Industry Ponders Whether The Current Attribution Model Needs To Be Addressed

Industry Ponders Whether The Current Attribution Model Needs To Be Addressed

Posted: April 5th, 2011 | Author: ExchangeWire | Filed under: Ad Network, Agency, Attribution, Data Strategy, Online Advertising, RTB | http://www.exchangewire.com/?p=8683">View Comments

The post view impression is a contentious conversion metric in ad land. Most ad networks make their living out of it. And it’s how the majority of agencies justify their DR display spend to advertisers. But questions are being raised about its viability from a number of senior ad execs. Cookie bombing is rife in the display marketplace with ad networks, DSPs et al chasing the last impression. More often than not the wrong vendor is being awarded the conversion – but current attribution models are failing to pick up on it. ExchangeWire asked a number of senior advertising players for their opinion on the current state of attribution in the European display marketplace.

The question posed: does the current attribution model for display and digital need to be addressed – and what do you think the industry will have to do to to fix some of the current failings?


Jon Baron, General Manager, TagMan

“Wherever the current attribution model is last click, then, yes, it needs to be addressed. Last click has driven the industry for 10 years and it has meant that certain channels and types of campaign have gained disproportionate credit and investment. It’s completely and, in our view, unfairly underpinned development in the digital industry.

But, now the tools exist for agencies and advertisers to understand how all channels play a role in delivering customers and to reward them appropriately. Consider digital where, for example, display campaigns that deliver the first touch point are actually awarded a share of the credit and commission of the final sale for doing so. It’s completely transformative.

The key for the rest of the industry is to help drive, not hamper the shift. We’re already seeing that clients are looking for providers that can help them develop better attribution models with strategic, big picture advice and analysis. Those that are still seeking to protect their piece of the pie in conflict with the advertiser’s best interests are being given short shrift.”

Daniel de Sybel, Director of Technology and Operations, Infectious Media

“As alluded to in my recent attribution post on the Infectious Media blog, relying on last-event-wins leads to networks working to game the system and attempt to run campaigns that display ads just before a user was going to convert anyway. Unless this changes, there will always be a lack of trust in the metrics used to measure campaign performance.

Fixing this needn’t be too hard. By first addressing the measurement issue, marketers can begin to have more faith that their online ads are engaging prospective customers.
Understanding this alone would answer many of the critics of post-view conversions since you could tangibly measure whether the ad was actually seen or not.

But we can do better than this. Once you have your augmented impression and click data, you can begin to build a pattern of user behaviour to determine which ads from which channels actually made a difference to a user’s propensity to convert. This forms the basis of your attribution model which would vary depending on advertiser and product.

However, until a repeatable, standard methodology for developing these models can be agreed, marketers will always resort to their old, dependable last-event-wins. At Infectious, we aim to help drive the move towards standardising better attribution models so that all marketers can feel confident in the power of their advertising.”

Paul Silver, Associate Director, Starcom Mediavest Group

“I firmly believe that without a new robust method for measurement, display advertising is dead. No one clicks on ads, but that’s not to say it has no affect or doesn’t work. Current post view methods of measurement are not right either; we’ve created a monster. Everyone operating in this space is competing (to put it mildly) to be attributed the last click or view; it’s not conducive and I’d argue it’s not adding any real value. I am a big fan of likelihood and propensity models and this is something we are exploring right now, something that can be actionable everyday.

More broadly than that, we also now have a new product around cross media attribution modelling called PathFinder. I am a huge advocate of attribution modelling but the approach and model has to be data driven. I am not convinced either that buying some tech and selecting a pre-defined model to re-attribute and re-scale conversions is helping but perhaps its better than nothing?

I hear calls that attribution modelling should be standardised, but maybe it shouldn’t. If every advertiser has a unique way of re-attributing performance, maybe it will ensure we maintain a varied and dynamic use of this channel and the accompanying technology utilised to optimise. But we have to act soon. Staff up. Invest. Start making this happen. It could be the end of display if we don’t.”

Adam Pace, Managing Director, Access

“My view is that the attribution model is set by the agency and the client. This is done in context of the consideration cycle for each product and the price of other media and acquisition channels. The agencies have teams of planners, econometricians and research teams to set the flighting and weights of their media campaigns. We have to remember that it is not just the RTB element of digital campaigns that run as a result of all this work. TV, sponsorship, print ads and poster campaigns are all fighted to maximise consideration and purchase. At Access we simply optimise as briefed by the agency and the client.

