Punching the Monkey

BannerAdI recently tried to explain what I do for a living to my 14-year-old son. I found myself telling him about ad tech.

“Basically, we make technology that helps marketers buy different kinds of banner ads,” I told him.

“You mean the kind of annoying pop-up ads that everyone hates?” he asked.

His look of profound disappointment said it all. I explained that the kind of work we do wasn’t just about populating the Internet with the “Lose five pounds with one stupid trick” type of banner. But even though we are getting a lot right, my explanations eventually started sounding pretty weak.

I have been working in this business since 1995. Aside from doing some ad implementation testing, I have probably clicked on about a dozen banner ads in as many years. Today’s robust, real-time ad tech “stack” has been purpose-built to optimize the delivery of the kind of banner ads most people already hate: standardized IAB units, retargeted ads, auto-play video pre-roll units and even the dreaded pop-up and pop-under.

Publishers without robust direct sales options depend on networks and exchanges to monetize the endless streams of traffic they create, and they happily collect their $1.10 eCPM (cost per mille) payments. Advertisers looking for cheap reach and performance plumb the depths of such inventory to find the rare conversion, and hope they are getting what they pay for rather than a shady “last view” attributed banner.

Today, the highest and best use of the standardized banner has enabled marketers to leverage their first-party data to bombard site visitors with retargeted ads – an effective tactic, since they are essentially paying to accelerate a conversion that has a great chance of happening on its own.

As an industry, it seems pretty clear that we will look back on this era in digital ad technology and see how primitive it was. Have we built a trillion-dollar real-time ad serving machine for punch-the-monkey ads, or have we really innovated and created disruption?

RTB Is Dead, Long Live RTB

The recent acquisition of [X+1] by Rocket Fuel is a great sign for our industry. It basically validates the idea that, for programmatic RTB to be effective, real data science must inform targeting. [X+1] is one of the best at cross-channel targeting, and they have already started to figure out the cross-channel attribution puzzle. An everlasting always-on stream of RTB banners for branding and retargeting might prove to be a hugely important part of unlocking a broader multi-channel strategy – if the data can dictate it. If data management platform technology can be leveraged to truly optimize addressable marketing, then RTB will survive and thrive. With consumers always on the move, and every form of media starting to be addressable, real-time programmatic will be something marketers have permanently switched on, and we’ll see the true value of the pipes we have created.

Programmatic Direct

How about inventory that is relatively standard, but a bit nicer than that found within the exchange environment? Transacting on this tier of inventory works quite nicely with all kinds of one-to-one connections within RTB, and buyers and sellers are quickly leveraging the pipes to make private marketplace deals.

If I am a quality financial publisher, why wouldn’t I sell within RTB for $8 CPMs, rather than pay a $200,000 salesperson to sell at $12 CPMs? The math just makes sense. Delivering higher tiers of inventory at scale to private buyers is a great use of RTB, but not a panacea for overall inefficiency in media procurement. But, we have seen those RTB pipes service entire new classes of inventory, and start to appeal to brand marketers.

Workflow Automation

The problem with getting really good inventory has always been the difficulty understanding rates and availability. That’s why the RFP exists today, and isn’t going away anytime soon. Publishers will always want full control over the really good stuff. Because they know their inventory better than any algorithm, there will always be a need for human control and creativity. Big, custom sponsorships and custom-curated native executions will only increase over time, as more television and print budgets shift into addressable digital. You just can’t automate those deals. Marketers and agencies will demand programmatic efficiency to compress an expensive, 42-step process for securing guaranteed inventory. This is one area that programmatic RTB has not been built to handle (these deals are neither “real time” nor “bidded”), but we are seeing real innovation from a number of companies trying to bring programmatic efficiency to guaranteed deals.

It’s hard to explain everything that we are getting right to a 14-year-old who spends more time on mobile apps than in an Internet browser. His assessment, in surfing the desktop Internet, is probably right – it looks like a lot of weight loss ads and sneaker retargeting. But, it’s still early days nearly 20 years after the first banner ad was served.

Fraud and the Pivot Towards Programmatic Direct

ImageThere has been a lot of talk about the pervasive amount of click fraud and bot traffic happening in digital. Marketers are reportedly spending anywhere from 30% to 70% of their digital budgets on fake impressions and clicks, and an entire cottage industry is cropping up to help marketers combat fraud and try and protect their digital marketing investments.

Some people claim that price of fraud is already built into the programmatic RTB ecosystem. Marketers are using programmatic RTB for direct marketing, and they are measuring sales using CPA metrics. If they are paying $100 per verified acquisition, should they care whether it takes 10 million or 20 million impressions to produce a conversion? Some say that they don’t, and take the view that they only pay for results, justified by their backend conversion metrics which take media cost into consideration.

