Machine Learning (ML)

Machine Learning (ML) explained

In marketing, ML can help organise customer feedback, estimate demand or suggest relevant products. Its value depends on the specific task, the data and how it fits into the working process. A model identifies statistical relationships; its prediction does not automatically explain why a customer acts or which intervention would change that behaviour.

Supervised learning uses examples with known target values or labels. Unsupervised learning looks for structures in data, such as groups of similar cases. Reinforcement learning adjusts strategies for taking actions based on feedback about their results. Generative AI also uses ML and produces content such as text, images or audio. The task determines which method is appropriate.

A useful starting point is a clearly defined decision with a measurable objective. First, establish how well the current approach works. Then prepare suitable data, train models and evaluate them on separate validation and test data. Information that would not be available at the time of a real decision must not inadvertently enter the prediction. Otherwise, measured performance can look better than performance in practice.

Better can mean more relevant recommendations, fewer incorrectly routed enquiries or more reliably identified themes in customer feedback. These quality goals should be assessed alongside the effort involved, including data preparation, integration, operation and review. Comparison with the existing approach shows whether the additional value justifies the effort. It does not establish a universal conversion uplift or cost saving.

At Davies Meyer, AI is part of connecting strategy, creativity and technology. We take responsibility for concept and quality. When evaluating an application, this means defining the objective and quality criteria, reviewing results with subject expertise and making limitations clear. A model score alone does not authorise a customer communication.

Data and behaviour can change during operation. Regular monitoring, checks for systematic disadvantage and a clear process for uncertain results are therefore necessary. When personal data is involved, permitted purposes and legal requirements must be established for the specific use. The following scenarios describe possible applications.