Measuring Brand Activity through RTB

Harley Norrgren studied Statistics at University College London and after a two year tenure is currently heading up Infectious Media’s Analytics Team. Being with Infectious throughout their entire working relationship with RTB he’s had the opportunity to watch the space develop since the start of RTB’s European adoption.

If you’re spending large portions of your media budget on brand activity, you’ll want to ensure that you’re getting the best performance for your money, yet the effect of brand activity is typically hard to measure as it poses a few difficult questions to advertisers:

• What metrics can we use for measuring brand engagement?
• How can we get access to user level data from predominantly offline channels?
• How can we integrate cross-channel data to get a full picture of the effect of our branding spend?

It is our proposition that buying via RTB on Ad Exchanges makes brand advertising measurable and increases the accuracy of any direct response path to conversion analysis and attribution modelling. We currently create bespoke branding metrics (in conjunction with our data partners) and marginal attribution models, which help to give advertisers better insight into the effect of their brand spend and facilitate optimisation towards branding goals. These bespoke metrics are called ‘Brand Units’, and are a standardised unit of any combination of different brand measurements, such as exposure time or brand related search uplift, which can either be a proxy for conversion or reflect other incomplete path brand objectives.

Exchange generated impression level data feeds provide unique opportunities for data providers to integrate their data with unprecedented granularity. We are currently working with data partners to provide us with impression level exposure time metrics, enabling us to optimise towards advertising face time and to buy display in terms of view duration rather than per impression. The combination of engagement, search uplift and on-site activity density means that we can measure brand effect on a per user basis, rather than resorting to surveys, and optimise campaigns against these metrics in real time.

The unique user level insights generated from brand activity can be coupled with post brand engagement and ultimately conversion/post-conversion activities, allowing us to fit brand activity directly into the conversion path, as well as attribution models, providing advertisers with more insight about the path to conversion than was ever available before from a single channel.

One of the main considerations about running brand activity through exchanges is the perceived ‘remnant’ status of most inventory available. It’s important to remember that just because an impression is remnant, it doesn’t mean it’s of poor quality. Through the use of ad exposure times we can identify and optimise towards the best value Cost per Face Time inventory sources, giving advertisers more clarity and better value than direct buying could. Furthermore, the presence of private exchanges and the market wide increasing adoption of ad exchanges means that more and more ‘premium’ inventory is made available every day. Via exchange buying, adverts can be purchased on premium inventory on a per-impression basis and made measurable enough to get a stronger indication of the real value of each advert shown.

These insights represent a large step forward for a communication goal which is generally hampered by industry standard attribution models. We hope that the unprecedented feasibility of marginal attribution models and access to user level data means that more opportunities can arise for branding activity to be bought on exchanges and start to drive a shift away from historically retargeting heavy DR budgets.

For more information please visit http://www.infectiousmedia.com/index.php?page=our-products

Measurement – The Elephant in the (Attribution) Room

Attribution is a massive issue right now and there are a number of innovative technology solutions that have been developed to give advertisers the ability to understand how different channels interact with each other. These solutions tend to focus on attributing value to impressions and clicks (interactions) further up the funnel and whilst this is a sensible step, it’s only half the story. It’s no good understanding that Display has a positive impact on Search without knowing how your activity can be altered to improve that impact. This has to start with measurement. We must delve deeper and assess whether these “interactions” were interactions at all.

Online was supposed to be easy. Actual, attributable intent and sales from advertising without having to spend the company’s pension fund on a piece of econometric analysis that you would neither understand nor trust. Last-click-wins gave us a benchmark to measure all our digital activity, allowing us to compare and contrast different channels and strategies in the same way.

But it was broken. Assuming that only the last click (or impression if no clicks were recorded) influenced a user to make a sale was not just wrong, it was misleading. Yet Display and Affiliate networks made huge sums building CPA businesses on this flawed methodology and when Search exploded on to the scene, no-one seemed to realise that Google had effectively stumbled upon the best exploitation of last-click-wins, using it to build the largest online advertising business in the world. Even now we have Criteo-style product retargeting and Affiliate voucher code sites that often snipe the last click like a seasoned eBay auction bidder, winning the best deals just before the timer runs out.

Times are changing, however. Marketers are now more savvy and are demanding a more accurate solution to the attribution problem. But before this can happen, we need to understand the complexities of online tracking a little more deeply.

When analysing online path to conversion data, you can typically find between 5 and 100 events that may or may not have influenced a customer before conversion. Since most adservers only record clicks and impressions, your conversion path will only include these metrics as events. Herein lies the first problem. Impressions are not ad views. In other words, just because your adserver has recorded an ad call, it doesn’t mean that the ad was actually served. Even if the ad was served, it maybe was not even seen, especially if served below the fold. In essence, although impressions appear more tangible, they are no more accountable than newspaper impacts.

The second problem is clicks. Clicks are seen as the only measure of engagement and intent online. But advertisers should be asking for more. Anyone who has done some click to page landing analysis will know you see anywhere from a 15% to 50% drop off. If your clickers aren’t even waiting until your landing page loads up, how engaged were they? Similarly, what about all the people that read the ad, maybe played with it a little, then returned to what they were doing before?

Clearly not all clicks/impressions are equal and on their own, do not provide us with enough information to form robust attribution models. Whilst you can create a model based on your current media activity, all it takes is some additional spend on some cheap, below the fold inventory or some incentivised click sites to skew attribution and devalue the sites that actually do generate true ROI. What is required is more information. Impressions need to be augmented with above / below the fold data, time and position on page and page context / quality. Clicks need to be supplanted by interactions and page landings. With these enhanced metrics we can begin to understand whether our adverts were indeed seen and how they engaged our target audience. Once we understand this, we can start to model it.

Luckily there are now many companies that can provide this data. The likes of AdXpose and Flashtalking can all provide interaction data and some of the impression tracking enhancements mentioned above. Page landings can already be recorded using existing pixel technology and many data companies such as Peer39 can provide page context. What stops us from running all these technologies across all campaigns now are the current incremental costs of such solutions as well as the technical difficulties in integrating this data with standard adserving data in one place.

Of course, having the capability to record this data is only half the story. Storing and analysing it at a time when most companies struggle to store and analyse their click and impression data is arguably a larger issue. Add to this the lack of statistical and analysis skills in most marketing departments, is it any wonder that marketers hide away from the problem and merely discuss the fact that last-click-wins needs to be improved but have no idea where to start?

Here is where RTB can help. RTB provides an environment that allows any company to exchange data with another, server to server, in order to better understand the impression being served. By providing the APIs to allow companies such as AdXpose, Peer39, etc. to integrate directly with DSPs and adexchanges, the integration problem goes away. Data can still be collected in two separate places but you have a common unique user id to match up the sets. Once integration is solved, costs can come down as more advertisers will take up the service introducing economies of scale.

This still leaves the data storage and analysis issue, but creating a fast and scalable storage and analysis infrastructure is not as difficult as it used to be. Companies such as Netezza and Greenplum can do it for you for a price. Alternatively, if you can afford the time to investigate and implement open-source platforms, solutions such as Hadoop and InfoBright can also work just as well.

2011 is going to be a year where these technologies combine to allow us to better understand all that our advertising is delivering. Soon there will be no excuse for marketers to stick with last-click-wins as we will be able to provide robust attribution models to support or oppose our hypotheses. When this happens, we will not only be able to better understand the value of events leading up to conversion, we will also open up the door to more branding activity being placed online.