I hope this is not the case. Ignoring fraud with these justifications is what ultimately may kill the digital advertising business before we ever get to scale.

Another big problem is faulty, fraud-like attribution. Let’s take the case of the big programmatic marketing platform that has been getting great conversions for their customers. Marketers look at the results of such platforms and think that the technology has managed to effectively separate the wheat from the chaff in popular ad exchanges and find the “sweet spot” of cookie targeting that converts. But, dig a little deeper and you notice that many of the conversions are happening on webmail subdomains (mail.yahoo.com). In other words, the platform is getting last-view attribution from successful e-mail marketing. This is a more subtle case of fraud…but really more of a tax on digital ignorance for marketers. But again, the marketer sees this channel producing results that align with his CPA goals. Did the conversions get attributed correctly? Maybe not, but those questions get overlooked when the blended CPA is on target.

Cookie bombing and other types of fraud are just as likely to limit digital advertising to performance budgets, and keep real growth at bay.

If we are being honest with ourselves, we must admit that there doesn’t seem to be a ton of desire to solve these inherent problems in programmatic RTB. There are too many people making too much money to want to fix it. And it’s going to destroy programmatic RTB as we know it. Who benefits from the current scenario?

  • Publishers: Most publishers benefit greatly from the programmatic RTB revenue stream.  Big publishers “fill” their long tail inventory with ads. Mid-sized publishers without large direct sales teams depend greatly on network and programmatic fill for their revenue. Long tail pubs are fully committed to their AdSense checks for survival. A lot of publishers’ Comscore numbers are a lot bigger than they should be, thanks to cheap inventory of unknown provenance.
  • AdTech: Every vendor in programmatic RTB benefits from inventory flowing through their pipes. Most charge on a percentage-of-spend, which means they might sacrifice 50% of their revenue if they had to stop charging for fraudulent impressions. New fraud detection and measurement firms are also profiting (albeit in a virtuous way).
  • Agencies: What would today’s big agencies do without the ability to leverage programmatic RTB to arbitrage inventory, or charge a premium for “unpacking the ad tech space” for their clients? The new programmatic landscape has been a boon to smart, nimble agencies that have built, bought, or leveraged ad technology to pivot their dying media businesses. How eager are agencies to expose the fundamental flaws within the programmatic RTB ecosystem?

The biggest loser in the entire room is the poor marketer, who ultimately pays the bills. But it’s easy to turn a blind eye, because the numbers look good. But how long will big marketers confuse true marketing success with today’s flawed digital attribution metrics? Marketers are starting to think about real measurement frameworks (net new customers), rather than CPA metrics. They are also keenly interested using brand messages to interact with their customers across screens. And they won’t be using CPA to measure brand growth.

So why do marketers continue to leverage programmatic RTB despite the inherent risk of fraud and current limitations for brand advertising? To paraphrase Clear Channel’s Bob Pittman at the recent IAB annual meeting, “Given a choice between quality and convenience, convenience always wins.”

The biggest question lately is whether or not we can make it as convenient (and cheap) to buy guaranteed media at scale. Seeing this opportunity, a lot of players in programmatic RTB are looking hard at the money being spent on guaranteed media (the “transactional RFP” channel), and trying to add new “programmatic direct” tools to their arsenals.  RTB players know that brands are still uncomfortable executing brand campaigns in the wilds of the open exchange, and they know truly premium inventory won’t be available unless publishers have more granular control over pricing, availability, and partner selection. Put more simply, the lion’s share of digital money still gets transacted manually, with paper insertion orders, and successful automation means a big piece of the action.

Providing a layer of automation for direct deals helps with fraud (guaranteed deals, by their nature, offer inventory transparency), and adds the ability to scale within higher classes of inventory.

Marketers are actually looking forward to having their agencies leverage new technology to secure quality digital placements. Whether these innovations come from tweaking existing programmatic RTB technology (private exchanges) or from new, API-driven “programmatic direct” providers doesn’t matter to them. They need to execute cross-channel digital campaigns at scale, and those campaigns (if they are for brand purposes) cannot contain fraud.

Does this spell the end of programmatic RTB? Nope. I think there will always be exchanges and technologies that let direct marketers plumb the depths of the Web to drive online sales. Ten years ago, folks were writing about the death of shady affiliate, click, and CPA networks—but they are still around. But, will today’s programmatic RTB business have to fundamental transform to win brand dollars? Yes, and the path to success is what we have been calling programmatic direct. It will be interesting to see the various technology executions of programmatic direct, as they form the gateway for branding to flourish online.

