Data Modelling

Identifying those patterns and intelligence is important as you can learn and understand what made your customer behave in that way, you can then influence other customers to do the same or put barriers if the behaviour was negative. Models are categorised into four main areas:

• Descriptive Analytics models, which describe what has happened in the past

• Diagnostic Analytics models consider why things are happening and determine a root cause

• Predictive models look to show future outcomes

• Prescriptive analytics models use Machine Learning and simulations to determine an overall goal

Smart Statistics will help you to define the business objectives, find the right strategy and lead you through the implementation. Our team of experts can build statistical models in FastStats, “R”, Python, SQL or SAS. It is important to always ensure that the model is working and retrain if needed. Our consultants will be on hand to oversee the process and adapt the models. Modelling is a tool that helps identify important characteristics within large data to make actionable decisions that can ultimately improve ROI. This can be through:

• Optimising customer journey programs that generate longer tenures and identifying where you could save on cost.

• Enhancing segmentations with varied data brings statistical analysis to back results and decisions.

• Identifying prospects that are likely to be interested in your products and services through profiling.

• Developing targeting models that identify customers who are most suited to cross-sell opportunities.

• Track behavioural patterns that lead to customer churn, enabling you to be proactive in your retention strategy.