Author Archive

Inefficiencies of Exchange Traded Media

January 21st, 2014

Encore’s latest POV on Inefficiencies of Exchange Traded Media was published by AdExchanger on January 21, 2014.  You can read the article on AdExchanger or read the POV below.

While exchange-traded media is praised for bringing  efficiency to the display ad market, a deeper dive reveals numerous factors are costing advertisers billions of dollars in wasted  spend.  While programmatic buying is relatively efficient (compared to other media), on absolute basis, a lot of wasted spend generally goes unnoticed.

Research shows that perverse incentives, a lack of controls and limited use of advanced analytical tools have made a majority of exchange-traded media worthless.  While we advance how we buy and sell media, there is still significant room for improvement in the quality and economic returns from real-time bidding (RTB).


Where Waste Occurs

Optimizing media starts with eliminating wasted spending. In the RTB world, waste can take many forms:

  • burningmoneyFraud: Either 1x1s sold into exchanges to generate revenue or impressions served to bots, or non-human traffic.
  • Non-viewable ads: These are legitimate ads that are not viewable by the user.
  • Low-Quality inventory: Refers to ads served on pages whose primary purpose is to house six, eight or more than 10 ads.
  • Insufficient frequency: Too few ads served per user – one or two – to create desired awareness.
  • Excessive frequency: Too many ads served to individual users – more than 100, 500 or more RTB impressions over 30 days
  • Redundant reach: Multiple vendors target the same users. This is often a consequence of vendors using the same retargeting or behavioral tactics to reach the same audiences.


Quantifying The Costs

The percentage of wasted impressions varies by campaign, but it’s usually quite significant. Here are some general ranges of wasted RTB impressions:

  • +/- 20% of exchange-traded inventory is deemed fraudulent, according to the Integral Ad Science Semi-Annual Review 2013[TH1] .
  • +/- 7% of viewable inventory is served on ad farm pages (more than six ads)
  • +/- 60% of legitimate inventory is not viewable per IAB standard
  • 10 to 40% of Imps are served to users with frequency that is too low to influence their behavior
  • 5 to 30% of Imps are served to users with frequency greater than 100 over the previous 30 days (the more vendors, the higher the waste due to redundant reach and excessive retargeting)

To put this in the context of a typical campaign, assume 100 million RTB Impressions are served in a given month.

RTB waste infographic crop


In most cases, less than 20% of RTB impressions are viewable by humans on legitimate sites with appropriate frequency. In other words, 20% of all Impressions drive 99% of the results from programmatic buying.  Because RTB impressions are so inexpensive, it’s still a very cost-effective channel.  That said, there is considerable room for improvement within RTB buying.

Who’s To Blame?

When we present these analyses to clients, the first question often asked is, “Who’s to blame?” Unfortunately, there is no single culprit behind the RTB inventory problem. As mentioned, the problem is due largely to a lack of controls and perverse incentives.

  • Lack of Controls: While a growing number of brands and agencies are incorporating viewability and using algorithmic analytical tools, most are still in the dark ages. Some feel their results are “good enough” and choose not to dig deeper. Others seem not to care. Hopefully this will change.
  • Perverse incentives: We live in a CPM world where everyone in the RTB value chain – save the advertiser) – profits from wasted spending. It’s not just the DSPs, exchanges and ad networks that benefit; traditional publishers now extend their inventory through RTB and unknowingly contribute to the problems mentioned above. While steps are being taken to address these issues, we’re not going to see dramatic improvement until the status quo is challenged.


How To Fix The Problem

The good news is that the RTB inventory problems are solvable. Some tactical fixes include:

  • We should invest in viewability, fraud detection and prevention, and algorithmic attribution solutions. While not expensive, they do require a modest investment of time, energy and budget. But when you consider the cost of doing nothing – and wasting 50 to 80% of spending – the business case for investing is very compelling.
  • We need to stop using multiple trading desks and RTB ad networks on a single campaign, or they’ll end up competing against each other for the same impressions. This will reduce the redundant reach and excessive frequency while keeping a lid on CPMs. It will also make it easier to pinpoint problems when they occur.
  • Finally, we need to analyze frequency distribution each month. Average frequency is a bad metric as it can mask a lot of waste. If 100 users are served only one ad, and one user is served 500 ads, the average frequency is six but 99% of those impressions are wasted. Look at the distribution of ads by frequency tier to see where waste is occurring.

For strategic change to occur, brands and their agencies must lead the way. In this case, “leading” means implementing controls and making their vendors accountable for quality and performance of display media.