Does Driving Efficiency Drive Profit (A Contrarian View of RTB)

unicorn

Online display would be like this, if branding metrics took profit into account.

I’ve always loved the notion of programmatic RTB. As a data hound and an early adopter of Appnexus , the notion that advertisers can achieve highly granular levels of targeting and utilize algorithms to impact performance is right in my wheelhouse. Today’s ad tech, replete with 300 companies that enable data-driven audience segmentation, targeting, and analytics is testament to the efficiency of buying ads one impression at a time.

But what if driving efficiency in display actually does more harm than good?

Today’s RTB practitioners have become extremely relentless in pursuit of the perfect audience. It starts with retargeting, which uses first party data to serve ads only to people who are already deeply within the customer funnel. No waste there. The next tactic is to target behavioral “intenders” who, according to their cookies, have done everything BUT purchase something. Guess what? If I have searched 4 times in the last three hours for a flight from JFK to SFO, eventually you will get last view attribution for my ticket purchase if you serve me enough ads. Next? Finding “lookalike” audiences that closely resemble past purchasers. Data companies, each of whom sell a variety of segments that can be mixed to create a 35 year-old suburban woman, do a great job of delivering audiences with a predilection to purchase.

But what if we are serving ads to people that are already going to buy? Is efficiency really driving new sales, or are we just helping marketers save money on marketing?

It seems like online display wants to be more and more like television. Television is simple to buy, it works, and it drives tons of top funnel awareness that leads to bottom funnel results. We know branding works, and even those who didn’t necessarily believe in online branding need look no further than Facebook for proof. With their Datalogix offline data partnership, Facebook conclusively proved that people exposed to lots of Facebook ads tended to grab more items off of store shelves. It just makes sense. So why are we frequency capping audiences at 3—or even 10? I can’t remember the last time I watched network television and didn’t see the same car ad about 20 times.

The other thing that RTB misses out on is profit. RTB drives advertising towards lowering the overall cost of media needed to drive a sale. Even if today’s attribution models were capable of taking into account all of the top-funnel activity that eventually creates an online shopping cart purchase (a ludicrous notion), we are still just measuring those things that are measureable. TV ads, billboard ads, and word of mouth never get online credit—yet I believe they drive most of the online sales. Sorry, but I believe the RTB industry creates attribution models that favor RTB buying. Shocking, I know.

So, what is true performance and what really drives it? For most businesses, performance is more profit. In other words, the notion that a sales territory that has 100 sales a day can generate 120 sales a day. That’s called profit optimization. If I can use advertising to create those additional 20 sales, and still make a profit after expenses, than that’s a winner. RTB makes it cheaper to get the 100 sales you already have, but doesn’t necessarily get the next twenty. Getting the next batch of customers requires spending more on media, and driving more top-funnel activity.

The other thing RTB tends to fumble is how real life sales actually happen. Sure, audience buying knows what type of audience tends to buy, and where to find them online, but misses with frequency capping and a lack of contextual relevance. Let me explain. In real life, people live in neighborhoods. The houses in those neighborhoods are roughly the same price, the kids go to the same school district, the people have similar jobs, and their kids do similar activities and play the same sports. The Smiths drive similar cars to the Joneses, they eat at the same restaurants, and shop at the same stores. If the Smiths get a new BMW, then it’s likely the Joneses will keep up with a new Audi or Lexus in the near future. When neighbors get together, they ask each other what they did on February Break, and they get their vacation ideas from each other. That’s how life works.

What media most closely supports this real-life model, where we are influenced most by our neighbors?  Is it serving the Jones family a few carefully selected banners on cheap exchange inventory, which is highly targeted and cost effective? Or is it jamming the Smiths and Joneses with top-funnel brand impressions across the web? The latter not only gets Smith, the BMW owner, to keep his car top-of-mind and be more likely to recommend it—but also predisposes Jones to regard his neighbor’s vehicle in a more desirable light. That takes a lot of impressions of various types of media. You can’t do that and remain efficient. The thing is—you can do that and create incremental profit.

Isn’t that what marketers really want?

[This post originally appeared in AdExchanger on 5/20/13]

Choosing a Data Management Platform

“Big  Data”  is  all  the  rage  right  now,  and  for a good reason. Storing tons and tons of data has gotten very inexpensive, while the accessibility of that data has increased substantially in parallel. For the modern marketer, that means having access to literally dozens of disparate data sources, each of which cranks out large volumes of data every day. Collecting, understanding, and taking action against those data sets is going to make or break companies from now on. Luckily, an almost endless variety of companies have sprung up to assist agencies and advertisers with the challenge. When it comes to the largest volumes of data, however, there are some highly specific attributes you should consider when selecting a data management platform (DMP).

