ADMA Digital Analytics Course

> ADMA Digital Analytics <
Measuring and optimising digital

> Digital analytics course overview
9 am start
§ Metrics framework
§ Campaign tracking
15 min coffee break
§ Measuring brand
§ Media attribution
12.30 pm 30 min lunch
§ Channel integration
§ Re-marketing
15 min coffee break
§ Landing pages
4.30 pm finish
November 2012 © ADMA & Datalicious Pty Ltd 2

> Digital analytics course rules
§ Get involved and be informal!
§ Ask questions, 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 criticize, point out opportunities

> Maximising course outcome
§ Share your expectations so I can adjust
§ Start an action sheet to collect ideas
§ Main digital analytics course outcomes
– Define a metrics framework
– Enable benchmarking across campaigns
– Effectively incorporate analytics into planning
– Understand digital data sources and their limitations
– Accurately attribute conversions across channels
– Develop strategies to extend optimisation past media
– Pull and interpret key reports in Google Analytics
– Impress with insights instead of spreadsheets

> Introductions & expectations
§ Your name
§ Your company
§ Your roles & responsibilities
§ Knowledge gaps you’re hoping to fill
§ Something else about yourself
– Ideal job
– Hobbies

> Metrics framework
101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010

Awareness Interest Desire Action Satisfaction
> AIDA and AIDAS formulas
Social media
New media
Old media

Reach
(Awareness)
Engagement
(Interest & Desire)
Conversion
(Action)
+Buzz
(Delight)
> Simplified AIDAS funnel

People
reached
People
engaged
People
converted
People
delighted
> Marketing is about people
40% 10% 1%

People
reached
People
engaged
People
converted
People
delighted
> Standardised roll-up metrics
Unique browsers,
search impressions,
TV circulation, etc
Unique visitors,
site engagements,
video views, etc
Online sales,
online leads, store
locator searches, etc
Facebook
comments, Tweets,
ratings, support calls, etc
Response rate,
Search response rate,
TV response rate, etc
Conversion rate,
engagement rate,
checkout rate, etc
10%40% 1%
Review rate,
rating rate, comment
rate, NPS rate, etc

People
reached
People
engaged
People
converted
People
delighted
> Provide context with figures
40% 10% 1%
New prospects vs. existing customers
Brand vs. direct response campaign

> Provide context with figures
§ Brand vs. direct response campaign
§ New prospects vs. existing customers
§ Competitive activity, i.e. none, a lot, etc
§ Market share, i.e. small, medium, large, et
§ Segments, i.e. age, location, 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

Exercise: Google Analytics

Exercise: Internal traffic

Exercise: Custom segments

Google: “google analytics custom variables”

> Conversion funnel 1.0
November 2012
Conversion funnel
Product page, add to shopping cart, view shopping cart,
cart checkout, payment details, shipping information,
order confirmation, etc
Conversion event
Campaign responses
© ADMA & Datalicious Pty Ltd 19

> Conversion funnel 2.0
November 2012
Campaign responses (inbound spokes)
Offline campaigns, banner ads, email marketing,
referrals, organic search, paid search,
internal promotions, etc
Landing page(hub)
Success events (outbound spokes)
Bounce rate, add to cart, cart checkout, confirmed order,
call back request, registration, product comparison,
product review, forward to friend, etc
© ADMA & Datalicious Pty Ltd 20

> Additional success metrics
Click
Through
Add To
Cart
Click
Through
Page
Bounce
Click
Through $
Click
Through
Call back
request
Store
Search ? $
$
$Cart
Checkout
Page
Views
?
Product
Views
Use additional metrics closer to the campaign origin

Exercise: Conversion goals

Exercise: Statistical significance

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 executions
if you serve 1,000,000 banners
Google “nss sample size calculator”

How many survey responses do you need
if you have 10,000 customers?
369 for each question 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 executions
if you serve 1,000,000 banners?
383 sales per banner execution or 383 x 6 = 2,298 sales

> Conversion metrics by category
Source: Omniture Summit, Matt Belkin, 2007

> Relative or calculated metrics
§ Bounce rate
§ Conversion rate
§ Cost per acquisition
§ Pages views per visit
§ Product views per visit
§ Cart abandonment rate
§ Average order value