  • Brands must demand more accountability from their agencies. They also need to equip them with the tools and solutions to address the underlying problems.
  • Agencies must demand better controls and make-goods from media vendors. Until we have better controls for preventing fraud and improving the quality of reach and frequency, media vendors need to stand behind their product, enforce frequency caps and make internal investments to improve the quality and efficiency of their inventory.
  • All buyers must make their DSPs and exchanges accountable for implementing more comprehensive solutions to address the fraud and frequency problems.


The Opportunity

We can’t expect a utopian world where no ads are wasted, but we can and should make dramatic improvements. By reducing waste, advertisers will see even greater returns from display media. Higher returns translate into larger media budget allocations, and that will benefit us all.

While fixing the problems may dampen near-term RTB growth prospects, it will serve everyone in the long run. Removing waste and improving quality of media will help avoid a bubble while contributing to the sustainable growth of the digital media industry.  Given the growing momentum in the public and private equity markets, I hope we as an industry take action sooner rather than later.

As always, comments are welcome.

Steve Latham

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Algorithmic Attribution SES Chicago

November 7th, 2013

Screen Shot 2013-11-07 at 11.29.01 AM At SES Chicago I introduced Algorithmic Attribution and discussed the implications for search marketers.  Please feel free to download and let me know if you have any questions!

Download pdf:  Algorithmic Attribution SESChicago2013

Steve Latham


Demystifying Attribution: Which Approach is Best?

June 23rd, 2012

As published by Adotas 6/22/12 (view article)

Digital media attribution is a hot topic, but it’s still a confusing concept for many. Given a lack of industry standards, and an ever-expanding list of attribution methodologies, it can be difficult for marketers to determine exactly what they need. This post intends to educate and enable marketers to optimize digital spend through advanced insights.

The Funnel and Attribution

Simply defined, attribution is allocating credit to each interaction that drives a desired action (visit, goal page view, conversion, etc.). Within this broad definition, there are two primary distinctions:

• Lower-funnel or click-based attribution incorporates assist clicks when allocating credit for conversions. Compared to last-click reporting, this is a step in the right direction. The limitation to lower-funnel attribution is that it severely discounts the role of display advertising while overstating the role of search, affiliate, email and other click-centric media. For online advertisers seeking a more complete picture, a full-funnel view is required.

• Full-funnel attribution builds on click-based attribution by incorporating assist impressions from display ads (video, rich media, flash and .gifs) when allocating credit for visits or conversions. Recognizing that display ads can be very effective, even in the absence of clicks, a full-Funnel attribution model is needed to quantify the true impact display ads have in creating awareness, consideration and preference.

Cross-Channel Attribution

Cross-channel attribution addresses the role of each digital channel (display, paid search, natural search, email, affiliate, etc.) plays in the customer engagement process. While conversion paths are interesting, they aren’t very actionable. To truly understand and optimize each channel, you must allocate fractional credit to each channel and placement that contribute to a measurable action. This generally results in a shifting of credit from non-paid channels (organic search, direct navigation, referring sites), back to paid media (display, paid search, email, etc.) that “fed” the non-paid channels. Before diving into weighting methodologies, let’s first look at leading approaches to attribution.

Three Approaches to Attribution

While there are many approaches to attribution, here are three you should be familiar with:

• Statistical attribution is based on traditional media-mix modeling, which relates to analysis of disparate data sets. In this approach, you would analyze three months of impression data and three months of conversion data and look for relationships between the data sets. At best, this approach can provide high-level directional signals. If you want granular insights into the impact of each channel, vendor, placement or keyword, you need a more granular approach.

• A/B testing seeks to attribute credit and validate causation by observing results from pre-defined combinations of media placements. A/B testing can be used to measure display’s impact on results from search, as well as the performance of a specific creative, vendor, market or channel. While A/B testing is a great way to observe directional insights, it’s nearly impossible to exclude or account for other factors (seasonality, competitors, macro-economics, weather, etc.) that might impact performance between the control and test groups.

Operational attribution takes a bottom-up (visitor-based) approach to analyzing and allocating credit to impressions, clicks and visits that precede each conversion. With operational attribution, there is no need to calculate possible conversion paths – you have the actual data, which provides for a more granular and accurate data set for advanced analysis. (i.e. heuristic and/or statistical modeling). As with any approach, caution must be exercised to define the appropriate look-back window and cleanse the data set and exclude factors (e.g. wasted impressions and post-conversion visits) that might skew the results.

Manual vs. Statistical Weightings

With the framework of operational attribution, weighing assist impressions and clicks is both art and science. Here are two primary approaches:

• Subjective weighting of impressions and clicks: First-generation platforms require the marketer to define the rules for allocating credit to each interaction. While this approach is easy and flexible, it lacks statistical rigor and entails too much guesswork. Moreover, it allows the operator to influence outcomes through the assumptions that drive the model. But the biggest problem is that it allocates credit based on the actual number of assist impressions, rather than using observable data to model how many are actually needed. For example, you may find that 12 impressions preceded an average conversion, when only six impressions were actually needed. If you give credit for wasted impressions, you end up rewarding vendors for over-serving customers.

