In highly competitive and consolidating markets, such as Real Estate Publishing, AI tools will make the difference between leaders and laggards.
AI solutions are evolving continuously, and the field of Real Estate Publishing is no exception. Competition is fierce and players aim to provide a better experience for both users and agents. Making proper use of the available data by leveraging the power of AI tools can significantly increase conversions, improve marketing budget ROI, and provide a better overall user experience.
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.
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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.
Users can be grouped in many ways, as they pertain to different segments and are at different stages of their journey.
Differentiating between users with real intentions to buy a property from the ones browsing out of curiosity can significantly improve marketing efforts and reduce costs. A user segmentation model creates meaningful clusters of similar users by employing Machine Learning Techniques to estimate the probability of conversion. It does so by finding patterns in user behavior (like time on site, type on different pages, page views, and filters used) that lead to conversion.
The clusters can be used to aid different groups at their respective stage of their user journey and substantially improve marketing ROI by focusing on users that are more likely to convert.
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The success of ads rests in finding the right audience and providing the right message at the right moment.
Ads have become an important tool in reaching out to users and driving conversion. Oftentimes, campaigns are driven by intuition-based calls that do not take into account the full potential of data. An ads optimization model extracts pattern information, conveying which keywords are likely to increase impressions and CTR. It can also indicate the perfect timing for launching a campaign, depending on responsiveness patterns and other events that might negatively affect the real estate market (Holidays or other seasonal events).
An ads optimization model can thus significantly increase the performance of marketing campaigns by leveraging valuable information that would otherwise not be fully tapped into.
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