Group M Analytics (Part 2)
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[ GroupM Analy.cs ] Advanced analy+cs training
[ Quick recap ]
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August 2010 © Datalicious Pty Ltd 2
[ Day 1: Basic Analy.cs ]
§ Defining a metrics framework – What to report on, when and why? – Matching strategic and tac+cal 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 limita+ons
§ Plus hands-‐on exercises August 2010 © Datalicious Pty Ltd 3
[ Day 1: Basic Analy.cs ]
§ Hands-‐on exercises and examples – Funnel breakdowns – Conversions metrics – Metrics framework – Search insights – Duplica+on impact – Sta+s+cal significance
August 2010 © Datalicious Pty Ltd 4
[ Course overview ]
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[ Day 2: Advanced Analy.cs ]
§ Campaign flow and media aSribu+on – Designing a campaign flow including metrics – Omniture vs. Google Analy+cs capabili+es
§ How to reduce media waste – Tes+ng and targe+ng in a media world – Media vs. content and usability
§ Plus hands-‐on exercises
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[ Media a?ribu.on ]
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Direct mail, email, etc
Facebook Twi?er, etc
[ Campaign flow and calls to ac.on ]
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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: Campaign flow
[ Unique 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 promo+onal codes, vouchers § Geographic loca+on (Facebook, FourSquare) § Regression analysis of cause and effect
August 2010 © Datalicious Pty Ltd 10
[ Search call to ac.on for offline ]
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TV audience
Search audience
Banner audience
[ Reach and channel overlap ]
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[ Indirect display impact ]
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[ Indirect display impact ]
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[ De-‐duplica.on across channels ]
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Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email Pla^orm
Google Analy.cs
$
$
$
Central Analy.cs Pla^orm
$
$
$
[ Success a?ribu.on 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 par.al credit
Paid Search
August 2010 16 © Datalicious Pty Ltd
[ First and last click a?ribu.on ]
August 2010 © Datalicious Pty Ltd 17
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 ]
August 2010 © Datalicious Pty Ltd 18
Closer
SEM Generic
Banner View
TV Ad
[ Full path to purchase ]
Influencer Influencer
August 2010 19 © Datalicious Pty Ltd
$
Banner Click $
SEO Generic
Affiliate Click $
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
August 2010 © Datalicious Pty Ltd 20
Closer
SEM Generic
Banner View
TV Ad
[ Impact of cookie expira.on ]
Influencer Influencer
August 2010 21 © Datalicious Pty Ltd
$
Banner Click $
SEO Generic
Affiliate Click $
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
Closer
25%
[ Success a?ribu.on models ]
Influencer Influencer
August 2010 22 © Datalicious Pty Ltd
$
25% Even A?rib.
Exclusion A?rib.
Pa?ern A?rib.
25% 25%
Introducer
33% 33% 33% 0%
30% 20% 20% 30%
[ Forrester media a?ribu.on ]
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Source: Forrester, 2009
Forrester adds another dimension to media aSribu+on by sugges+ng to change the allocated credit for each campaign touch point based on addi+onal factors such as site interac+on.
Exercise: A?ribu.on model
Closer
25%
[ Exercise: A?ribu.on models ]
Influencer Influencer
August 2010 25 © Datalicious Pty Ltd
$
25% Even A?rib.
Exclusion A?rib.
Custom A?rib.
25% 25%
Introducer
33% 33% 33% 0%
? ? ? ?
[ Exercise: A?ribu.on model ]
§ Allocate more conversion credits to more recent touch points for brands with a strong baseline to s+mulate repeat purchases
§ Allocate more conversion credits to more recent touch points for brands with a direct response focus
§ Allocate more conversion credits to ini+a+ng touch points for new and expensive brands and products to insert them into the mindset
August 2010 © Datalicious Pty Ltd 26
Channel Direct, Branded
Paid Search
Organic Search
Display Ads
Affiliates, Partners
Email Updates
Direct, Branded n/a
Paid Search n/a
Organic Search n/a
Display Ads n/a
Affiliates Partners n/a
Email Updates n/a
[ Understanding channel overlap ]
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display > sem > seo > affiliate > email > direct > $$$
[ Understanding channel overlap ]
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Display Ads
Paid Search
Direct
DM eDMs
Radio
Organic Search
Partners
Call Centre
[ Website entry survey ]
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Channel % of Conversions
Straight to Site 27%
SEO Branded 15%
SEM Branded 9%
SEO Generic 7%
SEM Generic 14%
Display Adver+sing 7%
Affiliate Marke+ng 9%
Referrals 5%
Email Marke+ng 7%
De-‐duped Campaign Report
} Channel % of Influence
Word of Mouth 32%
Blogging & Social Media 24%
Newspaper Adver+sing 9%
Display Adver+sing 14%
Email Marke+ng 7%
Retail Promo+ons 14%
Greatest Influencer on Branded Search / STS
Conversions aSributed 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.
