SunCorp Analytics

> Suncorp Analy.cs < Smart data driven marke-ng

> Short but sharp history

§  Datalicious was founded late 2007 §  Strong Omniture web analy-cs history §  Now 360 data agency with specialist team §  Combina-on of analysts and developers §  Carefully selected best of breed partners §  Evangelizing smart data driven marke-ng § Making data accessible and ac-onable §  Driving industry best prac-ce (ADMA)

November 2010 © Datalicious Pty Ltd 2

> Clients across all industries


> Wide range of data services


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 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

> Smart data driven marke.ng


Media AKribu.on

Op.mise channel mix

Tes.ng Improve usability

$$$

Targe.ng Increase relevance

15 tools are proposed largely centred on Adobe

6

Optimised marketing mix RIGHT OFFER

Optimised channel mix

RIGHT CHANNEL

Optimised customer

RIGHT CUSTOMER

Optimised Customer Journey

Once implemented these tools will deliver the capability for each LOB to leverage online data to optimise the customer's journey across any channel or brand within the Suncorp group

TOOL AQUIRE CONVERT GROW

1. Digital campaign tracking & attribution ü ü ü

2. Full digital pathway tracking & attribution ü ü ü

3. Onsite promotion tracking ü

4. Fallout & conversion analysis ü

5. Adv site navigation & form analysis ü

6. Internal search analysis ü

7. Brand portfolio level measurement ü ü ü

8. Site surveys ü

9. Advanced visitor segmentation ü ü ü

10. A/B & content testing ü ü ü

11. Campaign retargeting ü ü

12. Behavioural targeting ü ü

13. Syndicate personalised content ü

14. Digital identification & CRM integration ü ü ü

15. Online to offline conversion ü ü

Anonymous

Semi-Identified

Identified

Increased Personalisation

STRATEGIC GOAL DELIVERED

CAPABILITY EVOLUTION

CUSTOMER JOURNEY

Multi-Media Measurement & Attribution

Site Optimisation

Advanced Segmentation

& Targeting

Multi-Channel Optimisation

Personalised Targeting

CAPABILITY

> Media aKribu.on

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


Direct mail, email, etc

Facebook TwiKer, etc

> Campaign flow and calls to ac.on


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

C2

C3

= Paid media

= Viral elements

Call center, retail stores, etc

= Coupons, surveys

Display ads, affiliates, etc

C1

Exercise: Campaign flow


> Duplica.on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email Pla>orm

Google Analy.cs

$

$

$

> 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

Central Analy.cs Pla>orm

$

$

$

> De-‐duplica.on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$

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)



Quick win: Central pla>orm

TV/Print audience

Search audience

Banner audience

> Reach and channel overlap


> Indirect display impact


> 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

> Adobe stacking/par.cipa.on


Adobe can only stack direct paid and organic responses that end up on your website proper.es, mere banner impressions are missing from the stack and cannot be included via Genesis a`er the fact.

Closer

SEM Generic

Banner View

TV Ad

> Full path to purchase


Influencer Influencer $

Banner Click Online

SEO Generic

Affiliate Click Offline

SEO Branded

Direct Visit

Email Update Abandon

Direct Visit

Social Media

SEO Branded

Introducer

> Where to collect the data


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 banner impressions Less granular & complex

Lacking organic visits More granular & complex


Quick win: Data into ad server

> Search call to ac.on for offline


November 2010 © Datalicious Pty Ltd 27 Offline response tracking and improved experience


Quick win: Search call to ac.on

November 2010 © Datalicious Pty Ltd 30 hKp://www.suncorp.com.au?campaign=workshop

> Poten.al calls to ac.on §  Unique click-‐through URLs §  Unique vanity domains or URLs §  Unique phone numbers §  Unique search terms §  Unique email addresses §  Unique personal URLs (PURLs) §  Unique SMS numbers, QR codes §  Unique promo-onal codes, vouchers §  Geographic loca-on (Facebook, FourSquare) §  Plus regression analysis of cause and effect


> 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


> Jet Interac.ve phone call data



Quick win: Unique numbers

> PURLs boos.ng DM response rates


Text

> Media aKribu.on phases §  Phase 1: De-‐duplica-on

–  Conversion de-‐duplica-on across all channels –  Requires one central repor-ng plaiorm –  Limited to first/last click ajribu-on

§  Phase 2: Direct response pathing –  Response pathing across paid and organic channels –  Only covers clicks and not mere banner views –  Can be enabled in Google Analy-cs and Omniture

§  Phase 3: Full purchase path –  Direct response tracking including banner exposure –  Cannot be done in Google Analy-cs or Omniture –  Easier to import addi-onal channels into ad server


> Combining data sources


> Single source of truth repor.ng


Insights Repor.ng

> Understanding channel mix


> 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 ajributed 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.

> Adjus.ng for offline impact


+15 +5 +10 -‐15 -‐5 -‐10

Users are segmented before 1st ad is even served

> Ad server exposure test


Banner Impression $ TV/Print

Response Search

Response

Banner Impression $ Search

Response Direct

Response

Exposed group: 90% of users get branded message

Banner Impression $ Search

Response Direct

Response

Control group: 10% of users get non-‐branded message

Closer

SEM Generic

Banner View

TV Ad

> Full path to purchase



Banner Click Online

SEO Generic

Affiliate Click Offline

SEO Branded

Direct Visit

Email Update Abandon

Direct Visit

Social Media

SEO Branded

Introducer

> Cross-‐channel impact


> Offline sales driven by online


Website research

Phone order

Retail order

Online order

Cookie

Adver.sing campaign

Credit check, fulfilment

Online order confirma.on

Virtual order confirma.on

Confirma.on email

> Tracking offline conversions

§  Email click-‐through aoer purchase §  First online login aoer purchase §  Unique website or visitor phone number §  Call back request or online chat §  Unique website promo-on code §  Unique printable vouchers §  Store locator searches § Make an appointment online


Closer

25%

> Success aKribu.on models



25% Even AKrib.

