amnesia omniture training

> Omniture Training < Smart Data Driven Marke.ng

> Background

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March 2011 © Datalicious Pty Ltd 2

> What we do…


Data Pla;orms Data collec=on and processing Web analy=cs solu=ons Omniture, Google Analy=cs, etc Tag-‐less 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 aMribu=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 Alterian, Trac=on, Inxmail, etc Targe=ng and merchandising Internal search op=misa=on CRM strategy and execu=on Tes=ng programs

> Corporate data journey

May 2011 © Datalicious Pty Ltd 4

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, shiI 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!

> Smart data driven marke=ng


Media AMribu=on

Op=mise channel mix

Tes=ng Improve usability

$$$

Targe=ng Increase relevance

Metric

s Framew

ork

Benchm

arking and

tren

ding

Metrics Fram

ework

Benchmarking and trending

> Web Analy=cs

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> Measuring Online Success

March 2011

% Conversion funnel Product page, start a form, download content, watch content, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informa.on, order confirma.on, applica.on submiOed, etc

Conversion income ($$$$$)

Campaign spend ($$$)

© Datalicious Pty Ltd 7

> Addi=onal success metrics


Click Through

Add To Cart

Click Through

Page Bounce

Click Through $

Click Through

Product View

Call back request

Phone Sale $

$

$ Cart Checkout

Page Views

?

Product Views

?

> Conversion Funnel Maps


Awareness Interest Desire Ac=on Sa=sfac=on

> AIDA and AIDAS formulas


Social media

New media

Old media

Reach (Awareness)

Engagement (Interest & Desire)

Conversion (Ac.on)

+Buzz (Sa.sfac.on)

> Simplified AIDAS funnel


People reached

People engaged

People converted

People delighted

> It’s all about people numbers


40% 10% 1%

> New consumer decision journey


The consumer decision process is changing from linear to circular.

Change increases the importance of experience during research phase.

Online research

> 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

Capture internet traffic Capture 50-‐100% of fair market share of traffic

Increase consumer engagement Exceed 50% of best compe.tor’s engagement rate

Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% to 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%


> 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 marke.ng, 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

New vs. returning visitors

AU/NZ vs. rest of world

Skiing Content

Rugby Content

> Automa=c Affinity Segmenta=on


Affinity “Skiing”

What is likely to maximise conversion?

Purchase Cycle

Segments: Colour, price, product affinity, etc

Media Channels

Data Points

Default, awareness

Have you seen A?

Have you seen B?

Display, search, etc Default

Research, considera=on

A has great features!

B has great features!

Search, website, etc

Ad clicks, prod views

Purchase intent

A delivers great value!

B delivers great value!

Website, emails, etc

Cart adds, checkouts

Reten=on, up/cross-‐sell

Why not buy B?

Why not buy A?

Direct mails, emails, etc

Email clicks, logins, etc

> Targe=ng matrixes


> What is Omniture?

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> Omniture is a BEAST!


Omniture SiteCatalyst

> Omniture SiteCatalyst Outline


§ How it works §  The data structure § Key Variables § Classifica.ons §  Examples

> How it works


§  Collect info from the page (.tle, url, referrer, sec.on, content, .me, day, etc)

§  Collect info on the user (new/repeat, segments, customer/prospect, interests, etc)

§  Take all the above and request it as a URL §  Collect the info above into a database

> Basic Data Structure


.me Visitor ID Events campaign products pageName

12:30 Sat… 12345567 event1,event2 p:sem 102,103 travel:why:skiiing

12:31 Sat… 13323222 event3 direct travel:home

12:32 Sat… 13323222 event5 travel:why:skiiing

•  Every request (pageview) to Omniture becomes a new row •  Think of it as a giant spreadsheet •  Events are grouped together •  Products are grouped together •  Custom variables are also available

> Events become counters


pageName event1 event2 event3 event4 event5

travel:why:skiiing 1 1 0 0 1

travel:home 0 0 1 0 0

•  This is how reports show actual numbers •  Events are counted against each variable value in the same row

•  When thinking of a visit, all rows of a par.cular visitorID in a session .me window are added together

> Types of Variables


§ PageName à think site structure § Products à what are you selling § Campaign à where did people come from

§ Props à What happened on a page §  Evars à Something you learned about the visitor

> The PageName Variable

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> KPIs Depend On Solid Founda=on

Web Analy.c Data

Business Objec.ves

1

2

3

KPIs

Founda.on

Key Performance Indicators also depend on a solid founda.on of well-‐defined page names, content hierarchy, and report suite architecture. Without these building blocks in place, making decisions and taking ac.on on the data will prove difficult.