[ Ad server exposure test ]
August 2010 © Datalicious Pty Ltd 30
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
[ Research online, shop offline ]
August 2010 © Datalicious Pty Ltd 31
Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
[ Track offline sales driven by online ]
August 2010 © Datalicious Pty Ltd 32
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
Exercise: Offline conversions
[ Exercise: Offline conversions ]
§ Email click-‐through aner purchase § First online login aner purchase § Unique website phone number § Unique website promo+on code § Unique printable vouchers § Store locator searches § Make an appointment online
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[ Media a?ribu.on phases ] § Phase 1: De-‐duplica+on – Conversion de-‐duplica+on across all channels – Requires one central repor+ng plaoorm – Limited to first/last click aSribu+on
§ 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 Analy+cs and Omniture
§ Phase 3: Full purchase path – Direct response tracking including banner exposure – Cannot be done in Google Analy+cs or Omniture – Easier to import addi+onal channels into ad server
August 2010 © Datalicious Pty Ltd 35
[ Recommended resources ] § 200812 ComScore How Online Adver+sing Works § 200905 iProspect Research Study Search And Display § 200902 Forrester Mul+-‐Campaign ASribu+on § 200904 ClearSaleing American ASribu+on Index § 201003 Datalicious Tying Offline Sales To Online Media
August 2010 © Datalicious Pty Ltd 36
[ Reducing waste ]
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[ Reducing waste along the funnel ]
August 2010 © Datalicious Pty Ltd 38
Media a?ribu.on
Op.mising channel mix
Tes.ng Improving usability
$$$
Targe.ng Increasing relevance
[ Increase revenue by 10-‐20% ]
August 2010 © Datalicious Pty Ltd 39
By coordina.ng the consumer’s end-‐to-‐end experience, companies could enjoy revenue increases of 10-‐20%.
Google: “get more value from digital marke.ng” or h?p://bit.ly/cAtSUN
Source: McKinsey Quarterly, 2010
[ The consumer data journey ]
August 2010 © Datalicious Pty Ltd 40
To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
[ Prospect targe.ng parameters ]
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[ Coordina.on across channels ]
August 2010 © Datalicious Pty Ltd 42
Off-‐site targe.ng
On-‐site targe.ng
Profile targe.ng
Genera.ng awareness
Crea.ng engagement
Maximising revenue
TV, radio, print, outdoor, search marke+ng, display ads, performance networks, affiliates, social media, etc
Retail stores, call centers, brochures, websites, landing pages, mobile apps, online chat, etc
Outbound calls, direct mail, emails, SMS, etc
Off-‐site targe+ng
On-‐site targe+ng
Profile targe+ng
[ Combining targe.ng pla^orms ]
August 2010 © Datalicious Pty Ltd 43
On-‐site segments
Off-‐site segments
[ Combining technology ]
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[ Datalicious SuperTag ]
August 2010 © Datalicious Pty Ltd 47
§ Central JavaScript based container tag § One tag for all plaoorms incl. Omniture § Either hosted internally or externally § Faster tag implementa+on and updates § Consistent network wide re-‐targe+ng § Transfer or profiling data between sites § Iden+fica+on of exis+ng customers § Re-‐targe+ng by brand preferences
Campaign response data
[ Combining data sets ]
August 2010 © Datalicious Pty Ltd 48
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
[ Behaviours plus transac.ons ]
August 2010 © Datalicious Pty Ltd 49
one-‐off collec+on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira.on, etc predic+ve models based on data mining
propensity to buy, churn, etc historical data from previous transac+ons
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 promo+on responses
emails, internal search, etc
Site Behaviour
UPDATED CONTINUOUSLY
[ 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 iden+fica+on through Cookies
August 2010 50 © Datalicious Pty Ltd
[ Sample customer level data ]
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[ Sample site visitor composi.on ]
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30% exis.ng customers with extensive profile including transac+onal history of which maybe 50% can actually be iden+fied 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
Exercise: Targe.ng matrix
Phase Segment A Segment B Channels
Awareness
Considera.on
Purchase Intent
Up/Cross-‐Sell
[ Exercise: Targe.ng matrix ]
August 2010 54 © Datalicious Pty Ltd
Phase Segment A Segment B Channels
Awareness Seen this? Social, display, search, etc
Considera.on Great feature! Social, search, website, etc
Purchase Intent Great value! Search, site, emails, etc
Up/Cross-‐Sell Add this! Direct mail, emails, etc
[ Exercise: Targe.ng matrix ]
August 2010 55 © Datalicious Pty Ltd
Phase Segment A Segment B Data Points
Awareness Seen this? Default
Considera.on Great feature! Download, product view
Purchase Intent Great value! Cart add, checkout, etc
Up/Cross-‐Sell Add this! Email response, login, etc
[ Exercise: Targe.ng matrix ]
August 2010 56 © Datalicious Pty Ltd
[ Poten.al landing page layout ]
August 2010 © Datalicious Pty Ltd 57
Branded header
Email or campaign message match
Targeted offers
Passing data on user preferences through to the website via parameters in email click-‐through URLs to customise content delivery.
