Archive for the ‘Strategy’ category

Cross-Device Attribution

December 13th, 2016

Courtesy of TapadAdExchanger just published a great article about the latest cross-device attribution work we did for Monarch Airlines. By incorporating cookieless tracking and the Tapad device graph, we were able to deliver unparalleled insights into the cross-device customer journey.  In the article, our client discusses the growing need for cross-device insights and how the advanced insights enabled a new level of campaign optimization. Read the article on cookieless, cross-device attribution.

In conjunction, we also announced the successful results of our partnership with Tapad and the eye-popping results from the Monarch case study.  Read our cross-device partnership announcement.

What’s Wrong with Cookie-based tracking?
As data increasingly shows (see case study below), the cookie  is becoming less and less reliable for identifying users.  Whether on desktop (Safari, Firefox) or mobile / table (IOS), cookie blocking and deletion is disrupting the ability to capture the customer journey via cookies (even one the same browser).  If you take into account the growth in cross-platform engagement (searching on mobile, buying on desktop), the problem is exacerbated and the cookie-based customer journey is quickly disaggregated.

Solution: Cookieless Tracking + Cross-Device Attribution 
To address these challenges, two key ad-tech innovations are needed: cookieless tracking and device mapping from partners like Tapad and Oracle / Crosswise.  Cookieless tracking uses probabilistic matching of non-PII data to identify browsers and devices without the use of cookies.  Cross-Device data enriches fractional attribution by linking touchpoints across disparate browsers and devices, enabling marketers to stitch together the cross-device customer journey.

By incorporating cookieless tracking and 3rd party device graph to join user IDs we can:

  • Provide a more comprehensive view of browser-specific and cross-device engagement
  • Allocate fractional credit to Impressions and Clicks that would otherwise be undercounted
  • Deliver even more accurate insights and recommendations, enabling better optimization


monarch-logoCase study: Monach Airlines
With the objective of providing comprehensive, cross-device insights, Encore by Flashtalking utilized cookieless tracking and the Tapad Device Graph™ to provide cross-device, fractional attribution across mobile and desktop media.  A few of the key takeaways are summarized below:

  • 38% of visitors blocked or deleted cookies
  • 42% of converters were matched to the Tapad Device GraphTM
  • 61% of matched converting IDs were bridged (>1 browser ID)
  • Among bridged converters, there were 2.0 IDs per user
  • Unified, modeled data resulted in a 35% lift in ROI attributed to Display advertising

“Both desktop and mobile play important roles in the customer journey. As such, the need for accurate, cross-device insights is critical.  By analysing the cross-device customer journey, we can measure and optimise media investments with even greater confidence” – Monarch Airlines

For more on cross-device attribution, please feel free to contact me below.

@stevelatham
Find me on LinkedIn

It’s Time for Brands to Lead

January 23rd, 2016

vonage“Better” begins with the Advertiser
I love the new Vonage ad about “Better.” It’s not only clever and funny, it emphasizes that “Better” should be the goal of every brand.

When it comes to digital attribution, “Better” is being achieved by too few advertisers; most still rely on antiquated last-touch performance metrics for two reasons: operational friction (multi-touch attribution has historically been complex, costly and cumbersome) and the lack of involvement from the brands who rely on their agencies to figure it out on their own. A new breed of attribution vendors are taking the friction out of the measurement process.  It’s time for brands to become active participants in developing (and demanding) better insights that will drive better business results.

Brands and Digital Attribution

Smart advertisers realize they can no longer abdicate responsibility for insights and optimization to their agencies. They recognize the need to make insights a priority and participate in the process of developing strategy, assigning responsibility, defining requirements and selecting a solution. In other words, they need to lead. And, they know it.

As many early adopters of attribution have learned, not all solutions are the same and “one size fits all” does not apply. Without a clear understanding of goals, requirements, limitations and tradeoffs, advertisers often invest large sums in solutions that never meet expectations. So leading goes beyond approving a test. It means partnering with their agency to ensure they are aligned and have a shared understanding of what they need and what it will cost in terms of budget, time and energy – both upfront and over time.