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

Exercise: Metrics framework

Level Reach Engagement Conversion +Buzz
Level 1,
people
Level 2,
strategic
Level 3,
tactical
Funnel
breakdowns
> Exercise: Metrics framework

Level Reach Engagement Conversion +Buzz
Level 1,
people
People
reached
People
engaged
People
converted
People
delighted
Level 2,
strategic
Display
impressions ? ? ?
Level 3,
tactical
Interaction
rate, etc ? ? ?
Funnel
breakdowns
Existing customers vs. new prospects, products, etc
> Exercise: Metrics framework

> NPS survey and page ratings
Page ratings

Google: “google analytics custom events”

> Importance of calendar events
Traffic spikes or other data anomalies without context are
very hard to interpret and can render data useless

> Potential calendar events
§ Press releases
§ Sponsored events
§ Campaign launches
§ Campaign changes
§ Creative changes
§ Price changes
§ Website changes
§ Technical difficulties

Exercise: Calendar events

> Campaign tracking
101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010

Exercise: Track campaigns

Google: “google analytics url builder”

http://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
[...]
> Email click-through identification

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
[...]
> Personalised URLs for direct mail

Source Medium Term Content Campaign
Referrer Medium Keyword Creative Promotion
google cpc search term a red banner promo a
newsletter banner search term b black banner promo b
? ? ? ? ?
> Exercise: Naming convention

Google: “link google analytics webmaster tools”

Google: “link google analytics google adwords”

Exercise: Organic optimisation

> Importance of social media
Search
WOM, blogs, reviews,
ratings, communities,
social networks, photo
sharing, video sharing
November 2012 © ADMA & Datalicious Pty Ltd
Promotion
57
Company Consumer

> Social as the new search

> Measuring brand
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010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010

Search
Quantity
Social
Quality
> Measuring brand: Search vs. social

> Media attribution
101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010

> Duplication across channels
Banner
Ads
Email
Blast
Paid
Search
Organic
Search
$Bid
Mgmt
Ad
Server
Email
Platform
Google
Analytics
$
$
$

> Duplication across channels
Display
impression
Paid
search $
Ad
Server
Bid
mgmt.
Web
analytics
Display
click
Ad server
cookie
Organic
search
Analytics
cookie
Analytics
cookie
Analytics
cookie
Bid mgmt.
cookie
Ad server
cookie

Central
Analytics
Platform
$
$
$
> De-duplication across channels
Banner
Ads
Email
Blast
Paid
Search
Organic
Search
$

Direct mail,
email, etc
Facebook
Twitter, etc
> Campaign flows are complex
POS kiosks,
loyalty cards, etc
CRM
program
Home pages,
portals, etc
YouTube,
blog, etc
Paid
search
Organic
search
Landing pages,
offers, etc
PR, WOM,
events, etc
TV, print,
radio, etc
= Paid media
= Viral elements
Call center,
retail stores, etc
= Sales channels
Display ads,
affiliates, etc

Exercise: Campaign flow

> Success attribution models
Banner
Ad
$100
Email
Blast
Paid
Search
$100
Banner
Ad
$100
Affiliate
Referral
$100
Success
$100
Success
$100
Banner
Ad
Paid
Search
Organic
Search
$100
Success
$100
Last channel
gets all credit
First channel
gets all credit
All channels get
equal credit
Print
Ad
$33
Social
Media
$33
Paid
Search
$33
Success
$100
All channels get
partial credit
Paid
Search

> First and last click attribution
Chart shows
percentage of
channel touch
points that lead
to a conversion.
Neither first
nor last-click
measurement
would provide
true picture
Paid/Organic Search
Emails/Shopping Engines

> 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 advertising without an
immediate response so advertisers cannot rely on clicks as the sole
success measure but should instead focus on impressions delivered

> Indirect display impact

Closer
Paid
search
Display
ad views
TV/print
responses
> Full purchase path tracking
Influencer Influencer $
Display
ad clicks
Online
leads
Affiliate
clicks
Social
referrals
Offline
sales
Organic
search
Social
buzz
Retail
visits
Lifetime
profit
Organic
search
Emails,
direct mail
Direct
site visits
Introducer

Closer
Paid
search
Display
ad views
TV/print
responses
> Full purchase path tracking
Display
ad clicks
Online
leads
Affiliate
clicks
Social
referrals
Offline
sales
Organic
search
Social
buzz
Retail
visits
Lifetime
profit
Organic
search
Emails,
direct mail
Direct
site visits
Introducer

> Purchase path example

Closer
Channel 1
Channel 1
Channel 1
> Path across different segments
Channel 2
Channel 2 Channel 3
Channel 2 Channel 3 Product 4
Channel 3
Channel 4
Channel 4
Introducer
Product
A vs. B
Clients vs.
prospects
Brand vs.
direct resp.