To reduce subjectivity and improve accuracy in how impressions and clicks are weighted, we must use machine learning and algorithmic modeling.

• Algorithmic weighting: To remove the guesswork in attribution can use machine learning and proven algorithms to calculate probability-based weightings for assist impressions and clicks. This removes the arbitrary nature of manual weightings and provides much higher levels of confidence and comfort for marketers.

There are numerous approaches to statistical modeling and there are plenty of vendors vying for the “best math” award. While it’s hard to say which approach is best, we have seen that most prefer transparency vs. opacity, and known algorithms to proprietary models. If you’re going to put your neck on the line to defend a new measurement standard, you should be comfortable with the approach and underlying assumptions. In general, a transparent, statistically validated approach is best.

Understanding Tradeoffs

There is an endless number of attributes you can seek to measure, including segment, format, audience demographics, creative concept, message, frequency, day-part, sequence, etc.  While the idea of Metric Nirvana is appealing, it’s also very elusive if you try to get there overnight.

It’s important to recognize that the more granular you get, the more data you need and the more complex the setup, production, reporting and analysis. Many attempts to go directly from last-click to advanced micro-attribution fail due to the complexity of implementation and analysis.  We all crawled before we walked, and walked before we ran. Analytics should be no different.

If you’re still using last-click metrics, start with channel-level and publisher-level attribution. Once you’ve identified your top performing vendors, delve deeper sequentially (as opposed to all at once) by looking at recency, format, creative and other variables that may have incremental impact on the end results. Just remember that with each incremental value, the scale, complexity, and risk of failure increase dramatically. So start with the low-hanging fruit, and work your way up the tree.

To sum all this up, we are Encore and MediaMind advocate the following best practices:

Operational, full-funnel and cross-channel attribution.
Use machine learning to weigh and allocate fractional credit to assist impressions and clicks.
Improve your chances of success by starting with basics and getting more granular over time.

Hopefully you now have a better understanding of the attribution landscape and some of the distinctions within it.  As always, feel free to comment, tweet, like, post, share, or whatever it is you do in your own social sphere.  Thanks for stopping by!


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Takeaways: Display Ecosystem Panel Discussion

May 7th, 2012


Last month I had the pleasure of moderating the Display Ecosystem panel (View the Video) at Rapleaf’s 2012 Personalization Summit.  On my panel were experts from leading companies that represented numerous categories within the display landscape.  Panelists included:

  • Arjun Dev Arora – CEO/Founder, ReTargeter @arjundarora
  • Key Compton – SVP Corporate Development, Clearspring @keycompton
  • Tod Sacerdoti – CEO & Founder, BrightRoll @todsacerdoti
  • Mark Zagorski – CEO, eXelate @markzexelate

Our discussion addressed many of the issues that we are grappling with in the Ad-Tech industry, including:

  • Complexity: The challenges of planning, executing, measuring and optimizing display media are exacerbated by the complexity in our space.  How can we reduce the cost and level of effort required via integration, prioritization, standards, etc.?
  • Consolidation: What will the landscape look like in 2 years?  Will there be more or fewer players?  Where will consolidation take place?  Who will be acquired and by whom?
  • Effectiveness: What can the industry do to improve performance and effectiveness of advertising? How will better targeting, data-driven personalization, frequency management and 360 customer-centric approaches improve efficacy of online marketing?
  • Accountability: Where are the gaps today, and how should we be measuring results, performance, ROI, etc?• Outlook for publishers, ad networks, DSPs and agencies.  What must each do to survive / thrive in this hyper-competitive marketplace?
  • Other issues: privacy, legislation, new platforms, etc.  In order to fully realize the potential of display advertising (i.e. Google’s $200bn forecast) these will need to be addressed.

After our discussion, I thought about the implications for the Display Ad ecosystem, and for the Ad-Tech industry as a whole.  Here are a few of my thoughts…

