An AI engine that analyzes individual customer data (browsing history, purchase behavior) to deliver hyper-personalized experiences, product recommendations, and marketing messages.
Media, streaming and e-commerce businesses often show every visitor the same homepage, the same bestseller list and the same weekly email. Customers with very different interests get identical experiences, so relevant content stays buried and engaged users drift away when they cannot find something new. Manual segmentation into a few broad groups helps a little but cannot keep up with thousands of titles or products.
An AI personalisation engine combines a customer data platform with recommendation models to tailor what each person sees. It learns from viewing history, purchases, searches and similar customers to rank products, articles or shows for each individual across the website, app and email. This is a significant build that requires clean, consented customer data and careful measurement, but for businesses with large catalogues and many returning users it directly shapes engagement and retention.
Customer events such as page views, plays, purchases and searches are collected from the website, app and email platform into a customer data platform with a consistent customer identity.
Catalogue data, including categories, descriptions, prices and availability, is cleaned and enriched so the model understands how items relate.
Recommendation models, typically collaborative filtering combined with content-based and popularity signals, are trained to rank items for each user and context.
Rankings are served through an API to website components, app screens and email templates, with business rules for stock, rights, exclusions and promotions.
A/B tests compare personalised experiences with a control group, and models are retrained regularly as behaviour and the catalogue change.
A streaming service's recommendation engine analyzes viewing habits to suggest content tailored to each user, significantly increasing user engagement and retention.
Custom ML Models, Recommendation Engines, Customer Data Platforms (CDP)
Personalisation relies on personal information, so the Privacy Act 1988 and Australian Privacy Principles govern collection, use and disclosure, and your privacy policy should explain profiling in plain language. Direct marketing using this data must respect APP 7 and offer a simple opt-out, and personalised emails and SMS still need consent under the Spam Act 2003. Take extra care with children's data and inferences about sensitive information such as health or religion. Privacy reforms are progressing, including on targeting, so review changes. Host the customer data platform in an Australian cloud region where practical.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Yes. Many e-commerce platforms, email tools and customer data platforms include recommendation features that work with limited setup. A custom engine is worth considering when the catalogue is unusual, rights or stock rules are complex, or you need recommendations across several channels. Many businesses start with built-in features and move to custom models once they understand their data.
Most of the effort is data work: tracking events consistently, unifying customer identities and cleaning the catalogue. Model training and serving follow, then a proper test period to confirm the engine improves outcomes. Rushing the data stage produces recommendations that look random and erode confidence in the project.
Generally yes, provided you collect data lawfully, explain how it is used, use it for purposes customers would reasonably expect and honour opt-outs from direct marketing. Sensitive information has stricter rules. Because privacy law is being reformed, including around targeting, it is worth getting advice on your consent approach before launch.
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