Ad Tech Campaign Measurement

> Campaign Measurement < Digital Campaign Measurement

ad:tech 2011 Workshop

> Short but sharp history

§  Datalicious was founded late 2007 §  Strong Omniture web analyBcs history §  Now 360 data agency with specialist team §  CombinaBon of analysts and developers §  Carefully selected best of breed partners §  Evangelizing smart data driven markeBng § Making data accessible and acBonable §  Driving industry best pracBce (ADMA)

March 2011 © 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 Campaigns 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 AKribu?on

Op?mise channel mix

Tes?ng Improve usability

$$$

Targe?ng Increase relevance

Stan

dardised

Metric

s Be

nchm

arking and

tren

ding

Standardised Metrics

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 (AcBon)

+Buzz (SaBsfacBon)

> 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

> Addi?onal funnel breakdowns


40% 10% 1%

New prospects vs. exisBng customers

Brand vs. direct response campaign

New vs. returning visitors

AU/NZ vs. rest of world

Exercise: Funnel breakdowns

> Exercise: Funnel breakdowns

§  List potenBally insighXul funnel breakdowns –  Brand vs. direct response campaign – New prospects vs. exisBng customers –  Baseline vs. incremental conversions –  CompeBBve acBvity, i.e. none, a lot, etc –  Segments, i.e. age, locaBon, 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


People reached

People engaged

People converted

People delighted

> Mul?ple metrics data sources


QuanBtaBve and qualitaBve 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 informaBon, order confirmaBon, etc

Conversion event

Campaign responses

© Datalicious Pty Ltd 18

> Conversion funnel 2.0

March 2011

Campaign responses (inbound spokes) Offline campaigns, banner ads, email markeBng, referrals, organic search, paid search, internal promoBons, etc

Landing page (hub)

Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registraBon, product comparison, product review, forward to friend, etc

© Datalicious Pty Ltd 19

> 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 22

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 23

> 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

> Exercise: Metrics framework


Level Reach Engagement Conversion +Buzz

Level 1 People

People reached

People engaged

People converted

People delighted

Level 2 Strategic

Search impressions, UBs, etc

? ? ?

Level 3 Tac?cal

Keyword rank, click-‐through, etc

? ? ?

> Exercise: Metrics framework


> Media aKribu?on

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


Direct mail, email, etc

Facebook TwiKer, 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 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-‐counBng 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 reporBng but once de-‐duplicated they each only contributed 50% of conversions

–  What are the iniBal CPA values and what is the true CPA? §  SoluBon: $50 iniBal CPA and $100 true CPA

–  $5,000 / 100 = $50 iniBal 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 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

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

> Search call to ac?on for offline


March 2011 © Datalicious Pty Ltd 44 Offline response tracking and improved experience

March 2011 © Datalicious Pty Ltd 46 hKp://www.suncorp.com.au?campaign=workshop

> PURLs boos?ng DM response rates


Text

> 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 promoBonal codes, vouchers §  Geographic locaBon (Facebook, FourSquare) §  Plus regression analysis of cause and effect


> 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 interacBon 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 Bme 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

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

> 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 ater the fact.

> 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

> 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 AdverBsing 7%

Affiliate MarkeBng 9%

Referrals 5%

Email MarkeBng 7%

De-‐duped Campaign Report

} Channel % of Influence

Word of Mouth 32%

Blogging & Social Media 24%

Newspaper AdverBsing 9%

Display AdverBsing 14%

Email MarkeBng 7%

Retail PromoBons 14%

Greatest Influencer on Branded Search / STS

Conversions aoributed to search terms that contain brand keywords and direct website visits are most likely not the originaBng 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

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

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 sBmulate repeat purchases

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

§  Allocate more conversion credits to iniBaBng touch points for new and expensive brands and products to insert them into the mindset


> Media aKribu?on phases §  Phase 1: De-‐duplicaBon –  Conversion de-‐duplicaBon across all channels –  Requires one central reporBng plaXorm –  Limited to first/last click aoribuBon

§  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 AnalyBcs and Omniture

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


> Targe?ng

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


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

Increase consumer engagement Exceed 50% of best compeBtor’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 markeBng, 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 targeBng

On-‐site targeBng

Profile targeBng

> 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.

On-‐site segments

Off-‐site segments

> Combining technology


CRM

> SuperTag code architecture


§  Central JavaScript container tag § One tag for all sites and plaXorms §  Hosted internally or externally §  Faster tag implementaBon/updates §  Eliminates JavaScript caching §  Enables code tesBng on live site §  Enables heat map implementaBon §  Enables redirects for A/B tesBng §  Enables network wide re-‐targeBng §  Enables live chat implementaBon

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 collecBon of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira?on, etc predicBve models based on data mining

propensity to buy, churn, etc historical data from previous transacBons

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 promoBon 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 overesBmated visitors by up to 7.6 Bmes 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 idenBficaBon through Cookies

March 2011 93 © Datalicious Pty Ltd

> Maximise iden?fica?on points


Mobile Home Work

Online Phone Branch

> Sample customer level data


> Sample site visitor composi?on


30% exis?ng customers with extensive profile including transacBonal history of which maybe 50% can actually be idenBfied 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 consumpBon or profile data for exisBng 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 targeBng, 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 hKp://bit.ly/de70b7

> Ad-‐sequencing in ac?on


MarkeBng is about telling stories and

stories are not staBc but evolve over Bme

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

> 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 plaXorm to build business rules and customise content delivery.

NPS

> 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.

> 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 idenBfied offline and given most call center plaXorms are now web-‐based it would be possible to use online targeBng plaXorms 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


> Keys to effec?ve targe?ng

1.  Define success metrics 2.  Define and validate segments 3.  Develop targeBng and message matrix 4.  Transform matrix into business rules 5.  Develop and test content 6.  Start targeBng and automate 7.  Keep tesBng and refining 8.  Communicate results March 2011 © Datalicious Pty Ltd 112


Contact us [email protected]

Learn more

blog.datalicious.com

Follow us twiKer.com/datalicious

Data > Insights > Ac?on

Ad Tech Campaign Measurement

Technology

Transcript of Ad Tech Campaign Measurement