  • No other industry is as innovative, adaptive and hyper-competitive as ad-tech. Where else can new niches evolve to multi-million dollar categories overnight with hundreds of startups raising billions in financing every year?  We’ve all seen industries where startups disrupted an established ecosystem for a period of time.  But where else does this happen over and over and over again?  Our industry is all about disruption and it doesn’t take long for the challenger startups to become the established incumbents or targets.
  • No other industry creates wealth like ad-tech.  Where else can companies launch, raise capital and exit for hundreds of millions (or more) in less than 18 months?  Where else are so many successful entrepreneurs (and their benevolent VC backers) rewarded with lifetime wealth for 1-3 years of work?  It’s pretty amazing if you think about it… our modern day decade-long gold rush.
  • Success in our industry requires mastery of several disciplines: marketing, technology and data science.  You can’t be a world-class ad-tech company without expertise and experience in all 3 of these categories.
  • While we are making progress as an industry, we still have so far to go.  Despite the advances in targeting, real-time bidding dynamic creative optimization, analytics and optimization techniques, most media buying is still done the same way it was 5 years ago.
  • There is still much confusion about how real-time exchanges work, and how they can be utilized by agencies and advertisers.  When you overlay that with efforts to aggregate 1st party data, creating proprietary cookie pools and using that data to find new audiences, many marketers become quickly overwhelmed.
  • We still have a scale problem that must be addressed.  While there is a huge supply of impressions available for real time bidding, there are only so many unique audiences in the warehouses operated by the data providers.  The more granular you get from  a targeting standpoint, the smaller your reach wil be.  Frequency capping is challenging, so you end up with hundreds or event thousands of impressions being served to a small pool of unique users.
  • We still have a people problem.  All the technology in the world won’t save us if we don’t have people trained to leverage these capabilities.  We also need a deeper pool of managers and leaders who can bring operational excellence to a fledgling, always-evolving industry.

The wall mural below sums up the discussion – and made for a nice graphic snack for attendees.


As always, feel free to comment, tweet, like, post, share, or whatever it is you do in your own social sphere.  Thanks for stopping by!


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Ad-Tech Attribution Case Study

April 25th, 2012

In April 2012 I presented a case study on Full-Funnel Attribution at the granddaddy of all industry conferences: Ad-Tech in San Francisco.

I was honored to share the stage with Young-Bean Song, a pre-eminent thought leaders in digital media measurement and analytics (and a very nice guy).  After years of applying to speak at Ad-Tech, I was finally selected; not because I’m the world’s most pre-eminent speaker but because the case study we developed is so effective at presenting how advanced analytics and full-funnel, cross-channel Attribution can be utilized to maximize performance and boost Return On Spend.

Among the highlights of the case study, we demonstrated:

  • How converters who were exposed to display ads followed a range of conversion paths before taking the desired action(s).
  • How attributing fractional credit for assist impressions and clicks (beyond just the last click) yielded much deeper insights into the performance of each channel, vendor, placement and keyword.
  • How recency, or the time lag between the first impression, last impression, visit and conversion) impacted performance.
  • How frequency is still a big issue that needs to be addressed – especially when buying exchange-traded media.

For those who didn’t make the show, I’m happy to share the case study in two formats (both are hosted on slideshare):

If you’d like to learn more about Attribution or discuss the case study, please drop me a line (see Contact link below).  Also please feel free to comment, tweet, like, post, share, etc. as you see fit.  Thanks for your time and interest!


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It’s Hard to Solve Problems from an Ivory Tower

March 2nd, 2012

Today a colleague sent me a link to a new article on Attribution and media measurement with a request to share my thoughts. Written by a statistician, it was the latest in a series of published perspectives on how Attribution should be done. When I read it, several things occurred to me (and prompted me to blog about it):

  1. Are we still at a point where we have to argue against last-click attribution?  If so, who is actually arguing for it?  And are we already at a point where we can start criticizing those few pioneers who are testing attribution methodologies?
  2. Would a media planner (usually the person tasked with optimizing campaigns) understand what the author meant in his critique: “the problem with this approach is that it can’t properly handle the complex non-linear interactions of the real world, and therefore will never result in a completely optimal set of recommendations”?  It may be a technical audience, but we’re still marketers… right?
  3. The article discusses “problems” that only a few of the largest, most advanced advertisers have even thought about.  When it comes to analytics and media measurement, 95% of advertisers are still in first grade, using CTRs and direct-conversions as the primary metric for online marketing success. They have a lot of ground to cover before they are even at a point where they can make the mistakes the author is pointing out.

In reading the comments below the article, my mind drifted back to business school (or was it my brief stint in management consulting?) and the theoretical discussions that took place among pontificating strategists.   And then it hit me… even in one of the most innovative, entrepreneurial and growth-oriented industries, an Ivory Tower mindset somehow still persists in some corners of agencies, corporations, media shops and solution providers.  Not afraid to share my views, I responded to the article in what I hope was a polite and direct way of saying “stop theorizing and focus on the real problem.” Here is my post:

“…We all agree that you need a statistically validated attribution model to assign weightings and re-allocate credit to assist impressions and clicks (is anyone taking the other side of this argument?).  And we all agree that online is not the only channel that shapes brand preferences and drive intent to purchase.

I sympathize with Mr. X – it’s not easy (or economically feasible) for most advertisers to understand every brand interaction (offline and online) that influences a sale. The more you learn about this problem, the more you realize how hard it is to solve.  So I agree with Mr. Y’s comment that we should focus on what we can measure, and use statistical analyses (coupled with common sense) to reach the best conclusions we can. And we need to do it efficiently and cost-effectively.

