We have previously presented what the user will see in a cookieless future, what products will be displayed, and the end results—the visual consequences of the proposed changes to third-party cookies. In this article, we would like to focus on what is happening behind the scenes.
In this article, you will learn the answers to:
- Does the elimination of 3rd party cookies affect accurate valuations?
- What is the solution for the challenges ahead in a cookieless future
- Is it possible to maintain the current efficiency of advertising budget allocation when user data security is significantly improved?
Table of Contents:
- Accurate valuations and advertising budget allocation in the cookieless future
- What is Outcome-based TURTLEDOVE’s role in the Protected Audience API?
- Wrapping up
Accurate valuations and advertising budget allocation in the cookieless future
We use powerful algorithms to find the right user at the right time to display an ad at an optimal price to distribute the customer advertising budgets effectively. These algorithms are fed a lot of data, especially for impression valuation. The more information we deliver to the bidder—the more willing the bidder will be to buy.
Ultimately, the idea is to match the price that is paid for the impression to the likelihood of the purchasing parameters of the user. Today’s algorithms can accurately estimate this parameter and specify the price accordingly, which assists with keeping an advertising budget allocation in line.
By default, an impression served to a person clicking on many products, adding them to the basket, and frequently returning to the website is more expensive than the casual visitor. Such a pattern should be adequately mirrored in the page view price.
All the changes resulting from a cookieless future and the difference in the approach to targeting individuals as opposed to using third-party cookies appear to limit the possibilities of an accurate valuation. The apparent necessity of group valuations could leave several inaccuracies as compared to individual valuations.
The inaccuracy of impression valuations, especially in the context of first-price auctions, would increase the ineffectiveness of advertising budget allocations. A valuation that is too low would result in the inability to buy an impression, while a valuation that is too high would cause suboptimal budget spend.
RTB House’s Outcome-based TURTLEDOVE (OBTD), which is included in Protected Audience API (PAAPI), is the direct answer to these potential challenges.
What is Outcome-based TURTLEDOVE’s role in the Protected Audience API?
The main idea of Outcome-based TURTLEDOVE (OBTD) is to separate the mechanism of impression price valuation for a given user from the means of microtargeting prevention. As the authors of this proposal, we would like to show you how it works.
Thanks to OBTD, when a user visits the advertisers’ website, PAAPI can save additional information in the user’s browser as part of the so-called UserSignals. This data will be stored directly on the user’s device and will not be accessible to external companies.
The diagram below is designed to help you understand the process and show you how helpful this information can be for PAAPI:
= On the advertiser’s website, the user is added to an interest group: “t-shirts_buyers”
= Visits only premium t-shirts with an average conversion value of 100 PLN vs. just 30 PLN for the whole category.
How to improve the accuracy of this valuation compared to other members of the interest group for the auction?
= RTB House sends what the value of the “prod_conv_value” variable should be for this user based on first-party data from the advertiser.
= We define the bidding function for the whole interest group, for example:
BID = 0.01 * prod_conv_value.
= The variable, stored in the user’s browser, is then used in the on-device auction for displaying an ad on the publisher’s website while keeping everything privacy-friendly.
OBTD unlocked a wide range of precise signals to be used for bidding, which is crucial for bidding accuracy.
See also: Cookieless Retargeting—How Will It Work?
Since the industry needs to maintain the accuracy of valuations at current levels, RTB House has proposed a solution that makes this possible for Protected Audience API while protecting the identity of individual users.
The separation of the microtargeting prevention mechanism from the bidding mechanism allows for a flexibility increase—it can be adjusted without modifying the bidding module.
In addition, we have developed a proof of concept ad validation algorithm and analyzed its microtargeting prevention aspects. We carried out an extensive analysis and proved that the current separate mechanisms meet the highest mathematical guarantees of privacy and security.
If you have any questions or comments regarding the article, please let us know.