adma digital analytics

> ADMA Digital Analy-cs < Measuring and op.mising digital

> Digital analy-cs course overview

9 am start § Metrics framework §  Campaign tracking 15 min coffee break § Measuring brand § Media a8ribu.on

12.30 pm 30 min lunch §  Channel integra.on §  Re-‐marke.ng 15 min coffee break §  Landing pages 4.30 pm finish

October 2012 © Datalicious Pty Ltd 2

> Digital analy-cs course rules

§  Get involved and be informal! §  Ask ques.ons, share experiences §  Try to leave work outside the door §  Phones off or on mute please §  Toilet break whenever you like §  Different levels of experience §  Be open-‐minded and accept feedback §  I’m here to cri.cize, point out opportuni.es October 2012 © Datalicious Pty Ltd 3

> Maximising course outcome §  Share your expecta.ons so I can adjust §  Start an ac.on sheet to collect ideas §  Main digital analy.cs course outcomes –  Define a metrics framework –  Enable benchmarking across campaigns –  Effec.vely incorporate analy.cs into planning –  Understand digital data sources and their limita.ons –  Accurately a8ribute conversions across channels –  Develop strategies to extend op.misa.on past media –  Pull and interpret key reports in Google Analy.cs –  Impress with insights instead of spreadsheets


> Introduc-ons & expecta-ons

§  Your name §  Your company §  Your roles & responsibili.es §  Knowledge gaps you’re hoping to fill §  Something else about yourself –  Ideal job – Hobbies


> About Datalicious §  Datalicious was founded in November 2007 §  Official Adobe & Google Analy.cs partner §  360 data agency with team of data specialists §  Combina.on of analysts and developers §  Blue chip clients across all industry ver.cals §  Carefully selected best of breed partners §  Driving industry best prac.ce with ADMA §  Turning data into ac.onable insights §  Execu.ng smart data driven campaigns October 2012 © Datalicious Pty Ltd 6

> Smart data driven marke-ng

Media AKribu-on & Modeling

Op-mise channel mix, predict sales

Tes-ng & Op-misa-on Remove barriers, drive sales

Boos-ng ROMI

Targe-ng & Merchandising Increase relevance, reduce churn

“Using data to widen the funnel”

November 2012 © Datalicious Pty Ltd 7

> Wide range of data services

Data PlaTorms Data collec-on and processing Adobe, Google Analy-cs, etc Web and mobile analy-cs Tag-‐less online data capture Retail and call center analy-cs Big data & data warehousing Single customer view

Insights Analy-cs Data mining and modelling Tableau, Splunk, SPSS, R, etc Customised dashboards Media aKribu-on analysis Marke-ng mix modelling Social media monitoring Customer segmenta-on

Ac-on Campaigns Data usage and applica-on SiteCore, ExactTarget, etc Targe-ng and merchandising Marke-ng automa-on CRM strategy and execu-on Data driven websites Tes-ng programs


> Best of breed partners


> Internal product development

SCV2

Surveys Campaigns Promo.ons

Engage

Website/apps Social media eDMs/DMs

CRM1

1 Customer rela.onship management plaform containing all data necessary to manage campaigns 2 Single customer view plaform containing all data across all (customer) touch points

Mass media Social media Digital media

Measure Demographics Transac.ons Campaigns


> Clients across all industries


> Metrics framework

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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 (Delight)

> 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


> Standardised roll-‐up metrics

Unique browsers, search impressions, TV circula-on, etc

Unique visitors, site engagements, video views, etc

Online sales, online leads, store locator searches, etc

Facebook comments, Tweets,

ra-ngs, support calls, etc

Response rate, Search response rate, TV response rate, etc

Conversion rate, engagement rate, checkout rate, etc

10% 40% 1%

Review rate, ra-ng rate, comment rate, NPS rate, etc

People reached

People engaged

People converted

People delighted

> Provide context with figures


40% 10% 1%

New prospects vs. exis.ng customers

Brand vs. direct response campaign

> Provide context with figures §  Brand vs. direct response campaign §  New prospects vs. exis.ng customers §  Compe..ve ac.vity, i.e. none, a lot, etc §  Market share, i.e. small, medium, large, et §  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 §  Devices, i.e. home, office, mobile, tablet, etc October 2012 © Datalicious Pty Ltd 20

Exercise: Google Analy-cs



Exercise: Internal traffic


Exercise: Custom segments


Google: “google analy-cs custom variables”

