Online advertising used to be straightforward

WHAT HAPPENED?

And how to we get to grips with it?

AdServers

Ad Servers

£

Ten years ago everything was sold direct All a publisher needed was an ad server and a direct sales team

Dire

ct S

ales

Tea

m

£

Agencies

Performance Networks

Audience Networks

Non

-Gua

rant

eed

c. 6

0%G

uara

ntee

dc.

40%

Agencies

AdServers

MediaBuying

Platforms

DSPs

Ad Exchanges

Data Exchanges

Data Suppliers

Ad Servers

Ad NetworkOptimizers

Ad Security

International Networks

£££££

PUBLISHER PLATFORMS DEMAND PLATFORMS650+ CHANNELS

2 m

illion

+Today’s market is complex

Source: GCA Savvian Advisors, the Rubicon Project

Yield Optimization

Dire

ct S

ales

Tea

mAd

Net

wor

ks &

Exc

hang

es

Horizontal Networks

Vertical Networks

£ received

£ Spend

£ received

Scale IS important, so maximize synergies across silos

US: 23.1% share of display ad impressions in Q3 2010More than double #2 ranked company: Yahoo!

Competition has better tools and reach

TECHNOLOGY INVESTMENT

Publishers are being disintermediated by Ad technologies.

£20

£16

£12

£8

£4£3

£2

Advertisers buy media at every price point

Current sales channel management doesn’t capture all dollars

£20 – direct sales

£0.50 networks

“The saviour of this commoditization

is data.”

Greg Smith, COO, Neo@Ogilvy

PRODUCT

Packaging

Distribution

CRM

Advertising

CONSUMER

Old marketing

PRODUCT

Packaging

Distribution

CRM

Advertising

CONSUMER

New marketing

What are we doing at Trinity Mirror?

Mirror Digital Network - UK audience

National

Regional

Unique Users

5m

Unique User Visits7.6m

Page Impressions

23.6m

*Oct 2010

5.4m Unique Users

Fun, relevant quizzes ensure that rich, accurate data is collected

User takes Visual quiz, each image is tagged using a contextual taxonomy

Users provide brand preferences, age and gender

Ads offering personality tests drive traffic to the quizzes

– CTR to our quizzes of 0.2% to 1%– Quiz completion rate of 86% for Mirror.co.uk quiz– Quiz promoted as entertaining content rather than invasive data gathering

11 Collect Data

Data is collected by profiling users with visual quizzes and inference

22 Store Data

The data collected is held securely in a Data Bank

The data is made available via an API, DFP & other networks to deliver targeted ads & content

33 Access

The process: Collect, Store and Access

Scale Through Inference

• We collect accurate and rich profiles of a proportion of users through our quiz

• The technology tracks the behavior of all users of the site

• An inference engine then predicts the profiles of all users by comparing the behavior of non-profiled users to the behavior of profiled users

13 audience segments / life area groups

Publishers - sell high value audiences, not page impressionsAdvertisers - immerse their brands in relevant audiences

Content and Advertising targeting

129 audience ad groups

Detail of categories within groups

Normal behavioral profiling “guesses” at user profiles

Group: Auto buyerBrand: CitroenType: HatchbackType: Hybrid Group: Auto buyer

Brand: CitroenType: HatchbackType: HybridGroup: European VacationerDestination Country: GermanyDestination City: Berlin

Group: European VacationerDestination Country: GermanyDestination City: Berlin

Profile data is:1. Inaccurate2. Sparse3. Cannot be targeted at

scale

Page 1

Page 2

“Citroen Launches New Economy Hatchback”

ContentAnalysis

ContentAnalysis

4 tags per pageAggregate Profile

“Visit Berlin this summer”

The inference engine predicts full profilesor ‘personalities’

Gender: Female: 70%Gender: Male: 30%Age: 18-24 : 25%Age: 25-34 : 40%…Lifestage: Student : 30%Lifestage: Young family: 30%…Group: Auto buyer: 75%Group: European vacationer: 25%…Interests: Environment: 80%…Brand preference: Citroen : 35%Brand Preference: Mercedes : 20%

Gender: Female: 60%Gender: Male: 50%Age: 18-24 : 55%Age: 25-34 : 35%…Lifestage: Student : 45%Lifestage: Young family: 5%…Group: Auto buyer: 5%Group: European vacationer: 95%…Interests: Environment: 50%…Brand preference: Citroen : 15%Brand Preference: Mercedes :

50%

Profile data is:1. Statistically accurate2. Complete3. Can be targeted at scale

Predicted Profile

Gender: Female: 90%Gender: Male: 10%Age: 18-24 : 25%Age: 25-34 : 40%…Lifestage: Student : 85%Lifestage: Young family: 10%…Group: Auto buyer: 50%Group: European vacationer: 85%…Interests: Environment: 80%…Brand preference: Citroen : 40%Brand Preference: Mercedes : 50%

VisitorAnalysis

VisitorAnalysis

100 tags per page with associated

confidencePage 1

Page 2

“Citroen Launches New Economy Hatchback”

“Visit Berlin this summer”

Ad group CTR

Luxury Lovers 2.36%Home Improvers 2.31%Beach Lovers 2.19%

Ad group CTRFamily Car Buyers

0.87%

Clothing & Apparel Buyers

0.98%

Auto Enthusiasts 1.20%

Luxury Lovers, Home Improvers, Beach Lovers are all more likely to click on the Panasonic ad than other ad groups

Example Mirror campaign result

o Increase CPMs on your site and create incremental revenue

o Drive ROI for your advertiserso Convert non-premium site inventory to

premium audienceso Reduce media wastageo If you don’t sell audiences,

consequences can be high:

• Competitors beat you to the game• Buyers can leverage data to better

value and then arbitrage your inventory

Data drives revenue performance

2...then leaves to browse other websites1

A customer browses your website...

4One click brings them directly back to your site

3Targeting tech

displays a personalised ad to this prospect

How Personalised retargeting works

No single type of targeting is “best”

Consider the following factors:

- Data available (accurate, fresh, relevant)

- Objectives, persuasion or awareness

- Types of goods or services being sold

- Where audience is found in purchase funnel

The intertwined issues of data usage and audience privacy will affect display targeting for the next several years

To reach the audience with display ads, targeting needs to be multiple choice