Data for marketing, which has grown in both volume and availability, has become an essential foundation for modern marketing practices. With this growth has come a rise in the development of artificial intelligence capable of analysing and learning from vast quantities of data in order to make predictions about user behaviour.
This guide, an abridged version of Econsultancy’s AI, Machine Learning and Predictive Analytics Best Practice Guide, explores the many potential uses of AI and machine learning (ML) in digital marketing, and explains why good quality data should be the foundation of any data-driven marketing strategy. It covers:
- Origins and definitions: What was the genesis of AI technology, what stage has its development reached and where is it likely to go in future?
- Marketing significance: How are today’s marketers using AI capability to derive meaning from the wealth of available data, and what steps should they take to ensure true value is gained from machine learning and predictive analytics?
- The customer lifecycle: What are the potential applications for AI, ML and predictive analytics throughout the customer journey?
- Determining an approach: How can marketers build a strategy that draws the greatest benefit from AI and ML technology, and what models and principles are available to help choose a course of action?
- Governance: How can companies building machine learning and predictive analytics into their data strategy assess the level of governance and oversight required by the technology?