Attribution Readiness Roadmap

To avoid common pitfalls and realize the potential of attribution-informed planning, advertisers should work with their agency to define their needs, evaluate partners and choose the solution that best fits their needs. To assist in that process, here is an attribution readiness roadmap for success:

1. Prioritization: Make “Better” a priority by elevating measurement, insights and active optimization. Set clear expectations and work with your agency to ensure everyone is on the same page.

2. Active Involvement in Strategic Planning: Brands should either lead or be active participants in strategy development, resource allocation and operational oversight. It’s OK to delegate; just don’t abdicate. Work with your teams or agencies to outline a simple plan that defines conversions, performance metrics, criteria for success and a timeline for implementation. Keys to successful planning include:

Define key requirements and document what you need in a solution. Prioritize capabilities based on what you need vs. what is possible.

Crawl, walk, run. Before trying to tackle device bridging, multi-platform conversion pathing and audience integration, become proficient at fractional attribution and optimization of desktop media on a regular basis. Build a solid base from which you can grow.

Pursue quick but meaningful wins, with the goal of always getting “Better.” Remember Voltaire’s quote “perfect is the enemy of good” and focus on incremental progress.

Sourcing Up: After determining what you need, defining criteria for selecting a solution – before diving in to each vendor’s capabilities. Key considerations should include:

  • Accuracy: Fractional insights should be based on validated algorithmic models; it should be easy and efficient to ingest and match spend for each placement
  • Actionability: Insights should translate into clear recommendations, with customizable forecasts for quantifying expected improvements
  • Set-up: Confirm how much time and effort is required to onboard the solution, and if it will utilize existing data vs. redundant tagging of ads. Set-up times may range from one week to three months. Make sure you ask for details.
  • Usability: A good tool should be intuitive and easy to use. There may be a slight learning curve, but it should not take an analytics expert to get value from the solution.
  • Extensibility: How easily can 3rd party data (e.g. audience segments, device bridging, offline sales) be incorporated? Make sure you ask.
  • Portability: Make sure the underlying data is exportable and useful for other applications (i.e. your Big Data initiatives). Also ask who will own the data. You may be surprised.
  • Timeliness: Modeling, analysis and reporting cycles may range from one day to four weeks. If you want to fast insights, make sure your vendor can deliver them.
  • Support: Efficient onboarding, training and ongoing support is a key requirement for success. Find out what you’ll get and what it will cost.
  • Costs: Understand the fee structure and how it will scale as you grow. Given that fees may range from $2,500-$25,000 (or more) per month, you need to know the total cost of ownership. Make sure to include the cost of log file data in your analysis.
  • As mentioned earlier, vendors are taking friction out of the process by offering integration solutions. Tight integration between the ad server, site tagging, data unification and spend reporting are critical. Fully integrated systems now enable advertisers to receive better data and sharper insights while saving time, energy and money.

4. Reporting and Optimization: Schedule monthly meetings to review results, discuss the learning and agree on actions to optimize performance. Celebrate your successes and learn from your disappointments and make the ongoing pursuit of “Better” a priority.

Through active involvement and participation, brands can help their agencies be more successful and achieve “Better” results that will benefit us all.

As always, thanks for reading and sharing.

@stevelatham
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The Dark Side of Mobile Attribution

August 14th, 2015

Repost of my Data Driven Thinking byline published by AdExchanger August 2015.

The good news: Mobile will be the freight train that drives the media industry.

The bad news: The lack of data availability and transparency will cost marketers billions of dollars.

mobile ad spendSince the iPhone’s 2007 introduction, the media industry has deemed every year to be “The year of mobile.” It took longer than expected to mature, but desktop’s awkward little brother is about to dwarf big bro and steal his girlfriend along the way. Mobile surpassed desktop in consumption in 2014 and will surpass it in spending in 2016. eMarketer predicts mobile media will reach $65 billion by 2019, or 72% of digital spending.

