group m analytics (part 2)

[ 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


[ 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


[ Media a?ribu.on ]

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Direct mail, email, etc

Facebook Twi?er, etc

[ Campaign flow and calls to ac.on ]


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


[ Search call to ac.on for offline ]


TV audience

Search audience

Banner audience

[ Reach and channel overlap ]


[ Indirect display impact ]


[ De-‐duplica.on across channels ]


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 ]


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

Closer

SEM Generic

Banner View

TV Ad

[ Impact of cookie expira.on ]



$

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 ]



$

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 ]


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 ]



$

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


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 ]


display > sem > seo > affiliate > email > direct > $$$

[ Understanding channel overlap ]


Display Ads

Paid Search

Direct

DM eDMs

Radio

Organic Search

Partners

Call Centre

[ Website entry survey ]


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 ]


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 ]


Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School

[ Track offline sales driven by online ]


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


[ 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


[ 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


[ Reducing waste ]

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[ Reducing waste along the funnel ]


Media a?ribu.on

Op.mising channel mix

Tes.ng Improving usability

$$$

Targe.ng Increasing relevance

[ Increase revenue by 10-‐20% ]


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 ]


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 ]


[ Coordina.on across channels ]


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 ]


On-‐site segments

Off-‐site segments

[ Combining technology ]


[ Datalicious SuperTag ]


§  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 ]


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

[ Behaviours plus transac.ons ]


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


[ Sample customer level data ]


[ Sample site visitor composi.on ]


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 ]


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



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



[ Poten.al landing page layout ]


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 ]


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 ]


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 ]


[ ClickTale tes.ng case study ]


[ Bad campaign worse than none ]


[ 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


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)


[ 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


[ 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?


[ 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


[ Analysing content usage ]

§  Page traffic vs. engagement §  Entry vs. exit pages §  Popular page paths §  Internal search terms


[ Analysing conversion drop-‐out ]

§  Defining conversion funnels §  Iden+fying main problem pages §  Pages visited aner conversion barriers §  Conversion drop-‐out by segment


[ 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


© 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

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group m analytics (part 2)

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