The Pulse of RTB

Simmons Performance Solutions

The Pulse of RTB
A view through the Crystal Cube
8/14/2012

Table of Contents

1. Executive Summary…………………………………………………………………………………………..pg. 3

2. History, Industry vs. Market, & Financial Market Comparison…………………………..pg. 4

3. RTB Strategy Space…………………………………………………………………………………………...pg. 5

4. Current View of RTB…………………………………………………………………………………………pg. 7

5. Defining the RTB Supply Chain………………………………………………………………………… pg.8

6. A new RTB Model……………………………………………………………………………………………….pg. 9

7. Impact of a Dynamic Value Chain in RTB……………………………………………………………pg. 14

8. Possible Future States……………………………………………………………………………………….pg.15

9. Looking Forward……………………………………………………………………………………………….pg.19

10. References…………………………………………………………………………………………………………pg.20

The Pulse of RTB Page 1

Authors

Rufus Simmons Sunil Sharma
Founder and Principal CEO
Simmons Performance Solutions InferSystems Corp.

Copyright ©2012. InferSystems Corp., and Simmons Performance Solutions, All Rights Reserved


Executive Summary
Real time bidding (RTB) in media buying provides scale and efficiency but still has a long way to go in
being truly effective. This paper examines the vectors that the industry is likely to travel along from its
current state to becoming a highly effective media buying mechanism.

The new world of media buying, in which machines compete in nanosecond long second price auctions
to place billions of ad impressions has now received coverage by main stream media. But by no means
has this industry matured. In fact, it is not a single industry at all, but rather an interrelated collection of
sub-industries that formulate dynamically depending upon advertising objectives. We examine these
fluid formulations to provide more clarity about the strategy space and optimal strategy selection within
it.

Further, the transaction of buying and selling media is no longer executed directly between an
advertiser [represented by an agency] and a publisher. Rather, individual media impressions are bought
in auctions in media exchanges. These impressions are selected from pools of billions. The promise is
that rather than purchasing huge batches of inventory through inefficient offline transactions, RTB
enables more economical transactions and superior targeting; resulting in more efficiency at scale.

But RTB has the potential to provide a more dynamic impact that exceeds just efficiency and scale.
However, like many emerging industries, it is still defining its role within an overall advertising strategy.
But unlike many industries, RTB is an ecosystem and its ability to deliver value to advertisers will come
from how well it can reformulate supply chain components to deliver on different goals in near real
time. Certainly, a value chain is preferable to just a supply chain because each component provides
more value than it costs. But in RTB, the value chain should not be static. In order to achieve its
potential, RTB must be delivered through a dynamic value chain that reformulated at the campaign
level. In doing so, RTB will need to move beyond just audience data, and be able to simultaneously
extract and distribute valuable signals from all kinds of variables, such as creative type, ad location,
content, context, time of day, day of week, etc.

Data is the continuous pulse that feeds life into the ecosystem and provides a means to identify value.
But the approaches to dealing with data must be advanced further. Identifying the right data
combinations at the campaign level will enable the right value chain formulations and enable RTB to go
to the next level.

We introduce an industry design model that identifies areas of value within the industry, and identifies
key levers for more effective RTB advertising. We go on to discuss the four key competencies around
which an ad tech business can be built. Our model demonstrates that the RTB ecosystem's pulse is a
continuous flow of data; but that the meaning of data needs to be redefined.

Central to the RTB industry’s existence is the ability to deliver the optimal message on the right
impression, in the appropriate channels, and under the conditions that will result in overall advertising
effectiveness. As we will demonstrate, the ability to collaborate and bring the right tools to bear at the
campaign level is the key to achieving this.


