ADMA Digital Analytics

>
 ADMA
 Digital
 Analy-cs
 <
 
Measuring
 and
 op.mising
 digital
 

>
 Digital
 analy-cs
 course
 overview
 
9
 am
 start
  §  Metrics
 framework
  §  Campaign
 tracking
  15
 min
 coffee
 break
  §  Measuring
 brand
  §  Media
 a8ribu.on
 
 
October
 2012
 

12.30
 pm
 30
 min
 lunch
  §  Channel
 integra.on
  §  Re-­‐marke.ng
  15
 min
 coffee
 break
  §  Landing
 pages
  4.30
 pm
 finish
 

©
 Datalicious
 Pty
 Ltd
 

2
 

>
 Digital
 analy-cs
 course
 rules
 
§  §  §  §  §  §  §  § 
 
  Get
 involved
 and
 be
 informal!
  Ask
 ques.ons,
 share
 experiences
  Try
 to
 leave
 work
 outside
 the
 door
  Phones
 off
 or
 on
 mute
 please
  Toilet
 break
 whenever
 you
 like
  Different
 levels
 of
 experience
  Be
 open-­‐minded
 and
 accept
 feedback
  I’m
 here
 to
 cri.cize,
 point
 out
 opportuni.es
 
©
 Datalicious
 Pty
 Ltd
  3
 

October
 2012
 

>
 Maximising
 course
 outcome
 
§  Share
 your
 expecta.ons
 so
 I
 can
 adjust
  §  Start
 an
 ac.on
 sheet
 to
 collect
 ideas
 
  §  Main
 digital
 analy.cs
 course
 outcomes
 
 

–  Define
 a
 metrics
 framework
  –  Enable
 benchmarking
 across
 campaigns
 
  –  Effec.vely
 incorporate
 analy.cs
 into
 planning
  –  Understand
 digital
 data
 sources
 and
 their
 limita.ons
  –  Accurately
 a8ribute
 conversions
 across
 channels
  –  Develop
 strategies
 to
 extend
 op.misa.on
 past
 media
  –  Pull
 and
 interpret
 key
 reports
 in
 Google
 Analy.cs
  –  Impress
 with
 insights
 instead
 of
 spreadsheets
 
©
 Datalicious
 Pty
 Ltd
  4
 

October
 2012
 

>
 Introduc-ons
 &
 expecta-ons
 
§  Your
 name
  §  Your
 company
  §  Your
 roles
 &
 responsibili.es
  §  Knowledge
 gaps
 you’re
 hoping
 to
 fill
  §  Something
 else
 about
 yourself
 
–  Ideal
 job
  –  Hobbies
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  5
 

>
 About
 Datalicious
 
§  §  §  §  §  §  §  §  § 
 
  Datalicious
 was
 founded
 in
 November
 2007
  Official
 Adobe
 &
 Google
 Analy.cs
 partner
  360
 data
 agency
 with
 team
 of
 data
 specialists
  Combina.on
 of
 analysts
 and
 developers
  Blue
 chip
 clients
 across
 all
 industry
 ver.cals
  Carefully
 selected
 best
 of
 breed
 partners
  Driving
 industry
 best
 prac.ce
 with
 ADMA
  Turning
 data
 into
 ac.onable
 insights
  Execu.ng
 smart
 data
 driven
 campaigns
 
©
 Datalicious
 Pty
 Ltd
  6
 

October
 2012
 

>
 Smart
 data
 driven
 marke-ng
 
“Using
 data
 to
 widen
 the
 funnel”
 

Media
 AKribu-on
 &
 Modeling
Op-mise
 channel
 mix,
 predict
 sales
 


 

Targe-ng
 &
 Merchandising
 
Increase
 relevance,
 reduce
 churn
 

Tes-ng
 &
 Op-misa-on
 
Remove
 barriers,
 drive
 sales
 

Boos-ng
 ROMI
 
November
 2012
  ©
 Datalicious
 Pty
 Ltd
  7
 

>
 Wide
 range
 of
 data
 services
 
Data
 
PlaTorms
 

Insights
 
Analy-cs
 

  Data
 mining
 and
 modelling
 
  Tableau,
 Splunk,
 SPSS,
 R,
 etc
 
  Customised
 dashboards
 
  Media
 aKribu-on
 analysis
 
  Marke-ng
 mix
 modelling
 
  Social
 media
 monitoring
 
  Customer
 segmenta-on
 

Ac-on
 


  Data
 collec-on
 and
 processing
 
  Adobe,
 Google
 Analy-cs,
 etc
 
  Web
 and
 mobile
 analy-cs
 
  Tag-­‐less
 online
 data
 capture
 
  Retail
 and
 call
 center
 analy-cs
 
  Big
 data
 &
 data
 warehousing
 
  Single
 customer
 view
 

Campaigns
 


  Data
 usage
 and
 applica-on
 
  SiteCore,
 ExactTarget,
 etc
 
  Targe-ng
 and
 merchandising
 
  Marke-ng
 automa-on
 
  CRM
 strategy
 and
 execu-on
 
  Data
 driven
 websites
 
  Tes-ng
 programs
 

November
 2012
 

©
 Datalicious
 Pty
 Ltd
 

8
 

>
 Best
 of
 breed
 partners
 

November
 2012
 

©
 Datalicious
 Pty
 Ltd
 

9
 

>
 Internal
 product
 development
 
1
 Customer
 rela.onship
 management
 plaform
 containing
 all
 data
 necessary
 to
 manage
 campaigns
  2
 Single
 customer
 view
 plaform
 containing
 all
 data
 across
 all
 (customer)
 touch
 points
 


 

Mass
 media
  Social
 media
  Digital
 media
 
 

CRM1
 

Surveys
  Campaigns
  Promo.ons
 
 
  Website/apps
  Social
 media
  eDMs/DMs
 

Measure
 

  Demographics
  Transac.ons
  Campaigns
 

Engage
 

SCV2
 

November
 2012
 

©
 Datalicious
 Pty
 Ltd
 

10
 

>
 Clients
 across
 all
 industries
 

November
 2012
 

©
 Datalicious
 Pty
 Ltd
 

11
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Metrics
 framework
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  12
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

13
 

>
 AIDA
 and
 AIDAS
 formulas
 
 
Old
 media
  New
 media
 

Awareness
 

Interest
 

Desire
 

Ac-on
 

Sa-sfac-on
 

Social
 media
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

14
 

>
 Simplified
 AIDAS
 funnel
 
 

Reach
 
(Awareness)


 

Engagement
 
(Interest
 &
 Desire)
 

Conversion
 
(Ac.on)
 

+Buzz
 
(Delight)
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

15
 

>
 Marke-ng
 is
 about
 people
 
 

People
  reached
 

40%
 

People
  engaged
 

10%
 

People
  converted
 

1%
 

People
  delighted
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

16
 

>
 Standardised
 roll-­‐up
 metrics
 
People
  reached
 

40%
 

People
  engaged
 

10%
 

People
  converted
 

1%
 

People
  delighted
 

Unique
 browsers,
  search
 impressions,
  TV
 circula-on,
 etc
 

Unique
 visitors,
  site
 engagements,
  video
 views,
 etc
 

Online
 sales,
  online
 leads,
 store
 
  locator
 searches,
 etc
 

Facebook
 
  comments,
 Tweets,
 
  ra-ngs,
 support
 calls,
 etc
 

Response
 rate,
 
  Search
 response
 rate,
  TV
 response
 rate,
 etc
 

Conversion
 rate,
  engagement
 rate,
 
  checkout
 rate,
 etc
 

Review
 rate,
 
  ra-ng
 rate,
 comment
  rate,
 NPS
 rate,
 etc
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

17
 

>
 Provide
 context
 with
 figures
 
Brand
 vs.
 direct
 response
 campaign
 

People
  reached
 

40%
 

People
  engaged
 

10%
 

People
  converted
 

1%
 

People
  delighted
 

New
 prospects
 vs.
 exis.ng
 customers
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

18
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

19
 

>
 Provide
 context
 with
 figures
 
§  §  §  §  §  §  §  §  §  § 
 
  Brand
 vs.
 direct
 response
 campaign
  New
 prospects
 vs.
 exis.ng
 customers
  Compe..ve
 ac.vity,
 i.e.
 none,
 a
 lot,
 etc
  Market
 share,
 i.e.
 small,
 medium,
 large,
 et
  Segments,
 i.e.
 age,
 loca.on,
 influence,
 etc
  Channels,
 i.e.
 search,
 display,
 social,
 etc
  Campaigns,
 i.e.
 this/last
 week,
 month,
 year,
 etc
  Products
 and
 brands,
 i.e.
 iphone,
 htc,
 etc
  Offers,
 i.e.
 free
 minutes,
 free
 handset,
 etc
  Devices,
 i.e.
 home,
 office,
 mobile,
 tablet,
 etc
 
©
 Datalicious
 Pty
 Ltd
  20
 

October
 2012
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

21
 

Exercise:
 Internal
 traffic
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

22
 

Exercise:
 Custom
 segments
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

23
 

Google:
 “google
 analy-cs
 custom
 variables”
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

24
 

>
 Conversion
 funnel
 1.0
 
 
Campaign
 responses
  Conversion
 funnel
 

Product
 page,
 add
 to
 shopping
 cart,
 view
 shopping
 cart,
  cart
 checkout,
 payment
 details,
 shipping
 informa.on,
  order
 confirma.on,
 etc
 

Conversion
 event
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  25
 

>
 Conversion
 funnel
 2.0
 
 
Campaign
 responses
 (inbound
 spokes)
 
Offline
 campaigns,
 banner
 ads,
 email
 marke.ng,
 
  referrals,
 organic
 search,
 paid
 search,
 
  internal
 promo.ons,
 etc
 
 
 

Landing
 page
 (hub)
 

 
 

Success
 events
 (outbound
 spokes)
 

Bounce
 rate,
 add
 to
 cart,
 cart
 checkout,
 confirmed
 order,
 
  call
 back
 request,
 registra.on,
 product
 comparison,
 
  product
 review,
 forward
 to
 friend,
 etc
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  26
 

>
 Addi-onal
 success
 metrics
 
 
Click
  Through
  Use
 addi-onal
 metrics
 closer
 to
 the
 campaign
 origin
 

$
 

Click
  Through
 

Add
 To
 
  Cart
 

Cart
  Checkout
 

?
 

