Digital Measurement - a Determinant in Tracking and Measuring Marketing Performance

[ Digital Measurement ] More than just coun.ng hits

[ Company history ]

§  Datalicious was founded in 2006 §  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 §  Driving industry best prac.ce §  Evangelizing use of data

May 2010 © Datalicious Pty Ltd 2

[ Challenging clients ]


[ Data driven marke:ng ]


Data Pla<orms Data collec:on and processing Web analy:cs solu:ons Omniture, Google Analy:cs, etc Tagless online data capture End-‐to-‐end data pla<orms IVR and call center repor:ng Single customer view

Insights Repor:ng Data mining and modelling Customised dashboards Media aKribu: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

[ Digital metrics ]

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[ Data and what you pay for it ]


Source: Omniture Summit, MaN Belkin, 2007

HITS How Idiots Track Success


[ Basic website metrics ] §  Page view/impression: The number of .mes a page (an

analyst-‐definable unit of content) was viewed. §  Visit/session: A visit is an interac.on, by an individual,

with a website consis.ng of one or more requests for an analyst-‐definable unit of content (i.e. “page view”). If an individual has not taken another ac.on (typically addi.onal page views) on the site within a specified .me period, the visit session will terminate.

§  Unique visitor/browser: The number of inferred individual people (filtered for spiders and robots), within a designated repor.ng .meframe, with ac.vity consis.ng of one or more visits to a site. Each individual is counted only once in the unique visitor measure for the repor.ng period.


Source: Web Analy.cs Defini.ons, Web Analy.cs Associa.on, 2007

[ Browser side tracking process ]


Source: Google Analy.cs, Jus.n Cutroni, 2007

What if: Someone deletes their cookies? Or uses two computers, one at work and one at home? Or two

people use the same account or 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. The percentage error in cumula.ve unique visitor figures over a 28 day period on one of the sites can be seen in the graph above.

[ Overes:ma:on of unique visitors ]


Source: White Paper, RedEye, 2007

[ Mul:ply iden:fica:on points ]


0%

20%

40%

60%

80%

100%

120%

140%

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

Probability of iden.fica.on through cookie

[ Digital metric categories ]


Source: Accuracy Whitepaper for web analy.cs, Brian Cligon, 2008

+Social

[ Digital means global ]


Source: Carat/Isobar, 2007

“The image is a model of the Internet, based on how many people view different sites and how these sites are related to each other. There are 3 colours on this model. Red, Green and Blue. Each represents users from US, Europe and Asia.

The picture illustrates how non linear the digital world is. It also shows how some sites have a strong centre of gravity for mass audiences; others have strong centres of gravity for niche audiences.

It is important to iden.fy where marke.ng is going to have most impact -‐ crea.ng powerful programs on niche sites, which gradually extend an influence on the larger communi.es; or (more expensive) marke.ng ac.vity on mass sites, that will then generate a frenzy of interest in smaller niche groups.”

Reach (Awareness)

Engagement (Interest & Desire)

Ac:on (Ac.on)

+Buzz (Sa.sfac.on)

Quan.ta.ve and qualita.ve research data

Website, call center and retail data

[ Defining metrics frameworks ]


Social media data

Media and search data

Social media

[ Measuring reach ]

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[ New marketplace: Search ]


[ Search at all stages ]


Source: Inside the Mind of the Searcher, Enquiro 2004

[ Non-‐linear conversion funnel ]

14/11/12 © Datalicious Pty Ltd 18

Source: McKinsey, 2009


[ Importance of search ]


60-‐70%

30-‐40%

May 2010 21 © Datalicious Pty Ltd


[ Online insights influencing offline ]


[ Search data and media planning ]




[ 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

[ Hitwise Mosaic segment swing ]

australia.com vs. newzealand.com australia.com vs. bulafiji.com

May 2010 © Datalicious Pty Ltd 28 Source: Hitwise.com.au, 2008

[ Hitwise Mosaic segment swing ]

australia.com vs. newzealand.com australia.com vs. newzealand.com

May 2010 © Datalicious Pty Ltd 29 Source: Hitwise.com.au, 2008

[ Measuring engagement ]

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[ Conversion funnel 1.0 ]

May 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 31

[ Conversion funnel 2.0 ]

May 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 32

[ Addi:onal success metrics ]


Click Through

Add To Cart

Click Through

Bounce Rate

Click Through $

Click Through

Call back requests

Store Searches [ ... ] $

$

$ Cart Checkout

Pages Per Visit

?

