1. Personalized recommendation engine
User needs are complex and understanding them becomes crucial in providing relevant content.
A personalized recommendation engine provides users with individual property recommendations, allowing them to focus on content that matters to them instead of browsing through unrelated offers. This way, relevant content that might otherwise be overlooked, gets to users in a quick and clean manner.
A tailor-made recommendation engine can reach its audience through multiple channels: widgets on the home page, property lists (in combination with user defined filters), property detail pages, ads, and e-mail campaigns.
The Machine Learning Model works by considering the behavior of the active user (like pages visited, contacted properties, favorites lists, attributes of viewed properties), as well as the behavior of other users (to incorporate “hot” listings on the market). Moreover, the model should update every day to account for new property listings and to learn from new user interactions.
Personalized recommendations lead to an increase of up to 60% in viewed properties and up to 80% in leads, compared to traditional, similar listing models.
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Find out how imobiliare.ro successfully implemented this type of product recommendations on their website and the results they achieved using this type of AI technology.
2. User segmentation model
Users can be grouped in many ways, as they pertain to different segments and are at different stages of their journey.