$
 

Click
  Through
 

Page
  Bounce
 

Page
 
  Views
 

Product
 
  Views
 

$
 

Click
  Through
 

Call
 back
  request
 

Store
  Search
 

?
 

$
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

27
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

28
 

Exercise:
 Conversion
 goals
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

29
 

Exercise:
 Sta-s-cal
 significance
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

30
 

How
 many
 survey
 responses
 do
 you
 need
 
  if
 you
 have
 10,000
 customers?
  How
 many
 email
 opens
 do
 you
 need
 to
 test
 2
 subject
 lines
  if
 your
 subscriber
 base
 is
 50,000?
  How
 many
 orders
 do
 you
 need
 to
 test
 6
 banner
 execu-ons
 
  if
 you
 serve
 1,000,000
 banners
 

October
 2012
 

Google
 “nss
 sample
 size
 calculator”
 

©
 Datalicious
 Pty
 Ltd
 

31
 

How
 many
 survey
 responses
 do
 you
 need
 
  if
 you
 have
 10,000
 customers?
  369
 for
 each
 ques-on
 or
 369
 complete
 responses
  How
 many
 email
 opens
 do
 you
 need
 to
 test
 2
 subject
 lines
  if
 your
 subscriber
 base
 is
 50,000?
 And
 email
 sends?
  381
 per
 subject
 line
 or
 381
 x
 2
 =
 762
 email
 opens
  How
 many
 orders
 do
 you
 need
 to
 test
 6
 banner
 execu-ons
 
  if
 you
 serve
 1,000,000
 banners?
  383
 sales
 per
 banner
 execu-on
 or
 383
 x
 6
 =
 2,298
 sales
 

October
 2012
 

Google
 “nss
 sample
 size
 calculator”
 

©
 Datalicious
 Pty
 Ltd
 

32
 

>
 Conversion
 metrics
 by
 category
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 
Source:
 Omniture
 Summit,
 Ma8
 Belkin,
 2007
 

33
 

>
 Rela-ve
 or
 calculated
 metrics
 
§  Bounce
 rate
  §  Conversion
 rate
  §  Cost
 per
 acquisi.on
  §  Pages
 views
 per
 visit
  §  Product
 views
 per
 visit
  §  Cart
 abandonment
 rate
  §  Average
 order
 value
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  34
 

>
 Align
 metrics
 across
 channels
 
§  Paid
 search
 response
 rate
  §  Organic
 search
 response
 rate
  §  Display
 response
 rate
 
  §  Email
 response
 rate
 
 
=
 website
 visits
 /
 paid
 search
 impressions
  =
 website
 visits
 /
 organic
 search
 impressions
  =
 website
 visits
 /
 display
 ad
 impressions
  =
 website
 visits
 /
 emails
 sent
  =
 (website
 visits
 +
 phone
 calls)
 /
 direct
 mail
 pieces
 sent
  =
 (website
 visits
 +
 phone
 calls)
 /
 (TV
 ad
 reach
 x
 frequency)
 
©
 Datalicious
 Pty
 Ltd
  35
 

§  Direct
 mail
 response
 rate
 
  §  TV
 response
 rate
 
 
October
 2012
 

Exercise:
 Metrics
 framework
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

36
 

>
 Exercise:
 Metrics
 framework
 
 
Level
  Level
 1,
  people
  Level
 2,
  strategic
  Level
 3,
  tac-cal
  Funnel
  breakdowns
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  37
 

Reach
 

Engagement
 

Conversion
 

+Buzz
 

>
 Exercise:
 Metrics
 framework
 
 
Level
  Level
 1,
  people
  Level
 2,
  strategic
  Level
 3,
  tac-cal
  Funnel
  breakdowns
 
October
 2012
 

Reach
  People
  reached
  Display
  impressions
  Interac-on
  rate,
 etc
 

Engagement
  People
  engaged
 

Conversion
  People
  converted
 

+Buzz
  People
  delighted
 

?
  ?
 

?
  ?
 

?
  ?
 

Exis-ng
 customers
 vs.
 new
 prospects,
 products,
 etc
 

©
 Datalicious
 Pty
 Ltd
 

38
 

>
 NPS
 survey
 and
 page
 ra-ngs
 
Page
 ra.ngs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

39
 

Google:
 “google
 analy-cs
 custom
 events”
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

40
 

>
 Importance
 of
 calendar
 events
 
 

Traffic
 spikes
 or
 other
 data
 anomalies
 without
 context
 are
  very
 hard
 to
 interpret
 and
 can
 render
 data
 useless
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  41
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

42
 

>
 Poten-al
 calendar
 events
 
§  Press
 releases
  §  Sponsored
 events
  §  Campaign
 launches
  §  Campaign
 changes
  §  Crea.ve
 changes
  §  Price
 changes
  §  Website
 changes
  §  Technical
 difficul.es
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  43
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

44
 

Exercise:
 Calendar
 events
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

45
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Campaign
 tracking
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  46
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

47
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

48
 

Exercise:
 Track
 campaigns
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

49
 

Google:
 “google
 analy-cs
 url
 builder”
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

50
 

>
 Email
 click-­‐through
 iden-fica-on
 
h8p://www.company.com/email-­‐landing-­‐page.html?
 

 

utm_id=neNCu&
  CustomerID=12345&
  Demographics=M|35&
  CustomerSegment=A1&
  CustomerValue=High&
  ProductHistory=A6&
  NextBestOffer=A7&
  ChurnRisk=Low
  [...]
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  51
 

>
 Personalised
 URLs
 for
 direct
 mail
 
ChrisBartens.company.com
 >
 redirect
 to
 >
 company.com?
 

 

utm_id=neND&
  CustomerID=12345&
  Demographics=M|35&
  CustomerSegment=A1&
  CustomerValue=High&
  ProductHistory=A6&
  NextBestOffer=A7&
  ChurnRisk=Low
  [...]
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  52
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

53
 

>
 Exercise:
 Naming
 conven-on
 
Source
  Referrer
  google
  Medium
  Medium
  cpc
  Term
  Keyword
  search
 term
 a
  Content
  Crea-ve
  red
 banner
  Campaign
  Promo-on
  promo
 a
 

newsleKer
 

banner
 

search
 term
 b
  black
 banner
 

promo
 b
 

?
 
October
 2012
 

?
 

?
 
©
 Datalicious
 Pty
 Ltd
 

?
 

?
 
54
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

55
 

Google:
 “link
 google
 analy-cs
 webmaster
 tools”
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

56
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

57
 

Google:
 “link
 google
 analy-cs
 google
 adwords”
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

58
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

59
 

Exercise:
 Organic
 op-misa-on
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

60
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

61
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

62
 

>
 Importance
 of
 social
 media
 
Search
 

Company
 

Promo-on
 

Consumer
 

WOM,
 blogs,
 reviews,
  ra-ngs,
 communi-es,
  social
 networks,
 photo
  sharing,
 video
 sharing
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  63
 

>
 Social
 as
 the
 new
 search
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

64
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

65
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

66
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Measuring
 brand
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  67
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

68
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

69
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

70
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

71
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

72
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

73
 

>
 Measuring
 brand:
 Search
 vs.
 social
 

Search
  Quan-ty
 

Social
  Quality
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

74
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Media
 aKribu-on
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  75
 

>
 Duplica-on
 across
 channels
 
 
Paid
 
  Search
  Bid
 
  Mgmt
 

$
 

Banner
 
  Ads
 

Ad
 
  Server
 

$
 

Email
 
  Blast
 

Email
  PlaTorm
 

$
 

Organic
  Search
 

Google
  Analy-cs
 

$
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

76
 

>
 Duplica-on
 across
 channels
 
 
Display
  impression
  Paid
 
  search
  Display
  click
  Organic
  search
 

$
 

Ad
 server
  cookie
 

Ad
 server
  cookie
 

Ad
 
  Server
 

Bid
 mgmt.
  cookie
 

Bid
 
  mgmt.
 

