ADMA Course Analyse to Optimise

> Analyse to optimise <
ADMA short course on data,
measurement and ROI

> Course overview
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October 2010 © ADMA & Datalicious Pty Ltd 2

> Day 1: Basic Analytics
§ Defining a metrics framework
– What to report on, when and why?
– Matching strategic and tactical goals to metrics
– Covering all major categories of business goals
§ Finding and developing the right data
– Data sources across channels and goals
– Meaningful trends vs. 100% accurate data
– Human and technological limitations
§ Plus hands-on exercises

> Day 2: Advanced Analytics
§ Campaign flow and media attribution
– Designing a campaign flow including metrics
– Omniture vs. Google Analytics capabilities
§ How to reduce media waste
– Testing and targeting in a media world
– Media vs. content and usability

> Training outcomes
§ After successful completion of the training
course participants will be able to
– Define a metrics framework for any client
– Enable benchmarking across campaigns
– Incorporate analytics into the planning process
– Pull and interpret key reports in Google Analytics
– Impress with insights instead of spreadsheets
– Know how to extend optimisation past media buy
– Show the true value of digital media

Category Data Metrics Insights Platform
Why?
What?
How?
> Get the most out of the course

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

> Importance of social media
Search
WOM, blogs, reviews,
ratings, communities,
social networks, photo
sharing, video sharing
October 2010 © ADMA & Datalicious Pty Ltd
Promotion
9
Company Consumer

> Social as the new search

Reach
(Awareness)
Engagement
(Interest & Desire)
Conversion
(Action)
+Buzz
(Satisfaction)
> 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
> Additional funnel breakdowns
40% 10% 1%
New prospects vs. existing customers
Brand vs. direct response campaign

Prospect vs. customer
High vs. low value
Product affinity
Post code, age, sex, etc

> Exercise: Funnel breakdowns
§ List potentially insightful funnel breakdowns
– Brand vs. direct response campaign
– New prospects vs. existing customers
– Baseline vs. incremental conversions
– Competitive activity, i.e. none, a lot, etc
– 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

> Exercise: Conversion metrics
§ Key conversion metrics differ by category
– Commerce
– Lead generation
– Content publishing
– Customer service

> Exercise: Conversion metrics
Source: Omniture Summit, Matt Belkin, 2007

> Conversion funnel 1.0
October 2010
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 23

> Conversion funnel 2.0
October 2010
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 24

> 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

> 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

> eMarketer interactive metrics

Sentiment
ReachInfluence
> Measuring social media

Level Reach Engagement Conversion +Buzz
Level 1
People
Level 2
Strategic
Level 3
Tactical
> Exercise: Metrics framework

Level Reach Engagement Conversion +Buzz
Level 1
People
People
reached
People
engaged
People
converted
People
delighted
Level 2
Strategic
Search
impressions,
UBs, etc
? ? ?
Level 3
Tactical
Click-through
or interaction
rate, etc
? ? ?
> Exercise: Metrics framework

IR − MI
MI
= ROMI + BE
> ROI, ROMI, BE, etc
IR − MI
MI
= ROMI
R − I
I
= ROI
R Revenue
I Investment
ROI Return on
investment
IR Incremental
revenue
MI Marketing
investment
ROMI Return on
marketing
investment
BE Brand equity

> Success: ROMI + BE
§ Establish incremental revenue (IR)
– Requires baseline revenue to calculate additional
revenue as well as revenue from cost savings
§ Establish marketing investment (MI)
– Requires all costs across technology, content, data
and resources plus promotions and discounts
§ Establish brand equity contribution (BE)
– Requires additional soft metrics to evaluate subscriber
perceptions, experience, attitudes and word of mouth
IR − MI
MI
= ROMI + BE

> Process is key to success

> Recommended resources
§ 200501 WAA Key Metrics & KPIs
§ 200708 WAA Analytics Definitions Volume 1
§ 200612 Omniture Effective Measurement
§ 200804 Omniture Calculated Metrics White Paper
§ 200702 Omniture Effective Segmentation Guide
§ 200810 Ronnestam Online Advertising And AIDAS
§ 201004 Altimeter Social Marketing Analytics
§ 201008 CSR Customer Satisfaction Vs Delight
§ Google “Enquiro Search Engine Results 2010 PDF”
§ Google “Razorfish Actionable Analytics Report PDF”
§ Google “Forrester Interactive Marketing Metrics PDF”

