analyze to optimize (part 2)
DESCRIPTION
The presentation discusses training on data, measurement and ROI.TRANSCRIPT
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> Analyse to op-mise < ADMA short course on data,
measurement and ROI
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> Quick recap
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October 2010 © ADMA & Datalicious Pty Ltd 2
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> Day 1: Basic Analy-cs
§ Defining a metrics framework – What to report on, when and why? – Matching strategic and tacHcal 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 limitaHons
§ Plus hands-‐on exercises October 2010 © ADMA & Datalicious Pty Ltd 3
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> Day 1: Basic Analy-cs
§ Hands-‐on exercises and examples – Funnel breakdowns – Conversions metrics – Metrics framework – Search insights – DuplicaHon impact – StaHsHcal significance
October 2010 © ADMA & Datalicious Pty Ltd 4
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> Course overview
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> Day 2: Advanced Analy-cs
§ Campaign flow and media aWribuHon – Designing a campaign flow including metrics – Omniture vs. Google AnalyHcs capabiliHes
§ How to reduce media waste – TesHng and targeHng in a media world – Media vs. content and usability
§ Plus hands-‐on exercises
October 2010 © ADMA & Datalicious Pty Ltd 6
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> Get the most out of the course
Category Data Metrics Insights PlaForm
Why?
What?
How?
October 2010 © ADMA & Datalicious Pty Ltd 7
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> Media aJribu-on
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October 2010 © ADMA & Datalicious Pty Ltd 8
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Direct mail, email, etc
Facebook TwiJer, etc
> Campaign flow and calls to ac-on
October 2010 © ADMA & Datalicious Pty Ltd 9
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
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Exercise: Campaign flow
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Exercise: Calls to ac-on
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> Exercise: Calls to ac-on § 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 promoHonal codes, vouchers § Geographic locaHon (Facebook, FourSquare) § Regression analysis of cause and effect
October 2010 © ADMA & Datalicious Pty Ltd 12
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> Search call to ac-on for offline
October 2010 © ADMA & Datalicious Pty Ltd 13
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hJp://www.domain.com?campaign=outdoor
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TV audience
Search audience
Banner audience
> Reach and channel overlap
October 2010 © ADMA & Datalicious Pty Ltd 16
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> Indirect display impact
October 2010 © ADMA & Datalicious Pty Ltd 17
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> Indirect display impact
October 2010 © ADMA & Datalicious Pty Ltd 18
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> Indirect display impact
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> De-‐duplica-on across channels
October 2010 © ADMA & Datalicious Pty Ltd 20
Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email PlaForm
Google Analy-cs
$
$
$
Central Analy-cs PlaForm
$
$
$
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De-‐duplica-on across channels
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> Success aJribu-on models
October 2010 © ADMA & Datalicious Pty Ltd 22
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 par-al credit
Paid Search
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> First and last click aJribu-on
October 2010 © ADMA & Datalicious Pty Ltd 23
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
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> Paid and organic stacking
October 2010 © ADMA & Datalicious Pty Ltd 24
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Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
October 2010 © ADMA & Datalicious Pty Ltd 25
Influencer Influencer $
Banner Click $
SEO Generic
Affiliate Click $
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
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> Where to collect the data
October 2010 © ADMA & Datalicious Pty Ltd 26
Referral visits Social media visits Organic search visits Paid search visits Other paid visits
Email visits
Web Analy-cs Banner impressions
Banner clicks +
Paid search clicks
Ad Server
Paid/Organic Visits Paid Impressions/Clicks
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Closer
25%
> Success aJribu-on models
October 2010 © ADMA & Datalicious Pty Ltd 27
Influencer Influencer $
25% Even AJrib.
Exclusion AJrib.
PaJern AJrib.
25% 25%
Introducer
33% 33% 33% 0%
30% 20% 20% 30%
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Exercise: AJribu-on model
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Closer
25%
> Exercise: AJribu-on models
October 2010 © ADMA & Datalicious Pty Ltd 29
Influencer Influencer $
25% Even AJrib.
Exclusion AJrib.
Custom AJrib.
25% 25%
Introducer
33% 33% 33% 0%
? ? ? ?
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> Exercise: AJribu-on model
§ Allocate more conversion credits to more recent touch points for brands with a strong baseline to sHmulate repeat purchases
§ Allocate more conversion credits to more recent touch points for brands with a direct response focus
§ Allocate more conversion credits to iniHaHng touch points for new and expensive brands and products to insert them into the mindset
October 2010 © ADMA & Datalicious Pty Ltd 30
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> Understanding channel overlap
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> Website entry survey
October 2010 © ADMA & Datalicious Pty Ltd 32
Channel % of Conversions
Straight to Site 27%
SEO Branded 15%
SEM Branded 9%
SEO Generic 7%
SEM Generic 14%
Display AdverHsing 7%
Affiliate MarkeHng 9%
Referrals 5%
Email MarkeHng 7%
De-‐duped Campaign Report
} Channel % of Influence
Word of Mouth 32%
Blogging & Social Media 24%
Newspaper AdverHsing 9%
Display AdverHsing 14%
Email MarkeHng 7%
Retail PromoHons 14%
Greatest Influencer on Branded Search / STS
Conversions aWributed to search terms that contain brand keywords and direct website visits are most likely not the originaHng channel that generated the awareness and as such conversion credits should be re-‐allocated.