Analy-cs
  cookie
 

Analy-cs
  cookie
 

Analy-cs
  cookie
 

Web
  analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

77
 

>
 De-­‐duplica-on
 across
 channels
 
 
Paid
 
  Search
 

$
 

Banner
 
  Ads
  Central
  Analy-cs
  PlaTorm
  Email
 
  Blast
 

$
 

$
 

Organic
  Search
 

$
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

78
 

>
 Campaign
 flows
 are
 complex
 
=
 Paid
 media
  =
 Viral
 elements
  =
 Sales
 channels
 

Organic
 
  search
 

PR,
 WOM,
  events,
 etc
 

YouTube,
 
  blog,
 etc
 

Home
 pages,
  portals,
 etc
 

Paid
 
  search
 

TV,
 print,
 
  radio,
 etc
 

Direct
 mail,
 
  email,
 etc
 

Landing
 pages,
  offers,
 etc
 

Display
 ads,
  affiliates,
 etc
 

CRM
  program
 

Facebook
  TwiKer,
 etc
 

POS
 kiosks,
  loyalty
 cards,
 etc
 

Call
 center,
 
  retail
 stores,
 etc
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

79
 

Exercise:
 Campaign
 flow
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

80
 

>
 Success
 aKribu-on
 models
 
 
Banner
 
  Ad
  Paid
 
  Search
  Organic
  Search
  $100
  Success

$100
 


 

Last
 channel
  gets
 all
 credit
 

Banner
 
  Ad
  $100
 

Paid
 
  Search
 

Email
 
  Blast
 

Success

$100
 


 

First
 channel
  gets
 all
 credit
 

Paid
 
  Search
  $100
 

Banner
 
  Ad
  $100
 

Affiliate
 
  Referral
  $100
 

Success

$100
 


 

All
 channels
 get
  equal
 credit
 

Print
 
  Ad
  $33
 

Social
 
  Media
  $33
 

Paid
 
  Search
  $33
 

Success

$100
 


 

All
 channels
 get
  par-al
 credit
 
81
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

>
 First
 and
 last
 click
 aKribu-on
 
 
Chart
 shows
  percentage
 of
  channel
 touch
  points
 that
 lead
  to
 a
 conversion.
 

Paid/Organic
 Search
 

Emails/Shopping
 Engines
 

Neither
 first
 
  nor
 last-­‐click
  measurement
  would
 provide
  true
 picture
 
 
82
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

>
 Ad
 clicks
 inadequate
 measure
 

Only
 a
 small
 minority
 of
 people
 actually
 click
 on
 ads,
 the
 majority
  merely
 processes
 them
 (if
 at
 all)
 like
 any
 other
 adver.sing
 without
 an
  immediate
 response
 so
 adver.sers
 cannot
 rely
 on
 clicks
 as
 the
 sole
  success
 measure
 but
 should
 instead
 focus
 on
 impressions
 delivered
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  83
 

>
 Indirect
 display
 impact
 
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

84
 

>
 Indirect
 display
 impact
 
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

85
 

>
 Indirect
 display
 impact
 
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

86
 

>
 Full
 purchase
 path
 tracking
 
Introducer
  Influencer
  Influencer
  Closer
 

$
 

Paid
 
  search
 

Display
 
  ad
 clicks
 

Organic
  search
 

Direct
 
  site
 visits
 

Online
  leads
 

Display
 
  ad
 views
 

Affiliate
  clicks
 

Social
  referrals
 

Emails,
  direct
 mail
 

Offline
  sales
 

TV/print
 
  responses
 

Organic
  search
 

Social
 
  buzz
 

Retail
 
  visits
 

Life-me
  profit
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

87
 

>
 Full
 purchase
 path
 tracking
 
Introducer
  Influencer
  Influencer
  Closer
 

$
 

Paid
 
  search
 

Display
 
  ad
 clicks
 

Organic
  search
 

Direct
 
  site
 visits
 

Online
  leads
 

Display
 
  ad
 views
 

Affiliate
  clicks
 

Social
  referrals
 

Emails,
  direct
 mail
 

Offline
  sales
 

TV/print
 
  responses
 

Organic
  search
 

Social
 
  buzz
 

Retail
 
  visits
 

Life-me
  profit
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

88
 

>
 Purchase
 path
 example
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

89
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

90
 

>
 Path
 across
 different
 segments
 
Introducer
  Influencer
  Influencer
  Closer
 

$
 

Channel
 1
 

Channel
 2
 

Channel
 3
 

Channel
 4
 

Product
 
  A
 vs.
 B
 

Channel
 1
 

Channel
 2
 

Channel
 3
 

Channel
 4
 

Clients
 vs.
  prospects
 

Channel
 1
 

Channel
 2
 

Channel
 3
 

Product
 4
 

Brand
 vs.
  direct
 resp.
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

91
 

>
 Understanding
 channel
 mix
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

92
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

93
 

October
 2012
 

What
 promoted
 your
 visit
 today?
  q  Recent
 branch
 visit
  q  Saw
 an
 ad
 on
 television
  q  Saw
 an
 ad
 in
 the
 newspaper
  q  Recommenda.on
 from
 family/friends
  q  […]
 
  How
 likely
 are
 you
 to
 apply
 for
 a
 loan?
  q  Within
 the
 next
 few
 weeks
  q  Within
 the
 next
 few
 months
  q  I
 am
 a
 customer
 already
  q  […]
  ©
 Datalicious
 Pty
 Ltd
  94
 

>
 Website
 entry
 survey
 
 
De-­‐duped
 Campaign
 Report
  Greatest
 Influencer
 on
 Branded
 Search
 /
 STS
 

Channel
  Straight
 to
 Site
  SEO
 Branded
  SEM
 Branded
  SEO
 Generic
  SEM
 Generic
  Display
 Adver.sing
  Affiliate
 Marke.ng
  Referrals
  Email
 Marke.ng
 

%
 of
 Conversions
  27%
  15%
  9%
  7%
  14%
  7%
  9%
  5%
  7%
 

}
 

Channel
  Word
 of
 Mouth
  Blogging
 &
 Social
 Media
  Newspaper
 Adver.sing
  Display
 Adver.sing
  Email
 Marke.ng
  Retail
 Promo.ons
 

%
 of
 Influence
  32%
  24%
  9%
  14%
  7%
  14%
 

Conversions
 a8ributed
 to
 search
 terms
  that
 contain
 brand
 keywords
 and
 direct
  website
 visits
 are
 most
 likely
 not
 the
  origina.ng
 channel
 that
 generated
 the
  awareness
 and
 as
 such
 conversion
  credits
 should
 be
 re-­‐allocated.
 
 
©
 Datalicious
 Pty
 Ltd
  95
 

October
 2012
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

96
 

>
 Website
 entry
 survey
 example
 

In
 this
 retail
 example,
 the
  exposure
 to
 retail
 display
 ads
  was
 the
 biggest
 website
 traffic
  driver
 for
 direct
 visits
 as
 well
 as
  visits
 origina.ng
 from
 search
  terms
 that
 included
 branded
  keywords
 –
 before
 TV,
 word
 of
  mouth
 and
 print
 ads.
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  97
 

>
 Adjus-ng
 for
 offline
 impact
 
-­‐5
  +5
  -­‐15
  +15
  -­‐10
  +10
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

98
 

>
 Purchase
 path
 vs.
 aKribu-on
 
§  Important
 to
 make
 a
 dis.nc.on
 between
 media
  a8ribu.on
 and
 purchase
 path
 tracking
 
–  Not
 the
 same,
 one
 is
 necessary
 to
 enable
 the
 other
 

§  Tracking
 the
 complete
 purchase
 path,
 i.e.
 every
 paid
  and
 organic
 campaign
 touch
 point
 leading
 up
 to
 a
  conversion
 is
 a
 necessary
 requirement
 to
 be
 able
 to
  actually
 do
 media
 a8ribu.on
 or
 the
 alloca.on
 or
  conversion
 credits
 back
 to
 campaign
 touch
 points
 
 
–  Purchase
 path
 tracking
 is
 the
 data
 collec.on
 and
 
  media
 a8ribu.on
 is
 the
 actual
 analysis
 or
 modelling
 
 
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  99
 

>
 Where
 to
 track
 purchase
 path
 
Ad
 Server
 
Banner
 impressions
  Banner
 clicks
  +
  Paid
 search
 clicks
 

Web
 Analy-cs
 
Referral
 visits
  Social
 media
 visits
  Organic
 search
 visits
  Paid
 search
 visits
  Email
 visits,
 etc
  Lacking
 ad
 impressions
  Less
 granular
 &
 complex
 

Lacking
 organic
 visits
  More
 granular
 &
 complex
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

100
 

>
 Purchase
 path
 data
 samples
 
Web
 Analy-cs
 data
 sample
  LAST
 AD
 IMPRESSION
 >
 SEARCH
 >
 $$$|
 PV
 $$$
  AD
 IMPRESSION
 >
 AD
 IMPRESSION
 >
 SEARCH
 >
 $$$
 

 

Ad
 Server
 data
 sample
  01/01/2012
 11:45
 AD
 IMP
 YAHOO
 HOME 01/01/2012
 12:00
 AD
 IMP
 SMH
 FINANCE 01/01/2012
 12:05
 SEARCH
 KEYWORD
  07/01/2012
 17:00
 DIRECT
 
