Posts Tagged ‘Encore’

The Problem With Attribution

July 17th, 2015

Repost of my Data Driven Thinking byline published by AdExchanger

In recent months we’ve heard some noise about the problems with using multi-touch attribution to measure and optimize ad spend (see articles in Adexchanger and Digiday).  Some claim attribution is flawed due to the presence of non-viewable ads in user conversion paths. Others say attribution does not prove causality and should therefore be disregarded.

My view is that these naysayers are either painting with too big of a brush or they’re missing the canvas altogether.

Put The Big Brush Away 

broad-brushThe universe of attribution vendors, tools and approaches is large and diverse. You can’t take a broad-brushed approach to describe what they do.

If the critics are referring to static attribution models offered by ad servers and site analytics platforms, such as last touch, first touch, U-shaped, time-based and even weighting, I would agree that these are flawed because of the presence of non-viewable ads. Including every impression and click and arbitrarily allocating credit will do more harm than good. But if they’re referring to legitimate, algorithmic attribution solutions, they clearly don’t understand how things work.

First, not all attribution tools include every impression when modeling conversion paths. Occasionally, non-viewable impressions can be excluded from the data set via outputs from the ad server or a third-party viewability vendor. For the majority of cases where impression-level viewability is not available, there are proven approaches to excluding and/or discounting the vast majority of non-viewable ads. Non-viewable ads and viewable, low-quality ads almost always have a very high frequency among converters, serving 50, 100 or more impressions to retargeted users. By excluding the frequency outliers from the data set, you eliminate a very high percentage of non-viewable ads. You also exclude most viewable ads of suspect quality.

Second, unlike static models, machine-learning models are designed to reward ads that contribute and discount ads that are in the path but are not influencing outcomes. As cookie bombing is not very efficient, with lots of wasted impressions of questionable value, they are typically devalued by good algorithmic attribution models.

By excluding frequency outliers and using machine-learning models to allocate fractional credit, attribution can separate much of the signal from the noise, even the noise you can’t see. And while algorithmic attribution does not necessarily prove causality, a causal inference can be achieved by adding a control group. While not perfect, it’s more than sufficient for helping advertisers optimize spend.

You Missed The Entire Canvas

paint-on-childrenComplaining that attribution models are not accurate enough is like chiding Monet for being less precise than Picasso, especially when many advertisers are still painting with their fingers.

It’s easy to split hairs and poke holes in attribution, viewability, brand safety, fraud prevention, device bridging, data unification and other essential ad-tech solutions. But the absence of a bulletproof solution is not a valid reason to continue relying on last century’s metrics, such as click-through rates and converting clicks.

As Voltaire, Confucius and Aristotle said in their own ways, “Perfect is the enemy of good.”
Ironically, so is click-based attribution.

While no one claims to have all the answers with 100% accuracy, fractional attribution modeling can improve media performance over last-click and static models. And while not every advertiser can be the next Van Gogh, they can use the tools and data that exist today to get a solid “A” in art class.

The Picture We Should Be Painting
I’m a big fan of viewability tools and causality studies, and I’m an advocate for incorporating both into attribution models. I am not a fan of throwing stones based on inaccurate or theoretical arguments.
Every campaign should use tools to identify fraud, non-viewable ads and suspect placements. The outputs from these tools should be inputs to attribution models, and every advertiser should carve out a small budget for testing. While this is an idealistic picture, it may not be too far away. As the industry matures, capabilities are integrated and advertisers, including agencies and brands, learn to use the tools, we will get closer to marketing Nirvana.

In the mean time, advertisers should continue to make gradual improvement in how they serve, measure and optimize media. Even if it’s not perfect, every step counts.

puzzle-paintingAd-tech companies should remember we’re all part of an interdependent ecosystem. We need to work together to help advertisers get more from their media budgets. And we all need to have realistic expectations. From a measurement perspective, the industry will always be in catch-up mode, trying to validate the shiny new objects being created by media companies.

All that said, we can do much more today than only one year ago. We’ll continue to make progress. Advertisers will be more successful. And that will be good for everyone.

Steve Latham
@stevelatham

Shedding Light Beneath the Attribution Canopy

May 22nd, 2015

adexchanger_logoAdexchanger recently published a timely article “Breaking through the Attribution Canopy” on the Attribution marketplace (view it on Encore’s facebook page). Overall they did a good job of highlighting the conflicts of interest that are inherent when your media vendor is also your trusted source of insights.  They also touched on the emergence of new solutions that are designed to address the needs of the larger market.   Along with other industry executives, I was quoted in the interview.