As we move towards a “mobile-first” world, we need to address a very big problem: We still can’t accurately measure performance. The ability to target customers in new and innovative ways outpaces the ability to measure effectiveness of those tactics.

Mobile’s Measurement Problem

The digital media ecosystem was built on cookies to target, track and measure performance. Cookies are imperfect but good enough to develop accurate insights into customers’ journeys. Using cookie data to assemble and model conversion paths, marketers can use fractional or multi-touch attribution to optimize media campaigns much more effectively than with last-click metrics.

In mobile, third-party cookies are blocked on most devices and privacy regulations limit device tracking. Consequently, traditional ad servers are limited to reporting on last-click conversions where possible.

For brands seeking to drive app installs, mobile attribution companies like Kochava, Tune, Appsflyer and Apsalar can track the click that led to the download in Apple or Google stores. Some are working on post-click and post-view reports, but these will be of limited help to advertisers seeking actionable insights.

last-user-sessionThe lack of mobile data means advertisers cannot quantify reach and frequency across publishers. They also cannot measure performance across publishers via multi-touch attribution. The cost and complexity of device bridging further obfuscates user-level engagement.

Rays Of Light

Mobile data and measurement challenges won’t be solved overnight, but a convergence of factors point to a less opaque future. Here are my predictions:

encore-user-session

1. Ad servers will adapt to device IDs

Conceptually, a device ID is not unlike a cookie ID, privacy issues notwithstanding, but it takes time and money to introduce a cookie-less ID system. Following the lead of Medialets, traditional ad servers will introduce their own anonymous IDs, instead of cookies, that map to probabilistic and deterministic device IDs. Like cookies, these IDs will allow them to log user level data that can feed fractional attribution models. We’ll probably see some early announcements before the end of year, with more to come in 2016.

2. Data unification will become readily available

To date, demand-side platforms, data management platforms, tag managers and data connectors have fixated on using data to help advertisers target, retarget, cross-sell and remarket. The same data that is used to drive revenue can also be used to connect user-level data for measurement purposes. Companies, such as Liveramp, Signal, Exelate and Mediamath, are already unifying data for analysis. More will follow.

3. Device bridging will become ubiquitous

To date, connecting devices across publishers has been a luxury afforded by the largest advertisers. In time that will change as wireless carriers, and possibly some publishers, offer device graphs exclusive of media and standalone vendors, such as Tapad and Crosswise, will reach economies of scale. At the same time, ad servers and data connectors will build or license device graphs and offer bridging as an extension of their service.

As ad delivery, data management and device bridging become more integrated (e.g. see announcement by Tapad and Medialets), costs will come down and advertisers of all sizes will be able to measure engagement across devices.

4. Mobile attribution vendors will be forced to evolve

As ad servers and data connectors incorporate device-level conversions in their data sets, including app installs, mobile attribution companies will have to expand their offerings or risk becoming redundant. Some may stick to their knitting and delve deeper into mobile analytics and data management. Others may pivot towards media and expand into desktop or addressable TV. Others may just be acquired. Regardless, it’s unlikely this category will remain as-is for much longer.

5. Last-touch attribution may finally go away.

We’ve been predicting the end of the click as a key performance indicator for years. But inertia, apathy and a continuous stream of shiny objects have allowed last-touch metrics to survive while brands and agencies fought other battles.

Now that we’ve tackled video, programmatic, social, native, viewability, fraud and HTML5, the new focus on insights and big data may finally drive the roaches away. The click will be hard to kill, but as we become smarter about measurement, it will become much less visible.

As the mobile data gaps are filled, the promise of cross-platform, cross-device, cross-channel attribution can become a practical reality for advertisers of all sizes.  From a measurement perspective, our best days are still ahead.  But as mentioned in the headline, getting there is going to be quite costly.