History
Traditionally, advertisers either directly or through agencies bought media placements directly from
publishers. The system relied on faxes and insertion orders, and was for all intents and purposes the
same across media types – TV, radio, print, and digital. Search advertising introduced a new paradigm,
as search algorithms, most notably and successfully Google’s, displayed relevant Web pages and
advertisement based on the users’ search terms and other attributes. And, as Internet usage
skyrocketed in the early 2000s, and the supply of display advertising grew, intermediaries like ad
networks emerged to sell the remnant inventory that was left over after the publishers’ direct sales
efforts. Since then, media and data exchanges, demand side platforms with algorithms and bidders,
supply side optimizers, and other technologies have emerged to turn display advertising into a fully
programmatic industry which also includes premium media placements. These innovations have also
extended into mobile, video, and social.

Industry versus Market
Distinguishing industry from market may seem like minutia, but isn’t. The two have starkly different
meanings economically. A market is comprised of buyers and sellers, and in most cases intermediaries
and market makers. An industry is comprised of the supply side of a market, the sellers.

This distinction is significant for RTB because the market has traditionally been defined as supply and
demand. In this construct, publishers are considered the supply side, agencies and advertisers are
considered the demand side, and everyone else is considered to be intermediaries. However, only
advertisers are, in reality, buyers. Everyone else is a seller of something. Agencies are, in fact, selling a
service that enhances the ability to buy of advertisers the raw materials, the impressions. That agencies
do the buying on behalf of advertisers does not change the fact that they are actually sellers of a service.

This is why we have not included advertisers in this paper, to convey an analysis of industrial
development.

It’s NOT The NASDAQ
One popular axiom is that RTB is very similar to the stock market. But this is not really the case. It is true
that media trading looks more like The NASDAQ than the archetypical world of AMC's Man Men TV
series. Yet, while the programmatic, or machine driven, nature of RTB is similar to the financial markets,
key dissimilarities outweigh similarities.

The most significant difference is that the bulk of stocks are bought and sold by people who have
nothing to do with their creation. Once companies make stock available in initial sales, the equities are


simply traded among independent entities. This is a far cry from real time media, in which publishers
have to create content, worry about yield, manage multiple sales channels, learn to use technology, find
signals in data, and deal with a number of other key elements in the production and delivery of valuable
ad space to advertisers. Likewise, advertisers and agencies must deal with similar challenges to find the
optimal pockets of inventory.

Another stark contrast is that stock trading has been developed as a craft over more than a century of
practice. Traders can make reasonable bets by examining the economic cycle, sector strength, P/E
ratios, multiples, and news coverage. In contrast, programmatic media buying is less than 5 years old.
Buyers are still figuring out metrics and evaluation.

There are other key differences as well. But ultimately, RTB is a high growth, immature market whose
economic structure is still nascent. Therefore, while technologies can be borrowed from the financial
world, the real issue in programmatic media is that the industry, the supply side economic structure, is
still under construction. The book is yet mostly unwritten, and we strive to add another chapter.

The RTB Strategy Space
Before diving into the industry’s current state and potential future development, we will define its
current design and attempt to forecast future strategic moves by its participants. We will do this visually
using a tool developed by the late R. Jeffrey Ellis, former professor of management strategy at Babson
College, and call it the RTB Crystal Cube. Jeff’s consulting, research, and teaching in strategy spanned
decades and included extensive work with some of the leading global companies across industries. He
found that the companies that succeed consistently do so not by engaging in trench warfare with
competitors over minutia, but by anticipating the future and aligning strategy with operational
excellence to find a path to Utopia.

The model is based on anticipating the key vectors that an industry is likely to travel along to provide
maximum value to consumers (advertisers in this case). There is no shortcut to selecting the right
vectors. It can be done only by immersion into a market’s functions and then using that deep
understanding to sense the likely future direction. We use market here because anticipating the
evolution of buyer needs is paramount for charting the course of industrial development.

By definition, this cannot be an exact science. If it were, then everyone would do it and no one would
have a strategic advantage, except in scale; and the industry would be static. Our model of the path to
Utopia has been created by combining our own experience and knowledge of the space with
confidential discussions with some of the leading executives within digital media.


The RTB Crystal Cube

RTB’s ultimate goal, like that of any industry, is effectiveness; advertising effectiveness. We believe that
true effectiveness will be achieved by traveling along and maximizing on three key vectors – scale,
efficiency, and advertising impact.