> Data sources
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> Digital data is plentiful and cheap

> Digital data categories
Source: Accuracy Whitepaper for web analytics, Brian Clifton, 2008
+Social

> Customer data journey
To retention messagesTo transactional data
From suspect to To customer
From behavioural data From awareness messages
TimeTime
prospect

> Corporate data journey
Time, Control
Sophistication
Stage 1
Data
Stage 2
Insights
Stage 3
Action
Third parties control
most data, ad hoc
reporting only, i.e.
what happened?
Data is being brought
in-house, shift towards
insights generation and
data mining, i.e. why
did it happen?
Data is fully owned
in-house, advanced
predictive modelling
and trigger based
marketing, i.e. what
will happen and
making it happen!

> What analytics platform to use
Time, Control
Sophistication
Stage 1: Data Stage 2: Insights Stage 3: Action
Third parties control
most data, ad hoc
reporting only, i.e.
what happened?
Data is being brought
in-house, shift towards
insights generation and
data mining, i.e. why
did it happen?
Data is fully owned
in-house, advanced
predictive modelling
and trigger based
marketing, i.e. what
will happen and
making it happen!

People
Reached
People
Engaged
People
Converted
People
Delighted
> Potential data sources
40% 10% 1%
Quantitative and qualitative research data
Website, call center and retail data
Social media data
Media and search data
Social media

> Atomic Labs tag-less data capture
§ Keep all your favourite reports but
§ Eliminate tag maintenance and ensure
§ New pages/content is tracked automatically
§ Across normal websites, mobiles and apps

> Atomic labs integration model
§ Single point of data
capture and processing
§ Real-time queries to
enrich website data
§ Multiple data export
options for web analytics
§ Enriching single-customer
view website behaviour

> Google data in Australia
Source: http://www.hitwise.com/au/datacentre

> Search at all stages
Source: Inside the Mind of the Searcher, Enquiro 2004

> Search and brand strength

> Search and the product lifecycle
Nokia N-Series
Apple iPhone

> Search and media planning

> Search driving offline creative

> Exercise: Search insights
§ Identify key category search terms
– Data from Google AdWords Keyword Tool
– Search for “google keyword tool”
– Wordle and IBM Many Eyes for visualizations
– Search for “wordle word clouds” and “ibm many eyes”
§ Identify search term trends and competitors
– Google Trends and Google Search Insights
– Search for “google trends” and “google search insights”
§ Search and media planning
– DoubleClick Ad Planner by Google
– Search for “google ad planner”

> Cookie based tracking process
Source: Google Analytics, Justin Cutroni, 2007
What if: Someone deletes their cookies? Or uses a device
that does not support JavaScript? Or uses two computers
(work vs. home)? Or two people use the same computer?

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
October 2010 57© ADMA & Datalicious Pty Ltd
App
dow
nload/access

> De-duplication across channels
Banner
Ads
Email
Blast
Paid
Search
Organic
Search
$Bid
Mgmt
Ad
Server
Email
Platform
Google
Analytics
$
$
$
Central
Analytics
Platform
$
$
$

De-duplication across channels

Additional funnel breakdowns

> Exercise: Duplication impact
§ Double-counting of conversions across channels can have
a significant impact on key metrics, especially CPA
§ Example: Display ads and paid search
– Total media budget of $10,000 of which 50% is spend on paid
search and 50% on display ads
– Total of 100 conversions across both channels with a channel
overlap of 50%, i.e. both channels claim 100% of conversions
based on their own reporting but once de-duplicated they
each only contributed 50% of conversions
– What are the initial CPA values and what is the true CPA?
§ Solution: $50 initial CPA and $100 true CPA
– $5,000 / 100 = $50 initial CPA and $5,000 / 50 = $100 true
CPA (which represents a 100% increase)

