anz analytics

> ANZ Analy*cs Workshop < 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)

March 2011 © Datalicious Pty Ltd 2

> Clients across all industries

> Wide range of data services

Data PlaAorms Data collec*on and processing Web analy*cs solu*ons Omniture, Google Analy*cs, etc Tag-‐less online data capture End-‐to-‐end data plaAorms IVR and call center repor*ng Single customer view

Insights Repor*ng Data mining and modelling Customised dashboards Media aNribu*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

> Smart data driven marke*ng

Media ANribu*on

Op*mise channel mix

Tes*ng Improve usability

Targe*ng Increase relevance

Metric

s Framew

Benchm

arking and

Metrics Fram

Benchmarking and trending

> Metrics framework

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010

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

> Marke*ng is about people

40% 10% 1%

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

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

−−− Probability of iden.fica.on through Cookies

March 2011 11 © Datalicious Pty Ltd

Mobile Home Work

Online Phone Branch

People reached

People engaged

People converted

People delighted

> Addi*onal funnel breakdowns

40% 10% 1%

New prospects vs. exis.ng customers

Brand vs. direct response campaign

New vs. returning visitors

AU/NZ vs. rest of world

Exercise: Funnel breakdowns

> Exercise: Funnel breakdowns §  List poten.ally insighcul funnel breakdowns –  Brand vs. direct response campaign – New prospects vs. exis.ng customers –  Baseline vs. incremental conversions –  Compe..ve ac.vity, i.e. none, a lot, etc –  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

People reached

People engaged

People converted

People delighted

> Mul*ple metrics data sources

Quan.ta.ve and qualita.ve research data

Website, call center and retail data

Social media data

Media and search data

Social media

> 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

> Conversion funnel 1.0

March 2011

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

> Conversion funnel 2.0

March 2011

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

> 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

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” March 2011 © Datalicious Pty Ltd 25

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” March 2011 © 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

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

> Exercise: Conversion Funnel

> Media aNribu*on

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010

Direct mail, email, etc

Facebook TwiNer, etc

> Complex campaign flows

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

> Duplica*on across channels

Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaAorm

Web Analy*cs

> Cookie expira*on impact

Banner Ad Click

Email Blast

Paid Search

Organic Search

Bid Mgmt

Ad Server

Email PlaAorm

Google Analy*cs

Expira*on

Banner Ad View

> ANZ repor*ng plaAorms

Central Analy*cs PlaAorm

> 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)

TV/Print audience

Search audience

Banner audience

> Reach and channel overlap

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

> Indirect display impact

> Success aNribu*on models

Banner Ad $100

Email Blast

Paid Search $100

Banner Ad $100

Affiliate Referral $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 aNribu*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

Closer

SEM Generic

Banner View

> 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

> 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

> Search call to ac*on for offline

> PURLs boos*ng DM response rates

> Jet Interac*ve phone call data

> 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

> 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

Closer

SEM Generic

Banner View

> 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

Adobe campaign stack does not include organic channels or banner impressions and does not expire on any event, i.e. con*nues as long as the cookie is present.

> 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 ad impressions Less granular & complex

Lacking organic visits More granular & complex

Mobile Home Work

Online Phone Branch

> 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 arributed 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

Closer

> Success aNribu*on models

25% Even ANrib.

Exclusion ANrib.

PaNern ANrib.

25% 25%

Introducer

33% 33% 33% 0%

30% 20% 20% 30%

Closer

Channel 1

> Path across different segments

Channel 2

Channel 2 Channel 3

Channel 2 Channel 3 Product 4

Channel 3

Channel 4

Introducer

Product A vs. B

New prospects

Exis*ng customers

Exercise: ANribu*on model

Closer

> Exercise: ANribu*on models

25% Even ANrib.

Exclusion ANrib.

Custom ANrib.

25% 25%

Introducer

33% 33% 33% 0%

? ? ? ?

> Common aNribu*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

> Targe*ng and tes*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

> 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 plaAorms

ANZ Low Rate MasterCard

ANZ Business Debit Card

On-‐site segments

Off-‐site segments

> Combining technology

> SuperTag code architecture

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

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

−−− Probability of iden.fica.on through Cookies

Mobile Home Work

Online Phone Branch

> Sample customer level data

> 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 lited 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 hNp://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

Exercise: Targe*ng matrix

Purchase Cycle

Segments: Colour, price, product affinity, etc

Media Channels

Data Points

Default, awareness

Research, considera*on

Purchase intent

Reten*on, up/cross-‐sell

> Exercise: Targe*ng matrix

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

> Exercise: Targe*ng matrix

> 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

Exercise: Tes*ng matrix

Test Segment Content KPIs Poten*al Results

> Exercise: Tes*ng matrix

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

? ? ? ? ? ?

> Exercise: Tes*ng matrix

> 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 104

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