CRO Masterclass 21st August 2014

1. Using Web Analy.cs Data
To Challenge, Op.mise &
Support Your Marke.ng Mix
JOANNE CASEY | GLOWMETRICS
August 21st 2014

2. About GlowMetrics
• Focuses on providing a service
which helps businesses build
out digital strategies that are
underpinned by strong digital
data analysis.
• Have offices in both Belfast and
Dublin.
• Hold a range of clients
interna.onally and locally.
Clients

3. What We’ll Be Looking At:
• Tracking towards success
• Understanding mul.-­‐channel
funnels
• The importance of a[ribu.on
• The future of web analy.cs

4. Why Web Analy.cs ?
“Web Analy.cs is the measurement, collec.on, analysis and repor.ng
of Internet data for the purposes of understanding and op.mising web
usage”
-­‐ Web Analy.cs Associa.on
The Benefits of Analy.cs:
• get closer to the customer
• accurately gauge user experience
• increased accountability
• focus and priori.za.on of resources
• conversion rates and enhanced ROI

5. Repor.ng on Stat:
– Visits
– Unique Visitors
– Pageviews
– Pages-­‐Per-­‐Visit
– Time-­‐on-­‐Site
– New Visitors
– Returning Visitors
Reaping Ac.onable insights:
– Where does your most
valuable traffic come from?
– How do visitors from mobile
engage differently from
desktop visitors?
– Where and why are you
loosing visitors?
– How is your offline impac.ng
online sales?
Effec.ve Web Analy.cs

6. Web
Data
???
ROI
Effec.ve Web Analy.cs
A"ribu'on

7. STEP 1: Tagging

8. Metric Analysis in Marke.ng
“I no.ce increasing reluctance on the part
of marke.ng execu.ves to use judgement;
they are coming to rely too much on
research, and they use it as a drunkard
uses a lamp post for support, rather than
for illumina.on.”
-­‐ David Ogilvy
"If you torture data long enough, it will confess to
anything.”
-­‐ Hal Varian

9. Don’t believe the hype
“Your display campaign on MSN delivered
100,000 impressions and 10,000 clicks”
-­‐ Agency
“That Display campaign that you spend
€16,000 on drove €100 in revenue L ”
-­‐ Web Analy.cs

10. Use the URL Tool Builder to track ALL online
campaign traffic:
Campaign Traffic

11. Release the worms!

12. STEP 2: Mul.-­‐Channel Funnels

13. Consumer
journey
is
becoming
more
complex

14. Audience movement in the travel
market before a purchase

15. Understanding the length of the
purchase cycle
Source:
Google
ClickStream
Whitepaper,
August
8th,
2011

16. Campaign
TV
Ad
Radio
Ad
Outdoor
Ad
Print
Ad
Online
Ad
Search
Offline
Sale
Online
Booking
Website
Visit
Social
Media
Engage-­‐
ment
You should understand the impact each
campaign has on another:

17. First
Interac,on
Assist
Interac,ons
Last
Interac,on
Overall Path
Length: 4
Time Lag
(Time to Convert)
Top Paths
Mul.-­‐Channel Paths

18. Mul.-­‐Channel Paths
Remarke.ng
Display
Social Media
PPC
Direct
Organic Search
Assist
Convert
Email

19. Mul.-­‐Channel Funnels

20. STEP 3: A[ribu.on

21. A[ribu.on
Display
Search
Ad
Purchase
Affiliate
Display
Ad
Organic
Search
**I get the
conversion **
Abandon
**I get the
conversion **

22. A[ribu.on

23. A[ribu.on
16
___
17

24. The need for change…

25. Moving
from….
To…
€
Event
Event
Event
Event
Event
€5
€20
€5
€20
€50
Time
Recogni1on
for
all
events.
€
Event
5
Event
4
Event
3
Event
2
Event
1
The
last
click,
Event
5
gets
all
the
credit
€0
€0
€0
€0
€100
Time
Step out of the swimming lane with
a[ribu.on

26. A[ribu.on
The last interac.on will be given 100% of the
conversion credit.
The closer the channel was in driving the
conversion the more weight it gets.
Apply more weight to the channels that started
and finished the conversion.
Apply an equal weigh.ng to each channel that
was key in driving the conversion.

27. A[ribu.on

28. The Future of Web Analy.cs

29. Offline Marries Online:
Blurring of Mix

30. Tac.cs used to be[er analyse offline:
1
2
3

31. +
0
10000
20000
30000
40000
50000
60000
70000
80000
0
5000
10000
15000
20000
25000
30000
Sum
of
2011
Total
Radio
EQ
Sum
of
2011
TVEQ
Sum
of
2011
Press
Sum
of
Display
Impression
Sum
of
Search
Impressions
Sum
of
DI
Visits
2011
Tac.cs used to be[er analyse offline:

32. Key Trends: Removing Data Silos

33. Key Trends: Data Visualisa.on

34. h"p://www.consumerbarometer.com

35. Key Trends: Micro-­‐Personalised
Experiences

36. Remarke.ng

37. Remarke.ng

39. Using
Shopping
Behaviour
Data

40. Using Shopping Behaviour Data

41. Key Trends: Predic.ve Analy.cs

42. Predic.ve Personalisa.on

43. “We’re
in
a
new
era
of
retailing
–
the
era
of
mass
personalisa5on…
…It
will
offer
cheaper
products
to
price-­‐sensi5ve
customers
and
luxury
products
to
wealthier
customers…
…The
power
of
this
approach
was
born
out
by
a
test
we
did
to
sell
maBresses.
When
a
customer
visited
our
website,
we
would
use
Clubcard
data
to
tell
us
if
the
customer
was
more
swayed
by
price
or
quality.
We’d
then
display
the
type
of
maBress
that
best
reflected
that
shopper’s
characteris5c.
Sales
grew
by
10%.”
Source: Tesco’s chief execu.ve Philip Clarke, at the
Global Summit of the Consumer Goods Forum, Turkey
Tesco Clubcard

44. Predic.ve Real-­‐Time Auto Bidding

45. Wrap Up
• Tag, Tag, Tag
• Analyse behaviour between
channels
• Give channels he credit they deserve

46. Slide § 46
Ques.ons?
LinkedIn: h[p://ie.linkedin.com/in/joanneellencasey
Twi[er: @joannecasey
Email: joanne@glowmetrics.com