TV
audience
Search
audience
Banner
audience
> Reach and channel overlap

> Estimating reach and overlap
§ Apply average unique visitor count per recorded
unique user names to all unique visitor figures in
Google Analytics, Omniture, etc
§ Apply ratio of total banner impressions to unique
banner impressions from ad server to paid and
organic search impressions in Google AdWords and
Google Webmaster Tools
§ Compare Google Keyword Tool impressions for a
specific search term to reach for the same term in
Google Ad Planner
§ Custom website entry survey and campaign
stacking to establish channel overlap

Sentiment analysis: People vs. machine

> Altimeter social analytics
Social Marketing
Analytics is the
discipline that helps
companies measure,
assess and explain the
performance of social
media initiatives in the
context of specific
business objectives.

> Overall volume and influence
Data from

> Influence and media value
US
UK
AU/NZ
Data from

> Facebook insights
Using Facebook Like
buttons is a free and
powerful way to gain
additional insights
into consumer
preferences and
enabling social
sharing of content
as well as possibly
influence organic
search rankings in
the near future.

> Facebook Connect single sign on
Facebook Connect gives your
company the following data
and more with just one click
Email address, first name, last name,
gender, birthday, interests, picture,
affiliations, last profile update, time
zone, religion, political interests,
attracted to which sex, why they want to
meet someone, home town, relationship
status, current location, activities, music
interests, tv show interests, education
history, work history, family, etc Need anything else?

(influencers only)
(all contacts)
Appending social data to customer profiles
Name, age, gender, occupation, location, social
profiles and influencer ranking based on email

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
Google “nss sample size calculator”

> 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

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

§ 200311 UK RedEye Cookie Case Study
§ 200807 Kaushik Tracking Offline Conversion
§ 200904 Kaushik Standard Metrics Revisited
§ 201002 Kaushik 8 Competitive Intelligence Data Sources
§ 201005 Google Ad Planner Data Wrong By Up To 20%
§ 201005 MPI How Statistically Valid Is Your Survey
§ 201009 Google Analytics How To Tag Links
§ 200903 Coremetrics Conversion Benchmarks By Industry
§ 200906 WOM Online The People Vs Machines Debate
§ 201007 WSJ The Web's New Gold Mine Your Secrets
§ 201008 AdvertisingAge Are Marketers Really Spying On You

Why?
What?
How?

> Summary and action items
– Develop standardised metrics framework
– Define additional funnel breakdowns
– Establish baseline and incremental
– Define additional success metrics
– Ensure de-duplication via central analytics
– Check reports for statistical significance
– Check data sources and their accuracy
– Start populating a calendar of events

> Google Analytics practice
§ Describing website visitors
§ Identifying traffic sources (reach)
– Campaign tracking mechanics
§ Analyzing content usage (engagement)
§ Analyzing conversion drop-out (conversion)
§ Defining custom segments (breakdowns)

> Describing website visitors
§ Average connection speed
§ Plug-in usage (i.e. Flash, etc)
§ Mobile vs. normal computers
§ Geographic location of visitors
§ Time of day, day of week
§ Repeat visitation
§ What else?

> Identifying traffic sources
§ Generating de-duplicated reports
§ Campaign tracking mechanics
§ Conversion goals and success events
§ Plus adding additional metrics
§ Paid vs. organic traffic sources
§ Branded vs. generic search
§ Traffic quantity vs. quality

> Analysing content usage
§ Page traffic vs. engagement
§ Entry vs. exit pages
§ Popular page paths
§ Internal search terms

> Analysing conversion drop-out
§ Defining conversion funnels
§ Identifying main problem pages
§ Pages visited after conversion barriers
§ Conversion drop-out by segment

> Defining custom segments
§ New vs. repeat visitors
§ By geographic location
§ By connection speed
§ By products purchased
§ New vs. existing customers
§ By demographics, custom segments

