure.
Do you need high-quality Custom Essay Writing Services?