Real-time Interactive Event Analytics for Cross-Channel Visitor Data
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Solution Overview
Problem
Existing systems face challenges in analyzing cross-channel visitor interactions due to segregated data across different channels, requiring specialized code and resulting in time-consuming and error-prone processes to gain insights from disparate datasets.
Innovation Solution
A real-time interactive event analytics system that correlates multiple event datasets using a common visitor identifier, allowing users to select and combine datasets, generate requested information, and visualize results in chronological order through an analysis interface, without the need for specialized code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized code is prepared to query multiple disparate datasets, then analysis accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system pre-integrates multiple disparate datasets (web analytics, CRM, social media, etc.) into a unified data structure with standardized schemas before analysis is requested. This preliminary integration eliminates the need for time-consuming specialized code preparation during actual analysis, as data is already correlated and ready for querying.
Solution Approach 2:
The patent introduces an intermediary integration layer that sits between disparate data sources and analysis tools. This layer automatically correlates data from multiple channels using common identifiers and provides a unified interface, eliminating the need for specialized code while maintaining analysis accuracy through standardized data access.
2Loss of information
If multiple disparate datasets are integrated manually, then data completeness is improved, but operational complexity and error probability increase
Solution Approach 1:
The system merges multiple disparate datasets from different channels (web analytics, CRM, social media, mobile apps) into a single unified data structure. This consolidation achieves data completeness while eliminating operational complexity by providing a single integrated view instead of requiring manual handling of multiple separate datasets.
Solution Approach 2:
The patent creates a universal data integration framework that handles multiple data types and sources through a common architecture. This multi-functional system can ingest, correlate, and manage various dataset types uniformly, reducing operational complexity while maintaining complete data representation across all channels.
3Reliability
If data is segregated across different channels using different formatting and schema, then data integrity within each channel is preserved, but cross-channel analysis capability deteriorates
Solution Approach 1:
The system maintains segmented data storage for each channel to preserve data integrity and original formatting, while introducing a virtual integration layer that correlates these segments using common visitor identifiers. This segmentation approach allows both data integrity preservation and cross-channel analysis capability through the correlation mechanism.
Solution Approach 2:
The patent introduces an intermediary correlation layer that sits between segregated data channels. This layer uses common identifiers (such as visitor IDs) to link data across channels without requiring physical data movement or format changes, thereby preserving data integrity while enabling cross-channel analysis capability.
Data Source
AI summary
This disclosure involves performing event analytics on-the-fly based on user input to an analysis interface. An event analytics system correlates a plurality of event datasets to include a common visitor identifier. The system causes display, via the analysis interface, of information about the plurality of event datasets. The system receives, via the analysis interface, user selection of one or more event datasets, of the plurality of event datasets. Based on the selected one or more event datasets, the system generates a combined event dataset. The event analytics system receives, via the analysis interface, user input specifying information requested about the combined event dataset. The system obtains the requested information about the combined event dataset. The system causes display, via the analysis interface, of a visualization of the obtained information, wherein the visualization is based on event data from the combined event dataset in chronological order.