> User-‐friendly Page Names

Context: Include directory structure or content hierarchy in the page name to help users orient the page within the site and simplify report filtering.

Clarity: Ensure the page name is clear and easily iden.fiable for infrequent users.

Conciseness: Keep the page name as short as possible to maximize limited character space.

Three C’s of Effec.ve Page Naming §  Defining a good page naming strategy is

one of the most important steps in maximizing Web analy.cs success.

§  In order to help people understand the performance of site content, TA should consider crea.ng more user-‐friendly page names.

§  You will need to create page names that are contextual, clear, and concise.

A Friendly Page Name has two parts: URL structure stem and specific page name.

Clarity

> Structure of a Quality Page Name

directory:subdirectory:sub-‐subdirectory:specific page name

Context

Conciseness

Context focuses on the URL structure stem, which helps to iden.fy where a page resides.

Which neighborhood?

Conciseness primarily focuses on making the URL structure stem as short as possible. The specific page name part should also be as concise as possible but most of the emphasis will be on the stem.

Use “US” instead of “United States”?

Clarity is an overarching concern for the en.re page name.

Will users know what page this is?

URL structure stem

> PageNames should be like Pyramids

> Content Hierarchy

Site Sec.on Level

Sub Sec.on Level

Page Level

Page Type

Different aggrega.ons of content data will allow you to iden.fy key paOerns at higher levels and then drill into specific details at lower levels

> Page Naming Examples Good Page Naming Bad Page Naming vs.

§  Clear and user-friendly §  More concise §  Easy to filter and search §  Consistent format

§  Unclear and confusing §  Too long (URLs) §  Awkward to filter and search §  Inconsistent format

© 2007 Omniture Inc., Confiden.al & Proprietary

> Sample Page Naming

NOTE: Page names should not be exact copies of the bread crumb because for example “Home >” is unnecessary and other text in the bread crumb may need to be abbreviated for CONCISE page names.

Page names could leverage the bread crumbs on each page. The bread crumb captures the page CONTEXT as it reveals the loca.on of each page. The sec.on, department, and category of each page should go into each page name for page filtering purposes.

hOp://www.newzealand.com/things_to_do/snowboarding/index.html

Sample page name: Travel: things to do: snowboarding

Sub Sec.on sec.on site

> Page Naming Op=ons

Recommended 1.   Server-‐side: Use server-‐side logic to populate page name

for each web page. 2.   Hard-‐code: Manually set page name on each web page.

Use With Cau2on 3.   PageName plug-‐in: JavaScript plug-‐in strips “hOp://

www.domain.com/” from URL page names.

Not Recommended 4.   Leave blank: SiteCatalyst defaults to page URL. 5.   Document.=tle: Uses the .tle of each page instead of

URL.

> The Campaign Variable

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> External Campaign Tracking § Campaigns demand very special aOen.on within a global type structure.

–  Dont duplicate tracking codes for disparate campaigns –  Use structured tracking codes: cid=e:tar:0003

§ As a best prac.ce, we recommend crea.ng uniform tracking codes.

Examples: –  cid=a:033007 à “a” flags affiliate campaign –  cid=e:033007 à “e” flags email campaign –  cid=sem:g:033007 à”sem:g” signifies Google and is a paid

search campaign

§ U.lize SAINT to upload valuable meta data for analysis

Affiliates

Emails

Online Marke.ng

Redirects

Other

All Marke.ng

> TNZ Campaign Setup


> The Products Variable

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> The Products variable


§  Is for things that directly generate $$ § Can store mul.ple values § Can store $ value, quan..es § Can increment other events (tax, etc) §  Is commonly used for SKU codes, product codes, product names, etc

Product Classifica=ons §  Product pages are a key focus of repor.ng and

analysis. §  Populate the s.products variable with all product

IDs viewed on a page. §  In order to gain more insights into these

important pages, you can leverage SiteCatalyst’s SAINT classifica.on tool to upload metadata with different product aOributes into SiteCatalyst.

§  Able to analyze the conversion performance of its product pages by aggregated product aOributes such as category, subcategory, region, facili.es, accommoda.on type, star ra.ng, etc.

