Fledge

Google's First Locally-Executed Decision over Groups Experiment (FLEDGE) is a proposal to measure the effectiveness of Google's Turtledove auction mechanism as being a viable replacement for the interoperable identifiers that support the decentralized, open web.

FLEDGE has a goal to quantify the economic impact of Turtledove on publishers.

Experiment Design

FLEDGE will rely upon the following steps:

• Pre-auction Audience Segmentation
  • Marketers will periodically (maximum of once per day in this experiment) send two sets of information to a Google-controlled server
    • The first information set is a set of logic rules that determines marketer-defined audience segmentation
      • The Google-controlled server will send the audience segmentation logic to each Chrome browser
      • Each browser will processes each marketer's audience segmentation logic and return back with its unique identifier whether or not it qualifies for the audience segment to the Google-controlled server
      • The Google-controlled server will count distinct number of identifiers belong to each audience segment
      • If the number of identifiers exceeds a Google-defined threshold, then this server will notify these browsers that they may use such audience segments for auction logic
• Pre-auction Auction Desirability Logic
  • In addition to audience segmentation logic, marketers will send logic to determines marketer auction desirability
    • Marketer-specific desirability logic can include ad size, publisher domain, prior frequency of exposure to a give set of ads, and audience attributes
    • Marketers will also send budget information per campaign to a Google-controlled trusted server
  • The browser will separately request information from a Google-controlled trusted server to fetch marketer-specific desirability logic
    • The Google-controlled trusted server will apply the marketer desirability logic to generate a bid for each combination of ad size, audience information and context information per campaign independent of current context
• Pre-auction Publisher ad slot implementation
  • Publishers will implement a Fenced Frame to query the browser APIs for ads and render the resulting ad
    • The Fenced Frame will not communicate any information about the winning ad to the publisher
  • Publishers will load into the browser logic called "worklets" to select which bid response will win the on-device auction
    • Publisher logic can adjust desirability of each bid response, based on price and other factors
    • Publisher desirability logic can filter which marketer buying platforms can compete in the auction
    • Publisher can apply an out-of-band creative review process to be used as an input into this desirability logic
• Auction Mechanics
  • When triggered by the Fenced Frame, the browser will send a bid request to a limited number of marketers' buying platforms (e.g., DSPs) containing only the context of the given ad slot
  • DSPs receiving the browser request for a bid will determine whether they want to return a bid response
    • DSPs will return separate bids for context-only and others based on each combination of attributes the browser may or may not contain
  • The browser conducts an on-device auction to determine a local winning ad
    • The browser will filter the returned bids based on the presence of audience attributes
    • The browser will apply the publisher desirability logic to return the on-device auction winning ad
  • The publisher's monetization platform (e.g., Publisher Ad Server or SSP) will compare this ad and its bid price to other demand for this same ad slot and select the winning ad to render
• Post-auction
  • During this experiment, the browser will send reporting data to per-specified publisher and buyer end points with event level data as to the outcome of the auction
    • After this experiment, the browser will not send event-level data to the publisher or marketer
  • Marketer buying platforms that lose auctions will get access to aggregate metrics on some time-delayed basis

Impact

Given FLEDGE's design, the following are impacts to marketing effectiveness will not be measured:

• Cohort vs attribute level audience inputs to buyer algorithms
• Aggregate vs event-level feedback to buyer algorithms
• Time-delayed vs real-time event-level feedback to buyer algorithms

Open Questions

• What time delay, if any (e.g., on lost bids), will be used to quantify the impact of Turtledove on marketers' value of inventory?
• What limits on marketer audience segmentation logic will be placed?
• Who will pay for operating the Google-controlled servers that process marketer audience segmentation logic?
• How can marketers and their agents protect their intellectual property from being disclosed to Google?
• What is the maximum number of DSPs that can operate in the ecosystem under a model where the browser must directly contact each for bids?
• What is the acceptable level of impairment to marketing effectiveness, such that marketers will not reduce payments to publishers?
• Given publishers will be monetizing the same ad slot with existing monetization and Chrome-based monetization, how will the metrics associated with each be reported for comparison?
• Why are we not also evaluating the metrics proposed by Teetar, such as measuring experience and perception of users exposed to cohort-based advertising.

See Also

Teetar