Exponential is the parent company of a range of brands in the online ad space that deliver high-impact, high-engagement online advertising campaigns across display, video and mobile media. The best known is Tribal Fusion, global display advertising provider. But you might also know our engagement video division Firefly Video, in-stream advertising specialist Adotube and our new mobile engagement platform Appsnack.All these divisions use Exponential’s e-X Advertising Intelligence Platform, our integrated data and technology platform, to work out precisely which audiences an advertiser should be targeting and to reach those audiences at scale across media and device.
The e-X platform is the engine that drives Exponential and the way our audience engagement divisions can understand a brand’s best audiences and target them at scale. It is built from:Media – one of the world’s largest digital media footprints based on a premium publisher network made up of mid-tail sites that act as the best indicators of customer interest and intent – more than 450m worldwide reach, more than AOL and second only to Google, Microsoft, Facebook and Yahoo.Technology – our own technology for collating, aggregating and organizing data, plus for the delivery and optimization of online campaigns. Our contextualization engine is the best in the business, working at a page-level to understand the interests and intent associated with a visit to any page on our network.Data – our technology’s ability to process the 2 billion daily user ‘events’ occurring across our network into meaningful segmentation of audiences, means our data is truly actionable. We integrate 3rd-party segments from major data suppliers to perfect our audience targeting models.
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. Exponential has the same big data issue as many other businesses. We collect 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.[“The amount of data is meaningless”]All big data presentations start with stats about the vast scale of data now collected.But we would argue that the amount of data being collected today is now so vast as to be incomprehensible to the human mind. 5 years ago it was about how big your data warehouse was and how fast you could process data.Today, it really isn’t about how big it is but what you do with it.
Companies are making bets on the kinds of data that yield the most value. In truth, this is driven by where the company came from (search, traditional media, social media, display advertising) more than anything else, but all yield big data. Which of these kinds of data yields the most value is open to all sorts of argument. Our punt is on ‘behavioural’ – or interest-based – that is defining people’s interest by understanding the pages they have visited on the web. The point is all that – with scale – all can yield big data and that, if the data is big enough, all can act as a valuable proxy to people’s interests and, therefore, the kind of advertising that is likely to work for them.[PB: Doug, need examples of companies in each bucket…]
So we prefer to think about how you turn data from information then to insight and finally to action. This is not a new notion, we might even have stolen from Frank Zappa, but there’s still too much noise about data collection and not enough focus on data actionability.
Contextualization is the process by which online user behavior can be turned into meaningful audience segments. By working at page, rather than site-level, e-X:1. Aggregates the 2 billion daily user ‘events’ occurring across our network – page views and clicks2. Works out the interest and intentions that the most visited pages represent3. Attaches those interests and intentions to anonymous user profiles to create 50,000 potential audience segments in our topic tree
So now we can examine the data but it is here that we learn you cannot do without humans. Our machines tell us the online interests and intentions that represent a brand’s most likely customers – the dots are different online ‘behaviours’ – those showing the highest ‘lift’ are the behaviours that most indicate a propensity to buy. But the output of that analysis is the above. There’s little we do can do with this, especially since the chart – by definition - looks much the same for every brand. So the information has now been made relevant is STILL isn’t insight.
That’s where art needs to be layered over the science, that’s where we need humans to make sense of the information; to extract stories that:Validate: Sometimes analysis of the information yields findings that that simply validate our assumptions.Surprise: Sometimes the analysis tells us things that, in retrospect seem like common sense, but – because of the way we think and measure – haven’t been acted upon before.Sometimes it challenges your assumptions; it tells us things we’d never have thought of and gets us to rethink who we are targeting and why.
So what can we do with these insights? Well, firstly, these aren’t insights for optimising your display advertising. They are insights into the interest and intentions that define your customers – they are insights for your marketing strategy – who are we targeting and why? It informs your entire media buying and planning strategy, not just on, but offline. And they inform the kind of creative you should be using for your audiences – again, on and offline.
But, none of these places can the links be forged and acted upon programmatically. Even in online, this has been the problem with many of the planning tools in place - they delivered great insight but then still required you to find and buy a proxy to the audience you just identified. Fortunately digital advertising has become sophisticated enough that audiences can now be passed from planning application to media buying application, in our case on a single platform. The idea is that marketers should be able to buy what they learn.
But, we can go further that manually selecting the audience segments the combination of art and science identifies for us to buy media. We have found that for the most ‘audience efficiency’, ‘you can’t beat the model’.In our case we expose that audience model as an explicit trade off between the lift, or quality of fit, of the model to a brand’s target audience – the y-axis – and the reach you can achieve – the x-axis. The larger the model, the worse it gets, but at least it’s transparent. What we find now is that the traditional approach of manually selecting audiences based on their nearest analogue to your survey data, or even based on your audience insights discovery, is never as efficient as the model. In the chart above, the beer brand in question used its brand site visitors as a proxy to its customers. They could compare the lift and reach of their traditional, preferred audience targets – e.g. Men, Sports and Arts & Entertainment – to that suggested by a model. In all cases, their preferred audiences sacrificed significant lift for any given reach or vice versa. For a campaign with brand objectives this represents a missed opportunity and real wastage.So we’ve turned Big Data into a simple, scalable, repeatable action: “you can’t beat the model”
Validate, Surprise, Challenge
2A romp through the data fields:using big data to inform mediastrategy, planning and buying2Presented by:Doug Conely I VP, product strategy & operations@dougconely@exponentialinc