> Understanding channel mix

What promoted your visit today?
q Recent branch visit
q Saw an ad on television
q Saw an ad in the newspaper
q Recommendation 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 […]

> Website entry survey
Channel % of Conversions
Straight to Site 27%
SEO Branded 15%
SEM Branded 9%
SEO Generic 7%
SEM Generic 14%
Display Advertising 7%
Affiliate Marketing 9%
Referrals 5%
Email Marketing 7%
De-duped Campaign Report
}
Channel % of Influence
Word of Mouth 32%
Blogging & Social Media 24%
Newspaper Advertising 9%
Display Advertising 14%
Email Marketing 7%
Retail Promotions 14%
Greatest Influencer on Branded Search / STS
Conversions attributed to search terms
that contain brand keywords and direct
website visits are most likely not the
originating channel that generated the
awareness and as such conversion
credits should be re-allocated.

> 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 originating from search
terms that included branded
keywords – before TV, word of
mouth and print ads.

> Adjusting for offline impact
+15+5 +10
-15-5 -10

> Purchase path vs. attribution
§ Important to make a distinction between media
attribution 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 attribution or the allocation or
conversion credits back to campaign touch points
– Purchase path tracking is the data collection and
media attribution is the actual analysis or modelling

> Where to track purchase path
Referral visits
Social media visits
Organic search visits
Paid search visits
Email visits, etc
Web Analytics
Banner impressions
Banner clicks
+
Paid search clicks
Ad Server
Lacking ad impressions
Less granular & complex
Lacking organic visits
More granular & complex

> Purchase path data samples
Web Analytics data sample
LAST AD IMPRESSION > SEARCH > $$$| PV $$$
AD IMPRESSION > AD IMPRESSION > SEARCH > $$$
Ad Server data sample
01/01/2012 11:45 AD IMP YAHOO HOME $33
01/01/2012 12:00 AD IMP SMH FINANCE $33
01/01/2012 12:05 SEARCH KEYWORD -
07/01/2012 17:00 DIRECT $33
08/01/2012 15:00 $$$ $100

Closer
?%
?%
?%
> Media attribution models
?%
?% ?%
?% ?% ?%
?%
?%
?%
Introducer
Product
A vs. B
Prospects
vs. clients
Brand vs.
direct resp.

> Full vs. partial purchase path data
Display
impression
Display
impression
Display
impression
$
Display
impression $
Display
impression
Display
impression $
Display
impression
Search
response
Search
response $
Display
impression
Display
response
Direct
visit
✖ ✔ ✔✖
Display
impression
Display
impression
Email
response
Search
response
✖ ✔ ✔✔
✖ ✖ ✔ ✔
✖ ✔ ✔✔

> Full vs. partial purchase path data
Display
impression
Display
impression
Display
impression
$
Display
impression $
Display
impression
Display
impression $
Display
impression
Search
response
Search
response $
Display
impression
Display
response
Direct
visit
✖ ✔ ✔✖
Display
impression
Display
impression
Email
response
Search
response
✖ ✔ ✔✔
✖ ✖ ✔ ✔
✖ ✔ ✔✔
5% to 65% variance
in conversion attribution
for different channels due to
partial purchase path data

> Purchase path for each cookie
Mobile Home Work
Tablet Media Etc

0%
$100
0% Last click
attribution
Even
attribution
Weighted
attribution
0% 100%
25% 25% 25% 25%
Display
impression
Display
impression
Display
response
Search
response
X% X% Y% Z%

> Google Analytics models
§ The First/Last Interaction model plus …
§ The Linear model might be used if your
campaigns are designed to maintain
awareness with the customer
throughout the entire sales cycle.
§ The Position Based model can be used
to adjust credit for different parts of the
customer journey, such as early
interactions that create awareness and
late interactions that close sales.
§ The Time Decay model assigns the most
credit to touch points that occurred
nearest to the time of conversion. It can
be useful for campaigns with short sales
cycles, such as promotions.