In practice the amount of retargetting that goes on means that it can be really hard to show any benefit to display ads earlier in the purchse cycle or higher up the funnel, but isn’t that what TV and posters are for? We can do this in display, but we have to be really clear with the planners how it may affect CPA in the short term. Then it’s up to them how they use this infomation. That’s why it’s really important for us to work closely with the OMG agency planning teams.”

Erich Wasserman, Co Founder & GM, EMEA, MediaMath

“This is a great question and the short answer is: attribution definitely needs attention.

On a macro level, the obvious client question is: how is the totality of my spend accruing to sales? The answer is that different marketing channels (online, offline) naturally engender different levels of engagement and response. These channels should be utilised in different ways, according to distributed value. Searching for a product, for example, is generally the last thing users do before buying a product. We all know this and do this ourselves. We all also know that users need to be exposed to that product prior to searching for it.

When disparate digital media tracking systems (ad servers, affiliate networks, search providers, premium display, media networks, ad exchanges etc.) provide attributed sales analyses in isolation, the macro story is lost… to all of our detriment. Unfortunately, this is typically the way that media is bought and analysed. It is therefore typically the way that advertising budgets are allocated. Suffice it to say, we have seen, consistently and across clients, that turning on Display advertising (rich media, video, standard banners) increases Search efficiency — in addition to providing masses of new sales from display itself. Channels work together to grow sales for the client. The same goes for TV spend plus display – this even though I’ve yet to successfully click on a TV ad… working on it.

On a micro level and with regards to display, the client sees this channel vying for last-click or last-view attribution. Display typically uses an ad server to determine who clicked or saw an impression last, and on the basis of that last action, determines attribution to ad tag placement. Paired with standard methods of buying inventory, in which individual media properties or networks are given individual ad tags, the result is a war not over effectiveness of advertising nor the right price to pay for more qualified or less qualified users, but about who can get the last ad in front of the user last. The by-product of this dynamic is the need for massive volumes of media, which, to be efficient, must be priced lower and lower. Calling your media partner and getting them to lower your eCPMs becomes the name of the game. And they tell you to please turn off other high reach sources as their efficiency will undoubtedly increase. And on and on. Fun for all!

With the right systems in place, you can get control of attribution – cross channel. But if you don’t have the cross channel mandate, you can always look at the display channel in isolation and show incremental impact as a function of time since ad exposure. Results can be granular (hourly, even) and show the natural effects of ad effectiveness over time.”

Doug Conely, Senior Director for Global Data & Targeting, Tribal Fusion

“Display attribution models do need to make a leap forward but so does adoption of those models, even just of current best practice. Fortunately, there are lots of good things happening and the fact that attribution is now a topic in every conversation is a major step in the right direction that we wholeheartedly support.

We would like to see an end scenario where clients are able to pass us a dynamic feed of information on what events in the user journey have highest weighting in their attribution model and for our algorithms to optimise to those weightings; where every participant in the mix from paid search to social media to display is measured and rewarded accordingly both on client site and, where possible, off site; and where planners and buyers get easily understood, actionable recommendations without manual intervention.

There are major issues to be solved to achieve this around pixel distribution for measurement (unless anyone knows a better way), the cost of developing the product/ infrastructure to support this and standardising the outputs.

As I said at The Data Economy: I’d rather we didn’t leave work on this end scenario to Google. We’re happy to play our part in driving change and have internal projects so that we can contribute to the debate.”

Brian Fitzpatrick, Managing Director, MIG UK

“The best thing about Internet advertising is that you can track everything, on the other hand, the worst thing about internet advertising is that you can track everything.

While there is an enormous amount of data about online campaigns being collected, the current model used for attribution based on last action does not always give us the clearest picture of where the credit should be directed.

Often the media owners with the highest reach are the ones assigned credit rather than, for example, a site where a customer reads a product review and is convinced to buy the item, this lack of insight often results in quality sites being taken off plans.

As a response to the limitations of the current system and the lack of insight it provides agencies and clients, we are seeing the rise of dynamic attribution modeling, looking back at the user journey and dwell time to build a clearer picture for actual contribution, the results can be startling.

The technology we use to track, collect and action information about campaigns has come a long way, the new data companies entering the market should help shed more light on where customers are, however without the correct interpretation and analysis of the data by media planners and clients, budgets will still be directed towards what’s most obvious rather than what’s most effective.”

Ian Dowds, Vice President UK, Specific Media

“Attribution is not a challenge unique to digital. The entire advertising business has long been searching for a method of measuring the individual and cumulative effectiveness of each ad, in each medium, in driving awareness, consideration, engagement, purchase and re-purchase.