Advertising
Effectiveness
Efficiency

Scale

An industry’s true potential is reached when it is maximized on the three key vectors. In this case, when
scale, efficiency, and advertising impact are maximized, we reach the farthest point from the origin [of
the industry] and have advertising effectiveness. This is Utopia.

We have plotted the various participants within the RTB industry onto our RTB Crystal Cube later in the
paper. This shows where specific industry participants are strong and weak relative to full value
creation, and thus sheds light on possible future strategic investments.

But first, we examine how the RTB supply chain functions today, and can function in the future to create
more value.


Current View of Real Time Bidding

RTB is outpacing the growth of the rest of display media spending; Marketing
while display media spending is projected to increase by 10.5% CAGR
until 2015, RTB is projected to increase by as much as 66% CAGR
over the same span. With this rapid growth, RTB has caught the eyes
and dollars of many entrepreneurs and investors, resulting in more
than 250 companies staking out positions in the space. These new Raw
Materials
companies and the increased investment dollars are pushing the
industry to new heights.

With the flow of capital into RTB, many analysts are making The traditional one
assessments based on individual company operations and resources. size does not fit all
But to gain a better understanding of how the industry functions, an when it comes to
aggregate view of all the players and their interrelations is needed.
value chain models Procurement
The publishers, advertisers, DMPs, DSPs, ad exchanges, ad networks,
ad servers, buying platforms, and various optimizers are extremely
important; but all industries evolve as a whole system that delivers
value. Valuable product flow from seller to customer is the most
important factor for any industry. But in digital media, and more
specifically RTB, the ability to identify relevant impressions and place
the proper ads with the right messages under the optimal conditions Processing
requires a dynamic delivery system that is campaign specific.

When searching for a representation of the industry, one is likely to
find one of the many models which try to stay true to the traditional
straight line supply chain or value chain models, such as the
traditional liner model presented on the right. Distribution

But the key questions are: What is the central function which
produces the industry’s value? How is this best represented as a
model which serves the industry? Are the current models value
chains? Can a new model be used to either predict or prompt
discussions on how the industry will evolve? Sales


Defining the RTB Supply Chain
A straight line static model has been the standard bearer for representing the RTB industry.
Models like the one in figure one, which was produced by LUMA Partners LCC, have been very
useful in understanding the makeup of RTB. It provides an excellent visual representation of the
industry’s layout and its fragmented nature, thereby conveying the complexities, threats, and
opportunities within the space.

However, [we assume that] this model is not intended to be a functional representation of how
the industry operates. Yet, it is often misapplied that way.

Ultimately the RTB market is not about who is participating, but rather who is paying whom for
what.

The RTB Supply Chain

Figure 1

The current models show how impression sales flow from publisher to advertiser. This process
flow makes it a supply chain and not a value chain, which would show how value added activity
is added at each step. With the industry growing and attempting to attract a greater percentage


of online spending, the supply chain view is necessary at this step in the RTB evolution. It shows
a logical flow of available impressions and how ads are placed.

But can we present a true value chain model which will spur growth and show how value is
added to the supply chain? We believe so.

There are three key reasons that a straight line static supply chain model is not the best
representation of the RTB industry.

 The central process is not represented; models focus on identifying the sale and not
identifying value creation.
 Unlike the vast majority of industries, RTB is not only an ecosystem in name, but an
ecosystem in the way that it breaths through the flow of data.
 RTB and its players are quickly evolving, and with cross platform capabilities, some of
the players are changing the flow process from one available impression to the next.

Ultimately, RTB works best when the supply chain is reformulated continually at the campaign
level, and when the basic raw materials, the impressions, are treated uniquely based on their
inherent qualities to deliver value in the form of advertising results at the campaign level.

A New RTB Model
Ultimately, RTB is still a transaction between a publisher and an advertiser. That it is conducted
in media exchanges and marshals a variety of intermediary assets and data does not change its
fundamental nature; the difference is the added scale and efficiency to the transaction. But as
mentioned, effectiveness also requires advertising impact.