Exercise: Attribution models

Closer
?%
?%
?%
?%
?% ?%
?% ?% ?%
?%
?%
?%
Introducer
Product
A vs. B
Prospects
vs. clients
Brand vs.
direct resp.

> Media attribution example
COST PER CONVERSION
Last click
attribution
Even/weighted
attribution

COST PER CONVERSION
Last click
attribution
Even/weighted
attribution
?
Email
?
Direct
mail
?
Internal
ads?
Website
content
?
TV/Print

ROI FULL PURCHASE PATH
TOTALCONVERSIONVALUE
Increase
spend
Increase
spend
Reduce
spend

Exercise: Neglected keywords

> Channel integration
101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010

> Tracking offline responses online
§ Search calls to action for TV, radio, print
– Unique search term only advertised in print so all
responses from that term must have come from print
§ PURLs (personalised URLs) for direct mail
– Brand.com/customer-name redirects to new URL that
includes tracking parameter identifying response as DM
§ Website entry survey for direct/branded visits
– Survey website visitors that have come to site directly
or via branded search about their media habits, etc
§ Combine data sets into media attribution model
– Combine raw data from online purchase path, website entry
survey and offline sales with offline media placement data in
traditional (econometric) media attribution model

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
[...]
> Personalised URLs for direct mail

> Search call to action for offline

> Econometric media modelling
Use of traditional econometric
modelling to measure the
impact of communications on
sales for offline channels where
it cannot be measured directly
through smart calls to action
online (and thus cookie level
purchase path data).

> Tracking offline sales online
§ Email click-through
– Include offline sales flag in 1st email click-through URL after
offline sale to track an ‘assisted offline sales’ conversion
§ First login after purchase
– Similar to the above method, however offline sales flag
happens via JavaScript parameter defined on 1st login
§ Unique phone numbers
– Assign unique website numbers to responses from specific
channels, search terms or even individual visitors to match
offline call center results back to online activity
§ Website entry survey for purchase intent
– Survey website visitors to at least measure purchase
intent in case actual offline sales cannot be tracked

Confirmation
email, 1st login
> Offline sales driven by online
Website
research
Phone
sales
Retail
sales
Online
sales
Cookie
Advertising
campaign
Fulfilment,
CRM, etc
Online sales
confirmation
Virtual sales
confirmation

http://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
[...]
> Email click-through identification

> 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
[...]

Campaign response data
> Combining data sources
Customer profile data
+ The whole is greater
than the sum of its parts
Website behavioural data

> Transactions plus behaviours
+
one-off collection of demographical data
age, gender, address, etc
customer lifecycle metrics and key dates
profitability, expiration, etc
predictive models based on data mining
propensity to buy, churn, etc
historical data from previous transactions
average order value, points, etc
CRM Profile
Updated Occasionally
tracking of purchase funnel stage
browsing, checkout, etc
tracking of content preferences
products, brands, features, etc
tracking of external campaign responses
search terms, referrers, etc
tracking of internal promotion responses
emails, internal search, etc
Site Behaviour
Updated Continuously

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

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
overestimated visitors by up to 7.6 times whilst a cookie-based
approach overestimated visitors by up to 2.3 times.
> Unique visitor overestimation
Source: White Paper, RedEye, 2007

> Maximise identification points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
Cam
paign
response
Em
ailsubscription
Online
purchase
Repeatpurchase
Confirm
ation
em
ail
Em
ailnew
sletter
W
ebsite
login
Online
billpaym
ent
−−− Probability of identification through Cookies
November 2012 124© ADMA & Datalicious Pty Ltd
App
dow
nload/access

On-site
targeting
Off-site
targeting
> Combining targeting platforms
CRM

> Re-marketing
101011010010010010101111010010010101010100001011111001010101
010100101011001100010100101001101101001101001010100111001010
010010101001001010010100100101001111101010100101001001001010

> Importance of online experience
The consumer decision process is changing from linear to circular.
Consideration
set now grows
during online
research phase
which increases
importance of
user experience
during that phase
Online research

> Increase revenue by 10-20%

APPLY NOW

> Network wide re-targeting
Product A
Product B
prospect
Product A
prospect
Product A
customer
Product B Product C
Product C
prospect
Product B
prospect
Product B
customer
Product A
prospect
Product C
prospect
Product C
customer