© ADMA & Datalicious Pty Ltd
> Useful analytics tools
§ http://labs.google.com/sets
§ http://www.google.com/trends
§ http://www.google.com/insights/search
§ http://bit.ly/googlekeywordtoolexternal
§ http://www.google.com/webmasters
§ http://www.facebook.com/insights
§ http://www.google.com/adplanner
§ http://www.google.com/videotargeting
§ http://www.keywordspy.com
§ http://www.compete.com
October 2010 92

© ADMA & Datalicious Pty Ltd
§ http://bit.ly/hitwisedatacenter
§ http://www.socialmention.com
§ http://twittersentiment.appspot.com
§ http://bit.ly/twitterstreamgraphs
§ http://twitrratr.com
§ http://bit.ly/listoftools1
§ http://manyeyes.alphaworks.ibm.com
§ http://www.wordle.net
§ http://www.tagxedo.com
October 2010 93

> Quick recap
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– What to report on, when and why?
– Matching strategic and tactical goals to metrics
– Covering all major categories of business goals
– Data sources across channels and goals
– Meaningful trends vs. 100% accurate data
– Human and technological limitations

§ Hands-on exercises and examples
– Funnel breakdowns
– Conversions metrics
– Metrics framework
– Search insights
– Duplication impact
– Statistical significance

> Course overview
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> Day 2: Advanced Analytics
– Designing a campaign flow including metrics
– Omniture vs. Google Analytics capabilities
– Testing and targeting in a media world
– Media vs. content and usability

Why?
What?
How?

> Media attribution
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Direct mail,
email, etc
Facebook
Twitter, etc
> Campaign flow and calls to action
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
C2
C3
= Paid media
= Viral elements
Call center,
retail stores, etc
= Coupons, surveys
Display ads,
affiliates, etc
C1

> Exercise: Calls to action
§ Unique click-through URLs
§ Unique vanity domains or URLs
§ Unique phone numbers
§ Unique search terms
§ Unique email addresses
§ Unique personal URLs (PURLs)
§ Unique SMS numbers, QR codes
§ Unique promotional codes, vouchers
§ Geographic location (Facebook, FourSquare)
§ Regression analysis of cause and effect

> Search call to action for offline

http://www.domain.com?campaign=outdoor

TV
audience
Search
audience
Banner
audience
> Reach and channel overlap

> Indirect display impact

> De-duplication across channels
Banner
Ads
Email
Blast
Paid
Search
Organic
Search
$Bid
Mgmt
Ad
Server
Email
Platform
Google
Analytics
$
$
$
Central
Analytics
Platform
$
$
$

> 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

> Paid and organic stacking

Closer
SEM
Generic
Banner
View
TV
Ad
> Full path to purchase
Influencer Influencer $
Banner
Click $
SEO
Generic
Affiliate
Click $
SEO
Branded
Direct
Visit
Email
Update
Abandon
Direct
Visit
Social
Media
SEO
Branded
Introducer

> Where to collect the data
Referral visits
Social media visits
Organic search visits
Paid search visits
Other paid visits
Email visits
Web Analytics
Banner impressions
Banner clicks
+
Paid search clicks
Ad Server
Paid/Organic VisitsPaid Impressions/Clicks

Closer
25%
> Success attribution models
25% Even
Attrib.
Exclusion
Attrib.
Pattern
Attrib.
25% 25%
Introducer
33% 33% 33% 0%
30% 20% 20% 30%

Closer
25%
> Exercise: Attribution models
25% Even
Attrib.
Exclusion
Attrib.
Custom
Attrib.
25% 25%
Introducer
33% 33% 33% 0%
? ? ? ?

> Exercise: Attribution model
§ Allocate more conversion credits to more
recent touch points for brands with a strong
baseline to stimulate repeat purchases
§ Allocate more conversion credits to more
recent touch points for brands with a direct
response focus
§ Allocate more conversion credits to initiating
touch points for new and expensive brands and
products to insert them into the mindset

> Understanding channel overlap

> 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.

