Homsters, a leading provider of SaaS solutions for classifieds and property portals launched a new product, which rapidly increases the number of leads a portal can create for property developers.
The recently launched plug’n’play recommendation engine drives users to the new homes section by presenting a pre-selected set of new-homes which matches each user’s behavioral patterns. The engine called “Homsters Home Match” is easy to install and free to try.
“We noticed that most classifieds, who have created a new-home section face a major issue: even though they have a lot of traffic, they struggle to drive users to the developer landing pages," Said Andrew Olejnik, Co-founder and CEO, Homsters.
Adding, that this forces the classifieds to turn to search engines like Google, or other paid sources to deliver the necessary performance they promised to the property developers. "That’s extremely expensive and hardly sustainable. Which is why we created Home Match: an easy to install Ai-driven recommendation engine which drives more users to new homes, while providing a great user experience,” added Olejnik.
The engine is a free-to-try plug’n’play solution, easily installed on a property portal or on an online classified, Homsters Home Match starts monitoring the behavior of each user to search for new-developments matching its behavioral patterns.
If a match is found, the user is presented with a very relevant, personalized new-home offer in a form of a banner or a pop-up. The engine later uses machine learning to train itself and further improve the relevancy of the offers. The results can be easily tracked via an online dashboard.
“Our engine does the matching in the background and presents the user with a highly relevant offer, without them even doing a search. As a result we observe not only a massive spike in the number of users visiting the new-home section but also these users convert over 20% better than users acquired through SEO,” concluded Dmitriy Aleksandrov, Head of Product Development, Homsters.
SOURCE Homsters
Edited by Gordana Davila
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