> Network wide re-targeting
Product B
prospect
Product A
prospect
Product A
customer
Product C
prospect
Product B
prospect
Product B
customer
Product A
prospect
Product C
prospect
Product C
customer
Group wide campaign with approximate impression targets by product rather than hard budget limitations

Closer
Message 1
Message 1
Message 1
> Story telling or ad-sequencing
Message 2
Message 2 Message 3
Message 2 Message 3 Message 4
Message 3
Message 4
Message 4
Introducer
Product A
Product B
Product C

> Ad-sequencing in action
Marketing is about
telling stories and
stories are not static
but evolve over time
Ad-sequencing can help to
evolve stories over time the
more users engage with ads

> Targeting: Quality vs. quantity
30% existing customers with extensive
profile including transactional history of
which maybe 50% can actually be
identified as individuals
30% new visitors with no
previous website history
aside from campaign or
referrer data of which
maybe 50% is useful
10% serious
prospects
with limited
profile data
30% repeat visitors with
referral data and some
website history allowing
50% to be segmented by
content affinity

> ANZ home page targeting
© ADMA & Datalicious Pty Ltd 137November 2012
ANZ home page
re-targeting and
merchandising
combined with
landing page
optimisation
delivered an
increase in offer
response and
conversion rates
with an overall
project ROI of
578%

Exercise: Re-targeting matrix

Purchase
Cycle
Segmentation based on: Search keywords,
display ad clicks and website behaviour Data
Points
Default,
awareness
Default
Research,
consideration
Product
view, etc
Purchase
intent
Checkout,
chat, etc
Existing
customer
Login, email
click, etc
> Exercise: Re-targeting matrix

Purchase
Cycle
Points
Default Product A Product B
Default,
awareness
Acquisition
message D1
Acquisition
message A1
Acquisition
message B1
Default
Research,
consideration
Acquisition
message D2
Acquisition
message A2
Acquisition
message B2
Product
view, etc
Purchase
intent
Acquisition
message D3
Acquisition
message A3
Acquisition
message B3
Checkout,
chat, etc
Existing
customer
Cross-sell
message D4
Cross-sell
message A4
Cross-sell
message B4
Login, email
click, etc
> Exercise: Re-targeting matrix

Google: “enable remarketing google analytics”

Exercise: Remarketing lists

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

§ 1 unique phone number
– Phone number is considered part of the brand
– Media origin of calls cannot be established
– Added value of website interaction 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

§ 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
§ 100+ unique phone numbers
– Different numbers for different website visitors
– Call origin and time stamp enable individual match
– Call conversions matched back to search terms

Purchase
Cycle
Points
Default Product A Product B
Default,
awareness
1300 000 001 1300 000 005 1300 000 009 Default
Research,
consideration
1300 000 002 1300 000 006 1300 000 010
Product
view, etc
Purchase
intent
1300 000 003 1300 000 007 1300 000 011
Checkout,
chat, etc
Existing
customer
1300 000 004 1300 000 008 1300 000 012
Login, email
click, etc
> Website call center integration

> Landing pages
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Don’t reinvent the wheel

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

> The holy trinity of testing
1. The headline
– Have a headline!
– Headline should be concrete
– Headline should be first thing visitors look at
2. Call to action
– Don’t have too many calls to action
– Have an actionable call to action
– Have a big, prominent, visible call to action
3. Social proof
– Logos, number of users, testimonials,
case studies, media coverage, etc

> Best practice testing roadmap
§ Phase 1: A/B test
– Test same landing page
content in different
layouts
§ Phase 2: MV test
– Test different content
element combinations
within winning layout
§ Phase 3: Repeat
– Hero vs. challengers
§ Phase 4: Re-targeting
Element #1: Prominent headline
Element #2:
Call to action
Supporting
content
Element #3: Social proof / trust
Terms and conditions

> G&E Capital landing pages
Before
After
Removal of distractions
such as navigation and
search options resulted
in increased response
rates with ROI of 492%
Project platforms used: Adobe
SiteCatalyst and Test&Target

> Macquarie landing pages
Before
After
The small things count:
Simplification down to 1
set of buttons resulted in
increased response rate
and project ROI of 547%
Project platforms used: Adobe
SiteCatalyst and Test&Target

