Artificial intelligence. Machine Learning. Predictive Modelling. These terms have a good deal of overlap and can mean different things to different people.
Whatever your definition, data scientists employ these techniques to solve complex data problems. We use them not just because we can, but when they're the right tools for the job. Our goal is always to extract valuable insight from data in the simplest, most cost effective manner possible.
Predictive modelling is the process of identifying patterns in data that allow us to calculate the probability of potential future events occurring.
By applying predictive algorithms to past outcome data we can predict future outcomes. This lets our clients answer questions such as:
- Which customers are likely to leave or cancel their service?
- Which customers will respond to particular campaigns or offers, via which channel(s)?
- Which customers will be our most valuable in the future?
- Which prospects am I most likely to convert to customers?
These models enable our clients to take a proactive approach to their marketing. For example, our models can help you retain customers by predicting the most effective time to start retention marketing. We can help you take a data-driven approach to selecting the lowest-cost marketing channel mix that will result in a conversion. We can help you focus acquisition marketing on the prospects with the highest likelihood of becoming a customer. And much more.
We use machine learning models in a number of areas including customer decisioning and real-time personalisation. For more information, see Next Best Action & Decisioning.