Collection and Storage: Scale, Cost, and Ownership
First of all, before you can do anything with large amounts of data, you need a place to keep it. That  place  is  increasingly  becoming  “the  cloud”  (i.e.,  someone  else’s  servers),  but  it  can  also  be   your own servers. If you think you have a large amount of data now, you will be surprised at how much it will grow. As devices like the iPad proliferate, changing the way we find content, even more data will be generated. Companies that have data solutions with the proven ability to scale at low costs will be best able to extract real value out of this data. Make sure to understand how your DMP scales and what kinds of hardware they use for storage and retrieval.

Speaking of hardware, be on the lookout for companies that formerly sold hardware (servers) getting into the  data  business  so  they  can  sell  you  more  machines.  When  the  data  is  the  “razor,”   the  servers  necessarily  become  the  “blades.”  You  want  a  data  solution  whose  architecture  enables the easy ingestion of large, new data sets, and one that takes advantage of dynamic cloud provisioning to keep ongoing costs low. Not necessarily a hardware partner.

Additionally, your platform should be able to manage extremely high volumes of data quickly, have an architecture that enables other systems to plug in seamlessly, and whose core functionality enables multi-dimensional analysis of the stored data—at a highly granular level. Your data are going to grow exponentially, so the first rule of data management is making sure that, as your data grows, your ability to query them scales as well. Look for a partner that can deliver on those core attributes, and be wary of partners that have expertise in storing limited data sets.
There are a lot of former ad networks out there with a great deal of experience managing common third party data sets from vendors like Nielsen, IXI, and Datalogix. When it comes to basic audience segmentation, there is a need to manage access to those streams. But, if you are planning on capturing and analyzing data that includes CRM and transactional data, social signals, and other large data sets, you should look for a DMP that has experience working with first party data as well as third party datasets.

The concept of ownership is also becoming increasingly important in the world of audience data. While the source of data will continue to be distributed, make sure that whether you choose a hosted or a self-hosted model, your data ultimately belongs to you. This allows you to control the policies around historical storage and enables you to use the data across multiple channels.

Consolidation and Insights: Welcome to the (Second and Third) Party
Third party data (in this context, available audience segments for online targeting and measurement) is the stuff that the famous Kawaja logo vomit map was born from. Look at the map, and you are looking at over 250 companies dedicated to using third party data to define and target audiences. A growing number of platforms help marketers analyze, purchase, and deploy that data for targeting (BlueKai, Exelate, Legolas being great examples). Other networks (Lotame, Collective, Turn) have leveraged their proprietary data along with their clients to offer audience management tools that combine their data and third party data to optimize campaigns. Still others (PulsePoint’s  Aperture  tool  being  a  great  example)  leverage  all  kinds  of  third party data to measure online audiences, so they can be modeled and targeted against.

The key is not having the most third party data, however. Your DMP should be about marrying highly validated first party data, and matching it against third party data for the purposes of identifying, anonymizing, and matching third party users. DMPs must be able to consolidate and create as whole of a view of your audience as possible. Your DMP solution must be able to enrich the audience information using second and third party data. Second party data is the data associated with audience outside your network (for example, an ad viewed on a publisher site or search engine). While you must choose the right set of third party providers that provide the best data set about your audience, your DMP must be able to increase reach by ensuring that you can collect information about as many relevant users as possible and through lookalike modelling.

First Party Data

  • CRM data, such as user registrations
  • Site-site data, including visitor history
  • Self-declared user data (income, interest in a product)

Second Party Data

  • Ad serving data (clicks, views)
  • Social signals from a hosted solution
  • Keyword search data through an analytics platform or campaign

Third Party Data

  • Audience segments acquired through a data provider

For example, if you are selling cars and you discover that your on-site users who register for a test drive are most closely  matched  with  PRIZM’s  “Country  Squires”  audience,  it  is  not  enough  to  buy   that Nielsen segment. A good DMP enables you to create your own lookalike segment by leveraging that insight—and the tons of data you already have. In other words, the right DMP partner can help you leverage third party data to activate your own (first party) data.

Make sure your provider leads with management of first party data, has experience mining both types of data to produce the types of insights you need for your campaigns, and can get that data quickly.  Data  management  platforms  aren’t  just  about  managing  gigantic  spreadsheets.  They  are   about finding out who your customers are, and building an audience DNA that you can replicate.

