Digital Leaders Dinner (Sydney, Australia) - 6 February, 2013


Published on

Chief Product Officer Tim Brown's presentation from Exponential Sydney's Digital Leaders Dinner, delivering insights around the future and evolution of online advertising and attribution. For more details, tweet us at @exponentialinc or visit our website:

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • The chart shows that, come 2020, virtually all media will be ‘digital’ – that is delivered over IP. Since the web was invented in 1989, the growth of digital media has been one way (up) and at the direct ‘cost’ to ‘traditional’ media. But, what we also see is that – of course – that doesn’t mean ‘TV’ is dead – ‘televisual’ content is still the most compelling of all media forms – just that it will be delivered as data over IP rather than analogue signals over cables and frequency waves. As will games and even outdoor. That has enormous implications for distribution, consumption and, therefore, monetization.Distribution: Global, ‘free’ – limited barriers to entry – multi-screenConsumption: On demand, interactive, multi-screenMonetization: The price of ‘free’ content and services is our information. Services that users are not prepared to fund through subscription need to be ad-supported or owned.So, given advertising - and, more broadly, marketing - is likely to become an even greater piece of the way in which content and applications are funded, what are the implications of the media shift for advertisers?
  • Well the opportunity is clear - convergence means anything delivered over IP can potentially be planned and bought in the same way as we do online. If we consider that advertising is just another form of content or service then the same fundamentals apply to how we need to think about advertising in the digital age in terms of distribution and consumption:At a more practical level, advertising over IP means:Inventory can be ‘aggregated’ without media-owner consolidationIt can be planned and bought using automated tools and with automated optimization of audiences AND creativeAudience data becomes about real behavior and based on much bigger samplesAND it means we can behaviorally target…
  • So, if everything is going to work ‘like online’ then it’s useful to understand how online targeting has evolved over time. Online audience targeting, like all other media, started as untargeted media and evolved into ‘content targeting. But, as the possibilities became clear and the technology/infrastructure evolved, so it has been able to evolve far beyond this:Untargeted, contextual, demographic, retargeting, rules based, Predictive behavioral models. Takes the guess work out of finding your Target Audience and is the only way to do it when dealing with the amount of data available. In our case, which one of the 50,000 behavioral topics will you choose.
  • So there is enormous consumer behaviour change due to consumer technological advances. I was at CES in Vegas a few weeks ago and its amazing what is being show cased. Everything from your washing machine to your utilities and TV are going to be connected and controlled via personal devices. All the top Brands were there in force to figure out what this meant to them. One thing was clear, radical change for how Brands reach their target audiences to engage them in getting to know and buy their brands, is at our doorstep and people in this room and others like you around the world are going to be at the forefront of this change. B2B technology has also advanced, just look at Big Data – what can be collected, stored and processed today was unimaginable a few years ago and at a fraction of the cost. In 2011 IBM released a paper with the oft repeated comment that ““Everyday, we create 2.5 quintillion bytes of data–so much that 90% of the data in the world today has been created in the last two years alone.”When people engage online they create huge amounts of data. For example,Exponential collects 80 billion events a month across 450 million users worldwide and organise that into 50,000 categories. That’s equivalent to seeing more than 5600 Olympic Stadia of people more than 170 times a month each. Day to day online business dwarfs the data potential of the biggest events.
  • This advance has lead to, in my view, one of the most exciting milestones in the development of digital advertising. Google in September last year revamped their campaign reporting and attribution suite and is now part of your standard DFA license. They have also gone from making available 200 exposures proceeding a conversion from 20. Out of interest – who in the room uses Google adserving? How many of you are aware of the new reporting suite? How many of you have used some of the Multi Touch Attribution reports?So, whats the deal, let me start with some background?
  • SO, let’s look again at where we started in digital with answering that question ‘how Is my advertising working?’ Back when measurement was very hard on the internet, one thing that was easy to measure was the click.This has presented us with two problems – first, the assumption that ‘clickers’ are representative of the audience we’re seeking to target with any campaign. And, second, the assumption that ‘the click’ is the best representation of advertising ‘success’.We’ll come on to the second piece, but, examining the first of these challenges - that ‘clickers’ are representative of the audience we’re seeking to target – we can say that, initially, the click might well have been a good metric because, to be on using the internet in 1998-2003, you were likely to be technologically savvy and therefore a click probably was a fair representation of engagement. So the first attempt to measure effectiveness was the click or the click through rate.Now that internet usage has been mainstream for a number of years that no longer holds true and we see clear patterns of “clickers” appearing as distinct to who you think your target audience should be.
  • Here’s an example of what we mean. Look at the conversion behaviours for this UK business banking client. In this example, the converting behaviours are predominantly executive careers and executive cars. This is the profile of a small business owner. You can almost imagine them in the room. Compare that to the clicker audience for the same client. This looks nothing like the intended target.In this case the choice of measurement metric has skewed the delivery away from the target audience.
  • Now we start to look at the second piece of our click challenge – the assumption that ‘the click’ is the best representation of advertising ‘success’.Around 2004-2005 it became clear that measuring clicks was not necessarily driving online conversion, something that was distinctly measureable. So the question was, “how do you pay media owners for helping bring people to your site to convert?”. The result was last view attribution. The media partner showing the last ad before the user converts gets the full display ad conversion attribution. This is the prevalent model today for display and while we think it is the “least worst” option versus click through rate measurement and click to conversion attribution it does lead to bad practice. In our example here, a user has already visited the site. Now all the media partners in the plan with a retargeting pixel on the site are incentivised to show as many ads as possible to the use on the cheapest inventory they can find whether that ad was visible on the page or not. The infamous “spray and pray technique.
  • So what should we do about measuring effectiveness and attributing value?Well, there are as many ways to measure effectiveness as there are different ways to define your marketing objectives.List of effectiveness:OnlineGRP measurement can tie your TV plans to your online plansClosed loop purchase studiesFacebook likes can be used as proxy to creating awareness and favourability, though there is some healthy debate on the true value of thisClicksViewable ads measurement is a different perspective on your campaignBrand awareness lift studies on samples of exposed and non-exposed users can measure softer metricsSalesThe key for us is that we have to ensure we begin to more effectively match metrics with campaign objectives. If the overriding point of a campaign really is to lift brand awareness or shift brand perception – then we should measure ONLY those things. We must learn to stop measuring and/or ignore the click-through/last-view/CPA measures that come with every campaign report.
  • The big change in attribution is that it is now a hot topic with mainstream attention in the way it didn’t have 18 months ago, driven by an array of new tools and businesses focused on attribution, including tag management and attribution specialists.The arrival of attribution has led to some basic models of attribution – ways in which we can think about how we attribute the credit for campaign success.Brands, agencies, ad tech and media owners recognise that there are things in your control. The tools exist to employ a number of simple models. Last viewFirst viewLinearTime decay Some are even turning to customised models– the challenge with this is that data often has to be collected from disparate sources, is then run through complicated analysis by a team that is often disconnected from the rest of the marketing organisation, and then may well be delivered too late to apply to the next planning cycle.
  • Digital Leaders Dinner (Sydney, Australia) - 6 February, 2013