During the interview, we discussed a lot of issues surrounding media attribution and optimization.  But as with any interview, only a few of my comments were published.  To provide some context and clarify our POV, here are the key takeaways:

  • We are glad to see that Attribution has (finally) reached a tipping point.  Brands, agencies, DSPs and media platforms are scrambling to leverage machine-based insights to optimize media spend.  Continuing to rely on last-touch KPIs for is simply a lazy and irresponsible approach to measuring media.
  • We believe measurement, analysis and optimization decisions should be driven by the advertiser, its agency or an independent solution provider, not its media vendor.  Even if the fox is friendly, it shouldn’t be in the hen house.
  • We also believe data should be easily ported, integrated and made available for analysis, regardless of who sells the media or who serves the ads.  Openness, transparency and portability are not only ideological values; they also make business sense.
  • The growing concentration of power of leading media and technology vendors should be on everyone’s radar as a threat to transparency and openness.  If you look at the markets for programmatic display, video advertising, search, social marketing, mobile advertising* and ad serving, the dominant players are making it difficult and expensive to independently analyze their data in the context of other media. The path to marketing and advertising success does not end in a walled garden.
  •  To date, advanced insights (e.g. algorithmic attribution and data-driven optimization tools) have been reserved for the largest advertisers who can afford six-figure price tags.  As the article points out, there is a large unmet need beyond the top 200 advertisers.  To address the needs of the thousands of middle market advertisers, a new model (no pun intended) is needed.  Heavy, expensive and service-intensive solutions cannot scale across the broader market.  The next phase of adoption will be won by light and agile solutions that are affordable and easy to implement.
  • To deliver modeled insights at scale, the solution must be automated, efficient, flexible and customizable for each advertiser.  It should also be affordable.  On this point, we wholeheartedly agree with Forrester’s Tina Moffett “I think one advantage [attribution start-ups] do have is they were able to see the market needs and where the gaps were … and where existing players were falling short.”

For these reasons, we are very excited about the prospects for innovators who are able to address unmet needs for the large and growing middle market.

*For more on my quote that Google gets half of all mobile ad dollars, please see the emarketer report published earlier this year.

As always, thanks for reading and feel free to share comments or contact me if you have any questions.

Steve Latham
@stevelatham

Observations on the Attribution Market

July 7th, 2014


chart-blue
The market for Attribution companies has definitely heated up with high profile acquisitions by Google and AOL.  I view these transactions as strong proof points that brands and their agencies are starving for advanced data-driven insights to optimize their investments in digital media.  The same thesis that led us to start Encore several years ago still holds true today: traditional metrics are no longer sufficient and advanced insights are needed to truly understand what works, what doesn’t, and how to improve ROI from marketing dollars.

Over the years we’ve analyzed more than 100 brand and agency campaigns of all sizes – from the largest CPG companies in the world, to emerging challengers in Retail, Automotive, Travel and B2B.  Based on these experiences, here are 5 observations that I’ll share today:

