Analyze to Optimize

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

measurement and ROI

> Company history

§  Datalicious was founded in late 2007 §  Strong Omniture web analy@cs history §  1 of 4 Omniture Service Partners globally §  Now 360 data agency with specialist team §  Combina@on of analysts and developers § Making data accessible and ac@onable §  Evangelizing smart data driven marke@ng §  Driving industry best prac@ce (ADMA)

October 2010 © ADMA & Datalicious Pty Ltd 2

> Smart data driven marke-ng

Media A:ribu-on

Op-mise channel mix

Tes-ng Improve usability

Targe-ng Increase relevance

> Wide range of data services

Data PlaGorms Data collec-on and processing Web analy-cs solu-ons Omniture, Google Analy-cs, etc Tag-‐less online data capture End-‐to-‐end data plaGorms IVR and call center repor-ng Single customer view

Insights Repor-ng Data mining and modelling Customised dashboards Media a:ribu-on models Market and compe-tor trends Social media monitoring Online surveys and polls Customer profiling

Ac-on Applica-ons Data usage and applica-on Marke-ng automa-on Aprimo, Trac-on, Inxmail, etc Targe-ng and merchandising Internal search op-misa-on CRM strategy and execu-on Tes-ng programs

> Clients across all industries

> Course overview

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> 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 October 2010 © ADMA & Datalicious Pty Ltd 7

> Day 2: Advanced Analy-cs

§  Campaign flow and media a^ribu@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

> Training outcomes

§  Aber successful comple@on of the training course par@cipants will be able to – Define a metrics framework for any client –  Enable benchmarking across campaigns –  Incorporate analy@cs into the planning process –  Pull and interpret key reports in Google Analy@cs –  Impress with insights instead of spreadsheets –  Know how to extend op@misa@on past media buy –  Show the true value of digital media

Category Data Metrics Insights PlaGorm

> Get the most out of the course

> Metrics framework

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Awareness Interest Desire Ac-on Sa-sfac-on

> AIDA and AIDAS formulas

Social media

New media

Old media

> Importance of social media Search

WOM, blogs, reviews, ra-ngs, communi-es, social networks, photo sharing, video sharing

October 2010 © ADMA & Datalicious Pty Ltd

Promo-on

Company Consumer

> Social as the new search

Reach (Awareness)

Engagement (Interest & Desire)

Conversion (Ac@on)

+Buzz (Sa@sfac@on)

> Simplified AIDAS funnel

People reached

People engaged

People converted

People delighted

> Marke-ng is about people

40% 10% 1%

People reached

People engaged

People converted

People delighted

> Addi-onal funnel breakdowns

40% 10% 1%

New prospects vs. exis@ng customers

Brand vs. direct response campaign

New vs. returning visitors

AU/NZ vs. rest of world

Prospect vs. customer

High vs. low value

Product affinity

Post code, age, sex, etc

Exercise: Funnel breakdowns

> Exercise: Funnel breakdowns

§  List poten@ally insighful funnel breakdowns –  Brand vs. direct response campaign – New prospects vs. exis@ng customers –  Baseline vs. incremental conversions –  Compe@@ve ac@vity, i.e. none, a lot, etc –  Segments, i.e. age, loca@on, 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

> Exercise: Conversion metrics

§  Key conversion metrics differ by category – Commerce – Lead genera@on – Content publishing – Customer service

> Exercise: Conversion metrics

Source: Omniture Summit, Ma^ Belkin, 2007

Custom conversion goals

> Conversion funnel 1.0

October 2010

Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informa@on, order confirma@on, etc

Conversion event

Campaign responses

> Conversion funnel 2.0

October 2010

Campaign responses (inbound spokes) Offline campaigns, banner ads, email marke@ng, referrals, organic search, paid search, internal promo@ons, etc

Landing page (hub)

Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registra@on, product comparison, product review, forward to friend, etc

> Addi-onal 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

Pages per visit Time on site

> Rela-ve or calculated metrics

§  Bounce rate §  Conversion rate §  Cost per acquisi@on §  Pages views per visit §  Product views per visit §  Cart abandonment rate §  Average order value

> eMarketer interac-ve metrics

Sen@ment

Reach Influence

> Measuring social media

Exercise: Metrics framework

Level Reach Engagement Conversion +Buzz

Level 1 People

Level 2 Strategic

Level 3 Tac-cal

> 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 Tac-cal

Click-‐through or interac-on

rate, etc ? ? ?

