Machine learning
Machine learning is a branch of artificial intelligence in which computer systems learn from data to improve their performance on specific tasks over time, rather than following explicitly programmed rules for every situation. In a traditional computer programme, a developer writes specific rules: if condition A, then do B. In machine learning, the system is shown many examples of inputs and desired outputs, and it identifies the patterns that map one to the other. The more examples it sees, the better it gets.
Machine learning in marketing operates across a range of applications: predictive lead scoring (ranking prospects by their likelihood to convert), churn prediction (identifying customers showing early signs of leaving), content recommendations (surfacing the most relevant next piece of content for each visitor), send time optimisation (learning the best time to send to each individual contact), and customer segmentation (discovering natural clusters of behaviour in a contact database that rules-based segmentation would miss).
For B2B marketing teams, machine learning is most accessible through the features built into your existing platforms: the predictive lead scoring in your CRM, the send time optimisation in your email platform, the recommendation engine in your personalisation tool. Understanding what machine learning enables, even without building models from scratch, helps you use these features more intentionally and interpret their outputs more accurately to improve programme performance over time.
What is the difference between machine learning and artificial intelligence?
Artificial intelligence (AI) is the broad field of computer science concerned with building systems that can perform tasks that normally require human intelligence. Machine learning is a subset of AI: a specific approach to building AI systems that learn from data rather than being explicitly programmed. Not all AI is machine learning (some AI systems use rule-based logic), and machine learning is the technology behind many of the predictive and personalisation features found in modern marketing platforms.
How is machine learning used in email marketing?
Machine learning applications in email marketing include: send time optimisation (learning from individual engagement patterns to predict the best time to send to each contact), subject line optimisation (identifying patterns in what generates opens for specific audiences), predictive unsubscribe risk (identifying contacts likely to disengage and triggering re-engagement before they leave), bot click filtering (distinguishing machine-generated clicks from human ones using pattern recognition), and churn prediction (surfacing customers whose behaviour suggests they are at risk of leaving).
Do you need a data science team to use machine learning in marketing?
For most standard marketing applications, no. Many B2B marketing platforms include machine learning capabilities built directly into their features: send time optimisation, predictive lead scoring, and spam filtering are machine learning features that work out of the box without requiring data science expertise. Where custom machine learning models add significant value, such as building proprietary churn models on your specific customer data, a data science resource or a machine learning partner becomes relevant. The build-versus-buy decision depends on the complexity of the use case and the scale of the expected benefit.