Dynamic Bid Adjustment for RTB Data Quality
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Solution Overview
Problem
In online advertising, real-time bidding (RTB) exchanges face challenges in accurately determining bid prices due to varying amounts and types of data provided by publishers, which can hinder the probability of conversion and lead to inefficient ad placement, as some publishers withhold data to minimize costs, resulting in reduced bid prices for advertisers.
Innovation Solution
Implementing a system that dynamically adjusts bid prices based on the presence or absence of specific data types by applying penalty or bonus amounts, incentivizing publishers to provide more comprehensive data by linking bid price determination to the quality and quantity of data provided, thereby encouraging higher bid prices for publishers who offer valuable information and penalizing those who withhold it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If publishers withhold data to minimize costs, then publication costs are reduced, but bid price accuracy deteriorates and conversion probability decreases
Solution Approach 1:
The system implements feedback by adjusting bid prices based on the quality and quantity of data provided by publishers. Publishers receive feedback in the form of bid price adjustments that reflect the value of their data contribution, incentivizing them to provide more comprehensive data in future transactions.
Solution Approach 2:
The system changes the parameter of bid price dynamically based on data quality metrics. By varying the bid price parameter according to the completeness and quality of publisher-provided data, the system resolves the contradiction between minimizing publication costs and maintaining bid price accuracy.
2Measurement precision
If publishers provide comprehensive data, then bid price accuracy improves, but publication costs increase
Solution Approach 1:
The system applies dynamics by making bid prices flexible and adaptive rather than fixed. Bid prices dynamically adjust based on the data quality provided, allowing publishers to optimize their data provision strategy while maintaining accurate bid pricing.
Solution Approach 2:
The system allows for partial data provision with corresponding partial bid price adjustments. Publishers can choose to provide varying levels of data completeness, and the bid price reflects this partial action, rather than requiring full data provision for any bid.
3Reliability
If bid prices are adjusted based on data quality, then conversion probability increases, but system complexity increases
Solution Approach 1:
The system segments the bid price into base components and adjustment components. The base bid price remains simple, while data quality adjustments are applied as separate modular factors, reducing overall system complexity while maintaining conversion probability.
4Productivity
If real-time bid adjustments are implemented, then ad placement optimization improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing bid price adjustment rules and data quality metrics before actual bidding occurs. This preparation allows for rapid real-time adjustments during the bidding process without significant processing delays.
Data Source
AI summary
Within a computer networking environment, a first bid request having data is received. Bids are determined for advertisements based on the data associated with such first bid request. It may be determined that the first bid request's data includes one or more specific types of data for a bonus variable. At least a first portion of the bids that were determined for the first bid request are increased by a bonus amount in response to the one or more specific types of data for a bonus variable being determined to be present in the first bid request's data. For the first bid request, an optimum set of one or more of the bids are selected and transmitted, along with a reference to the associated advertisement, via to a sender of the first bid request.