Audience targeting is at the center of RTB. The idea is that rather than Web sites being a proxy
for audience, it is more effective to reach the right audiences across any Web site. But this is an
oversimplification.

The accuracy and scalability of audience segments is a controversial issue at best. The
traditional way of categorizing people into audience segment buckets by tracking their
browsing history has had limited success in real application. Alternative approaches are being
used more. These include applying offline transaction data, creating custom micro-segments,
etc. And, more precision is added when the characteristics of the Web site is taken into
account.

These more sophisticated approached to data analysis, and signal capture and distribution are
being made possible by increasingly sophisticated mathematical systems, rather than basic
algorithms. In fact, emerging math systems make it possible to simultaneously look at all
available parameters as data. For example, the number of ads on a site and its contextual


category are just two data points. Thus, the distinction between data and media is blurred for
analytical purposes. It is all data, which is a good thing for publishers with high impact sites.
Advanced mathematical systems are proving beyond any doubt that inventory characteristics
such as the number of ads on a page, ad location, contextual category, and URL can be
effectively used to optimize campaign performance without the need for audience segments.
When these attributes are combined with audience data, performance increases. However, it is
clear at this point that sites are not just a proxy for audience.

New mathematical systems can ingest and distribute signals from thousands of parameters
simultaneously. This alleviates the limitation of having to rely upon only one or two types of
data at the operational level. While this is not intended to be a paper on math, these
distinctions are important to conveying what we mean by audience. We are not referring to the
traditional “audience segment.” Rather, we are referring to real people who are the audiences
for advertising messages, and to the more sophisticated techniques being developed to reach
them with the right message, at the proper times, and within the best environments. The
combined weights of these factors have an impact on effectiveness, and new mathematical
systems can measure and act on this.

In any case, regardless of the distinction, reaching the right audience is still crucial; but not just
anywhere. Mathematical analysis clearly shows that ad placements matter.

Ultimately, the essence of the dynamic RTB value chain is that it brings to bare the right assets
at the campaign level.


Dynamic Value Chain RTB Model

The dynamic RTB value chain model shows how the RTB industry really functions. In spite of the
high number of intermediaries the industry is still comprised of transactions between
publishers and advertisers. The supporting entities, around the outer ring, provide the
infrastructure, data, and math to enable the transactions to be in real time and highly targeted.
However, all that is really necessary is included in the core set of middle circles. Essentially, all
that is needed is technology, math, data, and people to facilitate effective transactions.
Whether these capabilities are provided by support firms on the outer circle or by the principles
themselves is of no consequence. Currently, they are generally provided by the support firms.

We call the companies on the outer ring support firms and not intermediaries because they are
not in the middle of anything unless they support a transaction in providing greater value, at
least in a rational market. Therefore, only those support firms that can provide significant value
in the areas of data, math, and technology will be part of the broader market in the long run.

To be clear, we define data as any parameter for consideration that can be analyzed to make
decisions. Therefore, creative type, ad placement, and audience segment are all data points.

Ad Network DMP

Creative Technology Ad
Optimization
Exchange

Data

RTB Market

Math Publisher Advertiser Math
Data
DSP
Provider

Data

Media
Optimizer Buying
Technology Platform

Creative/
Ad Server
Agency


To further clarify, technology does not necessarily mean high tech in the computing or
hardware sense; though that is generally the meaning. We use the broader definition of
technology, including methods of production to induce better outcomes, such as work flow
design improvements.

But while there are many support firms within RTB with a variety of products, we believe that
there are only 4 key areas of excellence around which an ad tech business can be built – data,
technology, mathematical systems, and people. To reach Utopia, a firm must be able to deliver
excellence in each of these areas to advertisers. That requires collaboration and partnership, as
our research indicates that buyers are not satisfied with any single company in all 4 areas.