> Conversion funnel 1.0

October 2012

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 25

> Conversion funnel 2.0

October 2012

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 26

> 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

Use addi-onal metrics closer to the campaign origin


Exercise: Conversion goals


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” October 2012 © Datalicious Pty Ltd 31

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


> Conversion metrics by category


Source: Omniture Summit, Ma8 Belkin, 2007

> 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


> Align metrics across channels §  Paid search response rate

= website visits / paid search impressions §  Organic search response rate

= website visits / organic search impressions §  Display response rate

= website visits / display ad impressions §  Email response rate

= website visits / emails sent §  Direct mail response rate

= (website visits + phone calls) / direct mail pieces sent §  TV response rate

= (website visits + phone calls) / (TV ad reach x frequency)



Exercise: Metrics framework

Level Reach Engagement Conversion +Buzz

Level 1, people

Level 2, strategic

Level 3, tac-cal

Funnel breakdowns

> Exercise: Metrics framework


Level Reach Engagement Conversion +Buzz

Level 1, people

People reached

People engaged

People converted

People delighted

Level 2, strategic

Display impressions ? ? ?

Level 3, tac-cal

Interac-on rate, etc ? ? ?

Funnel breakdowns Exis-ng customers vs. new prospects, products, etc

> Exercise: Metrics framework


> NPS survey and page ra-ngs


Page ra.ngs


Google: “google analy-cs custom events”

> Importance of calendar events


Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless

> Poten-al calendar events

§  Press releases §  Sponsored events §  Campaign launches §  Campaign changes §  Crea.ve changes §  Price changes § Website changes §  Technical difficul.es



Exercise: Calendar events

> Campaign tracking

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Exercise: Track campaigns


Google: “google analy-cs url builder”

h8p://www.company.com/email-‐landing-‐page.html?

utm_id=neNCu& CustomerID=12345& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]

> Email click-‐through iden-fica-on


ChrisBartens.company.com > redirect to > company.com?

utm_id=neND& CustomerID=12345& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]

> Personalised URLs for direct mail


Source Medium Term Content Campaign

Referrer Medium Keyword Crea-ve Promo-on

google cpc search term a red banner promo a

newsleKer banner search term b black banner promo b

? ? ? ? ?

> Exercise: Naming conven-on



Google: “link google analy-cs webmaster tools”


Google: “link google analy-cs google adwords”


Exercise: Organic op-misa-on

> Importance of social media Search

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

October 2012 © Datalicious Pty Ltd

Promo-on

63

Company Consumer

> Social as the new search


> Measuring brand

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Search Quan-ty

Social Quality

> Measuring brand: Search vs. social


> Media aKribu-on

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> Duplica-on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaTorm

Google Analy-cs

$

$

$

> Duplica-on across channels


Display impression

Paid search $

Ad Server

Bid mgmt.

Web analy-cs

Display click

Ad server cookie

Organic search

Analy-cs cookie

Analy-cs cookie

Analy-cs cookie

Bid mgmt. cookie

Ad server cookie

Central Analy-cs PlaTorm

$

$

$

> De-‐duplica-on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$

Direct mail, email, etc

Facebook TwiKer, etc

> Campaign flows are complex


POS kiosks, loyalty cards, etc

CRM program

Home pages, portals, etc

YouTube, blog, etc

Paid search

Organic search

Landing pages, offers, etc

PR, WOM, events, etc

TV, print, radio, etc

= Paid media

= Viral elements

Call center, retail stores, etc

= Sales channels

Display ads, affiliates, etc


Exercise: Campaign flow

> 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

> First and 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

Paid/Organic Search

Emails/Shopping Engines

> Ad clicks inadequate measure


Only a small minority of people actually click on ads, the majority merely processes them (if at all) like any other adver.sing without an immediate response so adver.sers cannot rely on clicks as the sole success measure but should instead focus on impressions delivered

> Indirect display impact


Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking


Influencer Influencer $

Display ad clicks

Online leads

Affiliate clicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Life-me profit

Organic search

Emails, direct mail

Direct site visits

Introducer

Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking



Display ad clicks

Online leads

Affiliate clicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Life-me profit

Organic search

Emails, direct mail

Direct site visits

Introducer

> Purchase path example


Closer

Channel 1

Channel 1

Channel 1

> Path across different segments



Channel 2

Channel 2 Channel 3

Channel 2 Channel 3 Product 4

Channel 3

Channel 4

Channel 4

Introducer

Product A vs. B

Clients vs. prospects

Brand vs. direct resp.