  1. We are still early in the adoption curve.   While many brands and agencies have invested in pilots and proofs of concept, enterprise-wide (or agency-wide) adoption of fractional attribution metrics is still relatively low, and the big growth curve is still ahead of us.  About 18 months ago I wrote about 2013 being the year Attribution Crosses the Chasm.  I now see I was a bit early in my prediction – 2014 is clearly the year Attribution grows up.
  2. There is still some confusion about who should “own” cross-channel / full-funnel attribution.  Historically brands have delegated media measurement to their agencies.  We now see brands taking on a more active role in deciding how big data is used to analyze and inform media buys.  And as the silos are falling, the measurement needs of the advertiser often transcend the purview of their media agency.  In my opinion, responsibility for measurement of Paid, Owned and Earned media will increasingly shift from the agencies to the brands they serve.  This is already the case for many CPG companies we serve.  In measuring media for more than a dozen big consumer brands, we’re seeing the in-house teams setting direction and strategy, while agencies play a supporting role in the measurement and optimization process.  We’re happy to work with either side; they just need to decide who owns the responsibility for insights.
  3. Multi-platform measurement is coming, but not as fast as you might think.  We are big believers in the need for device bridging and multi-platform measurement and are working with great companies like Tapad to address the unmet need of unifying data to have a more comprehensive view of customer engagement.  To date we’ve presented Device Bridging POVs to most of our customers.  And while are interested in this subject, very few will invest this year.  It’s not that the demand isn’t there – it will just take some time to mature.
  4. Marketers need objective and independent insights – now more than ever.  Despite increasing efforts by big media companies to bundle analytics with their media, the days of relying on a media vendor to tell you how well their ads performed are limited.  It’s fine to get their take of how they contributed to your business goals, but agencies and brands need objective 3rd party insights to validate the true impact of each media buy.  And with the growing reliance on exchange-traded media and machine-based decisioning, objective, expert analysis is needed more than ever to de-risk spend and improve ROI.   We’ve found this approach works well – especially in days like these where it’s all about sales.  This leads to my 4th observation…
  5. In the end it’s about Sales.  While digital KPIs are great for measuring online engagement, we’re seeing more and more interest in connecting digital engagement to offline sales.  Again, we’re fortunate to work with great partners like (m)PHASIZE to connect the dots and show the true impact of digital spend on offline sales.  We’re also working on opportunities with LiveRamp and Mastercard to achieve similar goals.  Like device bridging, I see this becoming more of a must-have in 2015, but it’s good to have the conversations today.

There is so much more to discuss and I’m sure our market will continue to iterate and evolve quickly.  But to sum it up, it’s an exciting time to be in the digital media measurement space. Attribution is finally coming of age and it’s going to be a hell of a ride for the next few years.

As always, comments are welcome!

Steve Latham
@stevelatham


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Ad:tech NY Attribution Case Study

January 7th, 2013

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In November 2012, between a hurricane and a Nor-easter, I presented a case study on Full-Funnel Attribution at the one of the premier industry conferences: Ad:tech NYC.

For the presentation I joined by Brad May of KSL Media, who is not only a client but also an early adopter and supporter of Attribution.  Building on the insights from the Attribution Case study presented at Ad-Tech in San Fran, I was honored to speak again and present a case study illustrating 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:

  • After modeling the impact of assist impressions and clicks, Display advertising accounted for almost 20% of achieved actions.
  • Mobile ads generated low-cost mobile-generated actions (this year’s theme – mobile, mobile, mobile).
  • Search played largely a supporting role.
  • Frequency is an issues that all advertisers need to keep a close eye on.

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

 

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

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Conversion Paths vs. Full Attribution

February 24th, 2012

Attribution is a hot topic!  As marketers are shifting their focus to measurement and optimization, Attribution is rising to the top of the priority list for 2012.  However, like many things, Attribution has many flavors and often means different things to different people.  In this and future posts, I will shed some needed light on this topic and help marketers make sense of this complicated and ever-evolving discipline.

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

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Attribution 101: Full Funnel Media Measurement

March 17th, 2011

The What, Why and How of Online Media Attribution
[if you like presentations, view “Attribution 101” on slideshare]

Anyone who has ever bought (or sold) display ads is painfully aware of the need for new metrics for online media.  While “last-click wins” may work for paid search, it fails miserably in measuring the impact of display and other media at the top of the funnel.  Hence, the need for full-funnel Attribution, which allocates credit for “assists” in the customer engagement cycle.

By attributing credit to contributing impressions and clicks that precede subsequent visits and conversions, marketers can have a much more accurate and holistic view into the performance of each channel and vendor.  While most interactive marketers are familiar with Attribution, many are still trying to understand what it is and how it works.

The Need for New Metrics

While digital is the most measurable medium, the “one-size fits all” approach to online media measurement needs to be re-evaluated.  While click-through rates (CTRs), cost per click (CPC), direct conversion rates and cost per action (CPA) may be applicable for search and other “bottom-of-the-funnel” media, these metrics are not appropriate or insightful for measuring performance at the top of the funnel, where demand is created.

Display ads can be very effective in achieving their objectives (driving awareness) without any clicks or direct conversions.  A recent Media Math study showed that 80% of post-impression conversions are the result of viewing display ads without clicking and only 20% of conversions are the result of a click.  In other words, for every conversion that follows a click on a display ad, there are four (4) post-impression conversions without clicks.  The upshot: we need better tools and methodologies for measuring the performance of media at the top of the funnel.  This is where attribution comes into the picture.