Making it Work
At the end of the day, it’s  not  just  about  getting  all  kind  of  nifty  insights  from  the  data.  It’s   valuable to know that your visitors that were exposed to search and display ads converted at a 16% higher rate, or that your customers have an average of two females in the household.  But  it’s   making those insights meaningful that really matters.
So, what to look for in a data management platform in terms of actionability? For the large agency or advertiser, the basic functionality has to be creating an audience segment. In other words, when the blend of data in the platform reveals that showing five display ads and two SEM ads to a household with two women in it creates sales, the platform should be able to seamlessly produce that segment and prepare it for ingestion into a DSP or advertising platform. That means having an extensible architecture that enables the platform to integrate easily with other systems.

Moreover, your DSP should enable you to do a wide range of experimentation with your insights. Marketers often wonder what levers they should pull to create specific results (i.e., if I change my display creative, and increase the frequency cap to X for a given audience segment, how much will conversions increase)? Great DMPs can help built those attribution scenarios, and help marketers visualize results. Deploying specific optimizations in a test environment first means less waste, and more performance. Optimizing in the cloud first is going to become the new standard in marketing.

Final Thoughts
There are a lot of great data management companies out there, some better suited than others when it comes to specific needs. If you are in the market for one, and you have a lot of first party data to manage, following these three rules will lead to success:

  • Go beyond third party data by choosing a platform that enables you to develop deep audience profiles that leverage first and third party data insights. With ubiquitous access to third party data, using your proprietary data stream for differentiation is key.
  • Choose a platform  that  makes  acting  on  the  data  easy  and  effective.  “Shiny,  sexy”  reports  are   great, but the right DMP should help you take the beautifully presented insights in your UI, and making them work for you.
  • Make sure your platform has an applications layer. DMPs must not only provide the ability to profile your segments, but also assist you with experimentation and attribution–and provide you with ability to easily perform complicated analyses (Churn, and Closed Loop being two great  examples).  If  your  platform  can’t  make  the  data  dance,  find  another  partner.

Available DMPs, by Type
There are a wide variety of DMPs out there to choose from, depending on your need. Since the space is relatively new, it helps to think about them in terms of their legacy business model:

  • Third Party Data Exchanges / Platforms: Among the most popular DMPs are data aggregators like BlueKai and Exelate, who have made third  party  data  accessible  from  a  single  user  interface.  BlueKai’s  exchange approach enables data buyers  to  bid  for  cookies  (or  “stamps”)  in  a  real-time environment, and offers a wide variety of providers to choose from. Exelate also enables access to multiple third party sources, albeit not in a bidded model. Lotame offers  a  platform  called  “Crowd  Control”  which  was  evolved  from  social   data, but now enables management of a broader range of third party data sets.
  • Legacy Networks: Certain networks with experience in audience segmentation have evolved to provide data management capabilities, including Collective, Audience Science, and Turn. Collective is actively acquiring assets (such as creative optimization provider, Tumri14) to  broaden  its  “technology   stack”  in  order  to  offer  a  complete  digital  media  solution  for  demand  side customers. Turn is, in fact, a fully featured demand-side platform with advanced data management capabilities, albeit lacking  the  backend  chops  to  aggregate  and  handle  “Big  Data”  solutions  (although  that  may   rapidly change, considering their deep engagement with Experian). Audience Science boasts the most advanced native categorical audience segmentation capabilities, having created dozens of specific, readily accessible audience segments, and continues to migrate its core capabilities from media sales to data management.
  • Pure Play DMPs: Demdex (Adobe), Red Aril, Krux, and nPario are all pure-play data management platforms, created from the ground up to ingest, aggregate, and analyze large data sets. Unlike legacy networks, or DMPs that specialize in aggregating third party data sets, these DMPs provide three core offerings: a core platform for storage and retrieval of data; analytics technology for getting insights from the data with a reporting interface; and applications, that enable marketers to take action against that data, such as audience segment creation, or lookalike modeling functionality. Marketers with extremely large sets of structured and unstructured data that go beyond ad serving and audience data (and may include CRM and transactional data, as an example), will want to work with a pure-play DMP

This post is an excerpt of Best Practices in Digital Display Advertising: How to make a complex ecosystem work efficiently for your organization All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without prior permission in writing from the publisher.

Copyright © Econsultancy.com Ltd 2012

Best Practices in Digital Display Media (Interview)

Digital display is remarkably complex. Standard campaigns can involve multiple vendors of different technologies and types of media.

