M&M: A Passive Toolkit for Measuring, Correlating, and Tracking
Digital Measurement - a Determinant in Tracking and Measuring Marketing Performance
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Transcript of 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
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[ Challenging clients ]
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[ Data driven marke:ng ]
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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 ]
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Source: Omniture Summit, MaN Belkin, 2007
HITS How Idiots Track Success
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[ 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.
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Source: Web Analy.cs Defini.ons, Web Analy.cs Associa.on, 2007
[ Browser side tracking process ]
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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 ]
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Source: White Paper, RedEye, 2007
[ Mul:ply iden:fica:on points ]
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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 ]
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Source: Accuracy Whitepaper for web analy.cs, Brian Cligon, 2008
+Social
[ Digital means global ]
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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 ]
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Social media data
Media and search data
Social media
[ Measuring reach ]
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[ New marketplace: Search ]
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[ Search at all stages ]
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Source: Inside the Mind of the Searcher, Enquiro 2004
[ Non-‐linear conversion funnel ]
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Source: McKinsey, 2009
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[ Importance of search ]
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60-‐70%
30-‐40%
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[ Online insights influencing offline ]
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[ Search data and media planning ]
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[ Ad server exposure test ]
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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 ]
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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
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[ Conversion funnel 2.0 ]
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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
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[ Addi:onal success metrics ]
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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
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Customiza:on
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Crowdsourcing
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[ Book: Tuned In ]
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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 ]
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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 ]
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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
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[ De-‐duplica:on across channels ]
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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 ]
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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
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$
SEO Generic $
SEO Branded
Banner Click $
Social Media
Email Update
Direct Visit $
[ Paid and organic stacking ]
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[ Website entry survey ]
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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 ]
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[ Forrester media aKribu:on ]
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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 ]
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[ Research online buy in store ]
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Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
[ Store locator searches ]
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Facebook TwiKer, etc
[ Integrated campaign flow ]
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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 ]
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Cookie
Website.com Research
Credit Check Fulfilment
Phone Orders
Retail Orders
Online Orders
Credit Check Fulfilment
Credit Check Fulfilment
Website.com Research
Website.com Research
Online Order Confirma:on
Virtual Order Confirma:on
Virtual 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.
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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
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Promo:on
57
Source: Don Schultz, Northwestern University
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Social media analy:cs: People vs. machine
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Iden:fy influencers and advocates
[ Where to focus ]
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[ 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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Contact me [email protected]
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