 
  08/01/2012
 15:00
 $$$
 
 
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
 


 $33
 
 $33
 
 -­‐
 
 $33
 
 $100
 
101
 

>
 Media
 aKribu-on
 models
 
Introducer
  Influencer
  Influencer
  Closer
 

$
 

?%
 

?%
 

?%
 

?%
 

Product
 
  A
 vs.
 B
 

?%
 

?%
 

?%
 

?%
 

Prospects
  vs.
 clients
 

?%
 

?%
 

?%
 

?%
 

Brand
 vs.
  direct
 resp.
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

102
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

103
 

>
 Full
 vs.
 par-al
 purchase
 path
 data
 
Display
 
  impression
 

✖
  ✖
  ✖
  ✖
 

Display
 
  impression
 

✔
  ✖
  ✖
 

Email
  response
 

✔
  ✔
  ✔
  ✔
 

Search
  response
 

✔
  ✔
  ✔
  ✔
 

$
 

Display
 
  impression
 

Display
 
  impression
 

Display
 
  impression
 

Direct
 
  visit
 

$
 

Display
 
  impression
 

Display
 
  impression
 

Display
 
  impression
 

Display
 
  response
 

$
 

Display
 
  impression
 

Display
 
  impression
 

✔
 

Search
  response
 

Search
  response
 

$
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

104
 

>
 Full
 vs.
 par-al
 purchase
 path
 data
 
Display
 
  impression
 

✖
 

Display
 
  impression
 

✔
 

Email
  response
 

✔
 

Search
  response
 

✔
 

$
 

Display
 
  impression
 

Display
 
  impression
 

5%
 to
 65%
 variance
 
  ✖
  in
 conversion
 aKribu-on
 
  ✔
  ✔
  ✖
  for
 different
 channels
 due
 to
 
  par-al
 purchase
 path
 data
 
Display
 
  impression
  Display
 
  impression
  Direct
 
  visit
 

$
 

✖
  ✖
 

Display
 
  impression
 

✖
 

Display
 
  impression
 

✔
  ✔
 

Display
 
  response
 

✔
  ✔
 

$
 

Display
 
  impression
 

Display
 
  impression
 

✔
 

Search
  response
 

Search
  response
 

$
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

105
 

>
 Purchase
 path
 for
 each
 cookie
 

Mobile
 

Home
 

Work
 

Tablet
 

Media
 

Etc
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

106
 

>
 Media
 aKribu-on
 models
 
 
Display
 
  impression
  Display
 
  impression
  Display
  response
  Search
  response
 

$100
 

0%
 

0%
 

0%
 

100%
 

Last
 click
  aKribu-on
 

25%
 

25%
 

25%
 

25%
 

Even
 
  aKribu-on
 

X%
 

X%
 

Y%
 

Z%
 

Weighted
  aKribu-on
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

107
 

>
 Google
 Analy-cs
 models
 
§  The
 First/Last
 Interac-on
 model
 plus
 …
  §  The
 Linear
 model
 might
 be
 used
 if
 your
  campaigns
 are
 designed
 to
 maintain
  awareness
 with
 the
 customer
  throughout
 the
 en.re
 sales
 cycle.
  §  The
 Posi-on
 Based
 model
 can
 be
 used
  to
 adjust
 credit
 for
 different
 parts
 of
 the
  customer
 journey,
 such
 as
 early
  interac.ons
 that
 create
 awareness
 and
  late
 interac.ons
 that
 close
 sales.
  §  The
 Time
 Decay
 model
 assigns
 the
 most
  credit
 to
 touch
 points
 that
 occurred
  nearest
 to
 the
 .me
 of
 conversion.
 It
 can
  be
 useful
 for
 campaigns
 with
 short
 sales
  cycles,
 such
 as
 promo.ons.
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  108
 

Exercise:
 AKribu-on
 models
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

109
 

>
 Media
 aKribu-on
 models
 
Introducer
  Influencer
  Influencer
  Closer
 

$
 

?%
 

?%
 

?%
 

?%
 

Product
 
  A
 vs.
 B
 

?%
 

?%
 

?%
 

?%
 

Prospects
  vs.
 clients
 

?%
 

?%
 

?%
 

?%
 

Brand
 vs.
  direct
 resp.
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

110
 

>
 Media
 aKribu-on
 example
 
Even/weighted
  a8ribu.on
 

Last
 click
  a8ribu.on
 

COST
 PER
 CONVERSION
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  111
 

>
 Media
 aKribu-on
 example
 
TV/Print
 

?
 

Even/weighted
  a8ribu.on
 

Website
  content
 

?
 

Internal
  ads
 

?
 

Email
  Direct
  mail
 
COST
 PER
 CONVERSION
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  112
 

?
 

?
 

Last
 click
  a8ribu.on
 

>
 Media
 aKribu-on
 example
 
TOTAL
 CONVERSION
 VALUE
 

Increase
 
  spend
 

Reduce
  spend
 

Increase
 
  spend
 

ROI
 FULL
 PURCHASE
 PATH
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  113
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

114
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

115
 

Exercise:
 Neglected
 keywords
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

116
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Channel
 integra-on
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  117
 

>
 Tracking
 offline
 responses
 online
 
§  Search
 calls
 to
 ac.on
 for
 TV,
 radio,
 print
 
–  Unique
 search
 term
 only
 adver.sed
 in
 print
 so
 all
 
  responses
 from
 that
 term
 must
 have
 come
 from
 print
  –  Brand.com/customer-­‐name
 redirects
 to
 new
 URL
 that
  includes
 
 tracking
 parameter
 iden.fying
 response
 as
 DM
  –  Survey
 website
 visitors
 that
 have
 come
 to
 site
 directly
 
  or
 via
 branded
 search
 about
 their
 media
 habits,
 etc
  –  Combine
 raw
 data
 from
 online
 purchase
 path,
 website
 entry
  survey
 and
 offline
 sales
 with
 offline
 media
 placement
 data
 in
  tradi.onal
 (econometric)
 media
 a8ribu.on
 model
 
©
 Datalicious
 Pty
 Ltd
  118
 

§  PURLs
 (personalised
 URLs)
 for
 direct
 mail
 

§  Website
 entry
 survey
 for
 direct/branded
 visits
 

§  Combine
 data
 sets
 into
 media
 a8ribu.on
 model
 

October
 2012
 

>
 Personalised
 URLs
 for
 direct
 mail
 
ChrisBartens.company.com
 >
 redirect
 to
 >
 company.com?
 

 

utm_id=neND&
  Demographics=M|35&
  CustomerSegment=A1&
  CustomerValue=High&
  CustomerSince=2001&
  ProductHistory=A6&
  NextBestOffer=A7&
  ChurnRisk=Low
  [...]
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  119
 

>
 Search
 call
 to
 ac-on
 for
 offline
 
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

120
 

>
 Econometric
 media
 modelling
 

Use
 of
 tradi.onal
 econometric
  modelling
 to
 measure
 the
  impact
 of
 communica.ons
 on
  sales
 for
 offline
 channels
 where
  it
 cannot
 be
 measured
 directly
  through
 smart
 calls
 to
 ac.on
  online
 (and
 thus
 cookie
 level
  purchase
 path
 data).
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  121
 

>
 Tracking
 offline
 sales
 online
 
§  Email
 click-­‐through
  §  First
 login
 a{er
 purchase
  §  Unique
 phone
 numbers
 
–  Include
 offline
 sales
 flag
 in
 1st
 email
 click-­‐through
 URL
 a{er
  offline
 sale
 to
 track
 an
 ‘assisted
 offline
 sales’
 conversion
  –  Similar
 to
 the
 above
 method,
 however
 offline
 sales
 flag
  happens
 via
 JavaScript
 parameter
 defined
 on
 1st
 login
  –  Assign
 unique
 website
 numbers
 to
 responses
 from
 specific
  channels,
 search
 terms
 or
 even
 individual
 visitors
 to
 match
  offline
 call
 center
 results
 back
 to
 online
 ac.vity
  –  Survey
 website
 visitors
 to
 at
 least
 measure
 purchase
 
  intent
 in
 case
 actual
 offline
 sales
 cannot
 be
 tracked
 
©
 Datalicious
 Pty
 Ltd
  122
 

§  Website
 entry
 survey
 for
 purchase
 intent
 

October
 2012
 

>
 Offline
 sales
 driven
 by
 online
 
Adver-sing
 
  campaign
  Phone
  sales
  Fulfilment,
  CRM,
 etc
 

Retail
  sales
 

Confirma-on
  email,
 1st
 login
 

Website
  research
 

Online
  sales
 

Online
 sales
  confirma-on
 

Virtual
 sales
 
  confirma-on
 

Cookie
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

123
 

>
 Email
 click-­‐through
 iden-fica-on
 
h8p://www.company.com/email-­‐landing-­‐page.html?
 

 

utm_id=neNCu&
  CustomerID=12345&
  Demographics=M|35&
  CustomerSegment=A1&
  CustomerValue=High&
  ProductHistory=A6&
  NextBestOffer=A7&
  ChurnRisk=Low
  [...]
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  124
 

>
 Login
 landing
 and
 exit
 pages
 
Customer
 data
 exposed
 in
 page
 or
 URL
 on
 login
 or
 logout
 
 

 

CustomerID=12345&
  Demographics=M|35&
  CustomerSegment=A1&
  CustomerValue=High&
  ProductHistory=A6&
  NextBestOffer=A7&
  ChurnRisk=Low
  [...]
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

125
 

>
 Combining
 data
 sources
 
Website
 behavioural
 data
 

Campaign
 response
 data
 

+
 
©
 Datalicious
 Pty
 Ltd
 

The
 whole
 is
 greater
 
  than
 the
 sum
 of
 its
 parts
 

Customer
 profile
 data
 

October
 2012
 

126
 

>
 Transac-ons
 plus
 behaviours
 
CRM
 Profile
 
one-­‐off
 collec.on
 of
 demographical
 data
 
  customer
 lifecycle
 metrics
 and
 key
 dates
  predic.ve
 models
 based
 on
 data
 mining
 

Site
 Behaviour
 

age,
 gender,
 address,
 etc
 

profitability,
 expira-on,
 etc
  propensity
 to
 buy,
 churn,
 etc
 
historical
 data
 from
 previous
 transac.ons
 

average
 order
 value,
 points,
 etc
 

+
 
©
 Datalicious
 Pty
 Ltd
 

browsing,
 checkout,
 etc
 
tracking
 of
 content
 preferences
 

tracking
 of
 purchase
 funnel
 stage
 

products,
 brands,
 features,
 etc
 
tracking
 of
 external
 campaign
 responses
 

search
 terms,
 referrers,
 etc
 
tracking
 of
 internal
 promo.on
 responses
 

emails,
 internal
 search,
 etc
 

Updated
 Occasionally
 
October
 2012
 

Updated
 Con-nuously
 
127
 

>
 Customer
 profiling
 in
 ac-on
 
 
Using
 website
 and
 email
 responses
  to
 learn
 a
 li8le
 bite
 more
 about
  subscribers
 at
 every
 
  touch
 point
 to
 keep
 
 refining
 profiles
  and
 messages.
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

128
 

>
 Unique
 visitor
 overes-ma-on
 
 
The
 study
 examined
 
  data
 from
 two
 of
 
  the
 UK’s
 busiest
 
  ecommerce
 
  websites,
 ASDA
  and
 William
 Hill.
 