Today, eConsultancy launches Best Practices in Digital Display Advertising, a comprehensive look at how to efficiently manage online advertising. We asked the author, Chris O’Hara, about the report and work that went into it.

Why did you write Best Practices in Digital Display Media?

In my last job, a good part of my assignment was traveling around the country visiting with about 500 regional advertising agencies and marketers, large and small, over three years. I was selling ad technology. Most advertisers seemed extremely engaged and interested to find out about new tools and technology that could help them bring efficiency to their business and, more importantly, results to their clients. The problem was that they didn’t have time to evaluate the 250+ vendors in the space, and certainly didn’t have the resources (financial or time) to really evaluate their options and get a sense of what’s working and what isn’t.

First and foremost, I wanted the report to be a good, comprehensive primer to what’s out there for digital marketers including digital ad agencies. That way, someone looking at engaging with data vendors, say, could get an idea of whether they needed one big relationship (with an aggregator), no data relationships, or needed very specific deals with key data providers. The guide can help set the basis for those evaluations. Marketers have been basically forced to license their own “technology stack” to be proficient at buying banner ads. I hope the Guide will be a map through that process.

What was the methodology you used to put it together?

I essentially looked at the digital display ecosystem through the lens of a marketer trying to take a campaign from initial concept through to billing, and making sure I covered the keys parts of the workflow chain. What technologies do you employ to find the right media, to buy it, and ultimately to measure it? Are all of these technologies leading to the promised land of efficiency and performance? Will they eventually? I used those questions as the basis of my approach, and leveraged the many vendor relationships and available data to try and answer some of those questions.

What’s the biggest thing to take away from the report?

I think the one thing that really runs through the entire report is the importance of data. I think the World Economic Forum originally said the “data is the new oil” [actually, the earliest citation we can find is from Michael Palmer in 2006, quoting Clive Humby] and many others have since parroted that sentiment. If you think about the 250-odd technology companies that populate the “ecosystem,” most are part of the trend towards audience buying, which is another way of saying “data-driven marketing.” Data runs through everything the digital marketer does, from research through to performance reporting and attribution. In a sense, the Guide is about the various technologies and methodologies for getting a grip on marketing data—and leveraging it to maximum effect.

There’s an explosion of three letter acronyms these days (DSP, DMP, SSP, AMP, etc) that marketers are still trying to sort out. Do we need all of them? Is there another one around the corner?

I am not really sure what the next big acronym will be, but you can be certain there will be several more categories to come, as technology changes (along with many updates to Guides such as these). That being said, I think the meta-trends you will see involve a certain “compression” by both ends of the spectrum, where the demand side and supply side players look to build more of their own data-driven capabilities. Publishers obviously want to use more of their own data to layer targeting on top of site traffic and get incremental CPM lift on every marketable impression. By the same token, advertisers are finding the costs of storing data remarkably cheap, and want to leverage that data for targeting, so they are building their own capabilities to do that. That means the whole space thrives on disintermediation. Whereas before, the tech companies were able to eat away at the margins, you will see the real players in the space build, license, or buy technology that puts them back in the driver’s seat. TheBest Practices in Digital Display Advertising Guide is kind of the “program” for this interesting game.

To learn more about the Best Practices in Digital Display Advertising Guidedownload the report here.

Same Turkey, New Knife

The way the ad tech world looked pre-DSP...and pre-DMP

Technology may still capture the most advertising value, but what if publishers own it?

A few years ago, ad technology banker Terence Kawaja gave a groundbreaking IAB presentation entitled, “Parsing the Mayhem: Developments in the Advertising Technology Landscape.” Ever since then, his famed logo vomit slide featuring (then) 290 different tech companies has been passed around more than a Derek Jeter rookie card.

While the eye chart continues to change, the really important slide in that deck essentially remains the same. The “Carving up the stack” slide (see above), which depicts how little revenue publishers see at the end of the ad technology chain, has changed little since May 2010. In fact you could argue that it has gotten worse. The original slide described the path of an advertiser’s $5 as it made it’s way past the agency, through ad networks and exchanges, and finally into the publisher’s pocket.

The agency took about $0.50 (10%), the ad networks grabbed the biggest portion at $2.00 (40%), the data provider took two bits (5%), the ad exchange sucked out $0.35 (7%), and the ad server grabbed a small sliver worth $0.10 (2%), for a grand total of 64%. The publisher was left with a measly $1.80. The story hasn’t changed, and neither have the players, but the amounts have altered slightly.

