We were asked by Innovation Embassy to work with a large dataset centred around gambling investment, with the task of making a predictive function for computing Customer Lifetime Value (CLV), and also to see if there are ways of detecting fraudulent financial practices and addictive gambling patterns. We had moderate success with the data as it stands, but we were partly held back for two main reasons: the ability to discern a solid definition of CLV due to highly inconsistent data and data that contained many large and incomputable gaps. Different machine learning algorithms were used to find CLV functions based on key variables. We also describe a short and explicit list of ways where the base data can be improved to support effective calculation of CLV. Our key findings suggest that the average customer's CLV is 1035 and ~80% of revenue is brought in from ~10% of the clients.

This report addresses anti-social behaviour at green spaces in Wales. Using the data available, this report investigates classifying sites and site users. This classification is used to understand specific anti-social behaviours with agent-based modelling. Regression modelling is also used to calculate a site user’s impact on anti-social behaviour, and the scaling behaviour of associated quantities of interest as the number of site visitors increased was examined. Each area of research provides a different lens to understand what is happening at sites across Wales.