The digital channel has been fairly unique (along with direct mail) in delivering campaign measurability, in terms of message delivery, action and acquisition, with the click tending to be central to attribution models. That measurability has understandable appeal to marketers. But hanging everything on the last click fails to recognise that the digital channel works in conjunction with all other channels. No medium works in isolation and no campaign, be it display, video, search, mobile etc., works independently in the marketing funnel.

Technology does exist that enables fairer attribution measurement. The challenge is that not everyone is privy to all the data. Fair and agreed attribution becomes impossible: discrete channels have discrete measures of effectiveness – despite being part of a single campaign.

An agreed holistic approach to measurement is needed. It’s a difficult but not impossible task. Advertisers, agencies and sales organisations must engage with each other to create an integrated system that fairly, if not perfectly, attributes at least some credit where due.”

Cross-Visit Participation [Inside Omniture SiteCatalyst] | Omniture Industry Insights

Cross-Visit Participation [Inside Omniture SiteCatalyst]

Monday, 9 March 2009 @ 7:23, by Adam Greco

In one of my earliest blog posts, I discussed how Conversion Variables (eVars) have different types of allocation.  For example, if someone comes to your site multiple times from a few different campaign tracking codes, you can choose to give credit for any Success Event to the first tracking code or the last tracking code using allocation.  The same is true for every eVar, but there may be times when you want to see all of the values passed to an eVar prior to the Success Event taking place.  In this post I will explain how to do this in SiteCatalyst through Cross-Visit Participation.

Cross-Visit Participation
So what is Cross-Visit Participation?  In Omniture-speak, it is a JavaScript plug-in that concatenates a series of values into an eVar for attribution purposes.  As alluded to above, the most common use of this plug-in is for what we call Campaign Stacking.  For example, let’s say that a visitor comes to your site from a CNN Display Ad and the tracking code is “cnn_123″ and the same visitor comes the next day from a Google Paid Search keyword with the tracking code “ggl_456″ and then applies for a credit card.  In this case, if you were using the Campaign Conversion Variable (eVar) and the allocation was set to “Most Recent” then the credit card application success event would be attributed to the Google keyword.  However, that visitor may never have conducted a search on Google had he/she not seen the display advertisement on CNN so we may inadvertently reduce the budget for CNN and increase it for Google only to be surprised later that we are not getting as many credit card applications as before.  Sometimes a little (incorrect) data is worse than no data at all!

Omniture is well aware of this attribution issue and is taking steps to improve the overall accuracy in the future.  In the meantime, the Omniture Consulting team has created the Cross-Visit Participation plug-in (sometimes referred to as Campaign Stacking) that lets you see the entire picture.  So in the preceding example, while the Campaign eVar would always have the latest tracking code value, the plug-in would pass the historical string to a custom eVar:

By doing this, when the credit card application success event is fired, the Google tracking code will get credit in the Campaigns report, but in the eVar12 report, the string “cnn_123:ggl_456″ would get credit so you, as the online marketer, could see what percentage of all credit card applications involved each combination of tracking codes.  This information can then be moved to Excel using the ExcelClient, where more in-depth analysis can take place.

Variations of Cross-Visit Participation
However, the value of this plug-in reaches far beyond the realm of campaigns.  There are many fun and creative ways you can use this plug-in such as:

  1. Tracking which site tools/calculators visitors used prior to success
  2. Tracking which products visitors viewed prior to success
  3. Tracking which videos visitors viewed prior to success
  4. Tracking which search terms visitors entered on your site prior to success

The list goes on and on.  Any time you want to see a concatenated list of values in the order that they took place, you can consider using the Cross-Visit Participation plug-in.

Important Things To Know About Cross-Visit Participation
The following are some important things to know about Cross-Visit Participation:

  1. You need to work with Omniture Consulting to set-up this plug-in
  2. You must specify the maximum number of values you want to concatenate in the plug-in and the time period for them to be considered germane in impacting conversion
  3. The plug-in will not store duplicate values

Real-World Example
In this real-world example, I will build upon the preceding campaign example by using the Cross-Visit Participation plug-in to capture Marketing Channels.  Let’s imagine that your CMO comes to you and wants to know how often Greco Inc. is paying to drive people to its site from various Marketing Channels.  The CMO is primarily interested in seeing if paid acquisition converters are always coming to the site first from Paid Search and then Paid Display or vice versa.   Due to budget cuts, ad spending may have to be cut back and the CMO needs to justify which channels and channel combinations are important to continuing to drive applications.