While Kawaja correctly argued that DSPs provided some value back to both advertisers and publishers through efficiency, let’s look ahead through the lens of the original slide. Here’s what has happened to the players over the last 2 years:

  • Advertiser: The advertiser continues to be in the cat bird seat, enjoying the fact that more and more technology is coming to his aid to make buying directly a fact of life. Yes, the agency is still a necessary (and welcomed) evil, but with Facebook, Google, Pandora, and all of the big publishers willing to provide robust customer service for the biggest spenders, he’s not giving up much. Plus, agency margins continue to shrink, meaning more of their $5.00 ends up as display, video, and rich media units on popular sites.
  • Agency: It’s been a tough ride for agencies lately. Let’s face it: more and more spending is going to social networks, and you don’t need to pay 10%-15% to find audiences with Facebook. You simply plug in audience attributes and buy. With average CPMs in the $0.50 range (as opposed to $2.50 for the Web as a whole), advertisers have more and more reason to find targeted reach by themselves, or with Facebook’s help. Google nascent search-keyword powered display network isn’t exactly helping matters. Agencies are trying to adapt and become technology enablers, but that’s a long putt for an industry that has long depended on underpaying 22 year olds to manage multi-million dollar ad budgets, rather than overpaying 22 year old engineers to build products.
  • Networks: Everyone’s talking about the demise of the ad network, but they really haven’t disappeared. Yesterday’s ad networks (Turn, Lotame) are today’s “data management platforms.” Instead of packaging the inventory, they are letting publishers do it themselves. This is the right instinct, but legacy networks may well be overestimating the extent to which the bulk of publishers are willing (and able) to do this work. Networks (and especially vertical networks) thrived because they were convenient—and they worked. Horizontal networks are dying, and the money is simply leaking into the data-powered exchange space…
  • Data Providers: There’s data, and then there’s data. With ubiquitous access to Experian, IXI, and other popular data types through 3rd party providers, the value of 3rd party segments has declined dramatically. Great exchanges like eXelate give marketers a one-stop shop for almost every off-the-shelf segment worth purchasing, so you don’t need to strike 20 different license deals. Yet, data is still the lifeblood of the ecosystem. Unfortunately for pure-play segment providers, the real value is in helping advertisers unlock the value of their first party data. The value of 3rd party data will continue to decline, especially as more and more marketers use less of it to create “seeds” from which lookalike models are created.
  • Exchanges: Exchanges have been the biggest beneficiary of the move away from ad networks. Data + Exchange = Ad Network. Now that there are so many plug and play technologies giving advertisers access to the world of exchanges, the money had flowed away from the networks and into the pockets of Google AdX, Microsoft, Rubicon. PubMatic, and RMX.
  • Ad Serving: Ad serving will always be a tax on digital advertising but, as providers in the video and rich media space provide more value, their chunk of the advertiser pie has increased. Yes, serving is a $0.03 commodity, but there is still money to be made in dynamic allocation technology, reporting, and tag management. As an industry, we like to solve the problems we create, and make our solutions expensive. As the technology moves away from standardized display, new “ad enablement” technologies will add value, and be able to capture more share.
  • Publisher: Agencies, networks, and technologists have bamboozled big publishers for years, but now smart publishers are starting to strike back. With smart data management, they are now able to realize the value of their own audiences—without the networks and exchanges getting the lion’s share of the budget. This has everything to do with leveraging today’s new data management technology to unlock the value of first party data—and more quickly aggregate all available data types to do rapid audience discovery and segmentation.

 The slide we are going to be seeing in 2012, 2013 and beyond will show publishers with a much larger share, as they take control of their own data. Data management technology is not just the sole province of the “Big Five” publishers anymore. Now, even mid-sized publishers can leverage data management technology to discover their audiences, segment them, and create reach extension through lookalike modeling. Instead of going to a network and getting $0.65 for “in-market auto intenders” they are creating their own—and getting $15.00.

Now, that’s a much bigger slice of the advertising pie.

[This post originally appeared in ClickZ on 2/1/12]

The Data Driven Agency

Three ways you can supercharge your digital media agency with data

Today’s digital media agency has access to enormous amounts of data, but using it effectively is what is going to make the difference between the shops of the future and the also-rans. Delivering data-driven insights is the key to being a 21st century agency. Here are three ways you should be working with data to secure your future:

Visualize it

How much time are you and your colleagues spending collating data, building reports, and formatting spreadsheets and PowerPoint decks for your clients? Most of the agencies I have worked with over the years admit to dedicating an embarrassingly large amount of (highly expensive) time towards these menial tasks. It’s not that getting your clients the data they need is not worth the time, it’s simply that there are now so many automated ways to deliver the data without burning salary.

