3. Ad
Servers
Ad Servers
£
Ten years ago everything was sold direct
All a publisher needed was an ad server and a direct sales team
DirectSalesTeam
£
Agencies
7. 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
8. “The saviour of this
commoditization
is data.”
Greg Smith, COO, Neo@Ogilvy
12. Mirror Digital Network - UK
audience
National Regional
Unique Users
5m
Unique User Visits
7.6m
Page Impressions
23.6m
*Oct 2010
5.4m Unique Users
13. 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
14. 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
15. 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
16. 13 audience segments / life area groups
Publishers - sell high value audiences, not page
impressions
Advertisers - immerse their brands in relevant audiences
19. Normal behavioral profiling “guesses” at user
profiles
Group: Auto buyer
Brand: Citroen
Type: Hatchback
Type: Hybrid Group: Auto buyer
Brand: Citroen
Type: Hatchback
Type: Hybrid
Group: European Vacationer
Destination Country:
Germany
Destination City: Berlin
Group: European
Vacationer
Destination Country:
Germany
Destination City: Berlin
Profile data is:
1. Inaccurate
2. Sparse
3. Cannot be targeted at scale
Page 1
Page 2
“Citroen
Launches New
Economy
Hatchback”
Content
Analysis
Content
Analysis
4 tags per page
Aggregate Profile
“Visit Berlin
this summer”
20. The inference engine predicts full
profiles
or ‘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 accurate
2. Complete
3. 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%
Visitor
Analysis
Visitor
Analysis
100 tags per page
with associated
confidencePage 1
Page 2
“Citroen Launches
New Economy
Hatchback”
“Visit Berlin this
summer”
21. Ad group CTR
Luxury Lovers 2.36%
Home Improvers 2.31%
Beach Lovers 2.19%
Ad group CTR
Family 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
22. o Increase CPMs on your site and create
incremental revenue
o Drive ROI for your advertisers
o Convert non-premium site inventory to
premium audiences
o Reduce media wastage
o 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
23. 2...then leaves to browse
other websites
1
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
24. 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
Hinweis der Redaktion
Talk a bit about ad targeting http://www.youtube.com/watch?v=GYw4mpA8X5M
Made headlines this week – EU privacy directive: non-essential cookies can only be served with the explicit consent of the user
Potentially big ramifications for online advertising – will come to later in pres.
In the next 20 mins, take a look at why online ad targeting has become so important, and give some insight into some of the things we’re trialliung in this area
But worth starting off with trying to answer this question.
Lots of people in to my office trying to explain the various bits of the jigsaw puzzle, think I’m getting there now, so hopefully this presentation takes some of the mystery out of the seemingly dark arts of ad targeting. How did it get so complicated…. More importantly
We know that online ad spend continues to go up, but most publishers and ad agencies I talk to say they’re not experiencing revenue growth in line with the market
When we look at visual depiction of ecosystem – we can start to see why this is and so we can start to work out what to sort it out.
Ad agencies were the gateway to premium publishers
Publishers sales teams pitched to planners/buyers and ad servers took care of running the campaigns out across the pre-agreed sections of the target website
Relative scarcity of premium content sites, meant that money flowed through from agencies as had always been the way, and advertisers were happy with getting 0.01% click through rates because that was the accepted average.
FF 10 years, and you get to the industry's favourite slide – the current online ad ecosystem.
Here is
First came ad networks, which then fractured into specialist networks
Audience, Performance, Vertical.
Essentially, all of these technologies exist to increase efficiency for advertisers – they’re about reducing wastage.
All these new steps are in some way or another about ad targeting.
While the good news is that advertiser spend is still growing strongly…
There’s another major dynamic at play. That dynamic has a name:
The reason: Facebook is ginormous, what google is to search, facebook is fast becoming to display advertising
In the U.S facebook accounted for 23% of all display ad impressions in Q3
In fact Facebook's website racked-up more ad impressions than the next four companies combined: Yahoo, Microsoft, News Corporation's Fox Interactive Media and Google
So it’s setting market rates, and it’s setting those rates very, very low.
The takeaway from this is that scale is important, so as publishers of premium content, one of the things we absolutely must so is maximise synergies across our portfolios
Another clear advantage that facebook and all of the other new players in ad ecosystem have in common is that they’ve invested in tech.
And that imbalance needs addressing.
Those publishers who have premium audiences (attracted by premium content) – can’t unlock the advertiser value because they don’t have the technology.
And this is what’s happening
Publishers are being disintermediated by ad technologies.
Adv still buy at every price point
But publishers are generally seeing only 2. High(ish) or v. low
If we are to get proper value, we must move back to the chart on the left. We must get away from the commoditisation of our audiences. How?
