Automated Advertisement Selection via Engagement Probability
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Solution Overview
Problem
Current digital advertising platforms struggle to effectively display advertisements that interest customers, leading to low user interaction and reduced revenue for advertisers, as not all customers are interested in the same products.
Innovation Solution
An automated system that analyzes search requests, determines relevant keywords, and selects digital advertisements based on engagement probabilities, displaying the most likely-to-interact items on a website, thereby increasing advertiser revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital advertisements are displayed on a website, then advertisers can promote their items to potential buyers, but not all customers are interested in the advertised items leading to low user interaction
Solution Approach 1:
The system performs preliminary analysis of customer search requests and behavior patterns before displaying advertisements. By pre-calculating engagement probabilities based on historical data and customer preferences, the system prepares and displays the most relevant advertisements in advance, ensuring higher customer interest and interaction rates.
Solution Approach 2:
The system dynamically changes advertisement parameters such as selection criteria, engagement probability thresholds, and display priorities based on real-time customer behavior data. By adjusting these parameters according to customer interests and search patterns, the system optimizes advertisement relevance and interaction rates continuously.
2Quantity of substance
If advertisements are displayed based on general criteria, then a broad audience can be reached, but the relevance to individual customer interests is reduced
Solution Approach 1:
The system applies local quality by tailoring advertisement content and selection criteria to individual customer profiles and search contexts. Instead of using uniform display rules for all customers, the system customizes advertisement relevance based on each customer's interests, search history, and predicted engagement probability, thereby maintaining high relevance across diverse customer segments.
3Adaptability or versatility
If manual selection of advertisements is performed, then relevance can be controlled, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system performs self-service by automatically analyzing customer search requests, calculating engagement probabilities, and selecting appropriate advertisements without manual intervention. The automated system uses machine learning algorithms and historical data to make real-time advertisement selection decisions, eliminating time-consuming manual processes while maintaining high relevance through data-driven insights.
Solution Approach 2:
The system implements feedback loops where customer interaction data with displayed advertisements is continuously collected and used to refine future advertisement selections. By analyzing engagement patterns, click-through rates, and customer behavior feedback, the system automatically adjusts its selection criteria and improves relevance over time without requiring manual reconfiguration.
Data Source
AI summary
This application relates to apparatus and methods for determining data outputs to advertise on a platform such as a website. A computing device receives a website search request and determines a search term keyword. The computing device also determines a plurality of item accounts, such as sponsor campaigns, based on the search term keyword and a corresponding keyword of each item account. Each item account also includes a corresponding data output, such as a digital advertisement. The computing device identifies one of the item accounts based on determining an engagement probability for the digital advertisement of each item account. The engagement probability is determined based on aggregated impression and engagement data for the digital advertisement. The computing device provides the digital advertisement for the identified item account to a server, which may display the digital advertisement on the platform.