To answer this question, you would need to classify Campaign Tracking Codes by Marketing Channel and use Cross-Visit Participation to “stack” Marketing Channels (i.e. Paid Search, Display Ad, E-mail, etc…).  The classification of Campaign Tracking Codes by Marketing Channel should be straightforward (if you have read the SAINT Classifications post).  The second item needed is a concatenated string of “stacked” Marketing Channels so you can see the interplay between them prior to website conversion.  There are many ways to “stack” the channels ranging from creating SAINT Classifications of stacked campaign tracking codes to capturing the channel by using the Get Query Parameter plug-in (I suggest you work with Omniture Consulting to identify the best approach for your organization).

Once you have both of these set-up, you are ready for analysis, so let’s dig in.  Let’s say that Greco Inc. has a Campaign Channel Conversion report, which is the report of classified campaign tracking codes, set to “Most Recent” allocation as follows:

As you can see in this report, only 16.49% of Completed Applications are coming from Paid Display as compared to 66.59% coming from Paid Search (in this case, let’s assume e-mails are inexpensive) so your first thought might be to cut the Paid Display budget.  However, if we look at the report that shows the “stacked” Marketing Channels described above, we can see the interactions that visitors took with all Marketing Channels prior to completing an application as shown in this report:

When we look at this report we notice something interesting.  Display Ads are often times part of the mix that leads to success, but Paid Search is most often the one that is the last touch prior to conversion.  In fact, if we add up all of the times that Paid Display is part of the mix (or used individually) the percent impacted is 47.43%.  If you only looked at the first report (Campaign Channels) you would see a skewed picture since the “Most Recent” allocation would attribute most of the success to Paid Search.  Thus, you have to ask yourself, how much of my success would still come through if Paid Display were not in the mix?  At 16.49% you might be willing to take the risk to save some money, but at 47.43%, you may not be willing to do so and might consider some more in-depth testing prior to making any rash marketing spend decisions!

This is just one example of how Cross-Visit Participation can add more context to your analyses so that you can make the best marketing decisions possible.

Have a question about anything related to Omniture SiteCatalyst?  Is there something on your website that you would like to report on, but don’t know how?  Do you have any tips or best practices you want to share?  If so, please leave a comment here or send me an e-mail at insidesitecatalyst@omniture.com and I will do my best to answer it right here on the blog so everyone can learn! (Don’t worry - I won’t use your name or company name!).  If you are on Twitter, you can follow me at http://twitter.com/Omni_man.

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Attribution Modeling ~ Banners, Blondes, & the Bottom Line | Last Click News

Attribution Modeling ~ Banners, Blondes, & the Bottom Line

Published on February 22, 2011 by Mark Hughes   ·   No Comments

C3 Metrics CEO on solving the mystery of your missing profits

Raymond Chandler was the archetype detective storyteller, fond of using hard-boiled heroes, cold-blooded “thugs,” and hot-to-trot “dames” as his characters. One of his favorite quotes was: “I do a great deal of research…particularly in the apartments of tall blondes.”

But solving mysteries with diligent research is also part of our daily regimen in online advertising. Every network, keyword, and overall campaign is a puzzle that sometimes changes week to week. The puzzle is solved with attribution modeling, but we’re just now scratching the surface.

We’d like to think research methods we employ provide facts relevant to “our case.” But, incorrect facts will “never hold up in court.” Today, the facts are wrong.

Despite that Internet advertising (a $70 billion/yr global industry) is the most trackable form of advertising on earth–the facts determining success of those billions of dollars, won’t hold up much longer.

Why? Sadly, today’s outdated online ad tracking systems erroneously give 100% credit to the very last clicked or last viewed ad before a transaction.

Mark Hughes, CEO
C3 Metrics

Example: if four Internet ads contribute to a transaction; today’s outdated systems allocate entire credit to the very last ad, completely ignoring the first three ads which actually drove the revenue.

Zero credit to revenue drivers, and 100% credit to the last ad placed. It’s like discovering the prosecutor put away the wrong guy. Bad facts, bad decisions, bad outcome.

Members of the jury…this is a $20 billion global problem.

Now enter attribution modeling: at C3 Metrics (disclosure, I’m the CEO), a robust attribution model takes an enormous amount of ingredients, and reduces complexity to simplicity.

At a basic level, C3 Metrics’ attribution modeling system assigns credit to Originators, Assists, and Converters within a transaction. An attribution model should capture every online media source from the top of the funnel where sales originate…down to the very bottom of the funnel. So in a $100 transaction, an Originator would receive a fraction of $100 attributed to them—and the Assist and Converter would also receive fractional credit of the $100 attributed respectively.