> Ad server exposure test
User qualifies for the display campaign
(if the user has already been tagged go to step 3)
Audience Segmentation
10% of users in control group, 90% in exposed group
2
1
User tagged with segment
3
1st impression
N impressions
Control
(displayed non-branded message)
Exposed
(displayed branded message)
Measurement:
Conversions per
1000 unique
visitors
Control
(displayed non-branded message)
Exposed
(displayed branded message)
User remains in segment

> Research online, shop offline
Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School

> Offline sales driven by online
Website
research
Phone
order
Retail
order
Online
order
Cookie
Advertising
campaign
Credit check,
fulfilment
Online order
confirmation
Virtual order
confirmation
Confirmation
email

> Exercise: Offline conversions
§ Email click-through after purchase
§ First online login after purchase
§ Unique website phone number
§ Call back request or online chat
§ Unique website promotion code
§ Unique printable vouchers
§ Store locator searches
§ Make an appointment online

> Media attribution phases
§ Phase 1: De-duplication
– Conversion de-duplication across all channels
– Requires one central reporting platform
– Limited to first/last click attribution
§ Phase 2: Direct response pathing
– Response pathing across paid and organic channels
– Only covers clicks and not mere banner views
– Can be enabled in Google Analytics and Omniture
§ Phase 3: Full purchase path
– Direct response tracking including banner exposure
– Cannot be done in Google Analytics or Omniture
– Easier to import additional channels into ad server

§ 200812 ComScore How Online Advertising Works
§ 200905 iProspect Research Study Search And Display
§ 200904 ClearSaleing American Attribution Index
§ 201003 Datalicious Tying Offline Sales To Online Media
§ Google: “Forrester Campaign Attribution Framework PDF”

> Reducing waste
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> Reducing waste along funnel
Media attribution
Optimising channel mix
Testing
Improving usability
$$$
Targeting
Increasing relevance

Capture internet traffic
Capture 50-100% of fair market share of traffic
Increase consumer engagement
Exceed 50% of best competitor’s engagement rate
Capture qualified leads and sell
Convert 10-15% to leads and of that 20% into
sales
Building consumer loyalty
Build 60% loyalty rate and 40% sales conversion
Increase online revenue
Earn 10-20% incremental revenue online
> Increase revenue by 10-20%

> The consumer data journey
To retention messagesTo transactional data
From suspect to To customer
From behavioural data From awareness messages
TimeTime
prospect

> Coordination across channels
Off-site
targeting
On-site
targeting
Profile
targeting
Generating
awareness
Creating
engagement
Maximising
revenue
TV, radio, print,
outdoor, search
marketing, display
ads, performance
networks, affiliates,
social media, etc
Retail stores, in-store
kiosks, call centers,
brochures, websites,
mobile apps, online
chat, social media, etc
Outbound calls, direct
mail, emails, social
media, SMS, mobile
apps, etc

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

On-site
segments
Off-site
segments
> Combining technology

> Extended targeting platform
Brand
Network
Partners
Publishers

> SuperTag code architecture
§ Central JavaScript container tag
§ One tag for all sites and platforms
§ Hosted internally or externally
§ Faster tag implementation/updates
§ Eliminates JavaScript caching
§ Enables code testing on live site
§ Enables heat map implementation
§ Enables redirects for A/B testing
§ Enables network wide re-targeting
§ Enables live chat implementation

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

> Behaviours plus transactions
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

> 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
October 2010 145© ADMA & Datalicious Pty Ltd
App
dow
nload/access

> Sample customer level data

> Sample site visitor composition
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

> Potential home page layout
Branded header
Rule based offer
Customise content
delivery on the fly
based on referrer
data, past content
consumption or
profile data for
existing customers.
Targeted
offer Popular
links,
FAQs
Targeted
offer
Login

> Prospect targeting parameters

> Affinity targeting in action
Different type of
visitors respond to
different ads. By
using category
affinity targeting,
response rates are
lifted significantly
across products.
Message
CTR By Category Affinity
Postpay Prepay Broadb. Business
Blackberry Bold - - - +
5GB Mobile Broadband - - + -
Blackberry Storm + - + +
12 Month Caps - + - +
Google: “vodafone
omniture case study”
or http://bit.ly/de70b7

> Potential newsletter layout
Closest
stores,
offers
etc
Rule based branded header
Data verification
Rule based offer
Profile based offer
Using profile data
enhanced with
website behaviour
data imported into
the email delivery
platform to build
business rules and
customise content
delivery.
NPS

> 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.