While we’d all love to have a 99.9% answer to every question re: attribution and causation, there will always be some margin of error and/or room for disagreement. There are many practitioners (solution providers and in-house data science teams) that have studied the problem and developed statistical approaches to attributing credit in a way that is more than sufficient for most marketers.  Our problem is not that the perfect solution doesn’t exist. It’s that most marketers are still hesitant to change the way they measure media (even when they know better).

The roadblocks to industry adoption are not the lack of smart solutions or questionable efficacy, but rather the cost and level of effort required to deploy and manage a solution.  The challenge is exacerbated by a widespread lack of resources within the organizations that have to implement and manage them: the agencies who are being paid less to do more every year.  Until we address these issues and make it easy for agencies and brands to realize meaningful insights, we’ll continue to struggle in our battle against inertia. For more on this, see “Ph.D Targeting & First Grade Metrics…”

I then emailed one of the smartest guys I know (data scientist for a top ad-tech company) with a link to the article and thought his reply was worth sharing:

“I think people are entirely unrealistic, and it seems they say no to progress unless you can offer Nirvana.”

This brings me to the title of this post: It’s hard to solve problems from an Ivory tower.  Note that this is not directed at the author of the article, but rather a mindset that persists in every industry.  My point is that arm-chair quarterbacks do not solve problems. We need practical solutions that make economic sense.  Unless you are blessed with abundant time, energy and resources, you have to strike a balance between “good enough” and the opportunity cost of allocating any more time to the problem.   This is not to say shoddy work is acceptable; as stated above, statistical analysis and validation is the best practice we preach and practice.  But even so-called “arbitrary” allocation of credit to interactions that precede conversions is better than last-click attribution.  It all depends on your budget, resources and the value of advanced insights.  Each marketer needs to determine what is good enough, and how to allocate their resources accordingly.

Most of us learned this tradeoff when studying for finals in college: if you can study 3 hours and make a 90, or invest another 3 hours to make a 97 (recognizing that 100 is impossible), which path would you choose?  In my book, an A is an A, and with those 3 additional hours you could have prepared for another test, sold your text books or drank beer with your friends.  Either way, you would extract more value from your limited time and energy.

To sum up, we need to focus our energies away from theoretical debates on analytics and media measurement, and address the issues that prohibit progress.  The absence of a perfect solution is not an excuse to do nothing. And more often than not, the perfect solution is not worth the incremental cost and effort.

As always, feel free to comment, tweet, like, post, share, or whatever it is you do in your own social sphere.  Thanks for stopping by!


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OMMA Metrics Panel Video: Social Media ROI

June 30th, 2011

Encore founder and ceo Steve Latham recently moderated the “Measuring Social ROI” discussion at the OMMA Metrics NYC Conference in March 2011.  The big questions addressed were:

1. Social Media: Shiny Object or ROI Producer?
2. What are brands doing to measure the impact of social ROI?
3. What works and how do you know?

These questions were discussed by industry thought leaders and expert practitioners from across the country including:

- Adam Cahill, EVP Media Director, Hill Holliday
- Ben Straley, CEO & CO-Founder, Meteor Solutions                                                                  \
- Jonathan Mendez, Founder & CEO, Yieldbot
- John Lovett, Senior Partner & Principal Consultant, Web Analytics Demystified, Inc.
- Jascha Kaykas-Wolff, VP of Marketing, Involver
- Moderator: Steve Latham, Founder and CEO, Encore Media Metrics

A video of the panel is embedded for viewing below.  You may also view it on ustream.


As always, feel free to comment and share!

The Encore Team

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Display Advertising Landscape

June 10th, 2011

In early June I was fortunate to be one of 350 ad tech CEOs who attended LUMA Partners’ Digital Media Summit in NYC, featuring the best and brightest in the industry.  I’ve been to some great networking events before (IAB, 4A’s, etc.) but this was tough to beat.

In addition to meeting some amazing people, one of the highlights was the release of the latest display ad landscape or “LUMAscape” aka “the slide” that was originally produced by Terence Kawaja in 2010.  For those who are new to display advertising (or have been out of the market for the last 3 years), buying display media is like buying a house: you also need phone service, internet, cable, gas, electricity, dog-walking, etc.  In this case, Media is the house; ancillary services include ad verification, OBA compliance, data/tag management, audience measurement, ad serving, and our favorite: attribution.

The newest version of the slide is getting ever closer to accurately depicting all the segments and sub-segments that comprise the digital advertising landscape.  It also marked the debut of Encore Media Metrics as a recognized leader in the Attribution and Measurement category.

