Predictive Match

Real-time Listing Recommendations


PredictiveMatch’s SaaS offers an easy to implement, low cost and low risk way for online property portals to provide real time predictive recommendations that lead to higher engagement, increased listing discovery and leads to agents.

Behavioural based recommendations are common place in e-commerce, music, movies, content, books and have been proven to increase conversions…So why not harness the behaviours of online property portal site users to make predictive listing recommendations making it easier to find that perfect property?

Only PredictiveMatch’s advanced solution has been built from the ground up specifically to service the unique needs of an online classifieds portal. Unlike e-commerce sites where SKUs are wide and varied and reply on rules based configuration, PredictiveMatch understanding the deep dimensional data that exists for real estate. Utilising vertical specific domain knowledge and expertise enables us to develop unique predictive models that truly understand the behaviour of classifieds users.

Effortless Implementation
Collect behavioural data in real time via our flexible data collection methods. Ranging from our plug n play async javascript libraries and web beacons that make intgration as simple as dropping code snippets on relevant pages, through to our REST API for deep integration.

Real Time User Centric Recommendations
Our customisable widget serves user specific listing recommendations that are compiled by advanced machine learning predictive algorithms in real time. A/B testing of algorithm combined with auto optimisation ensures you have full control and transparency into what our users respond to. All aimed increasing leads to agents by making property portals more personalised and increasing listing discovery.

Advanced Analytics
Through our advanced technology PredictiveMatch continually tracks how each individual user interacts with key pages of your classified site and builds out rich, deep behavioural analytics, which not only feed our recommendation algorithms, but are also made available to you to better understand your user base.

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