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Blinding me with science: Ad optimizers

Dear, Startup Whisperer readers.  This is a post that I did for Adotas.  I hope that you enjoy it.


When one takes a step back and looks at the online ad market today,
there doesn’t seem to be a lot of good news: CPMs are dropping, display
ads are on life support, the marketplace is incredibly inefficient,
advertisers are demanding more transparency. The list goes on.

Despite all this, not all hope is lost. In fact, there is a lot of
innovation happening today, primarily coming from a bevy of startups
who are adding a tremendous layer of value between the supply and
demand. These companies are still getting funding when other sectors
have gone dry. Some even recently got acquired.

This innovation is coming from the optimizers. And they nest in what we in the space like to call the “ad optimization stack.” This ad optimization stack is made up of an increasing number of
companies. In fact, it can be quite confusing, so here’s a Beginner’s
Guide to understanding the optimization players and in what area of the
stack each plays:


Ad container optimization encompasses placement, ad creative, and
reach. Placement has to do with a set of technologies that optimize the
most appropriate place for any particular ad creative on a publisher
site, right down to the x and y coordinates. For example, knowing if an
ad is below or above the visible fold of a web page is an important
(and often unknown metric). Creative optimization refers to any technology that performs testing
related to colors, ad styles, engagement interactions, and ad creative.
Performing optimizations on creative attract the right types of
consumer behaviors for whatever offer is being presented.

Last, reach optimization focuses on analyzing the distribution path
of where ads are being seen. Whether the ad is being distributed
directly to a publisher site, an exchange, or an ad network (or all
three), is important to ensure that the appropriateness of a campaign
is maintained (e.g, some brands do not want to be on user generated
content sites or blogs).  Some interesting companies in this space: SnapAds, Tumri, Clearspring, Gigya, WidgetBucks.


There are many form forms of targeting, including behavioral,
demographic, contextual, category, and interest-based/affinity.
Basically all of these forms of targeting address different types of
objectives that primarily tradeoff between reach and scale. For
example, behavioral targeting, which is a set of segmentation
techniques to use consumer behavioral data to capture and use intent
data, is one of the best ways to target a consumer who is far down the
conversion funnel.

On the opposite end of the spectrum, category-level targeting is a
classic way in which traditional display ad networks target customers
using broad segmentation buckets (like “technology”, “business”, etc).
Thus, behavioral offers the most targeted but the least reach while the
inverse is true for category targeting. There are a bunch of
technologies in the middle of these approaches that use a combination
of publisher keyword content to derive interest in a specific product
or topic. These techniques fall under the contextual and interest-level
targeting. Some interesting companies in this space: BlueKai, Exelate,
Lookery, OthersOnline, WidgetBucks


Yield management technologies vary from companies that are focused
on yield ad inventory, ad networks themselves, or specific ad offers
within an ad placement. Some of the larger optimization companies in
the space have focused on being today’s answer to what broader ad
exchanges are purporting to offer. They are focused on using a single
dashboard across multiple geographies to manage hundreds of ad networks.

They also provide additional services like account management, other
types of specific optimization (e.g, text ad optimization), as well as
vetting technology (e.g, ensuring quality ad content being served thru
one ad placement). Additional optimization technologies provide
predictive and advanced A/B testing technologies that focus on
allocating specific inventory or offers inside the advertisement. Often
times, these technologies utilize sophisticated algorithms that take
cookie (person), domain, or aggregate behaviors to predict the most
appropriate ad offer.

Some interesting companies in this space: Pubmatic, Rubicon Project, YieldBuild, YieldX, WidgetBucks


The key distinction between marketplaces and ad exchanges are that
marketplaces aggregate advertiser demand on their own and exchanges
aggregate demand from other ad networks. Ad exchanges are quickly
becoming the favorite way to manage multiple remnant ad networks versus
the standard “daisy chain” approach that is applied today. Daisy chain
management is a process in which a publisher queues up multiple ad
networks behind one ad tag (or placement) in order to to get the
highest eCPM of each ad network, as well as managing the highest
fill-rate for one ad placement.

Some interesting companies in this space: Ad Exchanges: Right Media’s Publisher Media Exchange, Microsoft’s AdECN, DoubleClick

Advertising Exchange

Auction-based marketplaces: ContextWeb ADSDAQ, AdBite, Turn, Google AdSense, and Yahoo! Publisher Network There has been well over $300 million dollars invested in the ad
optimization space in the last year. As you look up-and-down the ad
optimization stack, you will notice that these players focus on math
version people.

The new players in this space have realized that the value added
services are those that take advantage of the huge hegemonic moves that
have been made by the larger players on both sides of the supply and
demand aisle. We are now in the phase of refining the online
advertising story. We’re in the age of bringing more science into the
existing landscape.


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