Rather than testing all combinations of alternative page content (i.e. A/B
testing), the Taguchi Method (i.e. multivariate MV testing) is a way of
reducing the number of different test scenarios (recipes) but still yield
useful test results. Essentially, the optimal page design is ‘predicted’
from the test results by analysing which page elements and element
combinations were most influential overall.
> A/B vs. MV (Taguchi) method
Test elements
(i.e. parts of page)
Test alternatives
(i.e. test content)
Full set of test
combinations (A/B)
Reduced Taguchi
test scenarios (MV)
3 2 8 4
7 2 128 8
4 3 81 9
5 4 1024 16

> Sufficient sample size for tests
§ MV testing requires a greater volume of visitors than
A/B testing. The volume required is dependent on:
– The number of elements on the page (and how many
alternatives for each element)
– Whether targeting 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 violating the test conditions
– Whether you can afford to present the test to all traffic

Exercise: Statistical significance

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
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?

How many click-throughs do you need to test 3
369 per test or 1,107 clicks in total
How many conversions do you need to test 3
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

> Telstra bundles pages
© ADMA & Datalicious Pty Ltd 167November 2012
Telstra bundles page optimisation 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%

> Other testing considerations
§ Avoiding ‘no results’ by making test executions
as obviously different as possible to consumers
§ Limit potential ‘negative’ test impact on
conversions by limiting the test to a smaller
sample size initially
§ Avoid launching tests during major above the
line campaign activity as this might magnify any
incremental gains of tested scenarios and the
test results can’t then be replicated in a non-
campaign period

> Introducing hero vs. challengers
Hero #1
CTR = 1%
Challenger #1
CTR = 0.5%
Challenger #2
CTR = 1.5%
Challenger #3
CTR = 1%
Challenger #4
CTR = 1%
New hero #2
= Challenger #2

Exercise: Optimisation ideas

> Eye tracking vs. mouse tracking
§ Eye tracking pros
– 100% accurate
– Controlled environment
– Open dialogue
§ Eye tracking cons
– High costs
– Limited scope
– Observer effect
§ Mouse tracking pros
– Natural environment
– No observer effect
– Global participation
– Low cost
§ Mouse tracking cons
– No pre-defined tests
– No research control
– No visitor feedback

> Segmented heat maps are key
Heat map for new visitors vs. existing customers
Independent research shows 84-88% correlation between mouse and eye movements*

> New approach to web design
§ Standard approach
– Analyst identifies issue
and briefs agency
– Agency develops new
designs, trashes some
– Agency or developers
implement new design
– Sometimes multiple
designs are tested
§ Try something new
– Analyst identifies issue
and briefs agency (incl.
current heat maps)
– Agency develops new
designs and tests them
(predictive heat maps)
– Winning designs are
developed and tested
(incl. new heat maps)
– Top performing design
is implemented

> New approach to web design
§ Step 1: Identify problem pages
§ Step 2: Prioritise pages for testing
§ Step 3: Pick page for testing and optimisation
§ Step 4: Implement and analyse heat-map
§ Step 5: Design test and brief creative agencies
§ Step 6: Pick best designs with predictive heat-maps
§ Step 7: Develop different page executions
§ Step 8: Execute, monitor (and refine) test
§ Step 9: Analyse test and verify predictive heat-maps
§ Step 10: Implement winning test design
§ Step 11: Pick next page & repeat steps 3-10

Targeting before testing

Exercise: Testing matrix

Test Segment Content Success Difficulty Potential
> Exercise: Testing matrix

Test Segment Content Success Difficulty Potential
Test 1 Product 1
Offer 1A
Clicks Low $100kOffer 1B
Offer 1C
Test 2 Product 2
Offer 2A
Clicks High $100kOffer 2B
Offer 2C
> Exercise: Testing matrix

> 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.

> Online form best practice
Maximise data integrity
Age vs. year of birth
Free text vs. options
Use auto-complete
wherever possible

> Social single-sign on services
http://vimeo.com/16469480
Gigya.com
Janrain.com

> Garbage in, garbage out
Avinash Kaushik:
“The principle of garbage in, garbage out
applies here. [… what makes a behaviour
targeting platform tick, 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.”

Contact us
cbartens@datalicious.com
Learn more
blog.datalicious.com
Follow us
twitter.com/datalicious

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