“The Slide”may also viewed on slideshare or you can download the LUMA Display Landscape here.

The industry is extremely fragmented, and is likely to stay that way for a while.  So if you want to play in the display advertising space (either as a buyer, seller or manager) you need to understand the difference between a DSP, DMP and SSP without yelling “WTF!”  Yes, it’s easier said than done but this map should help you get started.

Steve Latham (@stevelatham)

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The Five Forces Driving Attribution: Media Measurement Comes of Age

May 27th, 2011

Advertisers are (finally) looking beyond the last click.  Here is an overview of the Five Forces that are driving adoption (also published by MediaPost in May 2011)

It’s been 3 years since measurement buzzwords “attribution” and “engagement mapping” emerged with great anticipation and excitement in online advertising.  The idea of looking across digital channels and beyond the last click to measure media throughout the funnel was thought to be the holy grail in online marketing.  Recognizing that “last-click wins” is insufficient for measuring the brand-building attributes of display media, brands, agencies and media vendors saw Attribution as the next big thing in digital advertising.

Yet as we entered 2011, very few marketers were using Attribution to measure and optimize online media spend.  Despite the universal desire for better measurement, most were still using old metrics (click-through rates, cost per click and direct cost per action) to analyze paid media.  Greg Papaleoni, who develops Analytics and Insights for Yahoo! Advertising Solutions, sums it up well: “While Full Funnel Attribution is the future of the ever-evolving digital media measurement landscape – it should be the present.  Those advertisers who embrace and implement this logic, methodology and technology sooner rather than later will enjoy a massive advantage over their competition.”

While adoption has been slow to date, this is changing quickly due to the convergence of numerous factors.  Borrowing on Michael Porter’s “Five Forces” model for analyzing industries, here is my take on the Five Forces that are driving digital media attribution (author note – I received permission from Professor Porter to adopt his model to this category):

1. The continuing shift of media budgets from traditional to digital.

While total U.S. media spend will grow only 3% in 2011, digital spend will grow 14%, surpassing Newspaper as the #2 medium.  Accounting for almost 30% of daily media consumption, Digital spend will continue to outpace all other channels for the foreseeable future.

2. The resurgence of display advertising

Per eMarketer, Display media spend will grow 14% in 2011, outpacing 10.5% growth in paid search.  While there are many reasons behind the growth (consumption of social, video and mobile content, better targeting capabilities, real-time bidding, richer formats, etc.) I believe the resurgence of display is driven by two primary factors:

  • The maturing of search: There are only so many searches every day, and most marketers have optimized their paid search efforts.  For the big advertisers, there are no more keywords to buy.  As one search exec was recently quoted “paid search inventory is maxed out.” Incremental dollars will have to go elsewhere.  Display is the obvious choice.
  • The return of branding:  As the economy recovers, marketers are re-investing in their brands.  During lean times, online dollars focused on harvesting existing demand (via search).  But with the improving economy, brand-building is once again a strategic priority.  In the digital realm, display media offers the most efficient, effective and scalable way to create awareness, consideration and preference for brands, products and services.

3. Increasing focus on accountability

While marketing budgets may have loosened, the focus on results has not.  As a result, marketers are keeping a very close eye on ROI from “brand-building” media.  With the ever-increasing need to show ROI, brands now want branding plus performance.  To properly measure brand-building media, we need to measure engagement, not clicks.

4. Evolution of web architecture

Recent forays by IBM and Oracle into the marketing arena signal a new wave in convergence of IT and Marketing.  As the IT behemoths push technology-based marketing solutions, CIOs are becoming more attentive to the needs of the marketing department.  The deployment of Data Management and Universal Tagging Platforms enable advanced analytics and media measurement that were off-limits to marketers in the past.  With this roadblock removed, the stage is set for new measurement tools to be deployed across their digital infrastructure.

5. The emergence of better Attribution solutions.

While early Attribution solutions were expensive and limited in capabilities (e.g. couldn’t attribute credit for organic conversions), a new breed of point-solution vendors (including my company Encore Media Metrics), are now offering more effective, flexible and affordable solutions.  For a very modest investment (as low as 1-2% of media spend), advertisers can now have a much more holistic and accurate view into the performance of each channel, vendor, format, placement and keyword.  These insights are enabling advertisers to optimize media budgets, yielding 20-40% gains in revenue.  The immediate return on investment in Attribution solutions may exceed 1-20x (100%-2,000%).

The Five Forces Driving Attribution are illustrated below:

Five Forces Driving Attribution

As our business objectives change, so must the manner in which we measure results.  As dollars continue to flow into digital, brands and their agencies must use more efficient, accurate and effective metrics for measuring media throughout the funnel.  The emergence of more advanced and affordable Attribution solutions, supported by growing support from IT departments is paving the way for Attribution to become a foundational component within the digital marketing ecosystem.