Audiences Are Alive

The word audience is almost clinical, sterile. But we are referring to people, who eat, breath,
live, and react to their environments. Therefore, advertising has an impact on audiences. But
the impact changes over time as sensibilities, conditions, competitors’ strategies, and other
factors evolve. Therefore, yesterday’s strategy may not work today; resulting in the need for
constant data flow and analysis to extract RTB’s true power.

The actions of the advertisers, publishers, and audiences are interlocked and have an
interrelated effect on the desired outcome. Building brand equity and monetizing sites are two
primary publisher goals that drive user activity. Meanwhile, the goal of advertisers is efficacy,
whether branding or DR (which are interrelated with a two way causality). This creates the push
upon the audience in the form of advertising. However, as audiences react differently to the
same offers over time, they in turn push back on advertisers with changing behaviors.
Advanced math, tech, and data enable advertisers to decode these dynamic patters to deliver
relevant advertisements on appropriate content to the right audiences.

Data has been placed beyond the central industry because insight is pulled into this ring based
on the advertiser and publisher goals. The richness of available data is what enables optimizers
to create effective targeting rules and is paramount to the ecosystem’s progression. Technology
and math is layered on next because software pulls its information from the data and
manipulates it for effective targeting. Technology is paramount because it allows for the
identification of key audiences and relevant data to be obtained from audience activities and
provides the infrastructure for the bidding process. Advanced math is crucial because it extracts
the new signals that continually flow into the ecosystem, turning them into valuable insights.

As mentioned, the orbital ring contains the support firms in the RTB process. Each of these
strives to perform value added activities in the valuation, buying/selling, and delivery of ad
impressions with the right creative(s) to the appropriate people. They are pivotal as a whole,
but are still defining themselves and their positions, which is the reason that each arrow of the
orbital path flows back and forth. Which of these support a given transaction depends upon the
campaign objectives in a well functioning RTB strategy. Many support firms affect different
phases of the impression sale and placement process. This is also the reason that each has an
arrow flowing to and from the central value circles, because various stakeholders can bring an
impression request forward for, or place an ad creative in front of its targeted audience.

Ultimately, this dynamic flow and interrelation among industry participants creates a value
chain that reformulates for each campaign when all three key assets – technology, data, and
advanced math - are implemented to support the participants.


Impact of a Dynamic Value Chain in RTB
The key question: Is RTB better served by a supply chain model or a value chain model? The
current RTB supply chain models are important because they show how the industry processes
the flow of impressions from publishers to advertisers. But we must remember that the supply
chain demonstrates how revenue is derived, and the value chain defines how value is created.
The link between them is that in RTB the supply chain is being reformulated for every media
buy and thus creating a custom value chain in real time. As the industry evolves, the custom
value chain should become more defined and predictable. The expected consolidation of
stakeholders will force this. But in its present state, there is no exactly defined manner in which
the end to end process is completed every time. What is most important is that the identified
audience value shifts with the goals of each advertiser at the campaign level; and only by
acting upon this can RTB become truly effective. These forever shifting goals are increasing the
need for custom, dynamically reformulated value chains.

Data Has a Pulse

The RTB industry closely resembles a biological ecosystem. Unlike other industries, where value
chains are a product of stakeholders applying some value added activity to a raw material that
is processed and sent through the system to end up at a customer to be consumed and
discarded, RTB's value is derived from a central audience that emanates a pulse which is the
product from which stakeholders extract information and target a "receptive audience." Of
course, we cannot overlook the advanced processes by which impressions are placed in
exchanges for auction, and the advanced technology which enables the impression bids to be
processed and ads to be placed after applying analytics to the impressions within nanoseconds.
But all these capabilities are built around the ability to identify, extract, and evaluate data from
its source. But as mentioned, to overlook the value of reaching the right audiences within the
right environments, at the right time, and with the right messages and at the right price leaves
significant opportunity on the table. Therefore, maximum value is created only when all
available attributes can be quickly and simultaneously analyzed, and the key signals can be
distributed in nanoseconds to bid on the right inventory at the right prices.


