AI and Machine Learning for marketing briefing
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, every business can use Generative AI services like ChatGPT and Gemini to improve their marketing. Since ChatGPT launched in late 2022 we have created a suite of resources and templates to help businesses make more use of GenAI. These are signposted in our AI for Marketing Learning Path which we recommend you use for our latest practical guidance on AI governance and using AI for copywriting.
In this, our original briefing first produced in 2017, we make recommendations on tools and Machine Learning techniques that you can apply by:
- Applying dedicated AI-enabled tools that use machine learning to improve return-on-investment from existing activities like advertising, email marketing and your website.
- Enabling new machine learning features on existing platforms you use such as Google Ads, your email platforms or content management systems.
- Configuring data-mining tools and predictive analytics tools to develop your own analysis and predictive analytics for CRM and email marketing. This requires in-house skills with knowledge of the principles of machine learning which we explain in the last section of the report.
Who is this guide for?
This guide is aimed at anyone interested or responsible for learning about the techniques and technologies using the power of AI to improve results and reduce 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
Latest updates
- Introduction focuses on the Gartner Hype Cycle to illustrate AI’s many potential applications in marketing
- New examples of AI being used to improve marketing results, including ChatGPT, and examples of applying machine learning and AI across the customer lifecycle
- 10 questions marketers should be asking to generate their own use-cases for applying predictive analytics
Related member resources
Resource Details
- Author: Dr. Dave Chaffey, Smart Insights
- Resource format: Online hosted content with mindtools and examples
About the author
Dr. Dave Chaffey
Dave is co-founder of Smart Insights and creator of the Smart Insights RACE planning framework. For his 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 recognized by the Chartered Institute of Marketing as one of 50 marketing ‘gurus’ worldwide who have helped shape the future of marketing.
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