Tunable Algorithmic Segments for Look-Alike Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital marketers face challenges in identifying and targeting 'look-alike' consumer groups with characteristics similar to known target audiences due to the complexity and time-consuming nature of traditional demographic and behavioral data analysis, as well as the lack of control over automated tools that provide fixed audience segments without intuitive adjustments.
Innovation Solution
Tunable algorithmic segment techniques allow digital marketers to define a target audience, ascertain tuning parameters, and build a look-alike model that controls reach versus accuracy, enabling the generation of different market segments through user-configurable settings, providing transparency and flexibility in look-alike analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional demographic and behavioral data analysis is used to identify look-alike groups, then the analysis can be performed with existing tools, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces manual demographic and behavioral data analysis with an automated machine learning system that uses gradient boosting algorithms to identify look-alike groups, thereby eliminating the time-consuming manual process while maintaining or improving analysis accuracy
Solution Approach 2:
The system performs self-service by automatically executing the look-alike analysis through trained machine learning models, requiring minimal human intervention and eliminating the need for manual data processing while delivering timely results
2Productivity
If automated tools are used for look-alike analysis, then the process becomes faster, but digital marketers have little or no control over the analysis
Solution Approach 1:
The patent implements dynamic control by allowing digital marketers to adjust tuning parameters such as the proportion of training data allocated to different datasets and the number of boosting rounds, enabling flexible optimization of analysis results while maintaining automated processing speed
Solution Approach 2:
The system enables parameter changes by providing controls that allow users to modify analysis parameters including data proportions, model complexity, and performance thresholds, thereby maintaining both speed and user control over the automated analysis process
3Extent of automation
If existing black-box analysis tools are used, then automated look-alike analysis can be obtained, but digital marketers cannot adjust the analysis based on their intuition and experience
Solution Approach 1:
The patent implements feedback mechanisms by allowing digital marketers to review analysis results and adjust tuning parameters based on their intuition and experience, creating an iterative process that combines automated analysis with human expertise to improve campaign outcomes
Data Source
AI summary
Tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth. Tuning parameters designated for the look-alike model are ascertained and the look-alike model is built based on the target audience definition and the tuning parameters. The tuning parameters may include at least a setting selectable to control reach versus accuracy for the look-alike model. Segment data indicative of market segments generated according to the look-alike model may then be exposed for manipulation by a client. The manipulation may include selectable control over the tuning parameters to generate different look-alike groups from the segment data.