> Potential landing page layout
Rule based branded header
Campaign message match
Targeted offer
Passing data on user
preferences through
to the website via
parameters in email
click-through URLs
to customise
content delivery.
Call to action

> Exercise: Targeting matrix
Phase Segment A/B Channels Data Points
Awareness
Consideration
Purchase Intent
Up/Cross-Sell

> Exercise: Targeting matrix
Phase Segment A/B Channels Data Points
Awareness Seen this?
Social, display,
search, etc
Default
Consideration Great feature!
Social, search,
website, etc
Download,
product view
Purchase Intent Great value!
Search, site,
emails, etc
Cart add,
checkout, etc
Up/Cross-Sell Add this!
Direct mail,
emails, etc
Email response,
login, etc

> Quality content is key
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.”

> ClickTale testing case study

> Bad campaign worse than none

> Keys to effective targeting
1. Define success metrics
2. Define and validate segments
3. Develop targeting and message matrix
4. Transform matrix into business rules
5. Develop and test content
6. Start targeting and automate
7. Keep testing and refining
8. Communicate results

§ 201003 McKinsey Get More Value From Digital Marketing
§ 200912 Unbounce 101 Landing Page Optimization Tips
§ 201008 eConsultancy TV Ad Landing Pages
§ 200910 eMarketer Bad Campaign Worse Than None
§ 201003 WebCredible 10 Unexpected User Behaviours
§ 200910 Myth Of The Page Fold
§ 201008 Sample Size Currency Of Marketing Testing
§ 200409 Roy Taguchi Or MV Testing For Marketers
§ 200702 Internet Retailer Navigating Depths Of MV Testing
§ 201009 Six Revisions 10 Usability Tips Based On Research

Why?
What?
How?

> Summary and action items
– Draw campaign flow for your company
– Check platform cookie expiration periods
– Enable pathing of direct campaign responses
– Investigate how to track offline conversions
– Develop basic targeting matrix to get started
– Combine targeting platforms for consistency
– List all customer touch points for identification
– Check for common ID across all data sources

> Google Analytics practice
§ Describing website visitors
§ Identifying traffic sources (reach)
– Campaign tracking mechanics
§ Analyzing content usage (engagement)
§ Analyzing conversion drop-out (conversion)
§ Defining custom segments (breakdowns)

> Describing website visitors
§ Average connection speed
§ Plug-in usage (i.e. Flash, etc)
§ Mobile vs. normal computers
§ Geographic location of visitors
§ Time of day, day of week
§ Repeat visitation
§ What else?

> Identifying traffic sources
§ Generating de-duplicated reports
§ Campaign tracking mechanics
§ Conversion goals and success events
§ Plus adding additional metrics
§ Paid vs. organic traffic sources
§ Traffic quantity vs. quality

> Analysing content usage
§ Page traffic vs. engagement
§ Entry vs. exit pages
§ Popular page paths
§ Internal search terms

> Analysing conversion drop-out
§ Defining conversion funnels
§ Identifying main problem pages
§ Pages visited after conversion barriers
§ Conversion drop-out by segment

> Defining custom segments
§ New vs. repeat visitors
§ By geographic location
§ By connection speed
§ By products purchased
§ New vs. existing customers
§ By demographics, custom segments

§ http://labs.google.com/sets
§ http://www.google.com/trends
§ http://www.google.com/insights/search
§ http://bit.ly/googlekeywordtoolexternal
§ http://www.google.com/webmasters
§ http://www.facebook.com/insights
§ http://www.google.com/adplanner
§ http://www.google.com/videotargeting
§ http://www.keywordspy.com
§ http://www.compete.com

§ http://bit.ly/hitwisedatacenter
§ http://www.socialmention.com
§ http://twittersentiment.appspot.com
§ http://bit.ly/twitterstreamgraphs
§ http://twitrratr.com
§ http://manyeyes.alphaworks.ibm.com
§ http://www.wordle.net
§ http://www.tagxedo.com

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

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