Unified Customer Journey Analysis Across Multiple Communication Channels
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Solution Overview
Problem
Current customer service monitoring systems are inadequate as they primarily focus on call centers and do not effectively analyze diverse, connected customer interactions across multiple communication channels, such as web searches, chats, and phone calls, which limits their ability to provide a comprehensive understanding of customer experiences.
Innovation Solution
A system and method that collect and analyze data across multiple communication channels, enabling users to analyze customer journeys that span multiple interactions, identify problematic journeys, and visualize their timelines, text histories, and significant events, allowing for the formulation of hypotheses and quantification of issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a call center monitoring system is used to analyze customer interactions, then call center performance can be monitored, but diverse customer interactions across multiple communication channels cannot be effectively analyzed
Solution Approach 1:
The system is designed to handle multiple communication channels (voice calls, chats, emails, social media) through a single unified monitoring platform. The monitoring module can adapt to different channel types, and the data processing module normalizes data from various sources into a common format, enabling comprehensive analysis across all customer interaction channels without requiring separate specialized systems for each channel type.
2Measurement precision
If call center data is collected and analyzed, then call center inefficiencies can be identified, but complete customer journeys across multiple channels cannot be traced
Solution Approach 1:
The system implements a hierarchical data structure where individual interaction data is nested within customer journey contexts. Each customer journey contains multiple interactions across different channels, and each interaction contains detailed data points. This nested structure allows the system to maintain precise measurement of individual interactions while preserving the broader journey context, enabling both detailed issue identification and comprehensive journey tracking simultaneously.
3Productivity
If traditional monitoring systems are used, then call center operations can be supervised, but diverse customer interaction data cannot be integrated and visualized
Solution Approach 1:
The system introduces several intermediary components to bridge different communication channels and data formats. The data processing module acts as an intermediary that normalizes and standardizes data from various channels into a unified format. The visualization module serves as another intermediary that transforms processed data into comprehensive journey views. These intermediary layers enable efficient monitoring of diverse channels without requiring direct complex integration between all channel systems.
4Measurement precision
If individual interactions are analyzed in isolation, then specific interaction details can be examined, but patterns across multiple interactions and channels cannot be identified
Solution Approach 1:
The system merges individual interaction analyses with cross-channel pattern recognition through its journey-based architecture. While maintaining the ability to examine detailed individual interactions, the system simultaneously combines multiple interactions into unified customer journey views. The pattern recognition algorithms analyze aggregated journey data to identify cross-channel patterns, while still allowing drill-down to individual interaction details when needed.
Data Source
AI summary
To enable analysis of the performance of a customer contact center handling interactions across multiple types of communication channels, a system is provided that collects and analyzes data across the multiple channels and allows a user, such as an analyst, to analyze customer journeys that comprise multiple interactions, or contacts, across multiple communication channel types. That analysis can be used to identify journeys with similar, perhaps problematic, characteristics and provide the analyst user with the ability to examine the collected data for each identified journey individually. Once a journey is selected, the system displays the collected data in a coordinated display of journey timeline, text history, and sequence of automatically detected significant events. This single-journey display, applied to multiple journeys in succession, allows the analyst to explicitly annotate problems or formulate hypotheses about problems that can then be quantified for impact by refining the initial multi-journey analysis.