> Understanding channel mix



What promoted your visit today? q  Recent branch visit q  Saw an ad on television q  Saw an ad in the newspaper q  Recommenda.on from family/friends q  […] How likely are you to apply for a loan? q Within the next few weeks q Within the next few months q  I am a customer already q  […]

> 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%

De-‐duped Campaign Report

} 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%

Greatest Influencer on Branded Search / STS

Conversions a8ributed 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.

> Website entry survey example


In this retail example, the exposure to retail display ads was the biggest website traffic driver for direct visits as well as visits origina.ng from search terms that included branded keywords – before TV, word of mouth and print ads.

> Adjus-ng for offline impact


+15 +5 +10 -‐15 -‐5 -‐10

> Purchase path vs. aKribu-on

§  Important to make a dis.nc.on between media a8ribu.on and purchase path tracking –  Not the same, one is necessary to enable the other

§  Tracking the complete purchase path, i.e. every paid and organic campaign touch point leading up to a conversion is a necessary requirement to be able to actually do media a8ribu.on or the alloca.on or conversion credits back to campaign touch points –  Purchase path tracking is the data collec.on and media a8ribu.on is the actual analysis or modelling


> Where to track purchase path


Referral visits Social media visits Organic search visits Paid search visits Email visits, etc

Web Analy-cs Banner impressions

Banner clicks +

Paid search clicks

Ad Server

Lacking ad impressions Less granular & complex

Lacking organic visits More granular & complex

> Purchase path data samples

Web Analy-cs data sample LAST AD IMPRESSION > SEARCH > $$$| PV $$$ AD IMPRESSION > AD IMPRESSION > SEARCH > $$$

Ad Server data sample 01/01/2012 11:45 AD IMP YAHOO HOME $33 01/01/2012 12:00 AD IMP SMH FINANCE $33 01/01/2012 12:05 SEARCH KEYWORD -‐ 07/01/2012 17:00 DIRECT $33 08/01/2012 15:00 $$$ $100


Closer

?%

?%

?%

> Media aKribu-on models



?%

?% ?%

?% ?% ?%

?%

?%

?%

Introducer

Product A vs. B

Prospects vs. clients


> Full vs. par-al purchase path data


Display impression

Display impression

Display impression

$

Display impression $

Display impression


Display impression

Search response

Search response $

Display impression

Display response

Direct visit

✖ ✔ ✔ ✖

Display impression

Display impression

Email response

Search response

✖ ✔ ✔ ✔

✖ ✖ ✔ ✔

✖ ✔ ✔ ✔

> Full vs. par-al purchase path data


Display impression

Display impression

Display impression

$


Display impression


Display impression

Search response

Search response $

Display impression

Display response

Direct visit

✖ ✔ ✔ ✖

Display impression

Display impression

Email response

Search response

✖ ✔ ✔ ✔

✖ ✖ ✔ ✔

✖ ✔ ✔ ✔

5% to 65% variance in conversion aKribu-on

for different channels due to par-al purchase path data

> Purchase path for each cookie


Mobile Home Work

Tablet Media Etc

0%



$100

0% Last click aKribu-on

Even aKribu-on

Weighted aKribu-on

0% 100%

25% 25% 25% 25%

Display impression

Display impression

Display response

Search response

X% X% Y% Z%

> Google Analy-cs models §  The First/Last Interac-on model plus … §  The Linear model might be used if your

campaigns are designed to maintain awareness with the customer throughout the en.re sales cycle.

§  The Posi-on Based model can be used to adjust credit for different parts of the customer journey, such as early interac.ons that create awareness and late interac.ons that close sales.

§  The Time Decay model assigns the most credit to touch points that occurred nearest to the .me of conversion. It can be useful for campaigns with short sales cycles, such as promo.ons.



Exercise: AKribu-on models

Closer

?%

?%

?%




?%

?% ?%

?% ?% ?%

?%

?%

?%

Introducer

Product A vs. B

Prospects vs. clients


> Media aKribu-on example


COST PER CONVERSION

Last click a8ribu.on

Even/weighted a8ribu.on



COST PER CONVERSION

Last click a8ribu.on

Even/weighted a8ribu.on

? Email

? Direct mail

? Internal ads ?

