2. Goal and agenda
Goal
• Inform about recent disruption in the industry and the changes it will produce
Agenda
1. Industry moving to automated buying and selling via exchanges
2. Brands moving to targeted advertising
3. Winners and predicted developments
4. Risks
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3. Industry moving to automated buying via exchanges
Industry going through a similar transformation as the one that occurred in the finance industry
Finance
industry
Display 2002 2011 2015
Advertising
Buy and optimize via
real time exchanges
Buy and optimize via
real time exchanges
Buy directly with
networks (Old Way)
Buy directly with
networks (Old Way)
Old way
Data Sources: Econsultancy “DSP buying guide” , Forrester “RTB hits mainstream”
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4. Industry moving to automated buying via exchanges
The online advertising ecosystem is changing
Advertiser Ad Network Pub 1 BENEFITS of the RTB
Pub 2
ecosystem
Pub 3
The current system
Pub 4
Agency Ad Network
media buy • Efficiencies/ cost
saving – bidding
BIG Publisher allows for efficient
market
• ROI on ad spent -
more targeted ads
produce better ROI
Targeting
Technology Yield Optimizer
Agency • Transparency -
The RTB (real time bidding) ecosystem
Quantcast, (AdMelt, Rubicon,
BlueKai,Rapleaf, Pubmatic) advertisers knows
where ads will run
• Flexibility -
Advertiser can
quickly change
DSP Exchange campaign if needed
(tradedesk, (Google,
Advertiser Publisher
dataxu, Media Yahoo,
Math) OpenX)
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5. Industry moving to automated buying via exchanges
The agency account exec is being replaced by a data geek and software
The Old Way The New Way
• Snazzy account reps negotiate •Buyers and sellers enter bids for ad
inventory buys on behalf of the inventory into an exchange
advertiser • The exchange find matching bids
•Lots of phone calls and determines what inventory will
•Lots of negotiation be sold at what price
Inefficient • Just like the NYSE
-5-
6. Brands moving to more targeted advertising
Market conditions and new technologies driving advertisers to use targeted advertising
Technologies Market Trends
Behavioral Blue Kai
• Targets based on user’s previous comp
browsing behavior anies
• Often called “lookalike” modeling as Quantcast •Soft economy forces brands to be careful with
advertisers can target users that their ad budgets. Brands can no longer afford
“lookalike” their customers “machine gun” advertising approach.
Re-targeting Re-targeter
• Users becoming trained to avoid and mentally
•Display ads based on what action comp
user has performed on advertiser Ad roll block display ads that are not relevant to them
anies
website
Fetchback
•With the rise of social media and web 2.0 sites
users are revealing much more data about
themselves.
Search retargeting
Target users who have searched comp
for certain product on a search Chango
anies
engine
Magnetic
Social Media Targeting Compass Labs
Target users based on their social Graph Science
media activity comp
anies
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7. Projected Developments (1/2)
Google likely to be the big winner in the industry
• Exchanges are high margin businesses since they serve the role of “middle-men” between
buyers and sellers.
• Tough for new competitors to come in as creating the technology and building a network of
Google well publishers is quite costly.
• The other large players (YHOO, MSFT) have not executed well due to exec changes and
positioned for a strategy shifts. Most recent shift is that the two firms will partner on a common Ad Exchange.
home run • Combining forces will help YHOO/ MSFT, however, integration efforts are still unclear, and
there are several question marks about how exactly partnership will work.
• Google – has executed well, has shown strong commitment to display, has consistent
strategy, has significant resources to back the strategy.
• Large publishers likely to resist giving inventory to Google in order to maximize ad revenue.
Opportunity for • Many publishers are upset about the large power Google has in search and are afraid of
white label Google gaining similar power in display.
exchange • Thus larger publishers will be keen on licensing tech and running their own exchange.
• DSPs help large publishers with their own exchanges as advertisers can run ads on multiple
providers exchanges via same DSP.
• Unique “look alike modeling” technology creates huge value for advertisers
Quantcast well • Slow economy driving more advertisers to be more targeted increasing demand for Quantcast
• Value of Quantcast tool is tangible and easy to measure
positioned for a • Tough for competitors to duplicate Quantcast; network of publishers create significant barrier to
home run entry
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8. Projected Developments (2/2)
Google could use search data to improve display ads
Google search/ • Google could share its search data with its RTB exchange allowing advertisers to target users
based on their Google search history e.g Expedia could buy Hawaii hotels ad on CNN.com for
display a user who has searched for Hawaii flights on Google.
convergence • Such well targeted advertising creates enormous value for advertisers.
could rattle the • If the above happens the industry will be rattled once again.
industry
Firms will look • Proliferation of Social Media use among consumers creates a slew of new data that can be
used to better target ads across the web.
for ways to use • Increased use of FB connect by publishers (80K+ sites use it), allows for targeting of users
social media based on FB profiles data.
profiles to target • Compass Labs is leading the charge in this area however others are likely to follow.
ads
• Competition is high and likely to intensify as there are not strong barriers to entry
DSP’s likely to • SEM advertising companies likely to enter space (efficient frontier already has) further
see margin intensifying competition
squeeze • DSPs are having trouble coming up with differentiating features thus forced to compete on
price ( Triggit only firm has made some progress in differentiating features)
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9. Risks
•Currently Google RTB Exchange has under 50% market share
Google gaining •However, 70-80% of inventory is not traded through exchanges – “up for grabs”
too much power •If Google has too much power other players in the ecosystem will see their margins shrink
•Legislation that limits user tracking on the web currently being mulled by government
•Such legislation could kill the targeting technologies described here
“Do not track” •Some point to the success of “do not call” list as reason why this legislation will succeed
legislation
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