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.

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…

13. Small data can drive big outcomes

18. We must combine insights and data

20. That ‘D’ word…

22. That ‘D’ word…

23. 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

24. What activities would identify first home buyers?
Searching for low prices?
1 or 2 bedroom properties?
“Cheap” suburbs?
First home buyer developments?

28. 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?

29. How do we train our algorithm to detect first home buyers?

33. Take a survey
Not first home buyer
First home buyer

34. Take a survey
Not first home buyer
First home buyer

35. Machine learning in action: predicting first home buyers
Survey
Responses
Web Analytics
Data

36. 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

37. Predicting first home buyers
Anonymous
Consumer
Web Analytics
Data

38. Predicting first home buyers at scale

39. Predicting first home buyers at scale

40. Do first home buyers click more?
Ad targeting experiment: Who clicks more?

41. Continuing the cycle
Tweak model &
Adjust experiment
Analyze effect
Inspect methodology
What do we change?

42. Just one small piece of the puzzle!

43. • 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

44. • 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…

45. Thanks...
any questions?