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> Ad server exposure test
October 2010 © ADMA & Datalicious Pty Ltd 33
User qualifies for the display campaign (if the user has already been tagged go to step 3)
Audience Segmenta-on 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
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> Research online, shop offline
October 2010 © ADMA & Datalicious Pty Ltd 34
Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
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> Offline sales driven by online
October 2010 © ADMA & Datalicious Pty Ltd 35
Website research
Phone order
Retail order
Online order
Cookie
Adver-sing campaign
Credit check, fulfilment
Online order confirma-on
Virtual order confirma-on
Confirma-on email
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Exercise: Offline conversions
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> Exercise: Offline conversions
§ Email click-‐through aner purchase § First online login aner purchase § Unique website phone number § Call back request or online chat § Unique website promoHon code § Unique printable vouchers § Store locator searches § Make an appointment online
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> Media aJribu-on phases § Phase 1: De-‐duplicaHon – Conversion de-‐duplicaHon across all channels – Requires one central reporHng plaoorm – Limited to first/last click aWribuHon
§ 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 AnalyHcs and Omniture
§ Phase 3: Full purchase path – Direct response tracking including banner exposure – Cannot be done in Google AnalyHcs or Omniture – Easier to import addiHonal channels into ad server
October 2010 © ADMA & Datalicious Pty Ltd 38
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> Recommended resources § 200812 ComScore How Online AdverHsing Works § 200905 iProspect Research Study Search And Display § 200904 ClearSaleing American AWribuHon Index § 201003 Datalicious Tying Offline Sales To Online Media § Google: “Forrester Campaign AWribuHon Framework PDF”
October 2010 © ADMA & Datalicious Pty Ltd 39
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> Reducing waste
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> Reducing waste along funnel
October 2010 © ADMA & Datalicious Pty Ltd 41
Media aJribu-on
Op-mising channel mix
Tes-ng Improving usability
$$$
Targe-ng Increasing relevance
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Capture internet traffic Capture 50-‐100% of fair market share of traffic
Increase consumer engagement Exceed 50% of best compeHtor’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%
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> The consumer data journey
October 2010 © ADMA & Datalicious Pty Ltd 43
To reten-on messages To transac-onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
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> Coordina-on across channels
October 2010 © ADMA & Datalicious Pty Ltd 44
Off-‐site targe-ng
On-‐site targe-ng
Profile targe-ng
Genera-ng awareness
Crea-ng engagement
Maximising revenue
TV, radio, print, outdoor, search markeHng, 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
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Off-‐site targeHng
On-‐site targeHng
Profile targeHng
> Combining targe-ng plaForms
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On-‐site segments
Off-‐site segments
> Combining technology
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> Extended targe-ng plaForm
October 2010 © ADMA & Datalicious Pty Ltd 49
Brand
Network
Partners
Publishers
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> SuperTag code architecture
October 2010 © ADMA & Datalicious Pty Ltd 50
§ Central JavaScript container tag § One tag for all sites and plaoorms § Hosted internally or externally § Faster tag implementaHon/updates § Eliminates JavaScript caching § Enables code tesHng on live site § Enables heat map implementaHon § Enables redirects for A/B tesHng § Enables network wide re-‐targeHng § Enables live chat implementaHon
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Campaign response data
> Combining data sets
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
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> Behaviours plus transac-ons
October 2010 © ADMA & Datalicious Pty Ltd 52
one-‐off collecHon of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira-on, etc predicHve models based on data mining
propensity to buy, churn, etc historical data from previous transacHons
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 promoHon responses
emails, internal search, etc
Site Behaviour
Updated Con-nuously
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> Maximise iden-fica-on points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
−−− Probability of idenHficaHon through Cookies
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> Sample customer level data
October 2010 © ADMA & Datalicious Pty Ltd 54
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> Sample site visitor composi-on
October 2010 © ADMA & Datalicious Pty Ltd 55
30% exis-ng customers with extensive profile including transacHonal history of which maybe 50% can actually be idenHfied 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
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> Poten-al home page layout
October 2010 © ADMA & Datalicious Pty Ltd 56
Branded header
Rule based offer
Customise content delivery on the fly based on referrer data, past content consumpHon or profile data for exisHng customers.
