Attribution Model Integration in Analytics UI
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Solution Overview
Problem
Conventional attribution-modeling systems have isolated user interfaces that are incompatible with other software applications, limit dataset access, and restrict the use of attribution models and marketing channels, making it difficult to generate and modify attribution reports efficiently.
Innovation Solution
The system integrates attribution models as native components within the user interface, allowing users to configure analytics visualizations and apply attribution models to various event categories, dimensions, and parameters, enabling the generation of different attribution distributions and dynamic comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional user interfaces are used, then the system maintains a simple isolated architecture, but the interface cannot access datasets from other software applications and requires users to utilize multiple separate interfaces
Solution Approach 1:
The patent merges the attribution modeling interface with the analytics platform interface, allowing the attribution model to access and process datasets from multiple software applications through a unified interface. This eliminates the need for separate isolated interfaces while maintaining architectural coherence through standardized data access protocols.
Solution Approach 2:
The user interface is designed to be universal, capable of accessing and processing datasets from various software applications beyond just preconfigured datasets. The interface can handle multiple data sources, attribution models, and visualization types, making it multi-functional and adaptable to different analytical needs.
2Adaptability or versatility
If preconfigured datasets are used, then the system maintains simple data access, but the interface cannot facilitate users in generating attribution reports without preconfigured datasets or modifying existing reports
Solution Approach 1:
The system transitions from static preconfigured datasets to dynamic dataset selection, where users can flexibly choose and modify datasets in real-time. The interface allows dynamic configuration of attribution reports by enabling users to select from multiple data sources, adjust parameters, and generate customized reports without being constrained by preconfiguration.
Solution Approach 2:
The system performs preliminary actions by preloading and caching available datasets and attribution model configurations, allowing users to quickly access and modify reports without starting from scratch. This reduces user interaction complexity while maintaining flexibility through pre-prepared data structures and model templates.
3Productivity
If conventional user interfaces require reconfiguration for modifications, then the system maintains simple query execution, but the processing load increases by forcing the interface to construct queries anew for each modification
Solution Approach 1:
The system performs preliminary actions by precompiling and caching query structures, data access patterns, and attribution model configurations. When users modify reports, the system reuses these precompiled elements and applies only the necessary changes, rather than constructing queries entirely anew. This maintains high query execution efficiency while reducing processing time for modifications.
Solution Approach 2:
The query execution system becomes dynamic, allowing incremental updates and modifications to existing queries without full reconfiguration. The system can dynamically adjust query parameters, data sources, and model configurations based on user inputs, maintaining efficiency by building upon previously executed query structures rather than starting from scratch.
4Adaptability or versatility
If preset attribution models are used, then the system maintains simple model application, but the interface is limited to conventional attribution models and cannot apply models to properties outside canonical attribution models
Solution Approach 1:
The attribution modeling interface is designed to be universal, supporting both conventional preset attribution models and custom models applicable to properties outside canonical attribution frameworks. The system can handle multiple model types (first-touch, last-touch, linear, time-decay, etc.) and allow users to create and apply custom models to diverse properties such as product categories, geographic regions, or custom event types.
Solution Approach 2:
The system transitions from static preset models to dynamic model selection and configuration. Users can dynamically choose from multiple attribution models and customize model parameters based on specific analytical needs. The interface allows real-time model configuration and switching, enabling flexible application of attribution models to various properties and datasets without being constrained by preset limitations.
Data Source
AI summary
This disclosure relates to methods, non-transitory computer readable media, and systems that provide an attribution user interface that integrates attribution models as native components within the interface to configure analytics visualizations. By integrating attribution models and corresponding functions as native components of a user interface, the disclosed methods, non-transitory computer readable media, and systems can implement attribution models as parameters of attribution distributions or of any attribution visualizations, where the attribution models function as event categories. For instance, the disclosed methods, non-transitory computer readable media, and systems can provide analytics tools to generate visualizations of different attribution distributions of events across dimension values (or other visualizations) based on different attribution models. In some implementations, the disclosed methods, non-transitory computer readable media, and systems can also modify an attribution-distribution visualization extemporaneously given user inputs for a new event category, new dimension, new segment, or other parameter for the visualization.