Not Just Lead Generation: Real Estate Portals Getting Smarter Using N=1 Level Personalization
Leads are the core currency for digital real estate platforms, unfortunately most of them treat leads as an information set of email and/or phone numbers.
Photo Credit : Ritesh Sharma,
Personalisation is defined as the act of providing tailored products or services to specific individuals or, to groups or segments of individuals. In today’s world where technology has empowered platforms with big scale data processing, advanced machine learning algorithms, and capabilities to capture each and every user behaviour, personalisation has definitely become a key expectation from any good platform. The real estate ecosystem is no exception here.
With the ever increasing adoption by users and the manner in which they have evolved, digital real estate platforms have started playing a crucial role within the ecosystem. With technology at its core, digital real estate platforms are aptly positioned to leverage this opportunity in a much better way with longer purchase cycles and access to a lot more consumer data. This gives service providers better insights into what the consumers need allowing them to personalize the experience for each visitor, at every stage of the funnel in the purchase cycle. Here are the stages in the purchase cycle --
Search and Discovery:
Over the last few years an increasing number of real estate purchases have started to begin online – either through search on digital real estate portals or through agents who are online, and this phenomenon is gathering pace with every passing year.
With the advent of big data, it is possible to paint nearly accurate personas of the people who buy/rent houses, by understanding their browsing behaviour as well as search patterns on platforms. Instead of just welcoming returning users the traditional way, showcasing personalized and relevant content would be more beneficial to the users.
Most of the platforms provide the recommendations in different ways (e.g. by sending emails of relevant properties) but unfortunately their recommendations are mostly biased with mounting monetisation pressure. Unlike other platforms, Housing.com’s data team has come up a bold step by introducing the Housing Recommendation Engine as a key part of the search journey which can be accessed on mobile platforms as well. The recommendation engine takes millions of user actions of the search preferences and browsing history and applies insightful machine-learning algorithms, to best predict properties a user might be interested in.
Lead relevance matching:
Leads are the core currency for digital real estate platforms, unfortunately most of them treat leads as an information set of email and/or phone numbers. From a sale perspective, apart from the client information the relevance matching with the seller is another important aspect of a lead. The same lead can be of different relevance for different sellers, so the key aspect here is to improve the quality of relevance matching. With the right insights about what is happening with the leads after giving it to sellers, one can apply machine learning systems on the data for better classification of leads for relevant matching.
Being a transaction led platform, this is a key differentiator for makaan.com. The data related to meetings, site visits on the leads has enabled makaan.com to come up with better classification models and hence more relevant match of leads with the sellers.
There are some very simple use cases also where personalization can be provided for a set of visitors, for example customizing landing pages for NRIs based on the geography they are landing from which allows for better engagement and conversion metrics. A significant positive impact of such changes has been witnessed across all our platforms - PropTiger.com, Housing.com and Makaan.com.
The factors covered above are intrinsic to a platform and form only a part of what personalisation can do or how it can be deployed. Another facet which could provide even more insights into the consumer's mind is leveraging social media. Connecting with a visitor’s social media properties such as Facebook, LinkedIn and Twitter could potentially help real estate platforms to suggest the best options to buy or, rent based on their lifestyle, personal and professional circles or even their likes and preferences. And while it may sound like a thing of the future, in reality it is almost here. This signals exciting times ahead for digital real estate platforms and whoever exploits these opportunities in the best way will be the winner in the long run.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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