> Exercise: Metrics framework

IR −MIMI

= ROMI + BE

> ROI, ROMI, BE, etc

October 2010 © ADMA & Datalicious Pty Ltd 37 €

IR −MIMI

= ROMI

R − II

= ROI R Revenue I Investment ROI Return on

investment IR Incremental

revenue MI Marke@ng

investment ROMI Return on

marke@ng investment

BE Brand equity

> Success: ROMI + BE

§  Establish incremental revenue (IR) –  Requires baseline revenue to calculate addi@onal revenue as well as revenue from cost savings

§  Establish marke@ng investment (MI) –  Requires all costs across technology, content, data and resources plus promo@ons and discounts

§  Establish brand equity contribu@on (BE) –  Requires addi@onal sob metrics to evaluate subscriber percep@ons, experience, altudes and word of mouth

IR −MIMI

= ROMI + BE

> Process is key to success

> Recommended resources §  200501 WAA Key Metrics & KPIs §  200708 WAA Analy@cs Defini@ons Volume 1 §  200612 Omniture Effec@ve Measurement §  200804 Omniture Calculated Metrics White Paper §  200702 Omniture Effec@ve Segmenta@on Guide §  200810 Ronnestam Online Adver@sing And AIDAS §  201004 Al@meter Social Marke@ng Analy@cs §  201008 CSR Customer Sa@sfac@on Vs Delight §  Google “Enquiro Search Engine Results 2010 PDF” §  Google “Razorfish Ac@onable Analy@cs Report PDF” §  Google “Forrester Interac@ve Marke@ng Metrics PDF”

> Data sources

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> Digital data is plen-ful and cheap

> Digital data categories

Source: Accuracy Whitepaper for web analy@cs, Brian Clibon, 2008

+Social

> Customer data journey

To reten-on messages To transac-onal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

> Corporate data journey

Time, Control

is@ca@o

Stage 1

Data Stage 2

Insights Stage 3 Ac-on

Third par@es control most data, ad hoc repor@ng only, i.e. what happened?

Data is being brought in-‐house, shib towards insights genera@on and data mining, i.e. why did it happen?

Data is fully owned in-‐house, advanced predic@ve modelling and trigger based marke@ng, i.e. what will happen and making it happen!

> What analy-cs plaGorm to use

Time, Control

is@ca@o

Stage 1: Data Stage 2: Insights Stage 3: Ac-on

Third par@es control most data, ad hoc repor@ng only, i.e. what happened?

Data is being brought in-‐house, shib towards insights genera@on and data mining, i.e. why did it happen?

Data is fully owned in-‐house, advanced predic@ve modelling and trigger based marke@ng, i.e. what will happen and making it happen!

People Reached

People Engaged

People Converted

People Delighted

> Poten-al data sources

40% 10% 1%

Quan@ta@ve and qualita@ve 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 automa@cally §  Across normal websites, mobiles and apps

> Atomic labs integra-on model

§  Single point of data capture and processing

§  Real-‐@me queries to enrich website data

§ Mul@ple data export op@ons for web analy@cs

§  Enriching single-‐customer view website behaviour

> Google data in Australia

Source: h^p://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 crea-ve

Exercise: Search insights

> Exercise: Search insights §  Iden@fy key category search terms –  Data from Google AdWords Keyword Tool –  Search for “google keyword tool” – Wordle and IBM Many Eyes for visualiza@ons –  Search for “wordle word clouds” and “ibm many eyes”

§  Iden@fy search term trends and compe@tors –  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 Analy@cs, Jus@n 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 overes@mated visitors by up to 7.6 @mes whilst a cookie-‐based approach overes-mated visitors by up to 2.3 -mes.