Possible Future States: Using the Crystal Cube to Project RTB Industry
Development
Current strengths

Advertising
Effectiveness

DSP Network
Efficiency

Publisher Agency

Exchange

Scale

Having established the operational model for RTB, we can return to our strategic view of the
industry and plot the participants on the RTB Crystal Cube to project likely future moves. We
can do so by assessing their relative positions to the key vectors – scale, efficiency, and
advertising impact.

These positions represent what each of these participants can provide by themselves. For
example, a DSP can provide buying efficiency using its technology, algorithms, and managed
service teams. But, it cannot provide any scale without plugging into an exchange or a network,
which provide inventory at scale. A DSP can certainly work with these partners to provide both
efficiency and scale, but not by itself. In contrast, an ad network that has a buying platform can
provide both because it has an in house DSP and inventory at scale. In fact, when it comes to


efficiency and scale, ad networks are better positioned than DSPs, exchanges, individual
publishers, and agencies.

So much for the death of the network; an objective assessment reveals otherwise. In fact, not
only do networks provide scale through their own inventory and the ability to buy across
exchanges, but of all the entities plotted, they have the most experience in cultivating
inventory.

Certainly, agencies are the most proficient in designing impactful advertising strategies. They
also provide some scale in the form of buying power; thus enabling lower prices for given
batches of inventory.

Individual publishers are in the worst position, with a few exceptions like Yahoo, Microsoft, and
AOL because of their scale. By themselves, premium publishers have little scale in relation to
the size of the RTB industry and thus are price takers in the absence of yield management
strategies that can evaluate the non-audience value drivers within their inventory. On the other
hand, publishers have the opportunity to leapfrog legacy optimization systems because they
are just starting to bring these capabilities in house.

Possible Future Moves

Given that a number of assets already exist to provide scale and efficiency, such as DSPs and
exchanges, the most likely new investments will be in advertising impact. We believe that
advertising impact will be driven by three key developments:

 Better creative creation and delivery.
 Redefining data (not just audience segments; but all raw parameters).
 Advanced mathematical systems (to capture all signals and make decisions based on the
best ones at the campaign level).

In relation to these, the key industry participants shown in the RTB Crystal Cube would make
the following future state considerations:

DSPs: Excellent at providing efficiency, they do not by themselves provide scale or advertising
impact. Some have suggested that DSPs will become more like agencies in the future to be able
to provide creative and media planning services. This would likely create tension with their
agency clients, who currently represent the advertisers that they would be going after directly.

We believe that a more natural extension of the DSPs role is into scalable performance. This
would be different from the execution scale that exchanges provide by aggregating huge pools
of inventory. DSPs on the other hand, could provide impact through more scaled performance.
This is where advanced mathematical systems come into play, as this option has thus far been
limited by math limitations.


Large DSPs may look to develop advanced math in house. Smaller, hungry competitors will
likely to try to outflank them by acquiring such capabilities and implementing around
proprietary data assets to retain differentiation and gain the advantage of speed. It is too early
to tell which strategy will win.

Marketplaces (Exchanges): Are likely to continue being the standard bearers of supply side
liquidity. Their monetary incentives lie with providing optimal yield to publishers. This is
enabled by proper impression valuation and bidding by buyers. Therefore, we expect that
exchanges will not only invest in supply side yield formulas, but also push for more information
sharing between buyers and sellers, effective data aggregation, and more effective auction
strategies by DSPs. All of these would result in better yield for publishers and more effective
advertisement investments for media buyers.

Exchanges are also implementing and refining dynamic yield management systems that would
adjust price floors on the fly to get better yield for publishers. We believe that this will at best
provide mixed results at this stage, as these approaches rely upon big data capture and
distribution but grossly lack the sound econometrics and game theory modeling that is
required. It is a case of running before being able to walk because the market is not developed
enough at this stage to predict the probability of action and reaction in a bidding game. A major
problem with these strategies is that they rely upon reacting to a market whose rationality is a
long way from being defined. They also rely upon traditional auction theory, which is useful for
gaining a basic understanding of concepts for RTB, but is actually counterproductive given the
multi-stage, multi-unit nature of RTB auctions.

