group m analytics

[ GroupM Analy.cs ] Advanced analy+cs training

[ Company history ]

§  Datalicious was founded in 2007 §  Strong Omniture web analy+cs history §  One-‐stop 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)

August 2010 © 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

[ Main business units and services ]


Data PlaForms Data collec.on and processing Web analy.cs solu.ons Omniture, Google Analy.cs, etc Tag-‐less online data capture End-‐to-‐end data plaForms 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 August 2010 © Datalicious Pty Ltd 7

[ Day 2: Advanced Analy.cs ]

§  Campaign flow and media aZribu+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 ]

§  A^er successful comple+on of the training course par+cipants will be able to – Define a metrics framework for any client –  Incorporate analy+cs into the planning process –  Enable benchmarking across campaigns –  Iden+fy data gaps and recommend solu+ons – Use more than just ad server data for analy+cs –  Impress clients with insights not spreadsheets –  Know how to extend op+misa+on past media buy –  Show the true value of digital media


Plenty of hands on exercises

[ Prac.ce session prepara.on ]

§  Organise client placorm logins – Ad servers: DoubleClick, Atlas, Eyeblaster, etc – Bid management: Google AdWords, etc – Web analy+cs: Google Analy+cs, Omniture, etc – Social media: Radian6, S2M, etc

§  Plus any addi+onal data or logins – Google webmaster tools, Facebook fan pages – Phone calls, retail sales, etc


[ Metrics framework ]

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


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

August 2010 © Datalicious Pty Ltd

Promo.on

14

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

Exercise: Funnel breakdowns

[ Exercise: Funnel breakdowns ]

§  List poten+ally insighcul 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, MaZ Belkin, 2007

[ Conversion funnel 1.0 ]

August 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

© Datalicious Pty Ltd 24

[ Conversion funnel 2.0 ]

August 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

© Datalicious Pty Ltd 25

[ 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

[ 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

[ Pion 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

[ 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 ]


[ Forrester interac.ve metrics ]



Different metrics should be viewed as complementary parts of the measurement jigsaw.

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 ]

August 2010 © Datalicious Pty Ltd 36 €

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 so^ metrics to evaluate subscriber percep+ons, experience, amtudes 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 §  200805 Forrester Interac+ve Marke+ng Metrics Guide §  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 §  200612 Razorfish Ac+onable Analy+cs Report §  200708 Enquiro Search Engine Results 2010 §  201004 Al+meter Social Marke+ng Analy+cs §  201008 CSR Customer Sa+sfac+on Vs Delight


[ Data sources ]

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[ Digital data is plen.ful and cheap ]



[ Digital data categories ]


Source: Accuracy Whitepaper for web analy+cs, Brian Cli^on, 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

Soph

is+ca+o

n

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, shi^ 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 plaForm to use ]


Time, Control

Soph

is+ca+o

n

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, shi^ 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

[ Google data in Singapore]


Source: hZp://www.hitwise.com/sg/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 ]

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 58 © Datalicious Pty Ltd

[ 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

$

$

$

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)


Exercise: Web analy.cs

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.

[ Facebook insights ]


Using Facebook Like buZons 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! ID, first name, last name, middle name, picture, affilia+ons, last profile update, +me zone, religion, poli+cal interests, interests, sex, birthday, aZracted 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 and email Need anything else?

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

(influencers only)

(all contacts)

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

[ Recommended resources ] §  200311 UK RedEye Cookie Case Study §  200807 Kaushik Tracking Offline Conversion §  200906 WOM Online The People Vs Machines Debate §  201005 Google Ad Planner Data Wrong By Up To 20% §  201005 MPI How Sta+s+cally Valid Is Your Survey §  201005 Wikipedia Sta+s+cal Significance §  201005 Wikipedia Sta++cal Validity §  201005 Omniture Campaign Management §  200910 Eyeblaster Global Benchmark §  200903 Coremetrics Conversion Benchmarks By Industry §  201007 WSJ The Web's New Gold Mine Your Secrets §  201008 Adver+singAge Are Marketers Really Spying On You


Summary

[ Prac.ce session ]

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Exercise: Web analy.cs

[ Web analy.cs plaForm prac.ce ]

§  Google Analy+cs and Omniture SiteCatalyst – Placorm 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

semng 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 a^er 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 ] §  hZp://labs.google.com/sets §  hZp://www.google.com/trends §  hZp://www.google.com/insights/search §  hZp://www.google.com/sktool §  hZp://bit.ly/googlekeywordtoolexternal §  hZp://www.google.com/webmasters §  hZp://www.google.com/adplanner §  hZp://www.google.com/videotarge+ng §  hZp://www.keywordspy.com §  hZp://www.compete.com June 2010 87

© Datalicious Pty Ltd

[ Useful analy.cs tools ]

§  hZp://bit.ly/hitwisedatacenter §  hZp://www.socialmen+on.com §  hZp://twiZersen+ment.appspot.com §  hZp://bit.ly/twiZerstreamgraphs §  hZp://twitrratr.com §  hZp://bit.ly/listo^ools1 §  hZp://bit.ly/listo^ools2 §  hZp://manyeyes.alphaworks.ibm.com §  hZp://www.wordle.net June 2010 88

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