  Given
 that
 more
 
  than
 half
 of
 all
 page
 
  impressions
 on
 these
 
  sites
 are
 from
 logged-­‐in
 
  users,
 they
 provided
 a
 robust
 
  sample
 to
 compare
 IP-­‐based
 and
 cookie-­‐based
 analysis
 against.
  The
 results
 were
 staggering,
 for
 example
 an
 IP-­‐based
 approach
  overes.mated
 visitors
 by
 up
 to
 7.6
 .mes
 whilst
 a
 cookie-­‐based
  approach
 overes-mated
 visitors
 by
 up
 to
 2.3
 -mes.
 
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
 
Source:
 White
 Paper,
 RedEye,
 2007
 

129
 

>
 Maximise
 iden-fica-on
 points
 
 
160%
  140%
  120%
  100%
  80%
  60%
  40%
  20%
 
0
  4
  8
  12
  16
  20
  24
  28
  32
  36
  40
  44
  48
 

−−−
 Probability
 of
 iden.fica.on
 through
 Cookies
 

Weeks
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  130
 

>
 Combining
 targe-ng
 plaTorms
 

On-­‐site
 
  targe.ng
 

Off-­‐site
  targe.ng
 

CRM
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

131
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Re-­‐marke-ng
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  132
 

>
 Importance
 of
 online
 experience
 
The
 consumer
 decision
 process
 is
 changing
 from
 linear
 to
 circular.
 

Considera-on
 
  set
 now
 grows
  during
 online
  research
 phase
  which
 increases
  importance
 of
  user
 experience
  during
 that
 phase
 
October
 2012
 

Online
 research
 
 

©
 Datalicious
 Pty
 Ltd
 

133
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

134
 

>
 Increase
 revenue
 by
 10-­‐20%
 
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

135
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

136
 

APPLY
 NOW
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

137
 

>
 Network
 wide
 re-­‐targe-ng
 
Product
 A
  Product
 B
  Product
 C
 

Product
 A
  prospect
 

Product
 B
  prospect
 

Product
 C
  prospect
 

Product
 B
  prospect
 

Product
 C
  prospect
 

Product
 A
  prospect
 

Product
 A
  customer
 

Product
 B
  customer
 

Product
 C
  customer
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

138
 

>
 Network
 wide
 re-­‐targe-ng
 
Group
 wide
 campaign
 with
 approximate
 impression
 targets
 by
 product
 rather
 than
 hard
 budget
 limita-ons
 

Product
 A
  prospect
 

Product
 B
  prospect
 

Product
 C
  prospect
 

Product
 B
  prospect
 

Product
 C
  prospect
 

Product
 A
  prospect
 

Product
 A
  customer
 

Product
 B
  customer
 

Product
 C
  customer
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

139
 

>
 Story
 telling
 or
 ad-­‐sequencing
 
Introducer
  Influencer
  Influencer
  Closer
 

$
 

Message
 1
 

Message
 2
 

Message
 3
 

Message
 4
 

Product
 A
 

Message
 1
 

Message
 2
 

Message
 3
 

Message
 4
 

Product
 B
 

Message
 1
 

Message
 2
 

Message
 3
 

Message
 4
 

Product
 C
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

140
 

>
 Ad-­‐sequencing
 in
 ac-on
 
Marke.ng
 is
 about
  telling
 stories
 and
  stories
 are
 not
 sta.c
  but
 evolve
 over
 .me
 

Ad-­‐sequencing
 can
 help
 to
  evolve
 stories
 over
 .me
 the
 
  more
 users
 engage
 with
 ads
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  141
 

>
 Targe-ng:
 Quality
 vs.
 quan-ty
 
30%
 new
 visitors
 with
 no
  previous
 website
 history
  aside
 from
 campaign
 or
  referrer
 data
 of
 which
  maybe
 50%
 is
 useful
  30%
 repeat
 visitors
 with
  referral
 data
 and
 some
  website
 history
 allowing
  50%
 to
 be
 segmented
 by
  content
 affinity
  10%
 serious
  prospects
  with
 limited
  profile
 data
 
142
 

30%
 exis-ng
 customers
 with
 extensive
  profile
 including
 transac.onal
 history
 of
  which
 maybe
 50%
 can
 actually
 be
  iden.fied
 as
 individuals
 
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
 

>
 ANZ
 home
 page
 targe-ng
 
ANZ
 home
 page
  re-­‐targe.ng
 and
  merchandising
  combined
 with
  landing
 page
  op.misa.on
  delivered
 an
  increase
 in
 offer
  response
 and
  conversion
 rates
  with
 an
 overall
  project
 ROI
 of
  578%
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  143
 

Exercise:
 Re-­‐targe-ng
 matrix
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

144
 

>
 Exercise:
 Re-­‐targe-ng
 matrix
 
Purchase
  Cycle
  Default,
  awareness
  Research,
  considera-on
  Purchase
  intent
  Exis-ng
  customer
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
 

Segmenta-on
 based
 on:
 Search
 keywords,
  display
 ad
 clicks
 and
 website
 behaviour
 

Data
 
  Points
 

Default
  Product
 
  view,
 etc
  Checkout,
  chat,
 etc
  Login,
 email
  click,
 etc
 
145
 

>
 Exercise:
 Re-­‐targe-ng
 matrix
 
Purchase
  Cycle
  Default,
  awareness
  Research,
  considera-on
  Purchase
  intent
  Exis-ng
  customer
 
October
 2012
 

Segmenta-on
 based
 on:
 Search
 keywords,
  display
 ad
 clicks
 and
 website
 behaviour
  Default
  Acquisi-on
  message
 D1
  Acquisi-on
  message
 D2
  Acquisi-on
  message
 D3
  Cross-­‐sell
  message
 D4
  Product
 A
  Acquisi-on
  message
 A1
  Acquisi-on
  message
 A2
  Acquisi-on
  message
 A3
  Cross-­‐sell
  message
 A4
 
©
 Datalicious
 Pty
 Ltd
 

Product
 B
  Acquisi-on
  message
 B1
  Acquisi-on
  message
 B2
  Acquisi-on
  message
 B3
  Cross-­‐sell
  message
 B4
 

Data
 
  Points
 

Default
  Product
 
  view,
 etc
  Checkout,
  chat,
 etc
  Login,
 email
  click,
 etc
 
146
 

Google:
 “enable
 remarke-ng
 google
 analy-cs”
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

147
 

Exercise:
 Google
 Analy-cs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

148
 

Exercise:
 Remarke-ng
 lists
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

149
 

>
 Unique
 phone
 numbers
 
2
 out
 of
 3
 callers
  hang
 up
 as
 they
  cannot
 get
 their
 
  informa.on
 fast
  enough.
 
  Unique
 phone
  numbers
 can
  help
 improve
  call
 experience.
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

150
 

>
 Unique
 phone
 numbers
 
§  1
 unique
 phone
 number
 
 
–  Phone
 number
 is
 considered
 part
 of
 the
 brand
  –  Media
 origin
 of
 calls
 cannot
 be
 established
  –  Added
 value
 of
 website
 interac.on
 unknown
 

§  2-­‐10
 unique
 phone
 numbers
 
–  Different
 numbers
 for
 different
 media
 channels
  –  Exclusive
 number(s)
 reserved
 for
 website
 use
  –  Call
 origin
 data
 more
 granular
 but
 not
 perfect
  –  Difficult
 to
 rotate
 and
 pause
 numbers
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  151
 

>
 Unique
 phone
 numbers
 
§  10+
 unique
 phone
 numbers
 
–  Different
 numbers
 for
 different
 media
 channels
  –  Different
 numbers
 for
 different
 product
 categories
  –  Different
 numbers
 for
 different
 conversion
 steps
  –  Call
 origin
 becoming
 useful
 to
 shape
 call
 script
  –  Feasible
 to
 pause
 numbers
 to
 improve
 integrity
  –  Different
 numbers
 for
 different
 website
 visitors
  –  Call
 origin
 and
 .me
 stamp
 enable
 individual
 match
  –  Call
 conversions
 matched
 back
 to
 search
 terms
 
©
 Datalicious
 Pty
 Ltd
  152
 

§  100+
 unique
 phone
 numbers
 

October
 2012
 

>
 Website
 call
 center
 integra-on
 
Purchase
  Cycle
  Default,
  awareness
  Segmenta-on
 based
 on:
 Search
 keywords,
  display
 ad
 clicks
 and
 website
 behaviour
  Default
  Product
 A
  Product
 B
  Data
 
  Points
 

1300
 000
 001
  1300
 000
 005
  1300
 000
 009
 

Default
  Product
 
  view,
 etc
  Checkout,
  chat,
 etc
  Login,
 email
  click,
 etc
 
153
 

Research,
  1300
 000
 002
  1300
 000
 006
  1300
 000
 010
  considera-on
  Purchase
  intent
  Exis-ng
  customer
 
October
 2012
 

1300
 000
 003
  1300
 000
 007
  1300
 000
 011
  1300
 000
 004
  1300
 000
 008
  1300
 000
 012
 
©
 Datalicious
 Pty
 Ltd
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

154
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

155
 

October
 2012
 

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 Datalicious
 Pty
 Ltd
 

156
 

October
 2012
 

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 Datalicious
 Pty
 Ltd
 

157
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 Landing
 pages
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  158
 