While exchanges provide yield through liquidity, scale, and efficiency, we believe that dynamic
pricing systems would work far better if publishers could bring them in house.

Networks: As exchanges and DSPs emerged, a “death of the network” hysteria emerged in RTB
circles. While many networks failed, their fate was due to poor business models, often based
around questionable practices. However, the leading ad networks of today are positioned well
against DSPs and exchanges to drive advertisement effectiveness. This is because networks
often provide their own bidding technology (DSP) and own large pools of inventory that can be
bid on. While networks don’t have the pure liquidity of exchanges within their managed
inventory pools, they can source inventory from exchanges in real time, and therefore suffer
from no liquidity challenges.

In fact, networks pay for inventory up front, and are therefore able to select optimal pools.
Further, their existence has always been centered on cultivating and managing inventory. This
experience still provides value. Finally, that they buy inventory in bulk allows networks to enjoy
a cost advantage over DSPs.

Leading networks are making significant investments in other channels such as mobile and
video. By providing pools of inventory across these channels, and aggregating and distributing


multi-channel signals, networks can help advance multi-channel RTB, and therefore enhance
RTB’s impact, and ultimately effectiveness.

Agencies: Have always been able to provide a level of scale through their purchasing power.
Agency trading desks centralize work flow for media planners and add a level of efficiency.
Those that have purchased and/or licensed their own technology provide greater efficiency. We
expect that agencies will continue to acquire capabilities that create their own “secret sauce.”
They will continue to remain focused on extracting value from first party client data through
expertise and the implementation of tactical assets across multiple platforms.

As depicted in the RTB Crystal Cube with an upward arrow, agencies are now making progress
in improving their ability to impact scale and efficiency. They have always excelled in creating
advertising impact, and are therefore well positioned to gain back some of the control that they
have lost since the onset of RTB.

Publishers: We believe that premium publishers are in the weakest position, and as expected,
are scrambling to strengthen their hand. Because publishers lack the scale to be price makers,
they must look to do three things:

1) Differentiate their inventory: Many are currently investing on this front with
partners such as DSPs, SSPs, and third party data providers, as well as optimization
firms providing mathematical systems.

2) Achieve scale: The first iteration of the private exchange was private in name only.
But we expect that premium publishers will collaborate with each other going
forward to create more premium only exchanges to control inventory allocation
and yield.

3) Bring the intelligence in house: The limitation of traditional third party
optimization systems is that they are focused on becoming more efficient by
reacting to buyer behavior. This approach is fatally flawed because the limitation of
most buying algorithms is causing them to ignore the data that can evaluate
inventory value – ad location, URL, contextual category, etc. Therefore, publishers
must bring sophisticated intelligence in house and demonstrate value to buyers in a
way that it can be easily adopted for making objective buying decisions.


Looking Forward

A major obstacle to reaching Utopia is innovation fatigue. The companies that are in the best
position are not concentrated in any one part of the supply chain. Certain publishers, support
firms, and advertisers are making very effective strategic moves, while others are lagging
behind. The common denominator seems to be the vision and the will to invest of senior
executives in learning, assets, and people with an eye toward the future.

As RTB evolves, the Dynamic Value Chain RTB Model is likely to have fewer orbital support
firms, as many have overlapping capabilities. We expect that these support firms will
consolidate and pool resources. Most are likely to provide infrastructure, as agencies and
publishers will continue to make investments to bring capabilities in house. This will result in
more consistency in procurement, measurement, and evaluation of RTB activities.

Leading technology providers are likely build their businesses around providing crucial,
interoperable infrastructure at scale. However, we expect that advertisers (and agencies) and
publishers will improve their ability to glean data intelligence in house with the availability of
advanced mathematical layers that can extract key signals from proprietary data. These
systems will create stronger incentives to capture and organize better data, as their
proliferation will mean that differential value will lie in not only being able to analyze data, but
in collecting better data to analyze.