Avg Cart Value


Customiza:on



Crowdsourcing


[ Book: Tuned In ]


Source: hNp://www.pragma.cmarke.ng.com/tunedin

“70% or more of new products or new product decisions were made without

market data”

[ Social media data ]


Facebook Connect gives you the following 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, aNracted 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?

[ Measuring ac:on ]

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[ Key metrics by website type ]


Source: Omniture Summit, MaN Belkin, 2007

[ Success aKribu: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


[ De-‐duplica:on across channels ]


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email Pla<orm

Google Analy:cs

$

$

$

Omniture Pla<orm

$

$

$

[ First vs. last click aKribu:on ]


Chart shows percentage of channel touch points that lead to a conversion.

Neither first nor last-‐click measurement would provide true picture

Banner View

TV Ad

Print Ad

[ Path to purchase ]

Banner Click

SEM Generic

Partner Site

Direct Visit


$

SEO Generic $

SEO Branded

Banner Click $

Social Media

Email Update

Direct Visit $

[ Paid and organic stacking ]


[ 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%

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%

De-‐duped Campaign Report Greatest Influencer on Branded Search / STS

{Conversions aNributed 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.

Those who entered the website site directly or via branded organic search terms had the highest awareness and/or experience with retail display followed by having previously visited the site. Q: What was the reason or reasons for this visit to vodafone.com.au?

[ Reason for website visit ]


[ Forrester media aKribu:on ]


Chart shows an example only, aNribu.on model needs to be defined for each company separately based on their individual success metrics (and cookie expira.on policies).

[ Calls to ac:on ]


[ Research online buy in store ]


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

[ Store locator searches ]


Facebook TwiKer, etc

[ Integrated campaign flow ]

February 2010 © Datalicious Pty Ltd 53

Point of Sale, Kiosks, etc

CRM Program

Direct Mail, Email, etc

Home Page, Portal, etc

YouTube, Blog, etc

Paid Search

Organic Search

Landing Page, Compe::on

PR, Events, Social, etc

TV, Print, Radio, etc

V2

V3

= Paid Media

= Viral Element

V1

Retail Outlets

= Voucher

Sponsorships, Display Ads, etc

[ Offline sales driven by online ]

February 2010 © Datalicious Pty Ltd 54

Cookie

Website.com Research

Credit Check Fulfilment

Phone Orders

Retail Orders

Online Orders





Online Order Confirma:on

Virtual Order Confirma:on



@

@

@

Cookie Cookie

Adver:sing Campaign

Tying offline conversions back to online campaign and research behavior using standard cookie technology by triggering virtual online order confirma.on pages for offline sales using email receipts.


Sta:s:cal significance: Why does it maKer?

[ Measuring buzz ]

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Experience Brand

Service Product

[ Back to basics ]

Company Consumer

Search

Word of mouth, blogs, emails, tweets, reviews, social networks, social media, fan pages, etc

April 2010 © Datalicious Pty Ltd

Promo:on

57

Source: Don Schultz, Northwestern University



Social media analy:cs: People vs. machine


Iden:fy influencers and advocates

[ Where to focus ]


[ Useful links ]

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[ News and research ]

§  hNp://blog.datalicious.com §  hNp://www.emarketer.com §  hNp://www.marke.ngcharts.com §  hNp://www.techcrunch.com §  hNp://www.smartbrief.com/iab §  hNp://www.trendwatching.com §  hNp://www.springwise.com §  hNp://www.useit.com/alertbox §  hNp://weblogs.hitwise.com [ august 2008 ] [ datalicious.com ]

[ datalicious.com ]

[ Trends and data ]

§  hNp://www.google.com/trends §  hNp://www.google.com/sktool §  hNp://www.google.com/webmasters §  hNp://www.google.com/adplanner §  hNp://www.google.com/videotarge.ng §  hNp://www.hitwise.com.au/datacenter §  hNp://www.compete.com/ §  hNp://www.alexa.com/ §  hNp://wiki.kenburbary.com/ [ august 2008 ]


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Technology

Digital Measurement - a Determinant in Tracking and Measuring Marketing Performance