Download pdf - Ad Network Essentials

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.


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


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)


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


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


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.


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.


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.


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.


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).


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.


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.