Website content

? TV/Print



ROI FULL PURCHASE PATH

TOTA

L CO

NVE

RSION VALUE

Increase spend

Increase spend

Reduce spend


Exercise: Neglected keywords

> Channel integra-on

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> Tracking offline responses online

§  Search calls to ac.on for TV, radio, print –  Unique search term only adver.sed in print so all responses from that term must have come from print

§  PURLs (personalised URLs) for direct mail –  Brand.com/customer-‐name redirects to new URL that includes tracking parameter iden.fying response as DM

§  Website entry survey for direct/branded visits –  Survey website visitors that have come to site directly or via branded search about their media habits, etc

§  Combine data sets into media a8ribu.on model –  Combine raw data from online purchase path, website entry survey and offline sales with offline media placement data in tradi.onal (econometric) media a8ribu.on model


ChrisBartens.company.com > redirect to > company.com?

utm_id=neND& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& CustomerSince=2001& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]

> Personalised URLs for direct mail


> Search call to ac-on for offline


> Econometric media modelling


Use of tradi.onal econometric modelling to measure the impact of communica.ons on sales for offline channels where it cannot be measured directly through smart calls to ac.on online (and thus cookie level purchase path data).

> Tracking offline sales online §  Email click-‐through

–  Include offline sales flag in 1st email click-‐through URL a{er offline sale to track an ‘assisted offline sales’ conversion

§  First login a{er purchase –  Similar to the above method, however offline sales flag happens via JavaScript parameter defined on 1st login

§  Unique phone numbers –  Assign unique website numbers to responses from specific channels, search terms or even individual visitors to match offline call center results back to online ac.vity

§  Website entry survey for purchase intent –  Survey website visitors to at least measure purchase intent in case actual offline sales cannot be tracked


Confirma-on email, 1st login

> Offline sales driven by online


Website research

Phone sales

Retail sales

Online sales

Cookie

Adver-sing campaign

Fulfilment, CRM, etc

Online sales confirma-on

Virtual sales confirma-on

h8p://www.company.com/email-‐landing-‐page.html?

utm_id=neNCu& CustomerID=12345& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]

> Email click-‐through iden-fica-on


> Login landing and exit pages


Customer data exposed in page or URL on login or logout

CustomerID=12345& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]

Campaign response data

> Combining data sources


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Transac-ons plus behaviours


+ one-‐off collec.on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira-on, etc predic.ve models based on data mining

propensity to buy, churn, etc historical data from previous transac.ons

average order value, points, etc

CRM Profile

Updated Occasionally

tracking of purchase funnel stage

browsing, checkout, etc tracking of content preferences

products, brands, features, etc tracking of external campaign responses

search terms, referrers, etc tracking of internal promo.on responses

emails, internal search, etc

Site Behaviour

Updated Con-nuously

> Customer profiling in ac-on


Using website and email responses to learn a li8le bite more about

subscribers at every touch point to keep

refining profiles and messages.

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

October 2012 130 © Datalicious Pty Ltd

On-‐site targe.ng

Off-‐site targe.ng

> Combining targe-ng plaTorms


CRM

> Re-‐marke-ng

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> Importance of online experience


The consumer decision process is changing from linear to circular.

Considera-on set now grows during online research phase which increases importance of user experience during that phase

Online research

> Increase revenue by 10-‐20%


APPLY NOW


> Network wide re-‐targe-ng


Product A

Product B prospect

Product A prospect

Product A customer

Product B Product C

Product C prospect

Product B prospect

Product B customer

Product A prospect

Product C prospect

Product C customer

> Network wide re-‐targe-ng


Product B prospect

Product A prospect

Product A customer

Product C prospect

Product B prospect

Product B customer

Product A prospect

Product C prospect

Product C customer

Group wide campaign with approximate impression targets by product rather than hard budget limita-ons

Closer

Message 1

Message 1

Message 1

> Story telling or ad-‐sequencing



Message 2

Message 2 Message 3

Message 2 Message 3 Message 4

Message 3

Message 4

Message 4

Introducer

Product A

Product B

Product C

> Ad-‐sequencing in ac-on


Marke.ng is about telling stories and

stories are not sta.c but evolve over .me

Ad-‐sequencing can help to evolve stories over .me the more users engage with ads

> Targe-ng: Quality vs. quan-ty


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

> ANZ home page targe-ng

© Datalicious Pty Ltd 143 October 2012

ANZ home page re-‐targe.ng and merchandising combined with landing page op.misa.on delivered an increase in offer response and conversion rates with an overall project ROI of 578%


Exercise: Re-‐targe-ng matrix

Purchase Cycle

Segmenta-on based on: Search keywords, display ad clicks and website behaviour Data

