AI in Marketing

Marketing Exercise Sample

The book combines marketing analysis with applied exercises and discussion questions. This Chapter 25 sample asks readers to design an experimentation programme, justify a method, and protect the quality of its evidence and the customer’s interests.

Designing a CX Experimentation Programme

Three to four hours. Individual or small-group submission. Deliverable: a four-to-six-page experimentation memo addressed to a CX marketing director or experimentation lead.

Step 1. Select the organisation and map a customer journey. Choose an organisation with sufficient public information and map a customer journey across its touchpoints, identifying where the journey likely succeeds and fails.

Step 2. Form a hypothesis and apply the Experimentation Method Decision Tree. Form a specific, testable hypothesis about improving the journey, and use the decision tree to choose the method, A/B, bandit, contextual bandit, or agentic, justifying the choice by the question and context.

Step 3. Specify the statistical discipline. For the chosen experiment, specify the sample size and duration you would commit to in advance, how you would avoid peeking, and how you would account for the novelty effect, demonstrating the rigour the chapter insists on.

Step 4. Apply the CX Journey Optimisation Loop. Work the observe-hypothesise-design-test-learn-scale loop for the journey, specifying how AI would augment each stage and how the learning would feed back into the next cycle.

Step 5. Address the optimisation-outcome responsibility. State what the experiment would optimise for, and confirm that the outcome serves the customer's genuine benefit rather than a short-term metric that could lead the optimisation against the customer's interest.

Step 6. Submit. Submit a four-to-six-page memo with at least four referenced public sources. The memo is assessed on the soundness of the method choice, the seriousness of the statistical discipline, and the responsibility of the optimisation outcome, not on the number of experiments proposed.

Discussion questions

The chapter argues CX has been absorbed into experimentation infrastructure. For an organisation you know, is its CX run on experimentation, or on intuition and opinion? What would change if it experimented?

The chapter says confident wrong conclusions are worse than no experiment. Which statistical error, peeking, sample size, or novelty, have you seen produce a false conclusion, and what did it cost?

Using the Experimentation Method Decision Tree, choose a method for a CX question you know. Why does that method fit the question better than the others?

The chapter insists the journey, not the touchpoint, is the unit of optimisation. Give an example where optimising a touchpoint would improve it while degrading the journey.

Agentic personalisation constructs rather than selects experiences. For a use case you know, would the individualisation justify the governance burden, and how would you bound the agent's autonomy?

The Booking.com case attributes its success to culture more than tools. What are the cultural conditions for genuine experimentation, and why are they harder to build than the infrastructure?

The Sustainability Lens argues experimentation can be pointed against the customer's interest. How would you ensure an experimentation programme optimises for the customer's genuine benefit, and who should decide what it optimises for?

Frameworks behind the exercise

The Experimentation Method Decision Tree supports the choice of method. The CX Journey Optimisation Loop supports the observe, hypothesise, design, test, learn and scale cycle. Read both frameworks and their application in the sample chapter.