§  All meta data is .ed to the specific product id

12345

67891

23456

s.products="; 12345,; 67891,; 23456"

> Props

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> Props (custom traffic)


•  Informa.on related to a par.cular page load. i.e. not relevant outside the scope of that pageview

•  You get up to 75 to customise. •  They can contain lists of mul.ple items •  Are essen.ally counters for things that have names (as opposed to “events”, which are counters for specific events)

•  Example usage: Internal Search Keywords, Tags, etc

> Evars

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> Evars (custom conversion)


•  Evars are like props except the value remains with the user un.l it is set with a different value

•  Think of these as labelling the user •  User them to create segments, such as new/repeat visitor, category affinity, customer/non-‐customer, etc

•  Once you set an evar for a user, custom events will con.nue to register against the evars value

> Classifica=ons

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> Classifica=ons are labels

product Category Name Brand Profit Band TV Ad -‐ 7 days

001 TV Samsung plasma Samsung High No

002 Internet NetGear N50 Netgear Low No

003 Internet Netgear G800 Netgear Medium No

004 Mobile Phone Samsung Galaxy Samsung Low Yes

005 Accessory Headphones Sony Low No

006 TV Sony LCD Sony High Yes

007 Internet Wireless modem Alcatel Low No

Q. Show me revenue by brand? Q. Show me which product categories had the most views? Q. Did we see increased traffic for products we recently adver.sed on TV?

XID AOributes

> SAINT: Campaigns Classifica=ons §  By u.lizing the SAINT classifica.on template, you can

con.nue to upload meta data for each variable

§  Meta data examples: –  Campaign: Name, Channel, Owner, Paid vs Nonpaid,

Branded keywords vs non-‐branded, etc –  Product: Name, Category, Brand, etc –  Customer ID: Profitability, segment, churn risk,

demographics, loca=on, etc

§  The process of assigning aOributes through SAINT can be automated via FTP

§  Classifica.ons can be updated at any .me and will change all data retrospec.vely, because they are a label and do not change the underlying variable value and the data recorded against it.

SAINT

Things to think about

> Cookie expira=on impact


Banner Ad Click

Email Blast

Paid Search

Organic Search

Bid Mgmt

Ad Server

Email Pla;orm

Google Analy=cs

$

$

$

$

Expira=on

Banner Ad View

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

> 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

March 2011 57 © Datalicious Pty Ltd

Omniture Test and Target

> Sample site visitor composi=on


30% exis=ng customers with extensive profile including transac.onal history of which maybe 50% can actually be iden.fied 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

> Prospect targe=ng parameters


> Affinity re-‐targe=ng in ac=on


Different type of visitors respond to different ads. By using category affinity targe.ng, response rates are liIed 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 hMp://bit.ly/de70b7

> Customer profiling in ac=on


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

subscribers at every touch point to keep

refining profiles and messages.

Test Segment Content KPIs Poten=al Results

Test #1A New prospects

Conversion form A

Next step, order, etc ? ?

Test #1B New prospects

Conversion form B


Test #1N New prospects

Conversion form N


? ? ? ? ? ?

> Tes=ng matrixes


> Keys to effec=ve targe=ng

1.  Define success metrics 2.  Define and validate segments 3.  Develop targe.ng and message matrix 4.  Transform matrix into business rules 5.  Develop and test content 6.  Start targe.ng and automate 7.  Keep tes.ng and refining 8.  Communicate results March 2011 © Datalicious Pty Ltd 65

SuperTagging

> SuperTag code architecture


§  Central JavaScript container tag § One tag for all sites and pla|orms §  Hosted internally or externally §  Faster tag implementa.on/updates §  Eliminates JavaScript caching §  Enables code tes.ng on live site §  Enables heat map implementa.on §  Enables redirects for A/B tes.ng §  Enables network wide re-‐targe.ng §  Enables live chat implementa.on

Appendix

PageNaming Op=ons – Pros / Cons

Server-side

�  Provides flexibility to create friendly page names

�  Logic automates page naming process

�  Only applies to dynamic pages �  Flexibility depends on platform �  Ongoing diligence to ensure logic doesn’t break in future

Hard-code �  Free to create friendly page name that isn’t bound by URL or page title

�  Can be labor-intensive �  Doesn’t apply to dynamic pages �  Must be careful when using pages as boilerplates

Page Name Plug-in

�  Requires less effort to create page names

�  Shortens URL page names and makes them more manageable

�  Only recommended for sites with well-defined URL structures

�  Can still lead to long page names �  Won’t aggregate a page with different URL variations �  Problematic with dynamic pages

Leave Blank (defaults to URL)

�  Requires no effort �  URL page names can be long and unwieldy �  Limited character space is wasted on domain root �  Won’t aggregate a page with different URL variations �  Problematic with dynamic pages

Document.title �  Requires little effort to set up

�  Page titles aren’t always unique to a page �  Can be long and contain unnecessary keywords (SEO) �  May change frequently (SEO) �  Problems caused by translation tools (e.g., Babel Fish)

Pros Cons

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