Matt Miller, SVP of Strategy & Analytics at Performics, agrees, stating “Attribution is one of the top priorities for us and our advertisers.  Focus on attribution will only increase as advertisers build and implement strategies to maximize ROI across all digital channels.”

As always, comments are encouraged. And please feel free to share!

Steve Latham (@stevelatham)

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AdExchanger Q&A with Steve Latham, Encore CEO

April 12th, 2011

Encore’s CEO was recently interviewed by AdExchanger, a leading online marketing  news publisher, about the launch of Encore, the problems we solve and how we’re positioned.  You can read the article on AdExchanger or see the transcript below.  Enjoy and feel free free to share!

Encore Media Metrics Incorporating Attribution For Paid, Owned And Earned Media

Encore Media MetricsSteve Latham is CEO of Encore Media Metrics, an attribution technology company.

So, what problem are you solving with Encore?

We solve 2 problems for agencies and brands alike.  First, we provide advanced attribution and measurement, enabling them to see across channels and beyond the last click to measure performance of paid, owned and earned media.  While most marketers are aware of the need for attribution, very few are doing it.  Second, we allow them to offload the tedious, manual work of reporting and measurement, which is a loss-leader for most agencies.  They need good metrics, but it’s hard to justify the cost of large teams needed to manage all aspects of reporting.  We offer a cost-effective way to produce the insights they need to optimize budgets and maximize campaign ROI.  So to sum it up, we provide better reports and deeper insights in a way that saves them time and money.

I believe we’re hitting the market at a great time and that this will be the year Attribution goes mainstream for a few reasons.  First, paid search is maturing and expanding digital budgets will have to be deployed elsewhere (display, social, mobile, etc).  There are only so many searches every day and most companies have optimized their ppc campaigns.  The low hanging fruit in search has been picked; further gains will be in much smaller increments and will require buying short-tail terms that start conversations rather close them (hence the need for keyword attribution).  This is supported by the fact that Display will grow faster than search in 2011 and is expected to outpace it for coming years. Search is still the big dog, but display and other brand-building media are nipping at its heels.  If you believe Eric Schmidt’s prediction of a $200 billion global display market, we’re still very early in this game. Other factors driving Attribution are the increasing focus on accountability, the upgrading of web architecture (e.g. adoption of universal tags) and the emergence of affordable attribution solutions – such as ours.  These factors are converging to make 2011 a very exciting year for those of us in the attribution space.

What’s your view on the competitive set and where you’ve been and how would you say you differentiate?

To understand how we’re positioned, you must first understand how the Attribution marketplace is segmented. For starters, there are two different approaches to attribution: operational attribution and statistical or algorithmic modeling. Each approach has its place and I believe they are more complementary than competitive. Statistical modeling analyzes vast amounts of data to look for correlations that indicate how media channels (display, search, email, affiliate, etc.) work together to drive results. Modeling allows you to see which channels feed each other, and which mix should yield the best overall ROI.

In contrast, operational attribution creates detailed records for each visitor that enable you to see which ads were seen and clicked on, how the visitor found your site, what pages they viewed and what actions they took. You can then query the data to analyze engagement paths and assess the performance of each channel, vendor, keyword and placement. We believe operational attribution is the foundation for advanced analytics as it’s based on actual visitor data (vs. a black box) and provides much more granular insights into performance of all types of media. Once you have operational attribution, you can then do advanced modeling of that data to glean additional insights. But, operational attribution will provide 80-90% of the insight you need to optimize your spend.

Within the Operational segment, you then have to look at the extent of attribution: lower-funnel (click-based) vs. full-funnel (clicks and impressions). While click-based attribution is better than nothing, it doesn’t answer the question: “which media buys are creating demand?” The lower-funnel approach relies on clicks, which may be great for search, but insufficient for measuring the impact of display media. If you want a true picture of which ads are creating demand and which placements are satisfying demand, you need a full-funnel solution.

Now to the original question: how are we positioned vs. our competitors? While I can’t speak for our competitors, I can say we differentiate in a few ways: 1) we incorporate attribution from social media (even in the absence of referring clicks), allowing us to provide attribution for paid, owned and earned media, 2) we have a flexible approach that is designed to accommodate varying needs of agencies and brands (no long-term commitments, pay for what you use, etc.), and 3) we are affordable for most marketers. If a client spends between $50,000 and $5 million per month in online media, they can afford our solution.

Is scale of ad spend critical to Encore’s services – attribution, media mix modeling?

If you’re asking is Attribution is only suited for the biggest advertisers, the answer is no. It really doesn’t matter how much you spend; you still need to look across channels and beyond the last click to optimize your mix. Even if you’re only spending $50,000 a month, a small incremental investment can yield a dramatic improvement in Return on Spend. Any advertiser who is buying more than just search is going to benefit from Attribution.