Don’t
 reinvent
 the
 wheel
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

159
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

160
 

>
 Anatomy
 of
 a
 perfect
 landing
 page
 
1.  2.  3.  4.  5.  6.  7.  8.  9.  Page
 headline
 and
 ad
 copy
  Clear
 and
 concise
 headlines
  Impeccable
 grammar
  Taking
 advantage
 of
 trust
 indicators
  Using
 a
 strong
 call
 to
 ac.on
  Bu8ons
 and
 call
 to
 ac.on
 should
 stand
 out
  Go
 easy
 on
 the
 number
 of
 links
  Use
 images
 and
 video
 that
 relate
 to
 copy
  Keep
 it
 above
 the
 fold
 at
 all
 .mes
 
©
 Datalicious
 Pty
 Ltd
  161
 

October
 2012
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

162
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

163
 

>
 The
 holy
 trinity
 of
 tes-ng
 
1.
 The
 headline
 
–  Have
 a
 headline!
  –  Headline
 should
 be
 concrete
  –  Headline
 should
 be
 first
 thing
 visitors
 look
 at
  –  Don’t
 have
 too
 many
 calls
 to
 ac.on
  –  Have
 an
 ac.onable
 call
 to
 ac.on
  –  Have
 a
 big,
 prominent,
 visible
 call
 to
 ac.on
 

2.
 Call
 to
 ac-on
 

3.
 Social
 proof
 

–  Logos,
 number
 of
 users,
 tes.monials,
 
  case
 studies,
 media
 coverage,
 etc
 
  October
 2012
  ©
 Datalicious
 Pty
 Ltd
 

164
 

>
 Best
 prac-ce
 tes-ng
 roadmap
 
§  Phase
 1:
 A/B
 test
 
–  Test
 same
 landing
  page
 content
 in
  different
 layouts
 
Element
 #1:
 Prominent
 headline
 

§  Phase
 2:
 MV
 test
 
–  Test
 different
 content
  element
 combina.ons
  within
 winning
 layout
 

Suppor.ng
 
  content
 

Element
 #2:
 
  Call
 to
 ac.on
 

§  Phase
 3:
 Repeat
 
–  Hero
 vs.
 challengers
 

Element
 #3:
 Social
 proof
 /
 trust
  Terms
 and
 condi.ons
 

§  Phase
 4:
 Re-­‐targe.ng
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
 

165
 

>
 G&E
 Capital
 landing
 pages
 
Project
 plaforms
 used:
 Adobe
 
  SiteCatalyst
 and
 Test&Target
 

Before
  Removal
 of
 distrac.ons
  such
 as
 naviga.on
 and
  search
 op.ons
 resulted
 
  in
 increased
 response
  rates
 with
 ROI
 of
 492%
 
October
 2012
 

A{er
 
©
 Datalicious
 Pty
 Ltd
  166
 

>
 Macquarie
 landing
 pages
 
Before
  Project
 plaforms
 used:
 Adobe
 
  SiteCatalyst
 and
 Test&Target
  A{er
 

The
 small
 things
 count:
  Simplifica.on
 down
 to
 1
  set
 of
 bu8ons
 resulted
 in
  increased
 response
 rate
  and
 project
 ROI
 of
 547%
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  167
 

>
 A/B
 vs.
 MV
 (Taguchi)
 method
 
Rather
 than
 tes.ng
 all
 combina.ons
 of
 alterna.ve
 page
 content
 (i.e.
 A/B
  tes.ng),
 the
 Taguchi
 Method
 (i.e.
 mul.variate
 MV
 tes.ng)
 is
 a
 way
 of
  reducing
 the
 number
 of
 different
 test
 scenarios
 (recipes)
 but
 s.ll
 yield
  useful
 test
 results.
 Essen.ally,
 the
 op.mal
 page
 design
 is
 ‘predicted’
 
  from
 the
 test
 results
 by
 analysing
 which
 page
 elements
 and
 element
  combina.ons
 were
 most
 influen.al
 overall.
 
 
Test
 elements
 
  (i.e.
 parts
 of
 page)
 
3
  7
  4
  5
 
October
 2012
 

Test
 alterna-ves
 
  (i.e.
 test
 content)
 
2
  2
  3
  4
 

Full
 set
 of
 test
  combina-ons
 (A/B)
 
8
  128
  81
  1024
 

Reduced
 Taguchi
 
  test
 scenarios
 (MV)
 
4
  8
  9
  16
 
168
 

©
 Datalicious
 Pty
 Ltd
 

>
 Sufficient
 sample
 size
 for
 tests
 
§  MV
 tes.ng
 requires
 a
 greater
 volume
 of
 visitors
 than
  A/B
 tes.ng.
 The
 volume
 required
 is
 dependent
 on:
 
–  The
 number
 of
 elements
 on
 the
 page
 (and
 how
 many
  alterna.ves
 for
 each
 element)
  –  Whether
 targe.ng
 specific
 segments
 is
 part
 of
 the
 test
  or
 whether
 you
 want
 to
 examine
 success
 by
 different
  segments
 of
 traffic
  –  Expected
 control
 page
 conversion
 rates
  –  How
 long
 you
 can
 afford
 to
 have
 the
 test
 in
 market
  without
 viola.ng
 the
 test
 condi.ons
  –  Whether
 you
 can
 afford
 to
 present
 the
 test
 to
 all
 traffic
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  169
 

Exercise:
 Sta-s-cal
 significance
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

170
 

How
 many
 click-­‐throughs
 do
 you
 need
 to
 test
 3
 
  landing
 pages
 if
 you
 have
 30,000
 visitors?
  How
 many
 conversions
 do
 you
 need
 to
 test
 3
 
  landing
 pages
 if
 you
 have
 30,000
 visitors?
  How
 many
 click-­‐throughs
 do
 you
 need
 to
 test
 3
 landing
 pages
 
  if
 you
 have
 30,000
 visitors
 but
 only
 expose
 10%
 to
 the
 test?
 

October
 2012
 

Google
 “nss
 sample
 size
 calculator”
 

©
 Datalicious
 Pty
 Ltd
 

171
 

How
 many
 click-­‐throughs
 do
 you
 need
 to
 test
 3
 
  landing
 pages
 if
 you
 have
 30,000
 visitors?
  369
 per
 test
 or
 1,107
 clicks
 in
 total
  How
 many
 conversions
 do
 you
 need
 to
 test
 3
 
  landing
 pages
 if
 you
 have
 30,000
 visitors?
  369
 per
 test
 or
 1,107
 conversions
 in
 total
  How
 many
 click-­‐throughs
 do
 you
 need
 to
 test
 3
 landing
 pages
 
  if
 you
 have
 30,000
 visitors
 but
 only
 expose
 10%
 to
 the
 test?
  277
 per
 test
 or
 831
 clicks
 in
 total
 

October
 2012
 

Google
 “nss
 sample
 size
 calculator”
 

©
 Datalicious
 Pty
 Ltd
 

172
 

>
 Telstra
 bundles
 pages
 

Telstra
 bundles
 page
 op.misa.on
 combined
 call
 center
 data
 (each
 page
  had
 a
 unique
 phone
 number)
 with
 Adobe
 Test&Target
 online
 data
 and
  delivered
 a
 cross-­‐channel
 conversion
 rate
 increase
 with
 an
 ROI
 of
 647%
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  173
 

>
 Other
 tes-ng
 considera-ons
 
§  Avoiding
 ‘no
 results’
 by
 making
 test
 execu.ons
  as
 obviously
 different
 as
 possible
 to
 consumers
  §  Limit
 poten.al
 ‘nega.ve’
 test
 impact
 on
  conversions
 by
 limi.ng
 the
 test
 to
 a
 smaller
  sample
 size
 ini.ally
  §  Avoid
 launching
 tests
 during
 major
 above
 the
  line
 campaign
 ac.vity
 as
 this
 might
 magnify
 any
  incremental
 gains
 of
 tested
 scenarios
 and
 the
  test
 results
 can’t
 then
 be
 replicated
 in
 a
 non-­‐ campaign
 period
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  174
 

>
 Introducing
 hero
 vs.
 challengers
 

Hero
 #1
  CTR
 =
 1%
 

New
 hero
 #2
 
  =
 Challenger
 #2
 

Challenger
 #1
  CTR
 =
 0.5%
 
October
 2012
 

Challenger
 #2
  CTR
 =
 1.5%
 

Challenger
 #3
  CTR
 =
 1%
 

Challenger
 #4
  CTR
 =
 1%
 
175
 

©
 Datalicious
 Pty
 Ltd
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

176
 

Exercise:
 Op-misa-on
 ideas
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

177
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

178
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

179
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

180
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

181
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

182
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

183
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

184
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

185
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

186
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

187
 

>
 Eye
 tracking
 vs.
 mouse
 tracking
 
§  Eye
 tracking
 pros
 
–  100%
 accurate
  –  Controlled
  environment
  –  Open
 dialogue
 

§  Mouse
 tracking
 pros
 
–  Natural
 environment
  –  No
 observer
 effect
  –  Global
 par.cipa.on
  –  Low
 cost
 

§  Eye
 tracking
 cons
 
–  High
 costs
  –  Limited
 scope
  –  Observer
 effect
 
October
 2012
 

§  Mouse
 tracking
 cons
 
–  No
 pre-­‐defined
 tests
  –  No
 research
 control
  –  No
 visitor
 feedback
 
©
 Datalicious
 Pty
 Ltd
  188
 

>
 Segmented
 heat
 maps
 are
 key
 

Independent
 research
 shows
 84-­‐88%
 correla.on
 between
 mouse
 and
 eye
 movements*
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  189
 