Exclusion AKrib.

PaKern AKrib.

25% 25%

Introducer

33% 33% 33% 0%

30% 20% 20% 30%

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

New prospects

Exis.ng customers

Closer

Brand 1

Brand 1

Brand 1

> Paths across business units



Brand 2

Brand 2 Brand 3

Brand 2 Brand 3 Brand 4

Brand 3

Brand 4

Brand 4

Introducer

$

$

$

Exercise: AKribu.on model


Closer

25%

> Exercise: AKribu.on models



25% Even AKrib.

Exclusion AKrib.

Custom AKrib.

25% 25%

Introducer

33% 33% 33% 0%

? ? ? ?

> Common aKribu.on models

§  Allocate more conversion credits to more recent touch points for brands with a strong baseline to s-mulate repeat purchases

§  Allocate more conversion credits to more recent touch points for brands with a direct response focus

§  Allocate more conversion credits to ini-a-ng touch points for new and expensive brands and products to insert them into the mindset


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” November 2010 © Datalicious Pty Ltd 55

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” November 2010 © Datalicious Pty Ltd 56

> 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


Quick win: Addi.onal metrics

> Importance of calendar events


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

Calendar events to add context



Quick win: Event calendar

> Quick wins and geung started

§  Central analy-cs plaiorm §  Addi-onal data into ad server §  Unique phone numbers §  Search call to ac-on §  Addi-onal metrics §  Event calendar


> Targe.ng

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


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%


> New consumer decision journey


The consumer decision process is changing from linear to circular.

> 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






Time Time prospect

> 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

Off-‐site targe-ng

On-‐site targe-ng

Profile targe-ng

> Combining targe.ng pla>orms



Take a closer look at our cash flow solu.ons


+ Add website behaviour to submiKed contact form data


Take a closer look at our cash flow solu.ons


Save .me and get your business insurance online.


Our Flexi-‐Premium car insurance can help you save.


Our Flexi-‐Premium car insurance can help you save.

Save with our combine car and life insurance offer.

November 2010 © Datalicious Pty Ltd 81 It’s no accident we’re cheaper

On-‐site segments

Off-‐site segments

> Combining technology


CRM

> Extended targe.ng pla>orm


Brand

Network

Partners

Publishers

> SuperTag code architecture


§  Central JavaScript container tag § One tag for all sites and plaiorms §  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

Campaign response data

> Combining data sets


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Behaviours plus transac.ons


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

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

November 2010 89 © Datalicious Pty Ltd


Quick win: Iden.fying users

> Maximise iden.fica.on points


Mobile Home Work

Online Phone Branch

> Sample customer level data


> Facebook Connect single sign on


Facebook Connect gives your company the following data and more with just one click Email address, first name, last name, gender, birthday, interests, picture, affilia-ons, last profile update, -me zone, religion, poli-cal interests, ajracted 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, etc Need anything else?

(influencers only)

(all contacts)

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


> 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

> Poten.al home page layout


Branded header

Rule based offer

Customise content delivery on the fly based on referrer data, past content consump-on or profile data for exis-ng customers.

Targeted offer Popular

links, FAQs

Targeted offer

Login

> 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 lioed 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 hKp://bit.ly/de70b7

> 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


Quick win: Basic re-‐targe.ng

> Poten.al newsleKer layout


Closest stores, offers etc

Rule based branded header

Data verifica.on

Rule based offer

Profile based offer

Using profile data enhanced with website behaviour data imported into the email delivery plaiorm to build business rules and customise content delivery.

NPS

> Customer profiling in ac.on


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

subscribers at every touch point to keep

refining profiles and messages.

> Poten.al landing page layout


Rule based branded header

Campaign message match

Targeted offer

Passing data on user preferences through to the website via parameters in email click-‐through URLs to customise content delivery.

Call to ac.on

> Poten.al call center interface


Customers can also be iden-fied offline and given most call center plaiorms are now web-‐based it would be possible to use online targe-ng plaiorms to shape the call experience.

Call center menu op.ons

Customer contact history

Targeted offer Call script

Exercise: Targe.ng matrix



Purchase cycle

Segment A Segment B Media

channels Data points

Default, awareness

Research, considera.on

Purchase intent

Reten.on, up/Cross-‐Sell


Purchase cycle

Segment A Segment B Media

channels Data points

Colour, price, product affinity, etc

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, product views

Purchase intent

A delivers great value!

B delivers great value!

Website, emails, etc

Cart adds, checkouts, etc

Reten.on, up/Cross-‐Sell

Why not buy B?

Why not buy A?

Direct mails, emails, etc

Email clicks, logins, etc

> Quality content is key

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.”


> ClickTale tes.ng case study


> Bad campaign worse than none


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 AIDA funnel


People reached

People engaged

People converted

People delighted

> Standardised global metrics


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


Quick win: Global metrics

> 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 November 2010 © Datalicious Pty Ltd 116

> Quick wins and geung started

§  Iden-fica-on of individual users §  Simple home page re-‐targe-ng §  Simple ad server re-‐targe-ng §  Global targe-ng matrix §  Standardised metrics


> Resources

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010







Time Time prospect

> Forrester on web analy.cs


> Forrester on tes.ng/targe.ng


> Forrester on media aKribu.on


> Es.mate resource costs


Exercise: Return on investment



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