100% of revenue credit is attributed and split among Originators, Assists, and Converters–accounting for the actual drivers of revenue. Then revenue and respective costs from paid media sources converge in a single, elegant ratio in the attribution model: Attributed Revenue-to-Spend Ratio (ARSR™).

It’s a simple ratio any marketer can grasp: attributed revenue divided by corresponding spend. If you have a 4.0 ratio for a specific keyword, or specific Display campaign–you’re getting $4.00 in revenue for every dollar spent on that particular media source. If you have an ARSR of 1.25 for a particular media buy—you’re getting $1.25 in revenue for every dollar spent there.

For brands that don’t transact dollars on their site, they simply assign a revenue value for: a dealer zip code lookup, configuring a vehicle online, or scheduling an appointment online.

ARSR delivers knowledge ready to act on, versus information barely ready for analysis. The special sauce of the attribution model is the numerator of the ratio (attributed revenue). Media buyers easily identify media sources with high ratios to scale, and low ratios to cut or improve. Instead of taking weeks to analyze, it’s about an hour.

But the jury wants the facts, and here they are: in the longest running attribution modeling study of its kind (2 yrs) the results are enough to get anyone promoted:

a) 25%+ higher revenue on same ad-spend producing millions of dollars in incremental profit
b) Display ROI improvement of 160%
c) Search ROI improvement of 98%
d) Accurate economic model to measure affiliate performance

Case closed. The verdict: millions in profit added to the bottom line.

Are you ready to solve your case with the right facts?

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Attribution Technology: What’s Best For Your Needs?

Attribution Technology: What’s Best For Your Needs?

Apr 9, 2010 at 4:34pm ET by Adam Goldberg

A few months ago, I wrote an article titled Attribution: What It Is And Why It’s Important where I discussed two types of attribution: operational and project based attribution.

For this post, I want to go one step further and explain how you can use several different types of technologies for operational and project-based attribution. The tables below should help you select the most appropriate technology based on your own attribution needs.

Operational attribution allows an advertiser to see all the steps or clicks that led to conversion in real-time and continuously attributes conversion credit across the team of ads. The three most common technologies used for operational attribution are display ad servers, website analytics and advertising analytics.

Technology Pros Cons Audience

Display Ad Servers

Low level implementation; see how display clicks and impressions work alongside PPC. Focused more heavily on paid traffic sources such as display and PPC, and typically excludes other ad sources; revenue focus on ad delivery with limited insight into organic channels. Those with mainly display focus in marketing mix; focus on paid channel overlap.

Website Analytics

Strong digital channel coverage; ability to data mine against site traffic and CRM data. Heavy implementation effort; limited ability to de-duplicate post-impression data at user level. Current users of site analytics; those who have a limited desire to understand post-impression data.

Advertising Analytics

High level of accuracy due to consistent tracking methodology; ability to manage large volumes of data from internal and external systems. Moderate implementation effort; incremental investment to site analytics or ad serving. Those who desire complete channel coverage of digital landscape; those who seek to tie in product, customer and ad creative analytics into the overall value equation.

Project based attribution focuses on your overall marketing program and produces an optimized marketing spend plan, and its solutions include both technology and service providers. For project-based attribution, there are two commonly used technologies: business intelligence and advertising analytics.

Technology Pros Cons Audience
Business Intelligence Ability to pull in and interpret data from disparate sources; manage large volumes of data. Incremental expense to ad serving and site analytics; data from disparate sources can create accuracy and de-duplication concerns. Those who don’t have access to production site; those who are confident in “lift” metrics as opposed to actual metrics at the most granular level.
Advertising Analytics Accurate dataset to conduct comprehensive statistical modeling; ability to translate statistical analysis into day-to-day channel management. Requires code on site; incremental investment to site analytics or ad serving. Those who seek day-to-day dash boarding of their attribution efforts; those who seek to tie in granular data as well as larger econometric data into the equation.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Speakers posted for Search Engine Land’s SMX West search marketing conference – March 8-10 in San Jose. Check out the agenda!

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Adam Goldberg is the Co-Founder and Chief Innovation Officer at ClearSaleing, Inc., a technology provider of advertising analytics. Adam founded ClearSaleing after working at Google for 3 years, where he started their Inside Sales organization.

See more articles by Adam Goldberg >

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Interesting comparison between 'Operational' and 'Project based' attribution approaches.

Measurement – The Elephant in the (Attribution) Room | Infectious Media: Audience Targeted Display Advertising Specialists

Measurement – The Elephant in the (Attribution) Room

January 27, 2011 · Print This Article

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.

Written by Daniel de Sybel · Filed Under data, exchanges, tools 
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