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

Author

Affiliation

Dr Wei Miao

UCL School of Management

1 Business Objective

1.1 Background

Recently, M&S has launched its highly anticipated Beauty Advent Calendar for 2024, a curated selection of beauty and skincare products worth over £300. This limited-edition calendar is available to customers for only £50. 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 mailing marketing strategy, where customers receive color-printed leaflets via Royal Mail to their doorsteps. Each mail costs £1.5 to produce and another £0.5 to mail to the customers. 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 £50. The COGS for clothing, homeware, and beauty products is 85%. And the COGS for the advent calendar is 90%.

1.2 Cost-Benefit Analyses

• Cost: Each mail costs £1.5 to produce and another £0.5 to mail to the customers.
• The cost is the marketing offer we send, cost_per_offer

Based on the information provided, calculate the following values:

1. cost_per_offer: the cost of sending an 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

2. 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. And 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 from customers, which is the minimum response rate we need of a customer in order not to lose money from sending the marketing offer¹

• Only if a customer responds to us with at least the break-even response rate can we recover the costs of making an marketing offer.

• If we send offers to customers whose expected response rate is lower than the break-even response rate, we make a loss by expectation.

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 rate on the test set

• Business recommendations

  • Target customers based on predicted response rate

  • 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 (personalized 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 break-even is similar to the break-even quantity we learned in Week 1, the minimum incremental quantity we need to sell in order not to lose any money.