The remaining support firms will either provide low cost commodities at scale, or high value
enhancements in niche form. And, we expect the ecosystem as a whole to accelerate
investment in enhancing the advertising impact of RTB – the uncharted vector on the path to
Utopia.


References

In depth, single blind interviews with key executives across the supply chain – publishers, support firms, agencies, and
advertisers.

Glen Calvert ( January 17, 2012). The Collective Effort to Make The RTB Ecosystem A Better Proposition For Brands. Exchange
Wire EMEA, Retrieved from http://www.exchangewire.com/blog/2012/01/17/the-collective-effort-required-to-make-the-rtb-
eco-system-a-better-proposition-for-brands/

Forrester Consulting/ commissioned by Admeld (February 10,2011), RTB Hits The Main Stream, Retrieved from
http://www.admeld.com/download-rtb-hits-mainstream/

Ye, Chen, Pavel Berkhin, Bo Anderson, Nikhil Devanur,Real Time Bidding Algorithms for Performance-Based Display Ad
Allocation, Retrieved From http://research.microsoft.com/en-us/um/people/nikdev/pubs/rtb-perf.pdf

Adobe Maximize data insights with web analytics

Gridley & Company( December 2010). Ad Exchanges, Targeting & Optimization "From Mad Men to X-Men, Retrieved From
http://www.slideshare.net/Gridleyco/ad-exchanges-targeting-optimization-from-mad-men-to-xmen

Shar VanBostirk, Christine Spivey Overby, Sarah Tarvorien- Forrester Research ( August 24, 2011), US Interactive Industrying
Forecast 2011 To 2016

bluekai White Paper: Data Management Platforms Demystified , Retrieved From, http://www.bluekai.com/dmp/

Joanna O'Connell, Emily Riley, Shar VanBoskirk, Jennifer Wise, and Sarah Takvorian- Forrester Research ( December 14,2011),
The Forrester Wave: Demand Side Platforms, Q4 2011

Husam Janda- WSI (March 2011), Display Advertising: The Billboards of the Web, Retrieved From:
http://blog.wsidigitalindustrying.com/index.php/general/events/whitepaper-events/complimentary-whitepaper-display-
advertising-the-billboards-of-the-web/

Pubmatic (February 2010) Understanding Real-Time Bidding(RTB) From the Publisher Perspective, Retrieved From-
http://www.pubmatic.com/real-time-bidding

Doug Winz, iMedia connections( September 18, 2009) Closing the vast agency publisher knowledge gap. Retrieved From-
http://www.imediaconnection.com/content/24384.asp

Alex Baxter, Digital Media Summit (July 21, 2010). Real Time Bidding and the Ecosystem. Retrieved From-
http://www.minonline.com/minsiders/Alex-Baxter/14792.html

EW News Desk Team(August 03, 2011). Four Reasons for Digital Media Industry Growth. Economy Watch, Retrieved from
http://www.economywatch.com/in-the-news/four-reasons-for-digital-media-industry-growth.03.08.html

Kathryn Koegel ( February 27, 2011), Digital Industrying Guide: How Do You Slice and Dice Your Target Audience When Buying
an Ad? Advertising Age, Retrieved From http://adage.com/article/special-report-digital-industrying-guide/digital-industrying-
guide-buy-audience-web-ads/149104/

Doug Wintz iMedia Connection( September 8, 2008), How to sell your unsold ad inventory. Retrieved from-
http://www.imediaconnection.com//content//20616.asp

Frost Prioleau, Search Engine Land (Jan 11, 2012),The New Era Of Display: 5 Reasons Search Markets Should Care. Retrieved
from- http://searchengineland.com/the-new-era-of-display-5-reasons-search-industryers-should-care-106937


The Pulse of RTB

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (13)

Ähnlich wie The Pulse of RTB

Ähnlich wie The Pulse of RTB (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

The Pulse of RTB