Online advertising the challenges and opportunities
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Online advertising the challenges and opportunities
- 1. Online advertising used to be straightforward WHAT HAPPENED?
- 2. And how to we get to grips with it?
- 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
- 4. Performance Networks Audience Networks Non-Guaranteed c.60% Guaranteed c.40% Agencies Ad Servers Media Buying Platforms DSPs Ad Exchanges Data Exchanges Data Suppliers Ad Servers Ad Network Optimizers Ad Security International Networks ££££ £ PUBLISHER PLATFORMS DEMAND PLATFORMS650+ CHANNELS 2million+ Today’s market is complex Source: GCA Savvian Advisors, the Rubicon Project Yield Optimization DirectSalesTeamAdNetworks&Exchanges Horizontal Networks Vertical Networks £ received £ Spend £ received
- 5. Scale IS important, so maximize synergies across silos : 23.1% share of display ad impressions in Q3 2010 ore than double #2 ranked company: Yahoo!
- 6. Competition has better tools and reach TECHNOLOGY INVESTMENT
- 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
- 11. What are we doing at Trinity Mirror?
- 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
- 17. Content and Advertising targeting 129 audience ad groups
- 18. Detail of categories within groups
- 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