Targeted offer Popular
links, FAQs
Targeted offer
Login
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> Prospect targe-ng parameters
October 2010 © ADMA & Datalicious Pty Ltd 57
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> Affinity targe-ng in ac-on
October 2010 © ADMA & Datalicious Pty Ltd 58
Different type of visitors respond to different ads. By using category affinity targeHng, response rates are lined 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 hJp://bit.ly/de70b7
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> Poten-al newsleJer layout
October 2010 © ADMA & Datalicious Pty Ltd 59
Closest stores, offers etc
Rule based branded header
Data verifica-on
Rule based offer
Profile based offer
Using profile data enhanced with website behaviour data imported into the email delivery plaoorm to build business rules and customise content delivery.
NPS
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> Customer profiling in ac-on
October 2010 © ADMA & Datalicious Pty Ltd 60
Using website and email responses to learn a liWle bite more about
subscribers at every touch point to keep
refining profiles and messages.
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> Poten-al landing page layout
October 2010 © ADMA & Datalicious Pty Ltd 61
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 ac-on
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Exercise: Targe-ng matrix
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> Exercise: Targe-ng matrix
Phase Segment A/B Channels Data Points
Awareness
Considera-on
Purchase Intent
Up/Cross-‐Sell
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> Exercise: Targe-ng matrix
Phase Segment A/B Channels Data Points
Awareness Seen this? Social, display, search, etc Default
Considera-on 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
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> Quality content is key
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.”
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> ClickTale tes-ng case study
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> Bad campaign worse than none
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> Keys to effec-ve targe-ng
1. Define success metrics 2. Define and validate segments 3. Develop targeHng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targeHng and automate 7. Keep tesHng and refining 8. Communicate results October 2010 © ADMA & Datalicious Pty Ltd 68
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> Recommended resources § 201003 McKinsey Get More Value From Digital MarkeHng § 200912 Unbounce 101 Landing Page OpHmizaHon 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 MarkeHng TesHng § 200409 Roy Taguchi Or MV TesHng For Marketers § 200702 Internet Retailer NavigaHng Depths Of MV TesHng § 201009 Six Revisions 10 Usability Tips Based On Research October 2010 © ADMA & Datalicious Pty Ltd 69
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Summary
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> Get the most out of the course
Category Data Metrics Insights PlaForm
Why?
What?
How?
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> Summary and ac-on items
§ Campaign flow and media aWribuHon – Draw campaign flow for your company – Check plaoorm cookie expiraHon periods – Enable pathing of direct campaign responses – InvesHgate how to track offline conversions
§ How to reduce media waste – Develop basic targeHng matrix to get started – Combine targeHng plaoorms for consistency – List all customer touch points for idenHficaHon – Check for common ID across all data sources
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Exercise: Google Analy-cs
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> Google Analy-cs prac-ce
§ Describing website visitors § IdenHfying traffic sources (reach) – Campaign tracking mechanics
§ Analyzing content usage (engagement) § Analyzing conversion drop-‐out (conversion) § Defining custom segments (breakdowns)
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> Describing website visitors
§ Average connecHon speed § Plug-‐in usage (i.e. Flash, etc) § Mobile vs. normal computers § Geographic locaHon of visitors § Time of day, day of week § Repeat visitaHon § What else?
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> Iden-fying traffic sources
§ GeneraHng de-‐duplicated reports § Campaign tracking mechanics § Conversion goals and success events § Plus adding addiHonal metrics § Paid vs. organic traffic sources § Branded vs. generic search § Traffic quanHty vs. quality
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> Analysing content usage
§ Page traffic vs. engagement § Entry vs. exit pages § Popular page paths § Internal search terms
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> Analysing conversion drop-‐out
§ Defining conversion funnels § IdenHfying main problem pages § Pages visited aner conversion barriers § Conversion drop-‐out by segment
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> Defining custom segments
§ New vs. repeat visitors § By geographic locaHon § By connecHon speed § By products purchased § New vs. exisHng customers § Branded vs. generic search § By demographics, custom segments
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> Useful analy-cs tools § hWp://labs.google.com/sets § hWp://www.google.com/trends § hWp://www.google.com/insights/search § hWp://bit.ly/googlekeywordtoolexternal § hWp://www.google.com/webmasters § hWp://www.facebook.com/insights § hWp://www.google.com/adplanner § hWp://www.google.com/videotargeHng § hWp://www.keywordspy.com § hWp://www.compete.com October 2010 © ADMA & Datalicious Pty Ltd 80
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> Useful analy-cs tools § hWp://bit.ly/hitwisedatacenter § hWp://www.socialmenHon.com § hWp://twiWersenHment.appspot.com § hWp://bit.ly/twiWerstreamgraphs § hWp://twitrratr.com § hWp://bit.ly/listonools1 § hWp://bit.ly/listonools2 § hWp://manyeyes.alphaworks.ibm.com § hWp://www.wordle.net § hWp://www.tagxedo.com October 2010 © ADMA & Datalicious Pty Ltd 81