    1. 1. #exponentialDLD
    2. 2. The digital shift
    3. 3. All media will be ‘digital’ Global media consumption per week 90 Games 80Average hours per week Mobile 70 Outdoor 60 Cinema 50 Digital radio 40 Analogue radio 30 Digital TV 20 Analogue TV 10 Web 0 Print 1900 1920 1940 1960 1980 2000 2020 Source: Carat/World Media Trends Report 2008
    4. 4. Bringing together digital audience targeting opportunities
    5. 5. Context: the evolution of online audience targetingUntargetedAudience: N/A Contextual Audience: Content as a Demographic proxy to the right audience Audience: users with the right Retargeting age/gender/loca tion Audience: users Rule-based that have already visited your behavioral Predictive website behavioral Audience: users with the right, manually- Audience: users selected online with the behaviors right, auto- selected online behaviors
    6. 6. Lookalike modeling
    7. 7. Online data overshadows even the biggest events
    8. 8. 8
    9. 9. The click problem
    10. 10. The click problem – the click as a metric of ‘success’ConversionbehavioursClickbehaviours
    11. 11. The problem with last view
    12. 12. The effectiveness measurement
    13. 13. The hierarchy of attribution Last event First event Flat80 80 7060 60 5040 40 3020 20 10 0 0 1 2 3 4 5 1 2 3 4 5 -10 1 2 3 4 5 Bath-tub Time decay Custom70 70 7060 60 6050 50 5040 40 4030 30 3020 20 2010 10 10 0 0 0 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
    14. 14. Game Theory and axiomatic value attributionGame Theory proposed by Von Neumann andMorgenstern in Theory of Games and EconomicBehavior in 1944Described the strategy for players in games withcooperation or competition
    15. 15. Game Theory and axiomatic value attribution Axiomatic Value Attribution proposed in 1953 by Lloyd ShapleyA method to evaluate the ‘value’ of playing a game
    16. 16. Shapley value of playing a game Three player game exampleThe value of all combinations of players is knownThe Shapley Value is a numerical quantity thatassigns to each player their expected marginal contributions over all possible games
    17. 17. Shapley value exampleexample Shapley value = $0= $7 = $4 = $6= $7 = $15 = $9 = $19
    18. 18. Shapley value example= $0 Calculating Shapley value= $7= $4 $19= $6 $7 = $12= $7 $0= $15 1 3= $9= $19 2
    19. 19. Shapley value example= $0 Calculating Shapley value= $7= $4 $19= $6 $6 = $4= $7 $9= $15 1 3= $9= $19 2
    20. 20. Shapley value exampleexample Shapley value= $0 Shapley value= $7 = $7.7= $4= $6 = $3.2= $7= $15= $9 = $8.1= $19
    21. 21. Preliminary findings
    22. 22. Exposure attribution (1 of 2) Last View vs. Axiomatic attributionPercent of conversions 60%50% Last View40% Axiomatic30%20%10% 0% None 1 2 3 4 5 6 7 8 9-12 13-17 18-24 25+ Frequency of exposures
    23. 23. Exposure attribution (2 of 2)Ratio Axiomatic vs. Last View attribution ratio200% Frequency 1 and 2175% under-attributed by Last View150%125%100% Frequency > 3 75% over-attributed by Last View 50% Frequency > 9 25% grossly over-attributed by Last View 0% None 1 2 3 4 5 6 7 8 9-12 13-17 18-24 25+ Frequency of exposures
    24. 24. Ad Size attribution comparisonRatio Axiomatic vs. Last View attribution ratio 200% 175% This Ad Size under-attributed 150% by Last View 125% 100% 75% This Ad Size 50% grossly over- 25% attributed by 0% Last View None 468x60 120x600 160x600 728x90 300x250 300x600 Ad size
    25. 25. Conclusion1. Targeting the right audiences and engaging them with high impact ad formats works!2. Always think about the causal effect marketing activity has on your end goal3. Start measuring and testing these theories against new measurement and attribution solutions available
    26. 26. #exponentialDLD Thank you!Tim Brown I Chief Product