Marketing Analytics

The goal of marketing analytics is to make marketing spending more effective. Organisations spend significant amounts on campaigns and channels, and they need to know which of those investments actually drive results. Without good measurement, it is easy to spend money on channels that appear to perform well but are actually just reaching people who would have converted anyway.

The daily work involves pulling data from marketing platforms and internal systems, building models to measure campaign effects, and presenting findings to marketing and commercial teams. A/B testing is a core tool: you run controlled experiments to measure the causal effect of a campaign or product change. Attribution modelling is used to understand which touchpoints in a customer journey deserve credit for a conversion.

The main tools are Python and SQL for analysis, and platforms like Google Analytics or marketing data warehouses for data collection. Statistical knowledge is important for designing experiments correctly and avoiding common pitfalls like confounding or p-hacking. In e-commerce and digital platforms, the data volumes are large and experiments can be run quickly. In traditional retail, measurement is harder because not all customer behaviour is observable.

The work connects to the E-commerce & Retail and Digital Services & Platforms sectors, and to roles like Data Analyst, Data Scientist, and Market Researcher.