Personalised Recommendation Engine
Generic product suggestions get ignored. This agent analyses each customer’s purchase history and browsing patterns to surface products they’re genuinely likely to buy — increasing basket size and giving shoppers a reason to come back rather than browse your competitors.
About Personalised Recommendation Engine
The Problem
Most retail recommendation systems are basic: “customers also bought” lists that show the same suggestions to everyone. They miss the nuance of individual shopping patterns — what someone browsed but didn’t buy, which categories they return to, what price range they stick to. The result is irrelevant suggestions that customers learn to ignore, and missed opportunities to increase order value.
How It Works
This agent builds a profile for each customer based on their purchase history, browsing behaviour, wishlist activity, and loyalty data. It identifies patterns — like a customer who buys running shoes every six months, or one who always adds accessories after buying a main item — and surfaces recommendations at the right moment. It works across your website, email campaigns, and in-app experience, adjusting suggestions in real time as customers browse.
Built for Australian Retail
Australian retailers compete on experience as much as price. Personalised recommendations make your store feel like it understands what each customer wants, which builds the kind of loyalty that generic marketplaces struggle to match. The agent plugs into your existing e-commerce platform and CRM without disrupting what’s already working. Talk to our AI consulting team about how recommendation engines fit into your broader customer experience strategy.