When we say data, we mean audience insight
Fundamentally, an audience is any grouping (or “cluster”) of people –
Similar set of characteristics, gender, lifestyle, how they respond to things, their attitudes, what they buy, etc.
The reason that understanding audiences is so fundamental to success in online advertising is that there’s an oversupply of inventory, but a finite number of customers.
And the world has changed
Old marketing focused on the product as the starting point
Activity was focused on moving the towards the customer.
All about pushing things out at people. A one way process. Shove, shout, sell.
New marketing flips this on its head
Where there is an abundance of choice, an oversupply of inventory, the winners will be those who understand their customers, so that they can deliver the right ad at the right time for maximum efficiency.
Here’s how we’re trailling this at Mirror Group
We’re using a methodology that captures ALL four forms of targeting:
Demographic, Geographic, contextual in the first instance, and behavioural profile over time,
All of these ONLY with explicit consent from user
We’re working with Visual DNA, approach is to make the data capture a light-hearted, fun, relevant experience.
it’s very transparent, so we won’t fall foul of any future restrictions about the use of cookies to collect data
Addressable uk audience of 5m
And after duplification we have a further 5.4m unique users across out regional brands
Presentation of the quizzes is contextually relevant – promoted editorially
promoted as entertaining content rather than invasive data gathering
Quizzes themselves are fun, take 1 min to complete, and reward the user with personalized recommendations –helping them to get the best from the site.
Let’s do one…
As you’ve seen, the quiz provides very rich and accurate data – all volunteered by the user no guesswork involved
We then compare behaviour of all other site users against users who we’ve profiled,
The technology is able to make accurate inference about the non-profiled users, based on comparing their behaviour to the profiled ones
We end up with 13 broad audience segments or life area groups, incl
Heath & wellbeing, Travel, Entertainment, interest in Automotive, technology, nature etc.
And because of the granularity of the information provided by the profiled users, these can be further broken down into accurate audience ad groups
So those 13 audience segments / life area groups can be further categorized into 129 ad groups – for extremely accurate targeting.
This is the real strength of gathering data first hand, direct from users. If we look into one of these groups
Automotive – great to be able to target users who expressed interest in auto:
But within that, there are huge differences between say car enthusiasts, and car buyers
Within car buyers – there are wide variances too – those who want family car / sports car / luxury car.
This data gives us the ability to target with much greater precision
Take the auto sector example further – normal behavioral profiling kind of guesses at user profiles
Take a user who has looked at two pages – we might get 7 or 8 tags:: based on analyzing the content of the pages they look at
Lead to an aggregate profile that is pretty sparse, highly likely to be inaccurate, can’t be targeted at scale.
But when we have detailed visitor information to overlay against the content analysis, we get vastly richer information
If we know the profile of a visitor who looks at the same two pages, then we move from 7 or 8 tags or information nodes per page to almost 100.
The predicted is automatically infinitely more accurate and complete, and when we use inference data, allows us to target ads at scale
As an added bonus, we can also use the effectiveness of ad campaigns to target the most responsive group.
Here’s an example of the effectiveness of a recent campaign
Panasonic Lumix TZ10 camera
ran the campaign across 6 ad groups identified by visual DNA profiling,
All performed extremely well from a CTR pov, but the 3 groups that works best provided a CTR of over 2%
Really powerful and compelling info to take back to panasonic
So look, it’s clear that data drives revenue performance
But my recommendation is to look at a methodology that captures all 4 forms of targeting
If you do that, the outcomes are all positive: (read out)
PAUSE
So that’s what we’re doing from a site-centric perspective and I heartily recommend it as a sensible approach
But as we know – users don’t just come to our sites – they visit hundreds, so I also wanted to briefly touch on another form of personalised targeting –
Or rather – re-targeting: following customers as they move around the web. Here’s a summary of how it works
We know – it’s easy to be distracted during the purchase process
More than 95 percent of visitors who browse an ecommerce site leave before making a purchase.
This means retailers' investments in search and SEO resources, intended to bring potential customers to their ecommerce sites, are wasted.
Retargeted customers are 70 percent more likely to complete a sale than non-retargeted counterparts
It’s an area that we’re looking into – a few suppliers in the space, the one we’ve been most impressed by is Criteo, but if e-commerce forms a material part of revenues – worth a look
I mention this type of targeting as well because the approach to ad targeting really needs to be multifaceted.
So the summary is: No single type of targeting is best
You have to consider
Before I wrap up, worth noting that there are elements of advertising responsiveness that aren’t measurable, and targeting on its own isn’t enough.
That’s quite important