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Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, Technology Consultant, ThoughtWorks
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Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, Technology Consultant, ThoughtWorks

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Everybody is talking data in online industries, but how can we harness these insights and turn them into real sources of competitive advantage? …

Everybody is talking data in online industries, but how can we harness these insights and turn them into real sources of competitive advantage?

Visitors to the REA website generate huge amounts of data, which equates to a huge revenue generation opportunity. Through the power of analytics, REA hopes to gain greater insights into the intents and motivations of their visitors. We must all prepare for a data-driven future.

Published in: Marketing, Technology, Business

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  • 1. Building a data-driven future ThoughtWorks Live 2014 Jonas Jaanimagi (REA Group) Jennifer Smith (ThoughtWorks)
  • 2. Introduction to REA Group
  • 3. Introduction to realestate.com.au * Nielsen Online Ratings, October 2012 ** Nielsen Consumer & Media View, Survey 9, 2012 realestate.com.au is one of Australia’s most popular websites
  • 4. Who are our users?
  • 5. Who will you find at realestate.com.au? A diverse mix of ages and families 58% 42% Gender Age 78 % main grocery buyer 17% singles living alone or with others 28% Couples with no children 42% Families with children 16% 35% 33% 15% 14-24 25-39 40-54 55+
  • 6. A Month of Property Seeking with REA * Omniture Site Catalyst, October 2012 ** Nielsen Online Ratings, October 2012 *** Internal listings data 13,566 EMAILS SENT TO AGENTS POOL IS THE MOST POPULAR KEYWORD SEARCHED 1,565,978 UNIQUE BROWSERS USE A MOBILE 617,794,790 PHOTOS OF PROPERTIES ARE VIEWED 830,700 NEW VISITORS 65,651 INSPECTION TIMES SAVED 51MINUTES IS THE AVERAGE TIME SPENT ON OUR SITE 92,436,903 PAGE VIEWS WITH A TABLET 878,531 PROPERTY DETAILS PRINTED 3,195,000 UNIQUE AUDIENCE 97,903 NEW LISTINGS IN BUY 459,187 PROPERTIES SENT TO FRIENDS
  • 7. How do users access the site?
  • 8. 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% monday tuesday wednesday thursday friday saturday sunday Desktop Mobile Phone Tablet How do audiences engage with realestate.com.au? Adobe Site Catalyst, Device Type Report, March 4th to 31st March 2013 Visits Device Usage by Day of Week
  • 9. Empty Nesters •  Baby Boomers / Silent Generation •  No kids at home •  High level (70% +) home ownership •  Downgrading to smaller property / lifestyle change What property cycle are people in? * Residential Consumer Segmentation May 2012 * Residential Consumer Housing Affordability & Sentiment Index Study June 2012 * Consumer Purchase Intention Study BUY April 2012 * Consumer Retire Insights Nielsen CMV Survey 4 2012 Share Rent Buy Sell Invest Lifestyle Retire Buyers •  Mid 30’s •  Married, no kids yet •  Moderate to high household income ($70k+ pa) •  Intend to buy house within 5 years •  Just over 50% own property already Sellers •  Baby Boomers •  Married with a couple of kids •  Live in the suburbs •  Currently paying off debt (credit cards, home loan) •  Moderate to high household income ($70k+ pa) Renters •  Singles & Couples •  Mid twenties •  Low to moderate household income (<$70k pa) •  Live in suburbs close to the city •  82% don’t own property Sharers •  Single •  Early twenties •  Looking to live in the metro area, close to the city •  Sharing a 2 bedroom place Investors •  Aged 35 years and older •  High household income (>$100k) •  Looking for properties priced <$500k Retirees •  2.3m Aussies already retired •  Over 50% planning renovations •  1 in 3 retirees planning travel domestically & internationally
  • 10. That ‘D’ word…
  • 11. Small data can drive big outcomes
  • 12. We must combine insights and data
  • 13. That ‘D’ word…
  • 14. That ‘D’ word…
  • 15. Web Analytics: A trace of consumer activity 2013-10-23 09:00:22 | Searched for 1 bedroom units in North Fitzroy 2013-10-23 09:01:11 | Viewed property 1 2013-10-23 09:01:24 | Viewed image carousel 2013-10-23 09:02:50 | Clicked mail agent button 2013-10-23 09:03:36 | Viewed property 2
  • 16. What activities would identify first home buyers? Searching for low prices? 1 or 2 bedroom properties? “Cheap” suburbs? First home buyer developments?
  • 17. Applications of machine learning Handwriting/speech recognition Stock market analysis Medical diagnosis Bioinformatics Fraud detection Search engines http://en.wikipedia.org/wiki/Machine_learning#Applications … and first home buyer prediction?
  • 18. How do we train our algorithm to detect first home buyers?
  • 19. Take a survey Not first home buyer First home buyer
  • 20. Take a survey Not first home buyer First home buyer
  • 21. Machine learning in action: predicting first home buyers Survey Responses Web Analytics Data
  • 22. What does our model think makes a first home buyer? Searching with a low price band Sharing on social media Looking at property inspection times NOT searching for 4 car spaces NOT searching with a high price band
  • 23. Predicting first home buyers Anonymous Consumer Web Analytics Data
  • 24. Predicting first home buyers at scale
  • 25. Predicting first home buyers at scale
  • 26. Do first home buyers click more? Ad targeting experiment: Who clicks more?
  • 27. Continuing the cycle Tweak model & Adjust experiment Analyze effect Inspect methodology What do we change?
  • 28. Just one small piece of the puzzle!
  • 29. •  Better, stronger models! •  Diversify segments: general movers, investors •  Find further uses beyond ad targeting •  Unsupervised learning: what patterns exist purely in the data? Taking things further
  • 30. •  Start with an informed idea of your consumers •  Get data scientists, developers, ad folks working together closely •  Start small, learn from failure and stay skeptical •  Creating value as early as possible If you try this…
  • 31. Thanks... any questions?

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