Cross-Channel Impression Reporting System
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Solution Overview
Problem
Existing cross-channel performance measurement tools primarily focus on click-centric metrics, undervaluing the impact of content presented across various touchpoints and channels without direct clicks, leading to incomplete understanding of content effectiveness.
Innovation Solution
A computing system that periodically requests impression data from multiple data sources, processes this data using a machine-learned model to generate actionable insights, and outputs this information for user display, enabling comprehensive cross-channel content impression performance measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If click-centric measurement is used to evaluate content effectiveness, then the measurement system remains simple and focused, but the understanding of content impact across all touchpoints becomes incomplete
Solution Approach 1:
The patent segments the measurement system into multiple data sources (social media, retail websites, video platforms, etc.) and evaluates different types of user engagement (clicks, impressions, views, interactions) separately, then aggregates them to provide a comprehensive view of content effectiveness across all touchpoints
Solution Approach 2:
The patent creates a universal measurement framework that handles multiple types of content engagement (clicks, impressions, video views, social media interactions) through a single system, allowing content providers to evaluate their content performance across diverse channels and formats using consistent metrics
2Measurement precision
If internal tools with custom weighting models are used by sophisticated content providers, then content impact can be measured more accurately, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (the impression reporting system) that sits between raw data sources and content providers, automatically collecting, normalizing, and processing data from multiple channels using standardized protocols and pre-configured weighting models, thereby reducing the complexity burden on content providers while maintaining measurement accuracy
Solution Approach 2:
The patent enables the measurement system to automatically configure itself by collecting data from multiple sources, applying appropriate weighting models based on content type and channel, and generating reports without requiring content providers to manually set up complex tracking infrastructure or customize weighting parameters
3Loss of information
If impression tracking is implemented across multiple data sources, then the understanding of content reach improves, but the data collection and processing requirements increase
Solution Approach 1:
The patent extracts only the essential impression and engagement data from multiple data sources using standardized collection protocols, filtering out redundant information and focusing on key metrics (impressions, clicks, views, interactions) that directly measure content reach and effectiveness, thereby managing data volume while maintaining measurement quality
Data Source
AI summary
Computing systems and methods for surfacing impression data are disclosed herein. The method can include periodically providing a reporting data request to one or more data sources requesting impression data associated with content presented at the data sources. Reporting data is received and processed into a data format usable by the database. The reporting data is then saved in a database. In response to receiving a request from a user to generate a report the reporting data stored in the database is processed using a machine-learned model to generate a model output, and a portion of the reporting data and the model output are output for display to the user.