AI personalises product recommendations in emails by analysing customer data to predict which items an individual is most likely to purchase, and then dynamically inserting those suggestions into email content. This is achieved through machine learning algorithms that identify patterns in behaviour and preferences.
As of December 2025, many Australian small-to-medium businesses utilise marketing automation platforms like Klaviyo, ActiveCampaign, and Omnisend, which now include integrated AI recommendation engines. These systems typically work by tracking website activity (products viewed, added to cart), purchase history, email engagement (clicks on previous recommendations), and demographic data – all compliant with Australian Privacy Principles (APPs). The AI then segments customers based on these attributes and predicts future purchases. For example, a customer who frequently views running shoes might receive recommendations for new models or related accessories. These platforms currently offer features like ‘recommended for you’ blocks, personalised product carousels, and dynamic content that changes based on individual customer profiles. In 2026, we anticipate further integration with first-party data collection tools to improve accuracy, and increased use of collaborative filtering techniques. Pricing for these AI features varies, often based on the number of active subscribers and the complexity of the algorithms used.
Ultimately, these AI-powered systems function by continuously learning from customer interactions to deliver increasingly relevant product suggestions within email marketing campaigns.
