AI and Machine Learning for marketing
Our Artificial Intelligence for marketing guide focusing on Machine Learning and predictive analytics
How will this guide to Artificial Intelligence and Machine Learning help me and my business?
It used to be impossible for all but the largest businesses to harness Artificial Intelligence technology to their marketing. Today, now even smaller businesses can apply publicly available algorithms or off the shelf machine learning services to generate useful insights and create prediction models based on their customer’s behaviours.
Today, there are many new AI and Machine Learning services developing which mean that these techniques are now open to every business. The purpose of this guide is to cut through the hype and noise around these powerful technologies and show what you can put in place today to boost your business results. It provides models and strategies to successfully run these projects and gives examples and case studies of how the technology is used in businesses of all sizes, so you can understand how you can use it for your business.
The guide aims to help businesses of all sizes to apply to their marketing, focusing on Artificial Intelligence. All businesses can now use the services we recommend to implement Machine Learning. The guide explains why, how with an actionable and practical approach.
Who is this guide for?
This guide is aimed at all responsible for everyone interested in learning about the techniques and technologies using the power of AI to improving results and reducing costs in marketing including:
- Senior marketers including Heads and Directors of marketing and small business owners
- Marketing and digital marketing managers
- Brand innovation managers
How is the guide structured?
The guide is structured into the following sections:
- Introduction and definitions
- Artificial Intelligence applications for marketing including Chatbots
- Examples of using Machine Learning across the RACE customer lifecycle
- Machine learning for predictive analytics
- The business potential of applying Machine Learning to predictive data analytics
- The fundamental principles of Machine Learning – examples of theory in practice
- Common mistakes when using Machine Learning
- Planning an Artificial Intelligence or Machine Learning project
- Case Study: Propensity modelling of Smart Insights’ leads
- Resources – open source and paid tools and learning materials
- Five new examples of AI being used to improve marketing results from Facebook and email targeting, personalization, and B2B lead qualification
- Deep learning explanation added
- New learning resources and paid SaaS predictive analytics options and open source alternatives from Amazon, Facebook, and Google
- Author: Rob Allen
- Resource format: Online hosted content with mindtools and examples
About the author
Robert was the Editor of Smart Insights between 2015-2017. He managed the blog and you will find blog articles on a range of subjects- Marketing Technology trends and latest tech developments are a regular focus, as well as exploring key marketing concepts. You can get in touch with him on Twitter and connect on LinkedIn.
Dave Chaffey of Smart Insights worked with Robert on the first edition of this guide and updated the 2019 edition.
Dr. Dave Chaffey
Dave is co-founder and content director of Smart Insights. He is editor of the 100+ templates, ebooks and courses in our digital marketing resource library created by our team of 25+ digital marketing experts. Our resources used by our members in more than 100 countries to Plan, Manage and Optimize their digital marketing.
For my full profile, or to connect on LinkedIn or other social networks, see the About Dave Chaffey profile page on Smart Insights. Dave is author of 5 bestselling books on digital marketing including Digital Marketing Excellence and Digital Marketing: Strategy, Implementation and Practice. In 2004 he was recognised by the Chartered Institute of Marketing as one of 50 marketing ‘gurus’ worldwide who have helped shape the future of marketing.