Call to ac.on
[ Poten.al newsle?er layout ]
August 2010 © Datalicious Pty Ltd 58
Closest stores, offers etc
Rule based header theme
Data verifica.on
Rule based offer
Profile based offer
Using data on website behaviour imported into the email delivery plaoorm to build business rules to customise content delivery.
NPS
[ Affinity targe.ng in ac.on ]
August 2010 © Datalicious Pty Ltd 59
Different type of visitors respond to different ads. By using category affinity targe+ng, 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 h?p://bit.ly/de70b7
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.”
[ Quality content is key ]
August 2010 60 © Datalicious Pty Ltd
[ ClickTale tes.ng case study ]
August 2010 © Datalicious Pty Ltd 61
[ Bad campaign worse than none ]
August 2010 © Datalicious Pty Ltd 62
[ Recommended resources ] § 201003 McKinsey Get More Value From Digital Marke+ng § 200912 Unbounce 101 Landing Page Op+miza+on 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 Marke+ng Tes+ng § 200409 Roy Taguchi Or MV Tes+ng For Marketers § 200702 Internet Retailer Naviga+ng Depths Of MV Tes+ng
August 2010 © Datalicious Pty Ltd 63
Summary
[ Prac.ce session ]
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Exercise: Web analy.cs
[ Web analy.cs pla^orm prac.ce ]
§ Google Analy+cs and Omniture SiteCatalyst – Plaoorm basics and comparison – Describing website visitors – Iden+fying traffic sources (reach)
§ Campaign tracking mechanics
– Analyzing content usage (engagement) – Analyzing conversion drop-‐out (conversion) – Defining custom segments (funnel breakdowns)
August 2010 © Datalicious Pty Ltd 67
[ Top 5 Omniture usage .ps] § Bookmark interes+ng reports and frequently used report
sevng right away so they’re easy to find again later § Use mul+ple browser windows and con+nue browsing in
a new window once you find an interes+ng report to facilitate comparison and data explora+on
§ Set automa+c email alerts for all key metrics you come across right away so you are always the first to know about anomalies rather than the client telling you
§ Use short URLs next to all graphs used in client presenta+ons to facilitate naviga+on to the underlying report and to save +me on poten+al change requests
§ Read the ‘200708 Omniture SiteCatalyst Report Descrip+ons’ and ask for the clients’ Solu+on Design
August 2010 © Datalicious Pty Ltd 68
[ Describing website visitors ]
§ Average connec+on speed § Plug-‐in usage (i.e. Flash, etc) § Mobile vs. normal computers § Geographic loca+on of visitors § Time of day, day of week § Repeat visita+on § What else?
August 2010 © Datalicious Pty Ltd 69
[ Iden.fying traffic sources ]
§ Genera+ng de-‐duplicated reports § Campaign tracking mechanics – Google URL Builder and Omniture SAINT
§ Conversion goals and success events § Plus adding addi+onal metrics § Paid vs. organic traffic sources § Branded vs. generic search § Traffic quan+ty vs. quality
August 2010 © Datalicious Pty Ltd 70
[ Analysing content usage ]
§ Page traffic vs. engagement § Entry vs. exit pages § Popular page paths § Internal search terms
August 2010 © Datalicious Pty Ltd 71
[ Analysing conversion drop-‐out ]
§ Defining conversion funnels § Iden+fying main problem pages § Pages visited aner conversion barriers § Conversion drop-‐out by segment
August 2010 © Datalicious Pty Ltd 72
[ Defining custom segments ]
§ New vs. repeat visitors § By geographic loca+on § By connec+on speed § By products purchased § New vs. exis+ng customers § Branded vs. generic search § By demographics, custom segments
August 2010 © Datalicious Pty Ltd 73
© Datalicious Pty Ltd
[ Useful analy.cs tools ] § hSp://labs.google.com/sets § hSp://www.google.com/trends § hSp://www.google.com/insights/search § hSp://www.google.com/sktool § hSp://bit.ly/googlekeywordtoolexternal § hSp://www.google.com/webmasters § hSp://www.google.com/adplanner § hSp://www.google.com/videotarge+ng § hSp://www.keywordspy.com § hSp://www.compete.com June 2010 74
© Datalicious Pty Ltd
[ Useful analy.cs tools ]
§ hSp://bit.ly/hitwisedatacenter § hSp://www.socialmen+on.com § hSp://twiSersen+ment.appspot.com § hSp://bit.ly/twiSerstreamgraphs § hSp://twitrratr.com § hSp://bit.ly/listonools1 § hSp://bit.ly/listonools2 § hSp://manyeyes.alphaworks.ibm.com § hSp://www.wordle.net June 2010 75