Points

Default, awareness Default

Research, considera-on

Product view, etc

Purchase intent

Checkout, chat, etc

Exis-ng customer

Login, email click, etc

> Exercise: Re-‐targe-ng matrix


Purchase Cycle


Points Default Product A Product B

Default, awareness

Acquisi-on message D1

Acquisi-on message A1

Acquisi-on message B1 Default

Research, considera-on



Acquisi-on message B2

Product view, etc

Purchase intent



Acquisi-on message B3

Checkout, chat, etc

Exis-ng customer

Cross-‐sell message D4

Cross-‐sell message A4

Cross-‐sell message B4

Login, email click, etc

> Exercise: Re-‐targe-ng matrix



Google: “enable remarke-ng google analy-cs”


Exercise: Remarke-ng lists

> Unique phone numbers


2 out of 3 callers hang up as they cannot get their informa.on fast enough. Unique phone numbers can help improve call experience.

> Unique phone numbers

§  1 unique phone number –  Phone number is considered part of the brand – Media origin of calls cannot be established – Added value of website interac.on unknown

§  2-‐10 unique phone numbers – Different numbers for different media channels –  Exclusive number(s) reserved for website use –  Call origin data more granular but not perfect – Difficult to rotate and pause numbers


> Unique phone numbers §  10+ unique phone numbers – Different numbers for different media channels – Different numbers for different product categories – Different numbers for different conversion steps –  Call origin becoming useful to shape call script –  Feasible to pause numbers to improve integrity

§  100+ unique phone numbers – Different numbers for different website visitors –  Call origin and .me stamp enable individual match –  Call conversions matched back to search terms


Purchase Cycle


Points Default Product A Product B

Default, awareness 1300 000 001 1300 000 005 1300 000 009 Default

Research, considera-on 1300 000 002 1300 000 006 1300 000 010 Product

view, etc

Purchase intent 1300 000 003 1300 000 007 1300 000 011 Checkout,

chat, etc

Exis-ng customer 1300 000 004 1300 000 008 1300 000 012 Login, email

click, etc

> Website call center integra-on


> Landing pages

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Don’t reinvent the wheel

> Anatomy of a perfect landing page 1.  Page headline and ad copy 2.  Clear and concise headlines 3.  Impeccable grammar 4.  Taking advantage of trust indicators 5.  Using a strong call to ac.on 6.  Bu8ons and call to ac.on should stand out 7.  Go easy on the number of links 8.  Use images and video that relate to copy 9.  Keep it above the fold at all .mes October 2012 © Datalicious Pty Ltd 161

> The holy trinity of tes-ng 1. The headline – Have a headline! – Headline should be concrete – Headline should be first thing visitors look at

2. Call to ac-on – Don’t have too many calls to ac.on – Have an ac.onable call to ac.on – Have a big, prominent, visible call to ac.on

3. Social proof –  Logos, number of users, tes.monials, case studies, media coverage, etc


> Best prac-ce tes-ng roadmap

§  Phase 1: A/B test –  Test same landing page content in different layouts

§  Phase 2: MV test –  Test different content element combina.ons within winning layout

§  Phase 3: Repeat –  Hero vs. challengers

§  Phase 4: Re-‐targe.ng October 2012 © Datalicious Pty Ltd 165

Element #1: Prominent headline

Element #2: Call to ac.on

Suppor.ng content

Element #3: Social proof / trust

Terms and condi.ons

> G&E Capital landing pages


Before

A{er

Removal of distrac.ons such as naviga.on and search op.ons resulted in increased response rates with ROI of 492%

Project plaforms used: Adobe SiteCatalyst and Test&Target

> Macquarie landing pages


Before

A{er

The small things count: Simplifica.on down to 1 set of bu8ons resulted in increased response rate and project ROI of 547%

Project plaforms used: Adobe SiteCatalyst and Test&Target

Rather than tes.ng all combina.ons of alterna.ve page content (i.e. A/B tes.ng), the Taguchi Method (i.e. mul.variate MV tes.ng) is a way of reducing the number of different test scenarios (recipes) but s.ll yield useful test results. Essen.ally, the op.mal page design is ‘predicted’ from the test results by analysing which page elements and element combina.ons were most influen.al overall.