Steve Latham
@stevelatham

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

The Value of Data: Our POV on Verizon-AOL

May 19th, 2015

AdAge
I was recently interviewed by Advertising Age on the data angle of the Verizon-AOL deal (read the AdAge article).  While still fresh in my mind, I thought I’d share our POV.

First, Verizon already has unprecedented insight into what people are doing:

  • They know which device is speaking to its network and the packets and requests to each device (i.e. they capture all data sent and received)
  • They record every user session (e.g. using an app, typing an email or browsing the web)
  • Whether you’re using Verizon’s wireless service or local wifi to access the Internet, all data is captured
  • They track online behavior via cookies and relationships with 3rd parties (e.g. AOL owned Huffington Post)
  • They connect devices to each other and to desktops and households better than anyone else.
  • We believe Verizon already collects data than other providers

Acquiring AOL gives Verizon the ability to analyze the data and use it for advanced targeting of digital media.  While AOL’s sites (e.g. HuffPo) have some value, the real value is selling the ability to do advanced audience targeting to advertisers through AOL’s programmatic buying platforms for advertisers and publishers.  In short, Verizon has the diamond mines and AOL provides the mining equipment, sales and distribution.

And Verizon’s diamonds will have superior cut and clarity than what today’s competitors can offer as it can provide deeper insight to customer behavior across platforms and devices.  While competitors are trying to stitch together the pieces from the outside in, Verizon has already bolted them together from the inside out.

Not to say that AOL’s data isn’t valuable too.  In recent years AOL has done a great job of developing a very large proprietary data platform.  The Verizon deal will enhance AOL’s data in numerous ways:

  • Expand the reach of users
  • Expand the data on each user: demo, geo, behavioral, etc.
  • Enable better multi-platform / device bridging
  • Improve resolution and accuracy

So at the end of the day, this deal is about mining all that data and converting it into revenue.  As noted in the AdAge article, they will need to do this very carefully and responsibly. Verizon has a spotty record among privacy advocates so it would be smart to proceed with caution.

Thanks for your time and interest.  I look forward to your comments.

Steve Latham
@stevelatham


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Investing Confidently (and Safely) in Programmatic

March 28th, 2015

sparkle chartOver the past few years, we’ve spent a lot of time advising Brands and Agencies on the challenges and risks associated with Programmatic buying (which for this post will encompass exchange traded media, RTB, etc.).  While the idea of machine-based buying is exciting, it’s not without significant challenges and risks.  Having analyzed dozens of programmatic campaigns, we’ve found that a blind leap into Programmatic is almost always a costly endeavor.  The thesis for taking a smart approach to programmatic buying is summarized below:

  • While the promise of self-optimizing buying is intriguing, it doesn’t replace the need for objective, rational analysis.
  • Programmatic optimization is typically based on a broken model.  The continued reliance on clicks, post-click and post-view metrics may do more harm than good.
  • Algorithmic attribution is critical for measuring and optimizing media.  Fractional, statistical analysis is needed for accurate and impactful cross-channel, full-funnel insights.
  • As brands shift more of their budgets to programmatic, the need for objective, attribution-based insights will become even more critical

I recently put documented some of the key lessons learned to produce the embedded Presentation: “Investing Confidently in Programmatic“.  I thought about calling it “How to Avoid Wasting Half of Your Media Budget” but opted for the more positive spin. Either would be sufficiently accurate.

In it, I address some of the risks and challenges of Programmatic buying, along with recommendations for ensuring a successful investment in this rapidly changing arena.  Also included is a SWOT analysis to frame the strengths, weaknesses, opportunities and threats that advertisers must deal with to be successful in this new area of machine-based buying.

As always, your comments and questions are welcome – just post!  If you’d like a copy of this presentation please contact us.

Steve Latham
@stevelatham


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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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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
@stevelatham

 

Ad:tech NY Attribution Case Study

January 7th, 2013

3

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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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!

@stevelatham

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