Heat
 map
 for
 new
 visitors
 vs.
 exis-ng
 customers
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

190
 

>
 New
 approach
 to
 web
 design
 
§  Standard
 approach
 
–  Analyst
 iden.fies
 issue
  and
 briefs
 agency
  –  Agency
 develops
 new
  designs,
 trashes
 some
  –  Agency
 or
 developers
  implement
 new
 design
  –  Some.mes
 mul.ple
  designs
 are
 tested
 
 

§  Try
 something
 new
 
–  Analyst
 iden.fies
 issue
  and
 briefs
 agency
 (incl.
  current
 heat
 maps)
  –  Agency
 develops
 new
  designs
 and
 tests
 them
  (predic.ve
 heat
 maps)
  –  Winning
 designs
 are
  developed
 and
 tested
  (incl.
 new
 heat
 maps)
  –  Top
 performing
 design
  is
 implemented
 
191
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

>
 New
 approach
 to
 web
 design
 
§  §  §  §  §  §  §  §  §  §  §  Step
 1:
 Iden.fy
 problem
 pages
  Step
 2:
 Priori.se
 pages
 for
 tes.ng
  Step
 3:
 Pick
 page
 for
 tes.ng
 and
 op.misa.on
  Step
 4:
 Implement
 and
 analyse
 heat-­‐map
  Step
 5:
 Design
 test
 and
 brief
 crea.ve
 agencies
  Step
 6:
 Pick
 best
 designs
 with
 predic.ve
 heat-­‐maps
  Step
 7:
 Develop
 different
 page
 execu.ons
  Step
 8:
 Execute,
 monitor
 (and
 refine)
 test
  Step
 9:
 Analyse
 test
 and
 verify
 predic.ve
 heat-­‐maps
  Step
 10:
 Implement
 winning
 test
 design
  Step
 11:
 Pick
 next
 page
 &
 repeat
 steps
 3-­‐10
 
©
 Datalicious
 Pty
 Ltd
  192
 

October
 2012
 

Targe-ng
 before
 tes-ng
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

193
 

Exercise:
 Tes-ng
 matrix
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

194
 

>
 Exercise:
 Tes-ng
 matrix
 
Test
  Segment
  Content
  Success
  Difficulty
  Poten-al
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

195
 

>
 Exercise:
 Tes-ng
 matrix
 
Test
  Segment
  Content
  Offer
 1A
  Test
 1
  Product
 1
  Offer
 1B
  Offer
 1C
  Offer
 2A
  Test
 2
  Product
 2
  Offer
 2B
  Offer
 2C
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  196
 

Success
 

Difficulty
 

Poten-al
 

Clicks
 

Low
 

$100k
 

Clicks
 

High
 

$100k
 

>
 Response
 website
 design
 

Through
 fluid
 grids
 and
 media
 query
 adjustments,
  responsive
 design
 enables
 web
 page
 layouts
 to
 adapt
  to
 a
 variety
 of
 screen
 sizes.
 The
 content
 of
 the
 page
  does
 not
 change,
 just
 the
 way
 it
 is
 displayed
 for
 each
  screen
 size.
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  197
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

198
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

199
 

>
 Online
 form
 best
 prac-ce
 
Maximise
 data
 integrity
  Age
 vs.
 year
 of
 birth
  Free
 text
 vs.
 op.ons
 

Use
 auto-­‐complete
 
  wherever
 possible
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  200
 

>
 Social
 single-­‐sign
 on
 services
 
h8p://vimeo.com/16469480
 
 

Gigya.com
  Janrain.com
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

201
 

>
 Garbage
 in,
 garbage
 out
 
Avinash
 Kaushik:
 
  “The
 principle
 of
 garbage
 in,
 garbage
 out
  applies
 here.
 […
 what
 makes
 a
 behaviour
  targe;ng
 pla<orm
 ;ck,
 and
 produce
 results,
 is
  not
 its
 intelligence,
 it
 is
 your
 ability
 to
 actually
  feed
 it
 the
 right
 content
 which
 it
 can
 then
 target
  [….
 You
 feed
 your
 BT
 system
 crap
 and
 it
 will
  quickly
 and
 efficiently
 target
 crap
 to
 your
  customers.
 Faster
 then
 you
 could
 
  ever
 have
 yourself.”
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  202
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 About
 Datalicious
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  203
 

>
 Short
 but
 sharp
 history
 
§  §  §  §  §  §  §  §  § 
 
  Datalicious
 was
 founded
 in
 November
 2007
  Official
 Adobe
 &
 Google
 Analy.cs
 partner
  360
 data
 agency
 with
 team
 of
 data
 specialists
  Combina.on
 of
 analysts
 and
 developers
  Blue
 chip
 clients
 across
 all
 industry
 ver.cals
  Carefully
 selected
 best
 of
 breed
 partners
  Driving
 industry
 best
 prac.ce
 with
 ADMA
  Turning
 data
 into
 ac.onable
 insights
  Execu.ng
 smart
 data
 driven
 campaigns
 
©
 Datalicious
 Pty
 Ltd
  204
 

October
 2012
 

>
 Smart
 data
 driven
 marke-ng
 
“Using
 data
 to
 widen
 the
 funnel”
 

Media
 AKribu-on
 &
 Modeling
Op-mise
 channel
 mix,
 predict
 sales
 


 

Targe-ng
 &
 Merchandising
 
Increase
 relevance,
 reduce
 churn
 

Tes-ng
 &
 Op-misa-on
 
Remove
 barriers,
 drive
 sales
 

Boos-ng
 ROMI
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  205
 

>
 Wide
 range
 of
 data
 services
 
Data
 
PlaTorms
 

Insights
 
Analy-cs
 

  Data
 mining
 and
 modelling
 
  Tableau,
 Splunk,
 SPSS,
 etc
 
  Customised
 dashboards
 
  Media
 aKribu-on
 analysis
 
  Media
 mix
 modelling
 
  Social
 media
 monitoring
 
  Customer
 segmenta-on
 

Ac-on
 


  Data
 collec-on
 and
 processing
 
  Adobe,
 Google
 Analy-cs,
 etc
 
  Web
 and
 mobile
 analy-cs
 
  Tag-­‐less
 online
 data
 capture
 
  Retail
 and
 call
 center
 analy-cs
 
  Data
 warehouse
 solu-ons
 
  Single
 customer
 view
 

Campaigns
 


  Data
 usage
 and
 applica-on
 
  Alterian,
 SiteCore,
 Inxmail,
 etc
 
  Targe-ng
 and
 merchandising
 
  Marke-ng
 automa-on
 
  CRM
 strategy
 and
 execu-on
 
  Data
 driven
 websites
 
  Tes-ng
 programs
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

206
 

>
 Over
 50
 years
 of
 experience
 

Chris.an
 Bartens
 
Founder
 &
 Director
 
  §  Bachelor
 of
 Business
  Management
 with
  marke.ng
 focus
  §  Web
 analy.cs
 and
  digital
 marke.ng
 
  work
 experience
  §  Space2go,
 E-­‐Lo{,
  Tourism
 Australia
  §  SuperTag
 founder,
  ADMA
 Analy.cs
 Chair,
  I-­‐COM
 Board
 Member
 
  LinkedIn
 profile
  October
 2012
 

Elly
 Gillis
 

General
 Manager
 
  §  Bachelor
 of
  Communica.ons
 with
  print
 and
 digital
 focus
  §  Digital
 marke.ng
 and
  project
 management
  work
 experience
  §  M&C
 Saatchi,
 Mark,
  Holler,
 Tequila,
 IAG,
 
  OneDigital,
 Telstra
  §  Australian
 gold
 medal
  in
 surf
 boat
 rowing
 
  LinkedIn
 profile
 

Michael
 Savio
 

Head
 of
 Insights
 
  §  Bachelor
 of
 Arts
 &
  Science
 with
 applied
  mathema.cs
 focus
  §  CRM
 and
 marke.ng
  research
 and
 analy.cs
  work
 experience
  §  ANZ
 Bank,
 Australian
  Bureau
 of
 Sta.s.c,
  DBM
 Consultants
  §  ADMA
 lecturer
 on
  marke.ng
 tes.ng
 
  LinkedIn
 profile
 

Chaoming
 Li
 

Head
 of
 Data
 
  §  Bachelor
 of
 
  Technology
 with
  microelectronics
 focus
  §  So{ware
 and
 website
  development
 work
  experience
  §  Standards
 Australia,
 
  DF
 Securi.es,
 Globiz,
  Etang
  §  Developing
 his
 own
  CMS
 plaform
 
  LinkedIn
 profile
  207
 

©
 Datalicious
 Pty
 Ltd
 

>
 Best
 of
 breed
 partners
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

208
 

>
 Clients
 across
 all
 industries
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

209
 

>
 Great
 customer
 feedback
 
“[…]
 Datalicious
 quickly
 earned
 our
 respect
 and
 confidence
 […]
 understand
 our
  business
 needs,
 deliver
 value,
 push
 our
 thinking
 […].
 Likeable,
 transparent
 and
  trustworthy.
 I
 would
 be
 happy
 to
 recommend
 Datalicious
 to
 anyone.”
 Murray
  Howe,
 Execu.ve
 Manager,
 Suncorp
 Group
 
  "[…]
 Datalicious
 brought
 with
 them
 best
 prac@ce
 analy@cs
 to
 demonstrate
 the
  true
 value
 of
 our
 marke@ng
 dollars
 […]
 have
 become
 a
 cri;cal
 business
 partner
 […]
  provided
 great
 insights
 which
 have
 driven
 key
 business
 decisions.”
 Trang
 Young,
  Senior
 Marke.ng
 Manager,
 E*Trade
 Australia
 