> Unique visitor overes-ma-on

Source: White Paper, RedEye, 2007

Datalicious SuperCookie Persistent Flash cookie that cannot be deleted

> Maximise iden-fica-on points

0 4 8 12 16 20 24 28 32 36 40 44 48

−−− Probability of iden@fica@on through Cookies

October 2010 62 © ADMA & Datalicious Pty Ltd

> De-‐duplica-on across channels

Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaGorm

Google Analy-cs

Central Analy-cs PlaGorm

De-‐duplica-on across channels

Addi-onal funnel breakdowns

Exercise: Duplica-on impact

> Exercise: Duplica-on impact §  Double-‐coun@ng 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 repor@ng but once de-‐duplicated they each only contributed 50% of conversions

–  What are the ini@al CPA values and what is the true CPA? §  Solu@on: $50 ini@al CPA and $100 true CPA

–  $5,000 / 100 = $50 ini@al CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase)

TV audience

Search audience

Banner audience

> Reach and channel overlap

> Es-ma-ng reach and overlap §  Apply average unique visitor count per recorded unique user names to all unique visitor figures in Google Analy@cs, Omniture, etc

§  Apply ra@o 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

Sen-ment analysis: People vs. machine

> Al-meter social analy-cs

Social Marke@ng Analy@cs is the discipline that helps companies measure, assess and explain the performance of social media ini@a@ves in the context of specific business objec@ves.

Data from

> Overall volume and influence

Data from

> Influence and media value

Data from

> Facebook insights

Using Facebook Like bu^ons is a free and powerful way to gain addi@onal 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, affilia@ons, last profile update, @me zone, religion, poli@cal interests, a^racted to which sex, why they want to meet someone, home town, rela@onship status, current loca@on, ac@vi@es, music interests, tv show interests, educa@on history, work history, family, etc Need anything else?

(influencers only)

(all contacts)

Appending social data to customer profiles Name, age, gender, occupa-on, loca-on, social profiles and influencer ranking based on email

Exercise: Sta-s-cal 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 execu-ons 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 ques-on 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 execu-ons if you serve 1,000,000 banners?

383 sales per banner execu-on or 383 x 6 = 2,298 sales

Google “nss sample size calculator”

> Addi-onal 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

Calendar events

> Recommended resources §  200311 UK RedEye Cookie Case Study §  200807 Kaushik Tracking Offline Conversion §  200904 Kaushik Standard Metrics Revisited §  201002 Kaushik 8 Compe@@ve Intelligence Data Sources §  201005 Google Ad Planner Data Wrong By Up To 20% §  201005 MPI How Sta@s@cally Valid Is Your Survey §  201009 Google Analy@cs 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 Adver@singAge Are Marketers Really Spying On You October 2010 © ADMA & Datalicious Pty Ltd 86

Summary

Category Data Metrics Insights PlaGorm

> Get the most out of the course

> Summary and ac-on items

§  Defining a metrics framework – Develop standardised metrics framework – Define addi@onal funnel breakdowns –  Establish baseline and incremental – Define addi@onal success metrics

§  Finding and developing the right data –  Ensure de-‐duplica@on via central analy@cs –  Check reports for sta@s@cal significance –  Check data sources and their accuracy –  Start popula@ng a calendar of events

Exercise: Google Analy-cs

> Google Analy-cs prac-ce

§  Describing website visitors §  Iden@fying traffic sources (reach) – Campaign tracking mechanics

§  Analyzing content usage (engagement) §  Analyzing conversion drop-‐out (conversion) §  Defining custom segments (breakdowns)

> 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 §  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 aber 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

> Useful analy-cs tools §  h^p://labs.google.com/sets §  h^p://www.google.com/trends §  h^p://www.google.com/insights/search §  h^p://bit.ly/googlekeywordtoolexternal §  h^p://www.google.com/webmasters §  h^p://www.facebook.com/insights §  h^p://www.google.com/adplanner §  h^p://www.google.com/videotarge@ng §  h^p://www.keywordspy.com §  h^p://www.compete.com October 2010 97

> Useful analy-cs tools §  h^p://bit.ly/hitwisedatacenter §  h^p://www.socialmen@on.com §  h^p://twi^ersen@ment.appspot.com §  h^p://bit.ly/twi^erstreamgraphs §  h^p://twitrratr.com §  h^p://bit.ly/listobools1 §  h^p://bit.ly/listobools2 §  h^p://manyeyes.alphaworks.ibm.com §  h^p://www.wordle.net §  h^p://www.tagxedo.com October 2010 98

Contact us cbartens@datalicious.com

twi^er.com/datalicious

Learn more blog.datalicious.com

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