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!

@stevelatham

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):

• View the Presentation: Ad-Tech Attribution Case Study
• View the detailed writeup: Encore Attribution Case Study

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!

- @stevelatham

Latham to present Beyond the Last Ad: Better Decisions Through Better Attribution

Conversion reporting has become an everyday part of how campaigns are optimized.  As digital budgets grow in size and complexity, collective skepticism continues to build against the current “Last Ad” reporting standard.  The demand for more advanced attribution methods has spawned a host of new analytics and technology capabilities, yet going beyond the Last Ad has yet to “cross the chasm.”  Spiritually – advertisers, agencies and publishers are on board – but their reporting and optimization methods have yet to budge.  Led by Young-Bean Song, an expert in digital advertising effectiveness research, this session will reveal how current standards bias our view of the digital marketing world, and our spending as a result. We’ll also feature the latest data and case studies that quantify the impact of new attribution models.

Key takeaways:

• Learn how leading brands are tying display ads directly to purchases
• Discover pre- and post-campaign testing methods that are cost-effective and easy to execute
• Get insight into some of the most cutting-edge attribution research

Session Leader: Young-Bean Song, Principal and Founder – AnalyticsDNA

Presenter: Steve Latham, Founder and CEO – Encore Media Metrics

Session:

ad:tech San Francisco
Tuesday, April 3 at 3:45pm
http://na.ad-tech.com/sf/sessions/i-love-data-attribution-and-online-display-advertising-beyond-the-last-click/

MediaMind announced today its partnership with Encore Media Metrics

Read the Press Release

Read CEO Steve Latham’s Guest Post on MediaMind’s CreativeZone!

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!

@stevelatham

For starters, let’s define Attribution is simply the process of attributing credit to each interaction in a user’s path to conversion.  These interactions may include display ads, paid searches, natural searches, emails, social and other media.  To truly optimize your online marketing efforts, we must measure each channel, vendor, placement and keyword’s contribution, and give appropriate credit in the final analysis.  While the industry generally agrees on the problem (last-click measurement is woefully insufficient) and the objectives (give credit where it’s due), there are many divergent opinions on which approach is best for solving this problem.  With the goal of illuminating and educating (vs. selling) here is my perspective.

Analyzing Conversion Paths

Conversion path analysis is quite popular these days and is usually at the top of marketers’ wish lists.  Not to be confused with site-specific conversion analysis, media-centric “conversion path analysis” looks at the digital channels that influence customers throughout the conversion cycle.  In short, marketers want to a macro-view of all the touch points (we call them “assists”) that drive a conversion.

To capture the data needed to view conversion paths, you need to match impression cookies (set by your ad server when a user is exposed to display ads) and your site visitor cookies (set by your site analytics software).  You’ll also need to maintain all the details for each impression or visit as time-stamped, individual records are a key requirement for conversion path analysis and more advanced attribution.

Once you have the detailed history of impressions, clicks, visits and actions for each visitor, you can query the data to visualize the conversion paths for those who converted.

The table below shows the “average” path for all visitors, as well as the common paths for 4 unique groups of converters (segmented into natural clusters by a machine-learning algorithm).  As noted, the “average” converter saw 6.8 display ads and visited the site 2.9 times before converting, with natural search accounting for 0.4 visits, paid search 0.4 visits and display ads 0.9 visits.

Most marketers are content with channel-specific conversion paths, but we’re seeing more and more interest in vendor and placement specific paths and expect this will become more common over time.

Conversion path analysis is a good start towards cross-channel / full-funnel Attribution and should provide a foundation for more advanced (and necessary) analysis.  That said, there are a few limitations that marketers should be aware of when looking at conversion paths.

First, it’s important to note that Averages can be misleading and there is usually a broad distribution of paths that are not represented by the mean. While the average number of impressions was 6.8 in the case above, the number varied between 1.5 and 20 for each group (that’s a big range).  Likewise, while Display accounted for 70% (on average) of interactions that led to a conversion, it ranged between 38% and 88% among the four clusters.

Second, while conversion path analysis is insightful (and may help justify your display buys), you’ll need more information to truly understand campaign performance and determine how to optimize your media plan.  This is where Attribution comes into the picture.

Moving Beyond Conversion Paths to Full Attribution

If you have detailed conversion paths for each visitor, you have the data you need for advanced analysis.  Now you need a model that allocates credit for every impression and click assist in a way that makes sense.

And now we move into the realm of debate and disagreement that is characterized by “my math is better than your math.”  Truth is, Attribution models come in all shapes and sizes; some are proprietary and some are based on well-known statistical methodologies.  While there is no universally-accepted algorithm that constitutes the gold standard in Attribution modeling, there are numerous approaches that are more than sufficient.  The good news is that you don’t need a 99.9% solution to be successful.  In most cases, a 90% solution is sufficient and more cost-effective.

So without getting too deep into Attribution modeling, let’s talk about the Questions your attribution model should answer, such as:

• What is the relative contribution of each channel, vendor, placement or keyword (i.e. how many conversions should each get credit for)?
• What is the attributable cost per action (or return on spend) for each channel, vendor, placement or keyword? (see sample report below)
• How many impressions are required to influence a visit and/or a conversion?  (i.e. what is the optimal frequency?)
• How does the optimal frequency vary by vendor or placement?
• What was the actual frequency (and how many impressions were wasted)?
• What is the appropriate look-back period (how far back should we give credit for assist impressions and clicks)?

As always, feel free to comment and share!

The Encore Team

As always, feel free to comment and share!

The Encore Team

1) Conversion Path Analysis:

Encore aggregates and groups media conversion paths into natural clusters using statistical models that quantify the contribution of each channel in the engagement process.

2) Vendor Analysis:

Encore uses cluster sequencing algorithms and Markov probability analyses to measure performance of each vendor and placement.  We attributed credit for each vendor based on it’s statistically derived contributions (assists) and compute an Attributed CPV and CPA for each vendor.  We then group vendors as a “Winner,” “Laggard” or “Bubble” based on where they rank in the distribution.

3) Sequence-Based Impression Weighting: We calculate the relative value of each impression based on its sequence in reverse chronological order (starting with most recent) to help advertisers understand how recency and sequence impacts performance.

4) Lastly, we quantify wasted spend by calculating how many impressions are inefficiently served. We use the statistical modeling above to calculate how many impressions were required to drive a visit and a conversion, then calculate how much budget the Advertiser could have redeployed by reducing frequency and increasing reach.

As always, feel free to comment and share!

The Encore Team

Ideally advertisers are using multiple digital channels: display, search, email, affiliate, etc.

Seeking to drive traffic to a web site to take some type of action: view or submit content, visit a goal page, join a list, complete a form, submit a lead or make a purchase.

As always, feel free to comment and share!

The Encore Team

• What is the relative contribution of each channel, vendor, placement or keyword (i.e. how many conversions should each get credit for)?
• What is the attributable cost per action (or return on spend) for each channel, vendor, placement or keyword?
• How many impressions are required to influence a visit and/or a conversion?  (i.e. what is the optimal frequency?)
• How does the optimal frequency vary by vendor or placement?
• What was the actual frequency (and how many impressions were wasted)?
• What is the appropriate look-back period (how far back should we give credit for assist impressions and clicks)?

As always, feel free to comment and share!

The Encore Team