To paraphrase former agency head and Akamai leader David Kenny, if you are doing things with people that you can be doing with computers, you have already lost. Why spend time formatting Excel spreadsheets and populating PowerPoint report templates with data, when you can be spending salaried employee time selling more services, optimizing campaigns, and delivering great strategy and creative?  Today’s automated ad management solutions and DMPs offer powerful ways to port both audience and ad serving reporting data into a single interface, to get instant access to key metrics such as frequency to conversion, churn rate, and channel attribution.

Ask yourself if the cost of such a system is more than the cost of the time your employees you have been spending building reports—and, ultimately, more than the cost of your eventual demise, should you ignore the changes afoot in your business.

Aggregate and Activate it

Think of all the data you have access to from a digital media standpoint. If you are helping clients execute a digital media campaign, you have traditional serving data from your demand side server, such as DFA. You probably also have engagement data from your rich media ad server. If you have access to your clients’ website pages (or at least tags there), you have site-side data, including conversion event data. If you are using an audience measurement tool, or are doing audience-specific buying through a demand side platform, you also have audience measurement data. Great. What are you doing with all of it? Moreover, what kind of data does your client have that you can help them add to activate the common advertising data types I have just described?

Let’s take the example of an agency using an audience measurement reporting tool, alongside an ad server report. In this case, it is possible that the analyst knows that the highest frequency converters for his travel campaign belong to a popular PRIZM segment, and he may also know that visitors to a popular travel site are three times as likely to engage with his rich media ad creative. Now what? Obviously, the right move is to buy more of the audience segment and double up with guaranteed advertising on the travel site. But what about audience overlap?

How can the advertiser reduce ad waste by ensuring that members of his audience segment that he is securing for as little as $2.00 CPM on exchanges are not overrepresented on the premium site for which he is paying $18.00 CPM? Plus, how many members of that audience are also already registered as customers? If you are not deploying a DMP to aggregate your clients’ CRM (first-party) data alongside the site-side and ad serving (2nd party) data and the purchased (3rd party) data segments, then there is going to lots of duplicated uniques in your audience. Smart data aggregation creates ad activation through waste reduction, lifting conversion rates, while lowering cost per conversion. Getting an effective universal frequency cap across digital channels is very difficult, but every dollar not wasted on duplicate impressions is another dollar that may be spent finding a new audience member. Reducing waste adds reach—and performance, which every client likes.

Compare it

As a digital media agency, you’ve run hundreds, perhaps even thousands of campaigns, producing thousands of data-rich reports for your clients. How much of that knowledge are you leveraging? Although you might know the top travel sites and audience segments to reach “moms of school-age children in-market for a beach vacation,” how readily available is that knowledge? Is it sitting inside your Media Director’s head, or hidden in various documents that don’t talk to one another? How about access to normative campaign data? How quickly can you find out how certain sites performed against similar KPIs without doing hours of research?

Like or not, advertisers want to know how their campaigns are performing against known standards, and it’s gotten a lot more complicated than beating a 0.1% click-through rate lately. Knowing how your last 10 travel campaigns performed—from which guaranteed site buys succeeded, to which audience segments performed, to which creatives elicited the highest CTR—is just step one. Having that data available for quick reference means that every new campaign can start from an advanced performance level, and your media people don’t have to recreate the wheel every time you receive an RFP.

Today’s smart DMPs also feature the ability to leverage your data to an even greater extent, especially for audience buying. Why limit yourself to pre-packaged audience segments that do not include your client’s first-party data? Today’s more advanced DMPs give marketers the ability to create audience segments on the fly, building discrete segments from data that includes available third-party data—but also first-party data, such as registration details, transactional records, and signals from hosted social media listening solutions. It’s the difference between buying from an ad network and creating your own.

Summary

Buying into portals’ site sections was the first phase in the effort to bring contextual and audience relevance to ad buying. Networks followed, offering packaged audiences at scale. Then bidded exchange buying came, offering pre-packaged audience segments at the individual cookie level. Today’s best practices include marrying all available data types to give marketers the ability to create their own targeted buys, and modern data management platforms are helping the largest advertisers automate what they have been doing since the first direct mail piece went out: finding targeted audiences. Leveraging today’s DMP technology can not only help you find those audiences more easily, but help you understand who they are, why they respond, and help you find them again.

Chris O’Hara is head of strategic partnerships for nPario, a DMP with clients that include Yahoo! and Electronic Arts, among others. A frequent contributor to industry publications, this is his first column for The Agency Post. He can be reached through his blog on www.chrisohara.com

[This article originally appeared in The Agency Post on 1/25/12]