How Does AI Help with Product Recommendations?

Static product recommendations, whether manually curated best-sellers or rule-based cross-sells, treat all customers the same. A first-time visitor and a loyal repeat customer both see the same homepage. A customer who just bought a running shoe sees the same 'you might also like' as a customer who bought a formal shoe. The recommendation is imprecise and converts poorly. AI personalises recommendations based on what it knows about each visitor. For a new visitor with no history, AI uses contextual signals: what category they browsed first, how long they spent on each product, what search terms brought them to the site, and what similar visitors purchased. For a returning customer, AI adds purchase history, past browsing, loyalty tier, and any expressed preferences to build a richer model. The recommendation surfaces in multiple locations: the homepage, product pages, the basket, the checkout, and post-purchase emails. At each point, AI selects the products most likely to be relevant to this specific customer at this specific moment. The basket recommendation shows genuine complements to what they are already buying. The post-purchase email shows products that customers with the same first purchase typically buy next. The commercial impact is measurable. AI recommendation engines typically increase average order value by 10 to 30 percent and contribute a meaningful share of total ecommerce revenue. For physical retail and services businesses in Cyprus using digital channels, even a basic AI recommendation layer on the website or in email campaigns materially improves conversion. Related: AI personalisation and AI for customer lifetime value.