Class 10 (Case Study) Customer Targeting Using Supervised Learning for M&S

Author
Affiliation

Dr Wei Miao

UCL School of Management

Published

October 29, 2025

1 Business Objective

1.1 Background

Recently, M&S has launched its highly anticipated Beauty Advent Calendar for 2025, a curated selection of beauty and skincare products worth £330. This limited-edition calendar is available to customers for only £60 when a customer purchases it alongside other products. With the holiday season approaching, M&S wants to maximize the reach and response of its marketing campaign by promoting the advent calendar offer to the right customers.

M&S decides to use a conventional direct mail marketing strategy, where customers receive colour-printed leaflets via Royal Mail to their doorsteps. Each mailer costs £1.5 to produce and another £0.5 to mail to the customers. M&S also plans to include a small sample of the beauty product, which costs M&S £2 to produce. If a customer responds to the offer, M&S expects them to spend £35 on full-price clothing, homeware, or beauty, and purchase the advent calendar at £60. The COGS for clothing, homeware, and beauty products is 85%. The COGS for the advent calendar is 90%.

1.2 Cost-Benefit Analyses

  • Cost: Each mailer costs £1.5 to produce and another £0.5 to mail to the customers. M&S also plans to include a small sample of the beauty product, which costs M&S £2 to produce.
  • The cost is the expense of sending each marketing offer, cost_per_offer

Based on the information provided, calculate the following values:

  1. cost_per_offer: the cost of sending a marketing offer

  • Benefit: If customer responds to the offer, the management expects customers to buy our products and generate profits for M&S.

  • The benefit is the profit margin if a customer responds, profit_per_customer

  1. profit_per_customer: the profit from a customer if a customer responds to the marketing offer

1.3 ROI for Blanket Marketing

  • Blanket marketing: Send marketing offers to all 2000 customers. Compute the ROI for blanket marketing.

  • We already know the cost of sending an offer is cost_per_offer. We can calculate the total marketing costs by multiplying the cost per offer by the number of customers in the dataset.

  • Based on the Response variable in the dataset, calculate the total number of customers who responded to the marketing offer. Then calculate the total profit from the marketing campaign.

    • Tip: you can use data_full$Response to extract the Response variable as a vector in the dataset. Based on this vector, you can calculate the total number of responding customers and the total profit from the marketing campaign.

2 Targeting Using Supervised Learning

2.1 Break-Even Analysis: Break-Even Response Rate

  • In order to break even, we can calculate the break-even response rate, which is the minimum response rate we need from a customer in order to avoid losing money from sending the marketing offer1

  • Only if a customer’s expected response probability is at least the break-even response rate can we recover the costs of sending a marketing offer.

  • If we send offers to customers whose expected response rate is lower than the break-even response rate, we will lose money on average.

2.2 Workflow Using Supervised Learning

  • Data collection and cleaning

    • Send marketing offers to a random sample of customers and collect their responses (done by M&S)

    • Split the data into a training set and a test set

  • Data analytics

    • Train predictive models on the training set

    • Predict customer response probability on the test set

  • Business recommendations

    • Target customers based on predicted response probability

    • Compute and compare ROIs for each targeting method: (1) blanket marketing, (2) decision tree, (3) random forest

Let’s work on the Quarto document together!

3 Improve Marketing Efficiency Using Supervised Learning

3.1 Customer Life Cycle

  • Acquisition

    • Use predictive analytics to target responsive customers to reduce marketing costs

  • Development

    • Use predictive analytics to recommend products to customers (personalised recommendation system); for each customer, promote the item with the highest purchase probability

  • Retention

    • Use predictive analytics to find valuable customers who are likely to churn and conduct targeted churn management

Footnotes

  1. The idea of break-even is similar to the break-even quantity we learnt in Week 1, the minimum incremental quantity we need to sell in order not to lose any money.↩︎