What’s your view on the “view‑through conversion”?

View-throughs are good for ad networks seeking to optimize their media placement, but they are limited in what they offer advertisers.  If you are buying display media from 5-6 vendors, you’re likely to get some view-throughs from each buy.  While view-throughs tell you if an ad was seen they don’t tell you which ads were the most effective (and cost-effective) in creating demand, or how each media buy influenced results from paid or natural search.  You can’t analyze recency or frequency and you can’t tell the order in which ads were viewed.  You need more details to truly understand which placements created demand, the role they played in the engagement path, and how to attribute credit within the channel.  Yes, you need a full-funnel attribution solution.

What’s the difference between attribution modeling and media‑mix modeling?

In the context of measuring the impact of digital media, they’re effectively the same thing.  But for most marketers, media‑mix modeling encompasses all channels, including TV, print, radio and other traditional media.  Within that context, operational attribution should play an important role in providing the inputs that go into such a model.  We can provide a much more accurate and richer set of data inputs that will enable the global media mix model to produce more relevant and insightful outputs.  As mentioned earlier, it shouldn’t be “either / or” when evaluating operational vs. algorithmic attribution.  They can work in concert quite well.

What do you see out there as the most difficult channel to provide the sort of service you’re providing today?

Within digital media, Social is definitely the hardest to measure.  First, referring clicks are not good indicators as very few actually click-through from social sites to the brand’s web site (see “Connecting the Dots”).  But beyond clicks, how do you attribute credit back to people who are watching your You Tube channel, viewing comments on your Facebook page or reading a blog about you?  It’s hard because you can’t cookie browsers on 3rd party social media sites.  While Facebook now allows marketers to set cookies via iframes on company pages, very few are doing it.

Some try to do social attribution via correlation or looking at directional trends, where a social mentions drove a spike in traffic and a lift in conversions.  But this approach is, in technical terms, “squishy.”  For most, social attribution is a future goal more than a near term objective.

But since you asked, I should mention that we offer a unique solution to the social media attribution problem. We use a patent-pending tool that allows us to identify which visitors or purchasers have engaged with the brand in social media, regardless of whether or not they clicked through to the site.   Through this, we can draw a direct line between online conversions and the social interactions that preceded them.  We think it’s pretty cool and we’re seeing a lot of interest from brands, agencies and media vendors.

Do you see social media attribution as an opportunity?

It’s definitely something we see as a differentiator but it should be viewed as part of our solution for two reasons: 1) social should be integrated with other channels from a measurement perspective, and 2) it’s hard to make a ton of money on social media measurement.  A brand may spend $500-$1,000 to measure social interaction, but they’re not likely to spend more on the tool than they do on their social media marketing efforts.  You also don’t want to be a one-trick pony in the digital landscape.  Things move too quickly and one player (e.g. Google) can make render your product obsolete overnight.  So we see it as a differentiator and a conversation starter more than a standalone offering.

What is Encore’s target market?

We serve brands and agencies who are seeking to create demand and/or drive sales through paid, owned and earned digital media.  While we can accommodate budgets as low as $50k per month, our sweet spot is campaigns with budgets of $100,000 to $2 million per month.

In general, Attribution tends to be more appropriate for considered purchases, e.g. financial, auto, travel, health care, luxury goods and anything B-to-B.  The longer the sales cycle and the bigger the ticket, the more you need Attribution.

We work with brands, agencies and trading desks of all sizes, even those with internal ad ops teams.  Even if they have a bench, they still need better tools to produce the insights their planners and customers demand.

How does pricing work? Do you charge on according to media spend or is it a per seat?

We price our solution as a technology (vs. a flat % of media spend) that is tiered based on the scope and scale of the campaign.  In general, we charge a fixed fee that covers the planning, production and client services, along with a cpm-based fee that covers the cost of data capture, storage and analysis.  The fee as a percentage of the media budget will vary significantly.  If you’re buying premium placement media at $10cpm, our fees are tiny.  If on the other hand you’re going for scale (e.g. $2cpm), the fee will be slightly higher as a percentage of spend.  But in either scenario, we’re very affordable and the ROI is hard to beat.

What sort of milestones would you like the company to have accomplished?

My primary goal for 2011 is for Encore to become widely known as a leading provider of measurement, attribution and reporting services.  If there is a discussion about Attribution, I want us to be one of the solutions that are always mentioned.  Our value proposition (better reports, deeper insights, affordable and adaptable) is hard to beat, and we look forward to proving it to leading brands and agencies.

Follow Steve Latham (@stevelatham), Encore Media Metrics (@EncoreMetrics) and (@adexchanger) on Twitter.