Building a data-driven future
ThoughtWorks Live 2014
Jonas Jaanimagi (REA Group)
Jennifer Smith (ThoughtWorks)
Introduction to REA Group
Introduction to realestate.com.au
* Nielsen Online Ratings, October 2012
** Nielsen Consumer & Media View, Survey 9, 2012
...
Who are our users?
Who will you find at realestate.com.au?
A diverse mix of ages and families
58% 42%
Gender Age 78 % main grocery buyer
17%
...
A Month of Property Seeking with REA
* Omniture Site Catalyst, October 2012
** Nielsen Online Ratings, October 2012
*** In...
How do users access the site?
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
monday tuesday wednesday thursday friday saturday sunday
Desktop Mobile Phone Tablet...
Empty Nesters
•  Baby Boomers /
Silent Generation
•  No kids at home
•  High level (70%
+) home
ownership
•  Downgrading t...
That ‘D’ word…
Small data can drive big outcomes
We must combine insights and data
That ‘D’ word…
That ‘D’ word…
Web Analytics: A trace of consumer activity
2013-10-23 09:00:22 | Searched for 1 bedroom units in North Fitzroy
2013-10-23...
What activities would identify first home buyers?
Searching for low prices?
1 or 2 bedroom properties?
“Cheap” suburbs?
Fi...
Applications of machine learning
Handwriting/speech recognition
Stock market analysis
Medical diagnosis
Bioinformatics
Fra...
How do we train our algorithm to detect first home buyers?
Take a survey
Not first home buyer
First home buyer
Take a survey
Not first home buyer
First home buyer
Machine learning in action: predicting first home buyers
Survey
Responses
Web Analytics
Data
What does our model think makes a first home buyer?
Searching with a low price band
Sharing on social media
Looking at pro...
Predicting first home buyers
Anonymous
Consumer
Web Analytics
Data
Predicting first home buyers at scale
Predicting first home buyers at scale
Do first home buyers click more?
Ad targeting experiment: Who clicks more?
Continuing the cycle
Tweak model &
Adjust experiment
Analyze effect
Inspect methodology
What do we change?
Just one small piece of the puzzle!
•  Better, stronger models!
•  Diversify segments: general movers, investors
•  Find further uses beyond ad targeting
•  U...
•  Start with an informed idea of your consumers
•  Get data scientists, developers, ad folks working
together closely
•  ...
Thanks...
any questions?
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
Building a Data Driven Future | Jonas Jaanimagi, Head of Media Strategy & Operations, realestate.com.au | Jennifer Smith, ...
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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?

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.

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

  1. 1. Building a data-driven future ThoughtWorks Live 2014 Jonas Jaanimagi (REA Group) Jennifer Smith (ThoughtWorks)
  2. 2. Introduction to REA Group
  3. 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. 4. Who are our users?
  5. 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. 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. 7. How do users access the site?
  8. 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. 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. 10. That ‘D’ word…
  11. 11. Small data can drive big outcomes
  12. 12. We must combine insights and data
  13. 13. That ‘D’ word…
  14. 14. That ‘D’ word…
  15. 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. 16. What activities would identify first home buyers? Searching for low prices? 1 or 2 bedroom properties? “Cheap” suburbs? First home buyer developments?
  17. 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. 18. How do we train our algorithm to detect first home buyers?
  19. 19. Take a survey Not first home buyer First home buyer
  20. 20. Take a survey Not first home buyer First home buyer
  21. 21. Machine learning in action: predicting first home buyers Survey Responses Web Analytics Data
  22. 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. 23. Predicting first home buyers Anonymous Consumer Web Analytics Data
  24. 24. Predicting first home buyers at scale
  25. 25. Predicting first home buyers at scale
  26. 26. Do first home buyers click more? Ad targeting experiment: Who clicks more?
  27. 27. Continuing the cycle Tweak model & Adjust experiment Analyze effect Inspect methodology What do we change?
  28. 28. Just one small piece of the puzzle!
  29. 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. 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. 31. Thanks... any questions?

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