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Big Data Analysis of Property Guru


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Team 14 Presentation at DataDriven Hackathon, Singapore, Oct 22, 2016

Published in: Data & Analytics
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Big Data Analysis of Property Guru

  1. 1. Property Guru 4.5 million visits per month Generates over 500,000 inquiries / month 10% of Real estate transactions are in SG. Has yearly 43 % growth in indonesia. PropertyGuru transacts S$14B / year However ...
  2. 2. Problem Statement PropertyGuru’s conversion rate is less than 0.48% (2015).(See Notes) Property Listings have incomplete fields or wrong Region/District Codes.
  3. 3. Data Driven UI Property Recommendation Engine provides Machine curated Property Choices Smart Defaults (based on Data Analytics) enhances User experience
  4. 4. Data Driven Demo <Demo Here> Guru2015Listings/PropertyGuryDataAnal ysisbyEmpowerKaki?:embed=y&:display _count=yes
  5. 5. Business Goal Higher Conversion Rate Provide Users with Property Recommendation Engine based on both: A. Cluster Analysis of Property Listing (Similarity of Properties) B. Market Basket Analysis of Enquiries Database (Similarity of User Behavior)
  6. 6. Business Goal More Accurate Listings “Claim Your Property” enables property owners to help complete PG property listings fields. Big Data Analytics to detect Anomalies and Outliers
  7. 7. Expected Benefits Higher Conversion Rate (See Notes) ● 4X Increase in Conversion Rate ● 1.1M buying users monthly VS 200K ● 12% increased sales (i.e. more agents) ● $168M Increase in revenue for PropertyGuru SG Team #14 Data Driven Hackathon Oct 22, 2016
  8. 8. Notes Sources: “The group claims to serve 16 million users and generate 500,000 enquiries a month” - “According to PropertyGuru, its portals attract 11 million monthly consumer visits and 104 million page views.”