> A/B vs. MV (Taguchi) method


Test elements (i.e. parts of page)

Test alterna-ves (i.e. test content)

Full set of test combina-ons (A/B)

Reduced Taguchi test scenarios (MV)

3 2 8 4

7 2 128 8

4 3 81 9

5 4 1024 16

> Sufficient sample size for tests

§  MV tes.ng requires a greater volume of visitors than A/B tes.ng. The volume required is dependent on: –  The number of elements on the page (and how many alterna.ves for each element)

– Whether targe.ng specific segments is part of the test or whether you want to examine success by different segments of traffic

–  Expected control page conversion rates –  How long you can afford to have the test in market without viola.ng the test condi.ons

– Whether you can afford to present the test to all traffic



Exercise: Sta-s-cal significance

How many click-‐throughs do you need to test 3 landing pages if you have 30,000 visitors?

How many conversions do you need to test 3 landing pages if you have 30,000 visitors?

How many click-‐throughs do you need to test 3 landing pages if you have 30,000 visitors but only expose 10% to the test?


How many click-‐throughs do you need to test 3 landing pages if you have 30,000 visitors?

369 per test or 1,107 clicks in total

How many conversions do you need to test 3 landing pages if you have 30,000 visitors? 369 per test or 1,107 conversions in total

How many click-‐throughs do you need to test 3 landing pages if you have 30,000 visitors but only expose 10% to the test?

277 per test or 831 clicks in total


> Telstra bundles pages

© Datalicious Pty Ltd 173 October 2012

Telstra bundles page op.misa.on combined call center data (each page had a unique phone number) with Adobe Test&Target online data and delivered a cross-‐channel conversion rate increase with an ROI of 647%

> Other tes-ng considera-ons

§  Avoiding ‘no results’ by making test execu.ons as obviously different as possible to consumers

§  Limit poten.al ‘nega.ve’ test impact on conversions by limi.ng the test to a smaller sample size ini.ally

§  Avoid launching tests during major above the line campaign ac.vity as this might magnify any incremental gains of tested scenarios and the test results can’t then be replicated in a non-‐campaign period


> Introducing hero vs. challengers


Hero #1 CTR = 1%

Challenger #1 CTR = 0.5%

Challenger #2 CTR = 1.5%

Challenger #3 CTR = 1%

Challenger #4 CTR = 1%

New hero #2 = Challenger #2


Exercise: Op-misa-on ideas

> Eye tracking vs. mouse tracking

§  Eye tracking pros –  100% accurate –  Controlled environment

–  Open dialogue §  Eye tracking cons –  High costs –  Limited scope –  Observer effect

§  Mouse tracking pros –  Natural environment –  No observer effect –  Global par.cipa.on –  Low cost

§  Mouse tracking cons –  No pre-‐defined tests –  No research control –  No visitor feedback


> Segmented heat maps are key


Heat map for new visitors vs. exis-ng customers Independent research shows 84-‐88% correla.on between mouse and eye movements*

> New approach to web design

§  Standard approach –  Analyst iden.fies issue and briefs agency

–  Agency develops new designs, trashes some

–  Agency or developers implement new design

–  Some.mes mul.ple designs are tested

§  Try something new –  Analyst iden.fies issue and briefs agency (incl. current heat maps)

–  Agency develops new designs and tests them (predic.ve heat maps)

– Winning designs are developed and tested (incl. new heat maps)

–  Top performing design is implemented


> New approach to web design §  Step 1: Iden.fy problem pages §  Step 2: Priori.se pages for tes.ng §  Step 3: Pick page for tes.ng and op.misa.on §  Step 4: Implement and analyse heat-‐map §  Step 5: Design test and brief crea.ve agencies §  Step 6: Pick best designs with predic.ve heat-‐maps §  Step 7: Develop different page execu.ons §  Step 8: Execute, monitor (and refine) test §  Step 9: Analyse test and verify predic.ve heat-‐maps §  Step 10: Implement winning test design §  Step 11: Pick next page & repeat steps 3-‐10



Targe-ng before tes-ng


Exercise: Tes-ng matrix

Test Segment Content Success Difficulty Poten-al

> Exercise: Tes-ng matrix


Test Segment Content Success Difficulty Poten-al

Test 1 Product 1

Offer 1A

Clicks Low $100k Offer 1B

Offer 1C

Test 2 Product 2

Offer 2A

Clicks High $100k Offer 2B

Offer 2C

> Exercise: Tes-ng matrix


> Response website design


Through fluid grids and media query adjustments, responsive design enables web page layouts to adapt to a variety of screen sizes. The content of the page does not change, just the way it is displayed for each screen size.

