AI for Data-Driven Marketing Strategy and Operations

This course contains the use of learn artificial intelligence for entrepreneurs.

Most marketing teams are not short of data. They are short of data they trust, models they understand, and tests that prove anything. Dashboards multiply, attribution shifts underneath them, and the budget conversation still comes down to whoever argues best in the room.

This course fixes that chain end to end. You will not get a tour of vendors. You finish holding seven working artefacts, built against a single company so that every piece connects to the next.

What makes this course different

Most AI marketing courses teach you which buttons to press in this quarter’s popular platform. Platforms change constantly; the capability underneath them does not. This course teaches the capability — how to build a data foundation you can trust, produce predictions you can interrogate, run experiments that settle arguments, and move budget on evidence rather than on advocacy.

It is also unusually honest about what does not work. You will see a churn model with an AUC of 0.94 that turns out to be reading the cancellation itself. You will see a channel reporting 12.6x return that measures 0.8x when tested properly. You will see a customer value model that quietly excluded 9% of customers from decent service — designed by nobody, assembled from individually reasonable decisions.

Every technique is applied to Trellis Home Group, a £410M omnichannel home and garden retailer with 34 stores, 2.4 million loyalty members and 180,000 subscribers — plus a very specific problem. Three teams report three different revenue figures, £9.4M of media spend is allocated by a measurement everyone has stopped believing, and nobody can name which subscribers are about to leave.

The five capability areas

• Data management — resolved identities, metric definitions precise enough that three teams produce the same number, and the six quality checks that must pass before any model is worth building
• Predictive analytics — churn, lifetime value, propensity and demand forecasting, with the five questions that let you interrogate any model without a statistics background
• Automated testing — experiment design, significance and peeking, when a multi-armed bandit beats an A/B test and when it quietly destroys your evidence, plus geo holdouts that prove whether media works
• CRM integration — the five hops a score must travel to change what a customer receives, and why most predictive programmes die between hops two and three
• Campaign optimization — attribution, marketing mix modeling, automated bidding and cross-channel allocation, combined into one decision you could defend to a finance director

Two further sections make the system continuous and defensible: an always-on optimization loop with guardrails expressed in money, and governance covering privacy, the EU AI Act, fairness in targeting models, model risk, and reporting that survives board scrutiny.

No coding, no statistics background

You will not write code and you will not see a formula on screen. What you will develop is the ability to specify what a model should do, interrogate what it produced, and decide whether to trust it — which in most marketing organisations is scarcer and more valuable than the ability to fit the model.