Defining Attribution

Attribution is the art and science of allocating credit to all interactions that play a supporting role in the customer engagement process.  In other words, it’s the act of giving credit for assists.  Rather than viewing results from each digital channel in its own silo (a la traditional web analytics platforms), Attribution requires you to take a holistic approach to analyzing how each touch-point contributes to the overall goal (visits, conversions, etc.).

With the resurgence of display advertising, Attribution is becoming increasingly important for optimizing media budgets.  As shown in the Google trends chart below show, searches for “online attribution” have increased 150% over the past 36 months.

Approaches to Attribution

Generally speaking, there are two types of Attribution: Operational and Algorithmic / Media Mix Modeling.

  • Operational attribution consists of creating detailed records of every impression, click, visit and action for each visitor to your site, regardless of the source or channel (e.g. display, paid search, natural search, direct navigation, email, social, affiliate, etc.).  Data is then organized and reported in such a way that visitor paths and media placements can be effectively (and efficiently) analyzed.  By understanding which paid, owned and earned media placements are driving the most effective engagement, you can optimize spend and marketing efforts to boost ROI.
  • Media-Mix / Algorithmic Modeling consists of analyzing impression data, search data, email data and web log files to statistically correlate patterns and trends to fine tune campaigns.  This “black box” approach is useful but it depends entirely on the hard-coded assumptions and calculations in the model.

We believe operational attribution is the foundation for advanced measurement and analysis of media.  The operational approach of giving credit for assists is intuitive, logical and easy to understand.  Once the operational attribution model is defined, algorithmic modeling can be used to further optimize the media mix.

Channel Level Attribution

Channel level attribution addresses the relative roles of each media channel in driving traffic and conversions.  Attribution requires an algorithm that attributes partial credit to display impressions and clicks that precede visits and conversions.  The weighting of impressions relative to clicks will vary based on the type of ad, format, placement and other issues.  For example, highly-targeted rich media placements should have higher weighting than Run-of-network animated .gifs.  Weightings should be customizable for each vendor and placement.

The channel attribution report below shows the relative impact (last click vs. attributed) of each channel: direct navigation, natural search, referring sites, email, paid search, display advertising and 3rd party email.  As shown, attributable credit for display ads may be 50-400% higher than a last-click report would show. It should also be noted that paid search generally sees a net increase in attributable actions as short-tail keywords often play contributing roles in the customer engagement process.

After attributing credit for actions for each channel, spend data can be imported to show the adjusted cost per action for each channel, as shown below. As illustrated, we typically see a 30-80% decrease in attributable cost per action (CPA) for Display, and a slight drop in CPA for paid search (resulting from keyword assists)

Attribution chart

Vendor Level Attribution

Looking beyond channel level, we use the same approach to assess the performance of each media buy.  Shown below is a sample report showing the cost per action for each media vendor, both last-click and attributable.  As shown, some media buys can appear to be very poor performers on a last-click basis, but are in fact very effective for creating demand that is subsequently satisfied through other channels.

 

Keyword Attribution

Short-tail keywords (category terms, product terms, etc.) often play “assist” roles in the customer engagement process.  Just as it’s important to know which display ads precede visits and conversions, assist keywords should also be identified.  In many cases, assist keywords may perform poorly on a last-click basis, but perform very well in an attribution report.

The Business Case for Attribution

Attribution is more than just a buzzword – it is an essential part of campaign measurement and a requirement for optimizing media spend.  As illustrated below, moving “loser” budgets to the “winning” vendors can produce a dramatic improvement in revenue and return on spend.

Beyond the improvement in media efficiency and ROS, the economic benefits also accrue to:

  • Media planners: save wasted time and energy trying to replace ostensibly “bad” buys that are actually quite effective
  • Ad Ops and analytics teams who are tasked with aggregating silos of data into massive .xls workbooks (attribution vendors will do this for you)
  • Media vendors whose ads are actually engaging customers and creating demand that is satisfied through other channels.

As an industry, we have to do better.  We can’t use yesterday’s tools to measure tomorrow’s media. Attribution should no longer be an aspirational goal, but rather a key part of your 2011 digital marketing strategy.  The economic returns are compelling and there are numerous vendors (including us!) who would be happy to assist you in taking a more holistic approach to digital media measurement and optimization.

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

@stevelatham

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