Analyze to Optimize (Part 2)

> Analyse to op-mise < ADMA short course on data,

measurement and ROI

> Quick recap

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October 2010 © ADMA & Datalicious Pty Ltd 2

> 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

> Day 1: Basic Analy-cs

§  Hands-‐on exercises and examples – Funnel breakdowns – Conversions metrics – Metrics framework – Search insights – DuplicaHon impact – StaHsHcal significance


> 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


> Get the most out of the course

Category Data Metrics Insights PlaForm

Why?

What?

How?


> Media aJribu-on

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Direct mail, email, etc

Facebook TwiJer, 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

Exercise: Calls to ac-on

> 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


> Search call to ac-on for offline


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

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 PlaForm

Google Analy-cs

$

$

$

Central Analy-cs PlaForm

$

$

$

De-‐duplica-on across channels

> Success aJribu-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

> First and last click aJribu-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

> Where to collect the data


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

Closer

25%

> Success aJribu-on models



25% Even AJrib.

Exclusion AJrib.

PaJern AJrib.

25% 25%

Introducer

33% 33% 33% 0%

30% 20% 20% 30%

Exercise: AJribu-on model

Closer

25%

> Exercise: AJribu-on models



25% Even AJrib.

Exclusion AJrib.

Custom AJrib.

25% 25%

Introducer

33% 33% 33% 0%

? ? ? ?

> 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


> 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 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.

> 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

> 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 §  Call back request or online chat §  Unique website promoHon code §  Unique printable vouchers §  Store locator searches § Make an appointment online


> 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


> 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”


> Reducing waste

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> Reducing waste along funnel


Media aJribu-on

Op-mising channel mix

Tes-ng Improving usability

$$$

Targe-ng Increasing relevance

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%


> 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

> 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 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

Off-‐site targeHng

On-‐site targeHng

Profile targeHng

> Combining targe-ng plaForms


On-‐site segments

Off-‐site segments

> Combining technology


> Extended targe-ng plaForm


Brand

Network

Partners

Publishers

> SuperTag code architecture


§  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

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 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

> 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

October 2010 53 © ADMA & Datalicious Pty Ltd

> Sample customer level data


> Sample site visitor composi-on


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

> Poten-al home page layout


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

> Prospect targe-ng parameters


> Affinity targe-ng in ac-on


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

> Poten-al newsleJer layout


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

> Customer profiling in ac-on


Using website and email responses to learn a liWle bite more about

subscribers at every touch point to keep

refining profiles and messages.

> Poten-al 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 ac-on

Exercise: Targe-ng matrix

> Exercise: Targe-ng matrix

Phase Segment A/B Channels Data Points

Awareness

Considera-on

Purchase Intent

Up/Cross-‐Sell


> 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


> 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.”


> ClickTale tes-ng case study


> Bad campaign worse than none


> 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

> 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

Summary

> Get the most out of the course

Category Data Metrics Insights PlaForm

Why?

What?

How?


> 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


Exercise: Google Analy-cs

> 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)


> 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?


> 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


> Analysing content usage

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


> Analysing conversion drop-‐out

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


> 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


> 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

> 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

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Analyze to Optimize (Part 2)