> Online form best prac-ce


Maximise data integrity Age vs. year of birth Free text vs. op.ons

Use auto-‐complete wherever possible

> Social single-‐sign on services


h8p://vimeo.com/16469480

Gigya.com Janrain.com

> Garbage in, garbage out

Avinash Kaushik: “The principle of garbage in, garbage out applies here. [… what makes a behaviour

targe;ng pla<orm ;ck, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […. You feed your BT system crap and it will quickly and efficiently target crap to your

customers. Faster then you could ever have yourself.”


> About Datalicious

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


> Short but sharp history §  Datalicious was founded in November 2007 §  Official Adobe & Google Analy.cs partner §  360 data agency with team of data specialists §  Combina.on of analysts and developers §  Blue chip clients across all industry ver.cals §  Carefully selected best of breed partners §  Driving industry best prac.ce with ADMA §  Turning data into ac.onable insights §  Execu.ng smart data driven campaigns October 2012 © Datalicious Pty Ltd 204

> Smart data driven marke-ng


Media AKribu-on & Modeling

Op-mise channel mix, predict sales

Tes-ng & Op-misa-on Remove barriers, drive sales

Boos-ng ROMI

Targe-ng & Merchandising Increase relevance, reduce churn

“Using data to widen the funnel”

> Wide range of data services


Data PlaTorms Data collec-on and processing Adobe, Google Analy-cs, etc Web and mobile analy-cs Tag-‐less online data capture Retail and call center analy-cs Data warehouse solu-ons Single customer view

Insights Analy-cs Data mining and modelling Tableau, Splunk, SPSS, etc Customised dashboards Media aKribu-on analysis Media mix modelling Social media monitoring Customer segmenta-on

Ac-on Campaigns Data usage and applica-on Alterian, SiteCore, Inxmail, etc Targe-ng and merchandising Marke-ng automa-on CRM strategy and execu-on Data driven websites Tes-ng programs

> Over 50 years of experience


Chris.an Bartens Founder & Director §  Bachelor of Business

Management with marke.ng focus

§  Web analy.cs and digital marke.ng work experience

§  Space2go, E-‐Lo{, Tourism Australia

§  SuperTag founder, ADMA Analy.cs Chair, I-‐COM Board Member

LinkedIn profile

Elly Gillis General Manager §  Bachelor of

Communica.ons with print and digital focus

§  Digital marke.ng and project management work experience

§  M&C Saatchi, Mark, Holler, Tequila, IAG, OneDigital, Telstra

§  Australian gold medal in surf boat rowing

LinkedIn profile

Michael Savio Head of Insights §  Bachelor of Arts &

Science with applied mathema.cs focus

§  CRM and marke.ng research and analy.cs work experience

§  ANZ Bank, Australian Bureau of Sta.s.c, DBM Consultants

§  ADMA lecturer on marke.ng tes.ng

LinkedIn profile

Chaoming Li Head of Data §  Bachelor of

Technology with microelectronics focus

§  So{ware and website development work experience

§  Standards Australia, DF Securi.es, Globiz, Etang

§  Developing his own CMS plaform

LinkedIn profile

> Best of breed partners


> Clients across all industries


> Great customer feedback “[…] Datalicious quickly earned our respect and confidence […] understand our business needs, deliver value, push our thinking […]. Likeable, transparent and trustworthy. I would be happy to recommend Datalicious to anyone.” Murray Howe, Execu.ve Manager, Suncorp Group "[…] Datalicious brought with them best prac@ce analy@cs to demonstrate the true value of our marke@ng dollars […] have become a cri;cal business partner […] provided great insights which have driven key business decisions.” Trang Young, Senior Marke.ng Manager, E*Trade Australia “The Datalicious guys are great to work along side […] 'no stone unturned' approach to finding solu@ons to challenges […] knowledge and passion for web analy@cs and best of breed web op;miza;on was second to none” Steve Brown, Senior Business Analyst, Vodafone “[…] The Vodafone implementa@on of SiteCatalyst is one of the most impressive I have seen and ranks in the top 10 […]. It is an amazing founda@on for taking ac@on on the data and improving ROI.” Adam Greco, Consul.ng Lead, Omniture


