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

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

realestate.com.au is one of Australia’s most popular websites

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% 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+

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

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

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

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

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 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

What activities would identify first home buyers?

Searching for low prices? 1 or 2 bedroom properties? “Cheap” suburbs? First home buyer developments?

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?

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 property inspection times NOT searching for 4 car spaces NOT searching with a high price band

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 •  Unsupervised learning: what patterns exist

purely in the data?

Taking things further

•  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…

Thanks... any questions?