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An introduction to Ad Networks, incl. types, business models, pricing, reporting, etc. From the Innovation Works mentoring sessions (February 2010).
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Innovation Works Confidential and Proprietary
Wang Hua Founding Partner, Innova1on Works February 2010
What is an Ad Network?
• Aggrega&ng
• Media planning
• Ad crea&ng
• Ad serving
• Tracking
• Repor&ng
• Transac&on
© 2010 – Innovation Works – All rights reserved. 2
Media Media’s Posi0on
Adver+sers Adver0ser’s Demand
Ad Network Aggregate & Convert
• Both Media and Adver+ser are clients of the Ad Network, but fundamentally Adver+ser is the client of the whole value chain. • Branding ads network / performance ads network / ver+cal ads network.
Branding Ads vs. Performance Ads
Branding Ads Performance Ads
Brand Awareness, Reputa+on, etc.
Long-‐term Profit Short-‐term Profit
Immediate Transac+on
• Ads’ purpose is to impact user decision. • The closer ads to user decision, the higher the value of the ads.
© 2010 – Innovation Works – All rights reserved. 3
Advertiser’s Decision-Making Flow
Set Goal and Matrix
Ads Crea+ve
Media Planning
Small-‐Scale Running
Tracking Result
Stop
Large-‐Scale Running
Bad
Good
Medium
Crea+ve Agency Media Agency SEM
Tracking tool
© 2010 – Innovation Works – All rights reserved. 4
Why Ad Networks make money?
• Buy low -‐ Sell high
• Create addi&onal value and share a split:
Long-‐term strategies • Service (Double Click) • BeQer algorithm, beQer conversion (Google vs. Yahoo)
• Network effect to get monopoly posi+on
Short-‐term strategies • Control of specific traffic
• Control of specific adver+ser • Arbitrage • Re-‐package undervalued resources (Focus Media)
• Economy-‐of-‐scale (Allyes)
© 2010 – Innovation Works – All rights reserved. 5
Pricing Model
• Fixed Price Upside:
• very simple, easy to understand, easy to execute • predictable revenue stream or spending
Downside: • hard to price right • can’t capture the max value, and lose poten+al long tail revenue
• Bidding automa+c pricing, capture the most value
less transparency, harder to understand and use by adver+ser loss of revenue when there is not enough bidding pressure
• The evolvement of the Bidding System from Overture to Google
case study; why every winner only need to pay +1 cent of next place
© 2010 – Innovation Works – All rights reserved. 6
Tracking and Reporting
• Tracking and repor&ng is essen&al, as Adver&ser pays for matrix.
• One advantage of online ads is accurate tracking and fast itera&on.
• Compare to inven&ng a new matrix, it’s beIer to be compa&ble with the long-‐accepted old matrix.
Premium media Discount media New media
Main budget Experimental budget
© 2010 – Innovation Works – All rights reserved. 7
Matrix
• CPT
• CPM
• CPC
• CPA
• CPS
• CPR
• Mismatch (Adver+ser): Payment matrix & Internal matrix or real demands
Higher Adver+ser's risk Lower Adver+ser’s willingness
Lower Publisher's willingness Higher publisher’s risk
• The trend is going down.
• But it will require beQer tracking and targe+ng capability.
© 2010 – Innovation Works – All rights reserved. 8
Spam
• Destroy the adver&ser’s trust & kill the network (Alimama).
• Comes from a flaw of the business model.
• Misalignment between payment matrix & adver&ser’s real demand.
It’s almost impossible to spam CPS or CPR, the bigger the gap the harder for us to an+-‐spam.
© 2010 – Innovation Works – All rights reserved. 9
Ways to Anti-spam
• Business model: minimize the gap.
• Technology: gather as much informa&on as possible, and recognize the spam paIern:
• Tracking system on both publisher side and adver+ser side.
• Example: IP, user agent, cookie, referral, click through rate, click paQern, traffic paQern, user stay +me, user behavior on landing page, conversion rate.
• Adver&ser, user report and manual analysis.
© 2010 – Innovation Works – All rights reserved. 10
Serving & Matching (Ⅰ)
• Categorize traffic, recognize user inten&on, matching with right ads and landing page, and inspiring user conversion.
• Goal: maximize publisher revenue and adver&ser’s conversion.
Adver&ser inventory
Context info, user info, historical data
Ads content, historical data
Matching, serving, and conversion
© 2010 – Innovation Works – All rights reserved.
Publisher inventory
11
An Example:
Serving & Matching (Ⅱ)
• Contextual targe&ng, behavioral targe&ng, profile targe&ng:
Contextual targe+ng: • Upside: No need of addi+onal informa+on, current user status, best matching when user have clear intension
• Downside: No individual informa+on, worse when user does not have clear intension
(e.g. MySpace)
Profile targe+ng, Behavioral targe+ng: • Upside: BeQer when user haven’t clear intension
• Downside: No user current status informa+on, need specific user privacy data, hard to get.
© 2010 – Innovation Works – All rights reserved. 12
Serving & Matching (Ⅲ)
• Currently good ad networks normally use all three methods.
• Matching quality depends not only on quality of algorithm, but also quality of
publisher inventory, ads inventory size and data size.
• Normally, beIer algorithm and more input get beIer result, but must balance
with cost and speed.
• Ads format, ad quality, landing page quality, serving quality have big impact
(e.g. ads similar with main content).
© 2010 – Innovation Works – All rights reserved. 13
Serving & Matching (Ⅳ)
• Example system on slide 11.
• Star&ng from contextual targe&ng, now have profile and behavioral targe&ng,
support mul&ple form bidding, CPC and CPM bidding.
• Revenue = Traffic x RPM
• RPM = CPC x CTR x Quality Score (conversion)
• Extract keywords, calculate eCPM, trial running, refine eCPM, adjust with
conversion score.
© 2010 – Innovation Works – All rights reserved. 14
Ads Front-End
• Normally includes ads crea&on, media planning, report and transac&on part.
• Could be internal facing or external facing.
• Help user create and manage campaign, simplify user decision making.
© 2010 – Innovation Works – All rights reserved. 15