Interactive Communication Time Series Visualization
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Solution Overview
Problem
In customer relationship management (CRM) systems, it is challenging for users to track and analyze the vast volume and variety of communications across multiple interactions, making it difficult to determine the impact of communications on interaction outcomes and identify opportunities for intervention.
Innovation Solution
A cloud-based system processes and displays an interactive communication time series, analyzing communications to identify events and characteristics, and providing a real-time, filtered timeline with aggregate information, allowing users to filter, search, and intervene in interactions effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system stores and processes all communications in a database, then the volume and variety of communication data is captured, but it becomes difficult to determine what occurred over the course of an interaction and how to improve future interactions
Solution Approach 1:
The system segments the large volume of communication data into discrete communication events, each representing a specific interaction between two contacts. These events are organized into interaction groups that can be individually analyzed, transforming the overwhelming database into manageable, meaningful units that reveal interaction patterns and dynamics.
Solution Approach 2:
The system transforms the flat database storage into a multi-dimensional visualization displaying communication events along a time series axis. This temporal dimension allows users to see the sequence and evolution of interactions, adding a critical time-based perspective that reveals patterns invisible in traditional database views.
2Loss of information
If the system displays all communication events, then complete information is provided, but users cannot easily identify key events or intervene effectively in interactions
Solution Approach 1:
The system applies different visual qualities and properties to different communication events based on their characteristics. Key events are highlighted with distinct visual markers, while routine communications are displayed with standard styling. This selective emphasis allows users to quickly identify important interactions without losing access to the complete communication history.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes raw communication data and generates derived insights, such as interaction outcomes and key event identification. This intermediary layer acts as a bridge between the complete communication database and the user's need for actionable insights, automatically highlighting events that require attention.
3Loss of time
If the system analyzes communication content in real-time, then timely intervention opportunities are identified, but the complexity of data processing increases
Solution Approach 1:
The system performs preliminary analysis of communication patterns and establishes baseline expectations for normal interactions. By pre-processing and understanding typical communication flows, the system can quickly identify deviations and anomalies in real-time without requiring complex analysis of every single communication event, reducing processing complexity while maintaining timely detection capability.
Solution Approach 2:
The system automatically analyzes its own communication data without requiring manual configuration or complex external processing. The interaction analysis engine self-adapts to organization-specific communication patterns, learning from historical data to improve its ability to identify key events and intervention opportunities autonomously, reducing the complexity burden on the overall system.
Data Source
AI summary
A system may process and display communications data to a user. The system may receive data related to a time series of communication moments. The communication moments may include a property of a communication event that was derived based on an analysis of the communication event. The system may process the data to generate aggregate type information corresponding to one or more types of the communication moments. The system may display the communication moments and the aggregate type information to a user.