Multi-Channel Insight Extraction via Common Data Modeling
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Solution Overview
Problem
Cloud platforms struggle to efficiently synthesize customer feedback and interaction data from multiple data channels with different structures, preventing effective insight generation and action execution to enhance customer experiences.
Innovation Solution
A system that integrates data from various channels by transforming channel-specific data structures into a common format, generates insights using ML models, and executes actions based on these insights, facilitating improved user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple channels with different structures is collected, then the quantity and diversity of customer feedback increases, but the difficulty of synthesizing and processing the data increases
Solution Approach 1:
The system segments data processing by creating separate transformation pipelines for each data channel (e.g., social media, surveys, support tickets). Each channel's data is transformed independently into a standardized format before being aggregated for insight generation, reducing the complexity of handling heterogeneous data structures.
Solution Approach 2:
The patent introduces an intermediary standardized data model that acts as a mediator between diverse channel-specific data structures and the insight generation system. This intermediate layer translates various data formats into a common structure, enabling seamless synthesis without requiring complex direct mappings between all channel pairs.
2Reliability
If data structures from different channels are transformed into a common format, then the reliability of insight generation improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary transformation of channel-specific data into the standardized data model as data arrives, rather than transforming all data at once during insight generation. This advance preparation ensures data is ready for immediate analysis, reducing latency while maintaining transformation quality and reliability.
3Adaptability or versatility
If insights are generated from synthesized multi-channel data, then the value and applicability of customer feedback increases, but the complexity of the processing system increases
Solution Approach 1:
The standardized data model serves multiple functions: it acts as a transformation target for diverse input channels, a common representation for analysis, and a flexible structure that can accommodate various insight generation requirements. This universal model reduces system complexity by eliminating the need for separate processing paths for different insight types.
4Speed
If real-time data synthesis and insight generation is implemented, then customer experience enhancement speed improves, but the computational resources and system complexity increase
Solution Approach 1:
The system implements continuous data transformation and insight generation operations rather than periodic batch processing. Data from all channels is continuously transformed into the standardized model and fed into insight generation algorithms, enabling real-time customer experience enhancement while maintaining steady-state computational resource utilization.
Data Source
AI summary
A system may store data from data channels in a first data storage, where the data from each data channel corresponds to a channel-specific structure. The system may transform the data from a channel-specific structure into a common structure to obtain transformed data and store the transformed data in a second data storage. The system may store, in a data model, a channel-specific session and one or more channel-specific threads that are in association with the transformed data. Further, a channel-specific session may correspond to metadata representing a grouping of channel-specific threads. The system may generate, in accordance with a stored configuration and via machine learning models, insights on the transformed data stored and store the insights in the data model, where an insight may be associated with the channel-specific session and the channel-specific threads of the transformed data. The system may then execute actions based on the insight generation.