> Great customer feedback "[…] Datalicious understand the value of informa@on and how to leverage it using best of breed soFware. I would recommend the team without hesita@on [...]." James Fleet, Marke.ng Director, Appliances Online "[...] Datalicious have been in;mately involved in building our analy;cs solu;on. Most importantly their knowledge of best prac@ce combined with innova@ve solu@ons has allowed our business to remain nimble and current. They are also nice guys." Tzvi Balbin, Group Digital Marke.ng Lead, Catch of the Day "[...] Datalicious are helping us to move from a last click campaign measurement model to a more accurate media aGribu@on approach. [...] poten;al to significantly change our media planning [...]. Highly recommended." Keith Mirgis, Senior Digital & Social Media Marke.ng Manager, Telstra "We engaged Datalicious to support a strategic change in our business [...] understand our customers [and their transac@ons] beGer to ensure we retained as many as possible [...]" Natalie Farrell, Direct Marke.ng Manager, Luxo�ca


> About SuperTag

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> The Datalicious SuperTag

213

SuperTag

Conversion Tracking

Conversion De-‐duping

Media AKribu-on

Behavioral Targe-ng

A/B Tes-ng Heat Maps

Live Chat

Web Analy-cs

Any JavaScript

Easily implement and update any tag on any websites without or limited IT involvement De-‐duplicate conversions for CPA deals and align repor.ng figures across plaforms Collect accurate mul.-‐channel media a8ribu.on data to provide advanced insights Enable advanced features such as targe.ng, tes.ng and chat to op.mise user experience


> Unique SuperTag architecture

214

T

B

B

B

§ One tag for all sites and plaforms §  Hosted internally or externally §  Fast tag implementa.on/updates §  Increase analy.cs data accuracy §  Enables code tes.ng on live site §  Enables heat map implementa.on §  Enables A/B and MV test execu.on §  Enables cross-‐channel re-‐targe.ng §  Enables phone number targe.ng

T T

SuperTag


Injec.ng JavaScript tags into the page based on business rules using the SuperTag top and bo8om containers. The SuperTag top and bo8om containers are JavaScript func.ons called in the page code just a{er the opening <body> tag and just before the closing </body> tag on all page across all domains.

superT.t()

superT.b()

> Overcoming team barriers

215

Marke-ng SuperTag Technology

Easy to use online user interface enabling marketers to manage tags without intensive technology support


> Cross-‐plaTorm integra-on

216

Web Analy-cs Heat Maps Targe-ng Live Chat Tes-ng

SuperTag

CRM/eDMs Paid Search Ad Servers Affiliates DFPs

Centralised uniform business rules to trigger conversions and segment visitors across mul.ple marke.ng plaforms


$ $

$

$

$

$

> Conversion de-‐duplica-on

217

Centralised business rules to enable accurate conversion de-‐duplica.on across mul.ple marke.ng plaforms

Display ads

Paid search

Bid Mgmt

Ad server SuperTag


Affiliate referral

Affiliate system


Turn any page element into data or tes-ng areas

JavaScript hos-ng on client CDN

> SuperTag deployment op-ons

224

Client website

JavaScript hos-ng on client server

Email/FTP JavaScript publishing

Manual JavaScript

management

Client website

Client website

SuperTag JavaScript

management

Real-‐-me JavaScript publishing

JavaScript hos-ng on

SuperTag CDN

CDN = Content delivery network

Dedicated Github client code archive


> Unique selling points (USPs) §  Superior plaform architecture for more flexibility –  Turn any page element into variables for data collec.on or business rules for tag execu.on

–  Cross-‐plaform integra.on and data exchange –  Splunk integra.on for advanced data mining

§  Superior tes.ng, deployment and audit features –  Tes.ng of tags & business rules on the live website –  Complete audit trail of all tag changes and tests

§  No lock-‐in, stop using the SuperTag at any .me –  External and internal JavaScript hos.ng available –  Perpetual JavaScript usage rights & Github archive

§  All inclusive pricing structure incen.vizing use October 2012 © Datalicious Pty Ltd 225

> Blue chip SuperTag clients


> Great customer feedback

"Managing third party tags has never been easier [...] simplicity of seSng business rules [...] reduc;on in CPA [...].“ Jason Lima, Online Marke.ng, IMB "[...] SuperTag tool is so easy to use [...]. Live tes@ng is par@cularly useful [...] highly recommended [...]." Helene Cameron-‐Heslop, Analyst, Appliances Online "SuperTag speeds up tag implementa@on and gives us increased flexibility [...] manage media and website analy;cs [...].” Alex Crompton, Head of Digital, Aussie

227 October 2012 © Datalicious Pty Ltd


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