 
 “The
 Datalicious
 guys
 are
 great
 to
 work
 along
 side
 […]
 'no
 stone
 unturned'
  approach
 to
 finding
 solu@ons
 to
 challenges
 […]
 knowledge
 and
 passion
 for
 web
  analy@cs
 and
 best
 of
 breed
 web
 op;miza;on
 was
 second
 to
 none”
 Steve
 Brown,
  Senior
 Business
 Analyst,
 Vodafone
 
 
  “[…]
 The
 Vodafone
 implementa@on
 of
 SiteCatalyst
 is
 one
 of
 the
 most
 impressive
 
  I
 have
 seen
 and
 ranks
 in
 the
 top
 10
 […].
 It
 is
 an
 amazing
 founda@on
 for
 taking
  ac@on
 on
 the
 data
 and
 improving
 ROI.”
 Adam
 Greco,
 Consul.ng
 Lead,
 Omniture
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  210
 

>
 Great
 customer
 feedback
 
"[…]
 Datalicious
 understand
 the
 value
 of
 informa@on
 and
 how
 to
 leverage
 it
 using
  best
 of
 breed
 soFware.
 I
 would
 recommend
 the
 team
 without
 hesita@on
 [...]."
  James
 Fleet,
 Marke.ng
 Director,
 Appliances
 Online
 
  "[...]
 Datalicious
 have
 been
 in;mately
 involved
 in
 building
 our
 analy;cs
 solu;on.
  Most
 importantly
 their
 knowledge
 of
 best
 prac@ce
 combined
 with
 innova@ve
  solu@ons
 has
 allowed
 our
 business
 to
 remain
 nimble
 and
 current.
 They
 are
 also
  nice
 guys."
 Tzvi
 Balbin,
 Group
 Digital
 Marke.ng
 Lead,
 Catch
 of
 the
 Day
 
  "[...]
 Datalicious
 are
 helping
 us
 to
 move
 from
 a
 last
 click
 campaign
 measurement
  model
 to
 a
 more
 accurate
 media
 aGribu@on
 approach.
 [...]
 poten;al
 to
  significantly
 change
 our
 media
 planning
 [...].
 Highly
 recommended."
 Keith
 Mirgis,
  Senior
 Digital
 &
 Social
 Media
 Marke.ng
 Manager,
 Telstra
 
  "We
 engaged
 Datalicious
 to
 support
 a
 strategic
 change
 in
 our
 business
 [...]
  understand
 our
 customers
 [and
 their
 transac@ons]
 beGer
 to
 ensure
 we
 retained
 as
  many
 as
 possible
 [...]"
 Natalie
 Farrell,
 Direct
 Marke.ng
 Manager,
 Luxoƒca
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  211
 

101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010
 

>
 About
 SuperTag
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  212
 

>
 The
 Datalicious
 SuperTag
 
Conversion
  Tracking
  Any
  JavaScript
  Conversion
  De-­‐duping
 

Web
  Analy-cs
 

SuperTag
 

Media
  AKribu-on
 
 

Live
 
  Chat
  A/B
 Tes-ng
  Heat
 Maps
 

Behavioral
  Targe-ng
 

Easily
 implement
 and
 update
  any
 tag
 on
 any
 websites
 without
  or
 limited
 IT
 involvement
 
  De-­‐duplicate
 conversions
 for
  CPA
 deals
 and
 align
 repor.ng
  figures
 across
 plaforms
 
  Collect
 accurate
 mul.-­‐channel
  media
 a8ribu.on
 data
 to
  provide
 advanced
 insights
 
  Enable
 advanced
 features
 such
  as
 targe.ng,
 tes.ng
 and
 chat
 to
  op.mise
 user
 experience
 
213
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

>
 Unique
 SuperTag
 architecture
 
Injec.ng
 JavaScript
 tags
 into
 the
  page
 based
 on
 business
 rules
 using
  the
 SuperTag
 top
 and
 bo8om
 containers.
 
 
  The
 SuperTag
 top
 and
 bo8om
 containers
 are
  JavaScript
 func.ons
 called
 in
 the
 page
 code
  just
 a{er
 the
 opening
 <body>
 tag
 and
 just
  before
 the
 closing
 </body>
 tag
 on
 all
 
  page
 across
 all
 domains.
 
  T
 

SuperTag
 

B
 

superT.t()
 

T
 

T
 

superT.b()
 

B
 

B
 

§  One
 tag
 for
 all
 sites
 and
 plaforms
  §  Hosted
 internally
 or
 externally
  §  Fast
 tag
 implementa.on/updates
  §  Increase
 analy.cs
 data
 accuracy
  §  Enables
 code
 tes.ng
 on
 live
 site
  §  Enables
 heat
 map
 implementa.on
  §  Enables
 A/B
 and
 MV
 test
 execu.on
  §  Enables
 cross-­‐channel
 re-­‐targe.ng
  §  Enables
 phone
 number
 targe.ng
 
214
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

>
 Overcoming
 team
 barriers
 

Marke-ng
 

SuperTag
 

Technology
 

Easy
 to
 use
 online
 user
 interface
 enabling
 marketers
 to
 
  manage
 tags
 without
 intensive
 technology
 support
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  215
 

>
 Cross-­‐plaTorm
 integra-on
 
Web
 Analy-cs
  Heat
 Maps
  Targe-ng
  Live
 Chat
  Tes-ng
  CRM/eDMs
  Paid
 Search
  Ad
 Servers
  Affiliates
  DFPs
 

SuperTag
 

Centralised
 uniform
 business
 rules
 to
 trigger
 conversions
  and
 segment
 visitors
 across
 mul.ple
 marke.ng
 plaforms
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  216
 

>
 Conversion
 de-­‐duplica-on
 
Paid
 
  search
  Bid
 
  Mgmt
 

$
 

$
 

Display
  ads
 

Ad
 
  server
 

$
 

SuperTag
 

$
 

Affiliate
  referral
 

Affiliate
  system
 

$
 

$
 

Centralised
 business
 rules
 to
 enable
 accurate
 conversion
 
  de-­‐duplica.on
 across
 mul.ple
 marke.ng
 plaforms
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  217
 

Easy
 to
 use
 drag
 &
 drop
 interface
 to
 manage
 tags
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  218
 

Flexible
 business
 rule
 builder
 to
 suit
 all
 scenarios
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  219
 

Implement
 &
 maintain
 web
 analy-cs
 without
 IT
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  220
 

New
 more
 powerful
 re-­‐targe-ng
 segment
 builder
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  221
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

222
 

Turn
 any
 page
 element
 into
 data
 or
 tes-ng
 areas
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

223
 

>
 SuperTag
 deployment
 op-ons
 
Manual
 
  JavaScript
  management
  Email/FTP
  JavaScript
  publishing
  JavaScript
 
  hos-ng
 on
  client
 server
  Client
 
  website
 

Dedicated
 
  Github
 client
 
  code
 archive
 

CDN
 =
 Content
  delivery
 network
 

JavaScript
 
  hos-ng
 on
  client
 CDN
 

Client
 
  website
 

SuperTag
  JavaScript
  management
 

Real-­‐-me
  JavaScript
  publishing
 

JavaScript
 
  hos-ng
 on
  SuperTag
 CDN
 

Client
 
  website
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

224
 

>
 Unique
 selling
 points
 (USPs)
 
§  Superior
 plaform
 architecture
 for
 more
 flexibility
 
–  Turn
 any
 page
 element
 into
 variables
 for
 data
 
  collec.on
 or
 business
 rules
 for
 tag
 execu.on
  –  Cross-­‐plaform
 integra.on
 and
 data
 exchange
 
  –  Splunk
 integra.on
 for
 advanced
 data
 mining
 

§  Superior
 tes.ng,
 deployment
 and
 audit
 features
  §  No
 lock-­‐in,
 stop
 using
 the
 SuperTag
 at
 any
 .me
  §  All
 inclusive
 pricing
 structure
 incen.vizing
 use
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
 

–  Tes.ng
 of
 tags
 &
 business
 rules
 on
 the
 live
 website
  –  Complete
 audit
 trail
 of
 all
 tag
 changes
 and
 tests
  –  External
 and
 internal
 JavaScript
 hos.ng
 available
  –  Perpetual
 JavaScript
 usage
 rights
 &
 Github
 archive
 

225
 

>
 Blue
 chip
 SuperTag
 clients
 

October
 2012
 

©
 Datalicious
 Pty
 Ltd
 

226
 

>
 Great
 customer
 feedback
 
"Managing
 third
 party
 tags
 has
 never
 been
 easier
 [...]
  simplicity
 of
 seSng
 business
 rules
 [...]
 reduc;on
 in
 CPA
  [...].“
 Jason
 Lima,
 Online
 Marke.ng,
 IMB
 
  "[...]
 SuperTag
 tool
 is
 so
 easy
 to
 use
 [...].
 Live
 tes@ng
 is
  par@cularly
 useful
 [...]
 highly
 recommended
 [...]."
  Helene
 Cameron-­‐Heslop,
 Analyst,
 Appliances
 Online
 
  "SuperTag
 speeds
 up
 tag
 implementa@on
 and
 gives
 us
  increased
 flexibility
 [...]
 manage
 media
 and
 website
  analy;cs
 [...].”
 Alex
 Crompton,
 Head
 of
 Digital,
 Aussie
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  227
 

[email protected]
 
  blog.datalicious.com
 
  twiKer.com/datalicious
 
 
October
 2012
  ©
 Datalicious
 Pty
 Ltd
  228
 

Contact
 us
 

Learn
 more
  Follow
 us
 

Data
 >
 Insights
 >
 Ac-on
 

October
 2012
 

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 Datalicious
 Pty
 Ltd
 

229